




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

     if is_vision_available():
          from transformers import MaskaFormerImageProcessor

if is_vision_available():
     from PIL import Image





class 			__lowerCamelCase	:




    """simple docstring"""

    def __init__(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Any=2       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=True       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=False       ,     SCREAMING_SNAKE_CASE__					:					Dict=10       ,     SCREAMING_SNAKE_CASE__					:					Any=3       ,     SCREAMING_SNAKE_CASE__					:					Any=32 * 8       ,     SCREAMING_SNAKE_CASE__					:					Tuple=32 * 8       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=4       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=64       ,     )				->	Any:
         lowerCAmelCase__          =			parent
         lowerCAmelCase__          =			batch_size
         lowerCAmelCase__          =			is_training
         lowerCAmelCase__          =			use_auxiliary_loss
         lowerCAmelCase__          =			num_queries
         lowerCAmelCase__          =			num_channels
         lowerCAmelCase__          =			min_size
         lowerCAmelCase__          =			max_size
         lowerCAmelCase__          =			num_labels
         lowerCAmelCase__          =			hidden_dim
         lowerCAmelCase__          =			hidden_dim


    def        a				(	self					:					Any       )				->	Any:
         lowerCAmelCase__          =			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
             SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			torch.ones([self.batch_size, self.min_size, self.max_size]       ,     device=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			(
             torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]       ,     device=SCREAMING_SNAKE_CASE__       ) > 0.5
         ).float()
         lowerCAmelCase__          =			(torch.rand((self.batch_size, self.num_labels)       ,     device=SCREAMING_SNAKE_CASE__       ) > 0.5).long()

         lowerCAmelCase__          =			self.get_config()
         return config, pixel_values, pixel_mask, mask_labels, class_labels


    def        a				(	self					:					List[str]       )				->	Dict:
         lowerCAmelCase__          =			MaskaFormerConfig(
             hidden_size=self.hidden_dim       ,     )
         lowerCAmelCase__          =			self.num_queries
         lowerCAmelCase__          =			self.num_labels

         lowerCAmelCase__          =			[1, 1, 1, 1]
         lowerCAmelCase__          =			self.num_channels

         lowerCAmelCase__          =			64
         lowerCAmelCase__          =			128
         lowerCAmelCase__          =			self.hidden_dim
         lowerCAmelCase__          =			self.hidden_dim
         lowerCAmelCase__          =			self.hidden_dim
         return config


    def        a				(	self					:					List[Any]       )				->	Any:
         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			self.prepare_config_and_inputs()
         lowerCAmelCase__          =			{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
         return config, inputs_dict


    def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	str:
         lowerCAmelCase__          =			output.encoder_hidden_states
         lowerCAmelCase__          =			output.pixel_decoder_hidden_states
         lowerCAmelCase__          =			output.transformer_decoder_hidden_states

         self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__       )       ,     len(config.backbone_config.depths       )       )
         self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__       )       ,     len(config.backbone_config.depths       )       )
         self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__       )       ,     config.decoder_layers       )


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					str=False       )				->	Tuple:
         with torch.no_grad():
              lowerCAmelCase__          =			MaskaFormerModel(config=SCREAMING_SNAKE_CASE__       )
              model.to(SCREAMING_SNAKE_CASE__       )
              model.eval()

              lowerCAmelCase__          =			model(pixel_values=SCREAMING_SNAKE_CASE__       ,     pixel_mask=SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     output_hidden_states=SCREAMING_SNAKE_CASE__       )

         self.parent.assertEqual(
             output.transformer_decoder_last_hidden_state.shape       ,     (self.batch_size, self.num_queries, self.hidden_dim)       ,     )
         # let's ensure the other two hidden state exists
         self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None       )
         self.parent.assertTrue(output.encoder_last_hidden_state is not None       )

         if output_hidden_states:
              self.check_output_hidden_state(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	int:
         lowerCAmelCase__          =			MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE__       )
         model.to(SCREAMING_SNAKE_CASE__       )
         model.eval()

