






"""simple docstring"""


import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import SwiftFormerForImageClassification, SwiftFormerModel
			from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor






class 						snake_case  :





				def __init__(							self     : List[str]    ,		A     : Any    ,		A     : List[Any]=1_3    ,		A     : Optional[Any]=3    ,		A     : Union[str, Any]=True    ,		A     : List[Any]=True    ,		A     : Optional[Any]=0.1    ,		A     : int=0.1    ,		A     : Dict=2_2_4    ,		A     : int=1_0_0_0    ,		A     : List[str]=[3, 3, 6, 4]    ,		A     : Dict=[4_8, 5_6, 1_1_2, 2_2_0]    ,		):


						'''simple docstring'''




						a					:      Union[str, Any]    =       parent
						a					:      Union[str, Any]    =       batch_size
						a					:      Tuple    =       num_channels
						a					:      Optional[int]    =       is_training
						a					:      Union[str, Any]    =       use_labels
						a					:      Tuple    =       hidden_dropout_prob
						a					:      str    =       attention_probs_dropout_prob
						a					:      List[str]    =       num_labels
						a					:      Any    =       image_size
						a					:      Union[str, Any]    =       layer_depths
						a					:      List[Any]    =       embed_dims





				def     lowerCamelCase__				(							self     : Union[str, Any]      ):


						'''simple docstring'''




						a					:      int    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

						a					:      Tuple    =       None
						if self.use_labels:
								a					:      Dict    =       ids_tensor([self.batch_size]    ,		self.num_labels      )

						a					:      Optional[Any]    =       self.get_config()

						return config, pixel_values, labels





				def     lowerCamelCase__				(							self     : Optional[Any]      ):


						'''simple docstring'''




						return SwiftFormerConfig(
						    depths=self.layer_depths    ,		embed_dims=self.embed_dims    ,		mlp_ratio=4    ,		downsamples=[True, True, True, True]    ,		hidden_act='gelu'    ,		num_labels=self.num_labels    ,		down_patch_size=3    ,		down_stride=2    ,		down_pad=1    ,		drop_rate=0.0    ,		drop_path_rate=0.0    ,		use_layer_scale=A    ,		layer_scale_init_value=1E-5    ,		)





				def     lowerCamelCase__				(							self     : Dict    ,		A     : Tuple    ,		A     : int    ,		A     : Union[str, Any]      ):


						'''simple docstring'''




						a					:      Dict    =       SwiftFormerModel(config=A      )
						model.to(A      )
						model.eval()
						a					:      int    =       model(A      )
						self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.embed_dims[-1], 7, 7)      )





				def     lowerCamelCase__				(							self     : Tuple    ,		A     : Optional[Any]    ,		A     : Union[str, Any]    ,		A     : Union[str, Any]      ):


						'''simple docstring'''




						a					:      Any    =       self.num_labels
						a					:      Tuple    =       SwiftFormerForImageClassification(A      )
						model.to(A      )
						model.eval()
						a					:      Tuple    =       model(A    ,		labels=A      )
						self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)      )

						a					:      List[Any]    =       SwiftFormerForImageClassification(A      )
						model.to(A      )
						model.eval()

						a					:      Optional[int]    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )
						a					:      str    =       model(A      )
						self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)      )





				def     lowerCamelCase__				(							self     : Dict      ):


						'''simple docstring'''




						(a)					:      int    =       self.prepare_config_and_inputs()
						a					:      List[Any]    =       {'pixel_values': pixel_values}
						return config, inputs_dict






@require_torch
class 						snake_case  (  UpperCAmelCase       ,			UpperCAmelCase       ,			unittest.TestCase	):
				__magic_name__   					=					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
				__magic_name__   					=					(
				    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__magic_name__   					=					False
				__magic_name__   					=					False
				__magic_name__   					=					False
				__magic_name__   					=					False
				__magic_name__   					=					False





				def     lowerCamelCase__				(							self     : Optional[Any]      ):


						'''simple docstring'''




						a					:      str    =       SwiftFormerModelTester(self      )
						a					:      Optional[Any]    =       ConfigTester(
						    self    ,		config_class=A    ,		has_text_modality=A    ,		hidden_size=3_7    ,		num_attention_heads=1_2    ,		num_hidden_layers=1_2    ,		)





				def     lowerCamelCase__				(							self     : Optional[Any]      ):


