



import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 transformers import (
						    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    MODEL_MAPPING,
						    LevitForImageClassification,
						    LevitForImageClassificationWithTeacher,
						    LevitModel,
						)
						from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import LevitImageProcessor



class   UpperCAmelCase_    (       a):







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    self.config_class(**self.inputs_dict)
														self.parent.assertTrue(hasattr(__a,			"hidden_sizes"))
														self.parent.assertTrue(hasattr(__a,			"num_attention_heads"))



class   UpperCAmelCase_    :







							def __init__(   self,			__a,			__a=13,			__a=64,			__a=3,			__a=3,			__a=2,			__a=1,			__a=16,			__a=[128, 256, 384],			__a=[4, 6, 8],			__a=[2, 3, 4],			__a=[16, 16, 16],			__a=0,			__a=[2, 2, 2],			__a=[2, 2, 2],			__a=0.02,			__a=True,			__a=True,			__a=2,			):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    parent
														_lowerCAmelCase     :							Tuple    =    batch_size
														_lowerCAmelCase     :							Optional[int]    =    image_size
														_lowerCAmelCase     :							Any    =    num_channels
														_lowerCAmelCase     :							Dict    =    kernel_size
														_lowerCAmelCase     :							Optional[int]    =    stride
														_lowerCAmelCase     :							Tuple    =    padding
														_lowerCAmelCase     :							Optional[int]    =    hidden_sizes
														_lowerCAmelCase     :							int    =    num_attention_heads
														_lowerCAmelCase     :							List[str]    =    depths
														_lowerCAmelCase     :							Any    =    key_dim
														_lowerCAmelCase     :							Tuple    =    drop_path_rate
														_lowerCAmelCase     :							str    =    patch_size
														_lowerCAmelCase     :							Optional[Any]    =    attention_ratio
														_lowerCAmelCase     :							Union[str, Any]    =    mlp_ratio
														_lowerCAmelCase     :							List[Any]    =    initializer_range
														_lowerCAmelCase     :							Any    =    [
														    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
														    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
														]
														_lowerCAmelCase     :							int    =    is_training
														_lowerCAmelCase     :							Dict    =    use_labels
														_lowerCAmelCase     :							str    =    num_labels
														_lowerCAmelCase     :							Optional[Any]    =    initializer_range







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

														_lowerCAmelCase     :							Union[str, Any]    =    self.get_config()
														return config, pixel_values, labels







							def    snake_case__      (   self):

														'''simple docstring'''

														return LevitConfig(
														    image_size=self.image_size,			num_channels=self.num_channels,			kernel_size=self.kernel_size,			stride=self.stride,			padding=self.padding,			patch_size=self.patch_size,			hidden_sizes=self.hidden_sizes,			num_attention_heads=self.num_attention_heads,			depths=self.depths,			key_dim=self.key_dim,			drop_path_rate=self.drop_path_rate,			mlp_ratio=self.mlp_ratio,			attention_ratio=self.attention_ratio,			initializer_range=self.initializer_range,			down_ops=self.down_ops,			)







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    LevitModel(config=__a)
														model.to(__a)
														model.eval()
														_lowerCAmelCase     :							Tuple    =    model(__a)
														_lowerCAmelCase     :							int    =    (self.image_size, self.image_size)
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Tuple    =    image_size[0], image_size[1]
														for _ in range(4):
																					_lowerCAmelCase     :							Any    =    floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1)
																					_lowerCAmelCase     :							List[str]    =    floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1)
														self.parent.assertEqual(
														    result.last_hidden_state.shape,			(self.batch_size, ceil(height / 4) * ceil(width / 4), self.hidden_sizes[-1]),			)







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self.num_labels
														_lowerCAmelCase     :							Optional[Any]    =    LevitForImageClassification(__a)
														model.to(__a)
														model.eval()
														_lowerCAmelCase     :							Optional[Any]    =    model(__a,			labels=__a)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_labels))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    self.prepare_config_and_inputs()
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							str    =    config_and_inputs
														_lowerCAmelCase     :							Tuple    =    {"pixel_values": pixel_values}
														return config, inputs_dict



@require_torch
class   UpperCAmelCase_    (       a   ,					a   ,					unittest.TestCase):
							lowerCamelCase__   			=		(
							    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
							    if is_torch_available()
							    else ()
							)
							lowerCamelCase__   			=		(
							    {
							        'feature-extraction': LevitModel,
							        'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
							    }
							    if is_torch_available()
							    else {}
							)