         def comm_check_on_output(SCREAMING_SNAKE_CASE__					:					int       ):
              # let's still check that all the required stuff is there
              self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None       )
              self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None       )
              self.parent.assertTrue(result.encoder_last_hidden_state is not None       )
              # okay, now we need to check the logits shape
              # due to the encoder compression, masks have a //4 spatial size
              self.parent.assertEqual(
                  result.masks_queries_logits.shape       ,     (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)       ,     )
              # + 1 for null class
              self.parent.assertEqual(
                  result.class_queries_logits.shape       ,     (self.batch_size, self.num_queries, self.num_labels + 1)       )

         with torch.no_grad():
              lowerCAmelCase__          =			model(pixel_values=SCREAMING_SNAKE_CASE__       ,     pixel_mask=SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )

              comm_check_on_output(SCREAMING_SNAKE_CASE__       )

              lowerCAmelCase__          =			model(
                  pixel_values=SCREAMING_SNAKE_CASE__       ,     pixel_mask=SCREAMING_SNAKE_CASE__       ,     mask_labels=SCREAMING_SNAKE_CASE__       ,     class_labels=SCREAMING_SNAKE_CASE__       )

         comm_check_on_output(SCREAMING_SNAKE_CASE__       )

         self.parent.assertTrue(result.loss is not None       )
         self.parent.assertEqual(result.loss.shape       ,     torch.Size([1]       )       )






@require_torch
class 			__lowerCamelCase	(   UpperCamelCase__     ,    UpperCamelCase__     ,    unittest.TestCase   ):




    """simple docstring"""

    snake_case__ =				(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
    snake_case__ =				{"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False


    def        a				(	self					:					int       )				->	List[Any]:
         lowerCAmelCase__          =			MaskaFormerModelTester(self       )
         lowerCAmelCase__          =			ConfigTester(self       ,     config_class=SCREAMING_SNAKE_CASE__       ,     has_text_modality=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Tuple       )				->	Any:
         self.config_tester.run_common_tests()


    def        a				(	self					:					Optional[Any]       )				->	Optional[Any]:
         lowerCAmelCase__     ,		lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ,     output_hidden_states=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Tuple       )				->	Optional[Any]:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE__       )


    @unittest.skip(reason="Mask2Former does not use inputs_embeds"       )
    def        a				(	self					:					Union[str, Any]       )				->	Union[str, Any]:
         pass


    @unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"       )
    def        a				(	self					:					List[Any]       )				->	Dict:
         pass


    @unittest.skip(reason="Mask2Former is not a generative model"       )
    def        a				(	self					:					List[str]       )				->	List[Any]:
         pass


    @unittest.skip(reason="Mask2Former does not use token embeddings"       )
    def        a				(	self					:					Tuple       )				->	Optional[Any]:
         pass


    @require_torch_multi_gpu
    @unittest.skip(
        reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"       )
    def        a				(	self					:					Any       )				->	int:
         pass


    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
    def        a				(	self					:					Union[str, Any]       )				->	Tuple:
         pass


    def        a				(	self					:					Dict       )				->	str:
         lowerCAmelCase__     ,		lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowerCAmelCase__          =			model_class(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			inspect.signature(model.forward       )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              lowerCAmelCase__          =			[*signature.parameters.keys()]

              lowerCAmelCase__          =			["pixel_values"]
              self.assertListEqual(arg_names[:1]       ,     SCREAMING_SNAKE_CASE__       )


    @slow
    def        a				(	self					:					List[str]       )				->	Union[str, Any]:
         for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
              lowerCAmelCase__          =			MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE__       )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[int]       )				->	Tuple:
         lowerCAmelCase__          =			(self.model_tester.min_size,) * 2
         lowerCAmelCase__          =			{
             "pixel_values": torch.randn((2, 3, *size)       ,     device=SCREAMING_SNAKE_CASE__       ),
             "mask_labels": torch.randn((2, 10, *size)       ,     device=SCREAMING_SNAKE_CASE__       ),
             "class_labels": torch.zeros(2       ,     10       ,     device=SCREAMING_SNAKE_CASE__       ).long(),
         }
         lowerCAmelCase__          =			self.model_tester.get_config()

         lowerCAmelCase__          =			MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE__       ).to(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(**SCREAMING_SNAKE_CASE__       )
         self.assertTrue(outputs.loss is not None       )