						'''simple docstring'''




						self.config_tester.run_common_tests()





				@unittest.skip(reason='SwiftFormer does not use inputs_embeds'      )
				def     lowerCamelCase__				(							self     : Union[str, Any]      ):


						'''simple docstring'''




						pass





				def     lowerCamelCase__				(							self     : List[Any]      ):


						'''simple docstring'''




						a					:      List[Any]    =       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
								a					:      Dict    =       model_class(A      )
								a					:      Any    =       model.get_output_embeddings()
								self.assertTrue(x is None or isinstance(A    ,		nn.Linear      )      )





				def     lowerCamelCase__				(							self     : int      ):


						'''simple docstring'''




						a					:      List[Any]    =       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
								a					:      List[str]    =       model_class(A      )
								a					:      Optional[int]    =       inspect.signature(model.forward      )
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								a					:      Optional[int]    =       [*signature.parameters.keys()]

								a					:      Any    =       ['pixel_values']
								self.assertListEqual(arg_names[:1]    ,		A      )





				def     lowerCamelCase__				(							self     : str      ):


						'''simple docstring'''




						a					:      Union[str, Any]    =       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A      )





				def     lowerCamelCase__				(							self     : List[Any]      ):


						'''simple docstring'''




						a					:      Any    =       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A      )





				@slow
				def     lowerCamelCase__				(							self     : Tuple      ):


						'''simple docstring'''




						for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								a					:      List[str]    =       SwiftFormerModel.from_pretrained(A      )
								self.assertIsNotNone(A      )





				@unittest.skip(reason='SwiftFormer does not output attentions'      )
				def     lowerCamelCase__				(							self     : List[str]      ):


						'''simple docstring'''




						pass





				def     lowerCamelCase__				(							self     : List[str]      ):


						'''simple docstring'''




						def check_hidden_states_output(A     : Any    ,		A     : Union[str, Any]    ,		A     : int      ):
								a					:      Optional[Any]    =       model_class(A      )
								model.to(A      )
								model.eval()

								with torch.no_grad():
										a					:      List[Any]    =       model(**self._prepare_for_class(A    ,		A      )      )

								a					:      Optional[Any]    =       outputs.hidden_states

								a					:      Union[str, Any]    =       8
								self.assertEqual(len(A      )    ,		A      )  # TODO

								# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
								# with the width and height being successively divided by 2, after every 2 blocks
								for i in range(len(A      )      ):
										self.assertEqual(
										    hidden_states[i].shape    ,		torch.Size(
										        [
										            self.model_tester.batch_size,
										            self.model_tester.embed_dims[i // 2],
										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
										        ]      )    ,		)

						a					:      Dict    =       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
								a					:      List[str]    =       True
								check_hidden_states_output(A    ,		A    ,		A      )

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								a					:      Optional[int]    =       True

								check_hidden_states_output(A    ,		A    ,		A      )





				def     lowerCamelCase__				(							self     : Union[str, Any]      ):


						'''simple docstring'''




						def _config_zero_init(A     : List[str]      ):
								a					:      Any    =       copy.deepcopy(A      )
								for key in configs_no_init.__dict__.keys():
										if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
												setattr(A    ,		A    ,		1E-10      )
										if isinstance(getattr(A    ,		A    ,		A      )    ,		A      ):
												a					:      Any    =       _config_zero_init(getattr(A    ,		A      )      )
												setattr(A    ,		A    ,		A      )
								return configs_no_init

						a					:      int    =       self.model_tester.prepare_config_and_inputs_for_common()

						a					:      Optional[int]    =       _config_zero_init(A      )
						for model_class in self.all_model_classes:
								a					:      Optional[Any]    =       model_class(config=A      )
								for name, param in model.named_parameters():
										if param.requires_grad:
												self.assertIn(
												    ((param.data.mean() * 1E9) / 1E9).round().item()    ,		[0.0, 1.0]    ,		msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''    ,		)





				@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
				def     lowerCamelCase__				(							self     : Union[str, Any]      ):


						'''simple docstring'''




						pass




def       snake_case  ():







		'''simple docstring'''

		a					:      Optional[Any]    =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
		return image