							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    LevitModelTester(self)
														_lowerCAmelCase     :							Optional[Any]    =    ConfigTester(self,			config_class=__a,			has_text_modality=__a,			hidden_size=37)







							def    snake_case__      (   self):

														'''simple docstring'''

														self.create_and_test_config_common_properties()
														self.config_tester.create_and_test_config_to_json_string()
														self.config_tester.create_and_test_config_to_json_file()
														self.config_tester.create_and_test_config_from_and_save_pretrained()
														self.config_tester.create_and_test_config_with_num_labels()
														self.config_tester.check_config_can_be_init_without_params()
														self.config_tester.check_config_arguments_init()







							def    snake_case__      (   self):

														'''simple docstring'''

														return







							@unittest.skip(reason="Levit does not use inputs_embeds")
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							@unittest.skip(reason="Levit does not support input and output embeddings")
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							@unittest.skip(reason="Levit does not output attentions")
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Tuple    =    self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					_lowerCAmelCase     :							Optional[Any]    =    model_class(__a)
																					_lowerCAmelCase     :							Dict    =    inspect.signature(model.forward)
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					_lowerCAmelCase     :							List[Any]    =    [*signature.parameters.keys()]

																					_lowerCAmelCase     :							List[Any]    =    ["pixel_values"]
																					self.assertListEqual(arg_names[:1],			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														def check_hidden_states_output(__a,			__a,			__a):
																					_lowerCAmelCase     :							List[Any]    =    model_class(__a)
																					model.to(__a)
																					model.eval()

																					with torch.no_grad():
																												_lowerCAmelCase     :							int    =    model(**self._prepare_for_class(__a,			__a))

																					_lowerCAmelCase     :							str    =    outputs.hidden_states

																					_lowerCAmelCase     :							str    =    len(self.model_tester.depths) + 1
																					self.assertEqual(len(__a),			__a)

																					_lowerCAmelCase     :							Optional[int]    =    (self.model_tester.image_size, self.model_tester.image_size)
																					_lowerCAmelCase   ,    _lowerCAmelCase     :							List[str]    =    image_size[0], image_size[1]
																					for _ in range(4):
																												_lowerCAmelCase     :							List[str]    =    floor(
																												    (
																												        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																												        / self.model_tester.stride
																												    )
																												    + 1)
																												_lowerCAmelCase     :							Tuple    =    floor(
																												    (
																												        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																												        / self.model_tester.stride
																												    )
																												    + 1)
																					# verify the first hidden states (first block)
																					self.assertListEqual(
																					    list(hidden_states[0].shape[-2:]),			[
																					        height * width,
																					        self.model_tester.hidden_sizes[0],
																					    ],			)

														_lowerCAmelCase   ,    _lowerCAmelCase     :							str    =    self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					_lowerCAmelCase     :							str    =    True
																					check_hidden_states_output(__a,			__a,			__a)

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					_lowerCAmelCase     :							List[str]    =    True

																					check_hidden_states_output(__a,			__a,			__a)







							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							def    snake_case__      (   self,			__a,			__a,			__a=False):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    super()._prepare_for_class(__a,			__a,			return_labels=__a)

														if return_labels:
																					if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																												del inputs_dict["labels"]

														return inputs_dict







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														if not self.model_tester.is_training:
																					return

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Any    =    self.model_tester.prepare_config_and_inputs_for_common()
														_lowerCAmelCase     :							Dict    =    True

														for model_class in self.all_model_classes:
																					# LevitForImageClassificationWithTeacher supports inference-only
																					if (
																					    model_class in get_values(__a)
																					    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																					):
																												continue
																					_lowerCAmelCase     :							int    =    model_class(__a)
																					model.to(__a)
																					model.train()
																					_lowerCAmelCase     :							Union[str, Any]    =    self._prepare_for_class(__a,			__a,			return_labels=__a)
																					_lowerCAmelCase     :							Any    =    model(**__a).loss
																					loss.backward()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Dict    =    self.model_tester.prepare_config_and_inputs_for_common()
														if not self.model_tester.is_training:
																					return