    def        a				(	self					:					Tuple       )				->	Dict:
         lowerCAmelCase__     ,		lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ,     output_hidden_states=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					int       )				->	List[Any]:
         lowerCAmelCase__     ,		lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowerCAmelCase__          =			model_class(SCREAMING_SNAKE_CASE__       ).to(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			model(**SCREAMING_SNAKE_CASE__       ,     output_attentions=SCREAMING_SNAKE_CASE__       )
              self.assertTrue(outputs.attentions is not None       )


    def        a				(	self					:					List[Any]       )				->	Union[str, Any]:
         if not self.model_tester.is_training:
              return

         lowerCAmelCase__          =			self.all_model_classes[1]
         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()

         lowerCAmelCase__          =			model_class(SCREAMING_SNAKE_CASE__       )
         model.to(SCREAMING_SNAKE_CASE__       )
         model.train()

         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     mask_labels=SCREAMING_SNAKE_CASE__       ,     class_labels=SCREAMING_SNAKE_CASE__       ).loss
         loss.backward()


    def        a				(	self					:					Union[str, Any]       )				->	int:
         lowerCAmelCase__          =			self.all_model_classes[1]
         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         lowerCAmelCase__          =			True
         lowerCAmelCase__          =			True

         lowerCAmelCase__          =			model_class(SCREAMING_SNAKE_CASE__       ).to(SCREAMING_SNAKE_CASE__       )
         model.train()

         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     mask_labels=SCREAMING_SNAKE_CASE__       ,     class_labels=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			outputs.encoder_hidden_states[0]
         encoder_hidden_states.retain_grad()

         lowerCAmelCase__          =			outputs.pixel_decoder_hidden_states[0]
         pixel_decoder_hidden_states.retain_grad()

         lowerCAmelCase__          =			outputs.transformer_decoder_hidden_states[0]
         transformer_decoder_hidden_states.retain_grad()

         lowerCAmelCase__          =			outputs.attentions[0]
         attentions.retain_grad()

         outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE__       )

         self.assertIsNotNone(encoder_hidden_states.grad       )
         self.assertIsNotNone(pixel_decoder_hidden_states.grad       )
         self.assertIsNotNone(transformer_decoder_hidden_states.grad       )
         self.assertIsNotNone(attentions.grad       )






UpperCamelCase								=					1E-4



def 							_A       (						):
     """simple docstring"""

     lowerCAmelCase__          =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
     return image






@require_vision
@slow
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    @cached_property
    def        a				(	self					:					List[str]       )				->	Tuple:
         return "facebook/mask2former-swin-small-coco-instance"


    @cached_property
    def        a				(	self					:					List[str]       )				->	List[Any]:
         return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints       ) if is_vision_available() else None


    def        a				(	self					:					Union[str, Any]       )				->	Optional[Any]:
         lowerCAmelCase__          =			MaskaFormerModel.from_pretrained(self.model_checkpoints       ).to(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.default_image_processor
         lowerCAmelCase__          =			prepare_img()
         lowerCAmelCase__          =			image_processor(SCREAMING_SNAKE_CASE__       ,     return_tensors="pt"       ).to(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			inputs["pixel_values"].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0       )
         # check size
         self.assertEqual(SCREAMING_SNAKE_CASE__       ,     (1, 3, 384, 384)       )

         with torch.no_grad():
              lowerCAmelCase__          =			model(**SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			torch.tensor(
             [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]       ).to(SCREAMING_SNAKE_CASE__       )
         self.assertTrue(
             torch.allclose(
                 outputs.encoder_last_hidden_state[0, 0, :3, :3]       ,     SCREAMING_SNAKE_CASE__       ,     atol=SCREAMING_SNAKE_CASE__       )       )

         lowerCAmelCase__          =			torch.tensor(
             [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]       ).to(SCREAMING_SNAKE_CASE__       )
         self.assertTrue(
             torch.allclose(
                 outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]       ,     SCREAMING_SNAKE_CASE__       ,     atol=SCREAMING_SNAKE_CASE__       )       )

         lowerCAmelCase__          =			torch.tensor(
             [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]       ).to(SCREAMING_SNAKE_CASE__       )
         self.assertTrue(
             torch.allclose(
                 outputs.transformer_decoder_last_hidden_state[0, :3, :3]       ,     SCREAMING_SNAKE_CASE__       ,     atol=SCREAMING_SNAKE_CASE__       )       )


