




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 os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
     from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments





@require_torch
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	int:
         for model_result in results.values():
              for batch_size, sequence_length in zip(model_result["bs"]       ,     model_result["ss"]       ):
                   lowerCAmelCase__          =			model_result["result"][batch_size][sequence_length]
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       )				->	Any:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					int       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sgugger/tiny-distilbert-classification"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     only_pretrain_model=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Optional[Any]       )				->	int:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     torchscript=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    @unittest.skipIf(torch_device == "cpu"       ,     "Cant do half precision"       )
    def        a				(	self					:					Dict       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     fpaa=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Union[str, Any]       )				->	Tuple:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         # set architectures equal to `None`
         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Any       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    @unittest.skipIf(torch_device == "cpu"       ,     "Can't do half precision"       )
    def        a				(	self					:					int       )				->	Dict:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     fpaa=SCREAMING_SNAKE_CASE__       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					Optional[int]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Optional[Any]       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tinier_bart"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					List[str]       )				->	Dict:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					Optional[int]       )				->	Optional[int]:
         lowerCAmelCase__          =			"sshleifer/tinier_bart"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					List[Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         with tempfile.TemporaryDirectory() as tmp_dir:
              lowerCAmelCase__          =			PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     save_to_csv=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     inference_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_time.csv"       )       ,     train_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_mem.csv"       )       ,     inference_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_mem.csv"       )       ,     train_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_time.csv"       )       ,     env_info_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "env.csv"       )       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
              lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
              benchmark.run()
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_time.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_time.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_mem.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_mem.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "env.csv"       )       ).exists()       )


    def        a				(	self					:					Optional[Any]       )				->	Any:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"

         def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE__					:					List[Any]       ):
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "sequential"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "cumulative"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "current"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "total"       )       )

         with tempfile.TemporaryDirectory() as tmp_dir:
              lowerCAmelCase__          =			PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     log_filename=os.path.join(SCREAMING_SNAKE_CASE__       ,     "log.txt"       )       ,     log_print=SCREAMING_SNAKE_CASE__       ,     trace_memory_line_by_line=SCREAMING_SNAKE_CASE__       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
              lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			benchmark.run()
              _check_summary_is_not_empty(result.inference_summary       )
              _check_summary_is_not_empty(result.train_summary       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "log.txt"       )       ).exists()       )