@require_torch
@require_vision
class 						snake_case  (  unittest.TestCase	):





				@cached_property
				def     lowerCamelCase__				(							self     : Tuple      ):


						'''simple docstring'''




						return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'      ) if is_vision_available() else None





				@slow
				def     lowerCamelCase__				(							self     : Dict      ):


						'''simple docstring'''




						a					:      str    =       SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'      ).to(A      )

						a					:      Optional[int]    =       self.default_image_processor
						a					:      List[str]    =       prepare_img()
						a					:      Any    =       image_processor(images=A    ,		return_tensors='pt'      ).to(A      )

						# forward pass
						with torch.no_grad():
								a					:      List[str]    =       model(**A      )

						# verify the logits
						a					:      Optional[int]    =       torch.Size((1, 1_0_0_0)      )
						self.assertEqual(outputs.logits.shape    ,		A      )

						a					:      Union[str, Any]    =       torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]      ).to(A      )
						self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,		A    ,		atol=1E-4      )      )












"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase    :   Union[str, Any]   			=						logging.get_logger(__name__)

_UpperCamelCase    :   List[Any]   			=						{
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					'''beit'''





    def __init__(							self     : int    ,		A     : int=8_1_9_2    ,		A     : List[Any]=7_6_8    ,		A     : str=1_2    ,		A     : str=1_2    ,		A     : Dict=3_0_7_2    ,		A     : Optional[int]="gelu"    ,		A     : List[Any]=0.0    ,		A     : Union[str, Any]=0.0    ,		A     : Optional[Any]=0.02    ,		A     : Optional[int]=1E-12    ,		A     : Dict=2_2_4    ,		A     : str=1_6    ,		A     : Optional[Any]=3    ,		A     : List[Any]=False    ,		A     : Union[str, Any]=False    ,		A     : Optional[Any]=False    ,		A     : int=False    ,		A     : List[str]=0.1    ,		A     : Union[str, Any]=0.1    ,		A     : str=True    ,		A     : Tuple=[3, 5, 7, 1_1]    ,		A     : List[str]=[1, 2, 3, 6]    ,		A     : Optional[Any]=True    ,		A     : Union[str, Any]=0.4    ,		A     : Any=2_5_6    ,		A     : List[Any]=1    ,		A     : Optional[Any]=False    ,		A     : Any=2_5_5    ,		**A     : List[Any]    ,		):


      '''simple docstring'''




      super().__init__(**A      )

      a					:      Optional[int]    =       vocab_size
      a					:      Dict    =       hidden_size
      a					:      Optional[int]    =       num_hidden_layers
      a					:      Tuple    =       num_attention_heads
      a					:      Optional[int]    =       intermediate_size
      a					:      Optional[Any]    =       hidden_act
      a					:      Optional[int]    =       hidden_dropout_prob
      a					:      Optional[int]    =       attention_probs_dropout_prob
      a					:      Optional[Any]    =       initializer_range
      a					:      Union[str, Any]    =       layer_norm_eps

      a					:      Union[str, Any]    =       image_size
      a					:      str    =       patch_size
      a					:      Optional[Any]    =       num_channels
      a					:      List[str]    =       use_mask_token
      a					:      Optional[Any]    =       use_absolute_position_embeddings
      a					:      Any    =       use_relative_position_bias
      a					:      Any    =       use_shared_relative_position_bias
      a					:      Dict    =       layer_scale_init_value
      a					:      Optional[int]    =       drop_path_rate
      a					:      Dict    =       use_mean_pooling
      # decode head attributes (semantic segmentation)
      a					:      Optional[Any]    =       out_indices
      a					:      List[str]    =       pool_scales
      # auxiliary head attributes (semantic segmentation)
      a					:      Tuple    =       use_auxiliary_head
      a					:      Dict    =       auxiliary_loss_weight
      a					:      Any    =       auxiliary_channels
      a					:      Dict    =       auxiliary_num_convs
      a					:      List[str]    =       auxiliary_concat_input
      a					:      List[Any]    =       semantic_loss_ignore_index






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					version.parse('''1.11'''	)





    @property
    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




      return OrderedDict(
          [
              ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
          ]      )





    @property
    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      return 1E-4