    def        a				(	self					:					Dict       )				->	Optional[int]:
         lowerCAmelCase__          =			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(SCREAMING_SNAKE_CASE__       ).eval()
         lowerCAmelCase__          =			self.default_image_processor
         lowerCAmelCase__          =			prepare_img()
         lowerCAmelCase__          =			image_processor(SCREAMING_SNAKE_CASE__       ,     return_tensors="pt"       ).to(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			inputs["pixel_values"].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0       )
         # check size
         self.assertEqual(SCREAMING_SNAKE_CASE__       ,     (1, 3, 384, 384)       )

         with torch.no_grad():
              lowerCAmelCase__          =			model(**SCREAMING_SNAKE_CASE__       )
         # masks_queries_logits
         lowerCAmelCase__          =			outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape       ,     (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       )
         lowerCAmelCase__          =			[
             [-8.7_839, -9.0_056, -8.8_121],
             [-7.4_104, -7.0_313, -6.5_401],
             [-6.6_105, -6.3_427, -6.4_675],
         ]
         lowerCAmelCase__          =			torch.tensor(SCREAMING_SNAKE_CASE__       ).to(SCREAMING_SNAKE_CASE__       )
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,     SCREAMING_SNAKE_CASE__       ,     atol=SCREAMING_SNAKE_CASE__       )       )
         # class_queries_logits
         lowerCAmelCase__          =			outputs.class_queries_logits
         self.assertEqual(class_queries_logits.shape       ,     (1, model.config.num_queries, model.config.num_labels + 1)       )
         lowerCAmelCase__          =			torch.tensor(
             [
                 [1.8_324, -8.0_835, -4.1_922],
                 [0.8_450, -9.0_050, -3.6_053],
                 [0.3_045, -7.7_293, -3.0_275],
             ]       ).to(SCREAMING_SNAKE_CASE__       )
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,     SCREAMING_SNAKE_CASE__       ,     atol=SCREAMING_SNAKE_CASE__       )       )


    def        a				(	self					:					Optional[Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(SCREAMING_SNAKE_CASE__       ).eval()
         lowerCAmelCase__          =			self.default_image_processor

         lowerCAmelCase__          =			image_processor(
             [np.zeros((3, 800, 1_333)       ), np.zeros((3, 800, 1_333)       )]       ,     segmentation_maps=[np.zeros((384, 384)       ).astype(np.floataa       ), np.zeros((384, 384)       ).astype(np.floataa       )]       ,     return_tensors="pt"       ,     )

         lowerCAmelCase__          =			inputs["pixel_values"].to(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[el.to(SCREAMING_SNAKE_CASE__       ) for el in inputs["mask_labels"]]
         lowerCAmelCase__          =			[el.to(SCREAMING_SNAKE_CASE__       ) for el in inputs["class_labels"]]

         with torch.no_grad():
              lowerCAmelCase__          =			model(**SCREAMING_SNAKE_CASE__       )

         self.assertTrue(outputs.loss is not None       )






import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


UpperCamelCase								=					logging.get_logger(__name__)

# General docstring
UpperCamelCase								=					'PoolFormerConfig'

# Base docstring
UpperCamelCase								=					'sail/poolformer_s12'
UpperCamelCase								=					[1, 512, 7, 7]

# Image classification docstring
UpperCamelCase								=					'sail/poolformer_s12'
UpperCamelCase								=					'tabby, tabby cat'

UpperCamelCase								=					[
    'sail/poolformer_s12',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]



def 							_A       (						lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: float = 0.0       ,    lowerCAmelCase_					: bool = False      ):
     """simple docstring"""

     if drop_prob == 0.0 or not training:
          return input
     lowerCAmelCase__          =			1 - drop_prob
     lowerCAmelCase__          =			(input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
     lowerCAmelCase__          =			keep_prob + torch.rand(lowerCAmelCase_       ,    dtype=input.dtype       ,    device=input.device      )
     random_tensor.floor_()  # binarize
     lowerCAmelCase__          =			input.div(lowerCAmelCase_      ) * random_tensor
     return output





class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[float] = None       )				->	None:
         super().__init__()
         lowerCAmelCase__          =			drop_prob


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					torch.Tensor       )				->	torch.Tensor:
         return drop_path(SCREAMING_SNAKE_CASE__       ,     self.drop_prob       ,     self.training       )


    def        a				(	self					:					Optional[Any]       )				->	str:
         return "p={}".format(self.drop_prob       )






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					str=None       )				->	Optional[Any]:
         super().__init__()
         lowerCAmelCase__          =			patch_size if isinstance(SCREAMING_SNAKE_CASE__       ,     collections.abc.Iterable       ) else (patch_size, patch_size)
         lowerCAmelCase__          =			stride if isinstance(SCREAMING_SNAKE_CASE__       ,     collections.abc.Iterable       ) else (stride, stride)
         lowerCAmelCase__          =			padding if isinstance(SCREAMING_SNAKE_CASE__       ,     collections.abc.Iterable       ) else (padding, padding)

         lowerCAmelCase__          =			nn.Convad(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     kernel_size=SCREAMING_SNAKE_CASE__       ,     stride=SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			norm_layer(SCREAMING_SNAKE_CASE__       ) if norm_layer else nn.Identity()


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			self.projection(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.norm(SCREAMING_SNAKE_CASE__       )
         return embeddings






class 			__lowerCamelCase	(   nn.GroupNorm   ):




    """simple docstring"""

    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     **SCREAMING_SNAKE_CASE__					:					Any       )				->	Dict:
         super().__init__(1       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	List[Any]:
         super().__init__()
         lowerCAmelCase__          =			nn.AvgPoolad(SCREAMING_SNAKE_CASE__       ,     stride=1       ,     padding=pool_size // 2       ,     count_include_pad=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	Optional[Any]:
         return self.pool(SCREAMING_SNAKE_CASE__       ) - hidden_states






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Dict:
         super().__init__()
         lowerCAmelCase__          =			nn.Convad(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     1       )
         lowerCAmelCase__          =			nn.Convad(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     1       )
         lowerCAmelCase__          =			PoolFormerDropPath(SCREAMING_SNAKE_CASE__       )
         if isinstance(config.hidden_act       ,     SCREAMING_SNAKE_CASE__       ):
              lowerCAmelCase__          =			ACTaFN[config.hidden_act]
         else:
              lowerCAmelCase__          =			config.hidden_act


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	List[Any]:
         lowerCAmelCase__          =			self.conva(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.act_fn(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.drop(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.conva(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.drop(SCREAMING_SNAKE_CASE__       )

         return hidden_states






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Optional[Any]:
         super().__init__()
         lowerCAmelCase__          =			PoolFormerPooling(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PoolFormerOutput(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__       )

         # Useful for training neural nets
         lowerCAmelCase__          =			PoolFormerDropPath(SCREAMING_SNAKE_CASE__       ) if drop_path > 0.0 else nn.Identity()
         lowerCAmelCase__          =			config.use_layer_scale
         if config.use_layer_scale:
              lowerCAmelCase__          =			nn.Parameter(
                  config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE__)       )       ,     requires_grad=SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			nn.Parameter(
                  config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE__)       )       ,     requires_grad=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	int:
         if self.use_layer_scale:
              lowerCAmelCase__          =			self.pooling(self.before_norm(SCREAMING_SNAKE_CASE__       )       )
              lowerCAmelCase__          =			self.layer_scale_a.unsqueeze(-1       ).unsqueeze(-1       ) * pooling_output
              # First residual connection
              lowerCAmelCase__          =			hidden_states + self.drop_path(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			()

              lowerCAmelCase__          =			self.output(self.after_norm(SCREAMING_SNAKE_CASE__       )       )
              lowerCAmelCase__          =			self.layer_scale_a.unsqueeze(-1       ).unsqueeze(-1       ) * layer_output
              # Second residual connection
              lowerCAmelCase__          =			hidden_states + self.drop_path(SCREAMING_SNAKE_CASE__       )

              lowerCAmelCase__          =			(output,) + outputs
              return outputs

         else:
              lowerCAmelCase__          =			self.drop_path(self.pooling(self.before_norm(SCREAMING_SNAKE_CASE__       )       )       )
              # First residual connection
              lowerCAmelCase__          =			pooling_output + hidden_states
              lowerCAmelCase__          =			()

              # Second residual connection inside the PoolFormerOutput block
              lowerCAmelCase__          =			self.drop_path(self.output(self.after_norm(SCREAMING_SNAKE_CASE__       )       )       )
              lowerCAmelCase__          =			hidden_states + layer_output

              lowerCAmelCase__          =			(output,) + outputs
              return outputs






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	Any:
         super().__init__()
         lowerCAmelCase__          =			config
         # stochastic depth decay rule
         lowerCAmelCase__          =			[x.item() for x in torch.linspace(0       ,     config.drop_path_rate       ,     sum(config.depths       )       )]

         # patch embeddings
         lowerCAmelCase__          =			[]
         for i in range(config.num_encoder_blocks       ):
              embeddings.append(
                  PoolFormerEmbeddings(
                      patch_size=config.patch_sizes[i]       ,     stride=config.strides[i]       ,     padding=config.padding[i]       ,     num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]       ,     hidden_size=config.hidden_sizes[i]       ,     )       )
         lowerCAmelCase__          =			nn.ModuleList(SCREAMING_SNAKE_CASE__       )

         # Transformer blocks
         lowerCAmelCase__          =			[]
         lowerCAmelCase__          =			0
         for i in range(config.num_encoder_blocks       ):
              # each block consists of layers
              lowerCAmelCase__          =			[]
              if i != 0:
                   cur += config.depths[i - 1]
              for j in range(config.depths[i]       ):
                   layers.append(
                       PoolFormerLayer(
                           SCREAMING_SNAKE_CASE__       ,     num_channels=config.hidden_sizes[i]       ,     pool_size=config.pool_size       ,     hidden_size=config.hidden_sizes[i]       ,     intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio       )       ,     drop_path=dpr[cur + j]       ,     )       )
              blocks.append(nn.ModuleList(SCREAMING_SNAKE_CASE__       )       )

         lowerCAmelCase__          =			nn.ModuleList(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int=False       ,     SCREAMING_SNAKE_CASE__					:					List[str]=True       )				->	Dict:
         lowerCAmelCase__          =			() if output_hidden_states else None

         lowerCAmelCase__          =			pixel_values
         for idx, layers in enumerate(zip(self.patch_embeddings       ,     self.block       )       ):
              lowerCAmelCase__     ,		lowerCAmelCase__          =			layers
              # Get patch embeddings from hidden_states
              lowerCAmelCase__          =			embedding_layer(SCREAMING_SNAKE_CASE__       )
              # Send the embeddings through the blocks
              for _, blk in enumerate(SCREAMING_SNAKE_CASE__       ):
                   lowerCAmelCase__          =			blk(SCREAMING_SNAKE_CASE__       )
                   lowerCAmelCase__          =			layer_outputs[0]

              if output_hidden_states:
                   lowerCAmelCase__          =			all_hidden_states + (hidden_states,)

         if not return_dict:
              return tuple(v for v in [hidden_states, all_hidden_states] if v is not None       )

         return BaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE__       ,     hidden_states=SCREAMING_SNAKE_CASE__       )






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				PoolFormerConfig
    snake_case__ =				"poolformer"
    snake_case__ =				"pixel_values"
    snake_case__ =				True


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	List[Any]:
         if isinstance(SCREAMING_SNAKE_CASE__       ,     (nn.Linear, nn.Convad)       ):
              module.weight.data.normal_(mean=0.0       ,     std=self.config.initializer_range       )
              if module.bias is not None:
                   module.bias.data.zero_()
         elif isinstance(SCREAMING_SNAKE_CASE__       ,     nn.LayerNorm       ):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0       )


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=False       )				->	Tuple:
         if isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ):
              lowerCAmelCase__          =			value






UpperCamelCase								=					R'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

UpperCamelCase								=					R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`PoolFormerImageProcessor.__call__`] for details.\n'





@add_start_docstrings(
    "The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top."     ,    UpperCamelCase__     ,    )
class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Optional[Any]:
         super().__init__(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			config

         lowerCAmelCase__          =			PoolFormerEncoder(SCREAMING_SNAKE_CASE__       )

         # Initialize weights and apply final processing
         self.post_init()


    def        a				(	self					:					Optional[int]       )				->	Optional[Any]:
         return self.embeddings.patch_embeddings


    @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__       )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC       ,     output_type=SCREAMING_SNAKE_CASE__       ,     config_class=_CONFIG_FOR_DOC       ,     modality="vision"       ,     expected_output=_EXPECTED_OUTPUT_SHAPE       ,     )
    def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.FloatTensor] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     )				->	Union[Tuple, BaseModelOutputWithNoAttention]:
         lowerCAmelCase__          =			(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         lowerCAmelCase__          =			return_dict if return_dict is not None else self.config.use_return_dict

         if pixel_values is None:
              raise ValueError("You have to specify pixel_values"       )

         lowerCAmelCase__          =			self.encoder(
             SCREAMING_SNAKE_CASE__       ,     output_hidden_states=SCREAMING_SNAKE_CASE__       ,     return_dict=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			encoder_outputs[0]

         if not return_dict:
              return (sequence_output, None) + encoder_outputs[1:]

         return BaseModelOutputWithNoAttention(
             last_hidden_state=SCREAMING_SNAKE_CASE__       ,     hidden_states=encoder_outputs.hidden_states       ,     )






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	List[str]:
         super().__init__()
         lowerCAmelCase__          =			nn.Linear(config.hidden_size       ,     config.hidden_size       )


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       )				->	int:
         lowerCAmelCase__          =			self.dense(SCREAMING_SNAKE_CASE__       )
         return output






@add_start_docstrings(
    "\n    PoolFormer Model transformer with an image classification head on top\n    "     ,    UpperCamelCase__     ,    )
class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	Optional[int]:
         super().__init__(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			config.num_labels
         lowerCAmelCase__          =			PoolFormerModel(SCREAMING_SNAKE_CASE__       )

         # Final norm
         lowerCAmelCase__          =			PoolFormerGroupNorm(config.hidden_sizes[-1]       )
         # Classifier head
         lowerCAmelCase__          =			(
             nn.Linear(config.hidden_sizes[-1]       ,     config.num_labels       ) if config.num_labels > 0 else nn.Identity()
         )

         # Initialize weights and apply final processing
         self.post_init()


    @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__       )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT       ,     output_type=SCREAMING_SNAKE_CASE__       ,     config_class=_CONFIG_FOR_DOC       ,     expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,     )
    def        a				(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.FloatTensor] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.LongTensor] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     )				->	Union[Tuple, ImageClassifierOutputWithNoAttention]:
         lowerCAmelCase__          =			return_dict if return_dict is not None else self.config.use_return_dict

         lowerCAmelCase__          =			self.poolformer(
             SCREAMING_SNAKE_CASE__       ,     output_hidden_states=SCREAMING_SNAKE_CASE__       ,     return_dict=SCREAMING_SNAKE_CASE__       ,     )

         lowerCAmelCase__          =			outputs[0]

         lowerCAmelCase__          =			self.classifier(self.norm(SCREAMING_SNAKE_CASE__       ).mean([-2, -1]       )       )

         lowerCAmelCase__          =			None
         if labels is not None:
              if self.config.problem_type is None:
                   if self.num_labels == 1:
                        lowerCAmelCase__          =			"regression"
                   elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                        lowerCAmelCase__          =			"single_label_classification"
                   else:
                        lowerCAmelCase__          =			"multi_label_classification"

              if self.config.problem_type == "regression":
                   lowerCAmelCase__          =			MSELoss()
                   if self.num_labels == 1:
                        lowerCAmelCase__          =			loss_fct(logits.squeeze()       ,     labels.squeeze()       )
                   else:
                        lowerCAmelCase__          =			loss_fct(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
              elif self.config.problem_type == "single_label_classification":
                   lowerCAmelCase__          =			CrossEntropyLoss()
                   lowerCAmelCase__          =			loss_fct(logits.view(-1       ,     self.num_labels       )       ,     labels.view(-1       )       )
              elif self.config.problem_type == "multi_label_classification":
                   lowerCAmelCase__          =			BCEWithLogitsLoss()
                   lowerCAmelCase__          =			loss_fct(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         if not return_dict:
              lowerCAmelCase__          =			(logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output

         return ImageClassifierOutputWithNoAttention(loss=SCREAMING_SNAKE_CASE__       ,     logits=SCREAMING_SNAKE_CASE__       ,     hidden_states=outputs.hidden_states       )