														_lowerCAmelCase     :							int    =    False
														_lowerCAmelCase     :							Union[str, Any]    =    True

														for model_class in self.all_model_classes:
																					if model_class in get_values(__a) or not model_class.supports_gradient_checkpointing:
																												continue
																					# LevitForImageClassificationWithTeacher supports inference-only
																					if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																												continue
																					_lowerCAmelCase     :							Dict    =    model_class(__a)
																					model.gradient_checkpointing_enable()
																					model.to(__a)
																					model.train()
																					_lowerCAmelCase     :							Any    =    self._prepare_for_class(__a,			__a,			return_labels=__a)
																					_lowerCAmelCase     :							List[Any]    =    model(**__a).loss
																					loss.backward()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Dict    =    self.model_tester.prepare_config_and_inputs_for_common()

														_lowerCAmelCase     :							Optional[int]    =    [
														    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
														    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
														    {"title": "regression", "num_labels": 1, "dtype": torch.float},
														]

														for model_class in self.all_model_classes:
																					if (
																					    model_class
																					    not in [
																					        *get_values(__a),
																					    ]
																					    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																					):
																												continue

																					for problem_type in problem_types:
																												with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):
																																			_lowerCAmelCase     :							List[str]    =    problem_type["title"]
																																			_lowerCAmelCase     :							Dict    =    problem_type["num_labels"]

																																			_lowerCAmelCase     :							int    =    model_class(__a)
																																			model.to(__a)
																																			model.train()

																																			_lowerCAmelCase     :							Any    =    self._prepare_for_class(__a,			__a,			return_labels=__a)

																																			if problem_type["num_labels"] > 1:
																																										_lowerCAmelCase     :							List[str]    =    inputs["labels"].unsqueeze(1).repeat(1,			problem_type["num_labels"])

																																			_lowerCAmelCase     :							Optional[Any]    =    inputs["labels"].to(problem_type["dtype"])

																																			# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																																			# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																																			# they have the same size." which is a symptom something in wrong for the regression problem.
																																			# See https://github.com/huggingface/transformers/issues/11780
																																			with warnings.catch_warnings(record=__a) as warning_list:
																																										_lowerCAmelCase     :							int    =    model(**__a).loss
																																			for w in warning_list:
																																										if "Using a target size that is different to the input size" in str(w.message):
																																																	raise ValueError(
																																																	    f"Something is going wrong in the regression problem: intercepted {w.message}")

																																			loss.backward()







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_lowerCAmelCase     :							int    =    LevitModel.from_pretrained(__a)
																					self.assertIsNotNone(__a)






def 				A				(					):





							'''simple docstring'''




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



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







							@cached_property
							def    snake_case__      (   self):

														'''simple docstring'''

														return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0])







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
														    __a)

														_lowerCAmelCase     :							Optional[int]    =    self.default_image_processor
														_lowerCAmelCase     :							Optional[int]    =    prepare_img()
														_lowerCAmelCase     :							int    =    image_processor(images=__a,			return_tensors="pt").to(__a)

														# forward pass
														with torch.no_grad():
																					_lowerCAmelCase     :							Union[str, Any]    =    model(**__a)

														# verify the logits
														_lowerCAmelCase     :							Dict    =    torch.Size((1, 1000))
														self.assertEqual(outputs.logits.shape,			__a)

														_lowerCAmelCase     :							Tuple    =    torch.tensor([1.0_448, -0.3_745, -1.8_317]).to(__a)

														self.assertTrue(torch.allclose(outputs.logits[0, :3],			__a,			atol=1E-4))


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 				lowercase					(		unittest.TestCase   ):



		def __UpperCamelCase ( self				,      A_       )  ->					List[str]:
				"""simple docstring"""
				for model_result in results.values():
						for batch_size, sequence_length in zip(model_result['bs']				,      model_result['ss']       ):
								UpperCamelCase		=     model_result['result'][batch_size][sequence_length]
								self.assertIsNotNone(A_       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     'sgugger/tiny-distilbert-classification'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      only_pretrain_model=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      torchscript=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     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 __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      fpaa=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				# set architectures equal to `None`
				UpperCamelCase		=     None
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      configs=[config]       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     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 __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      fpaa=A_				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result       )
				self.check_results_dict_not_empty(results.memory_train_result       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      configs=[config]       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



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



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      configs=[config]       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result       )
				self.check_results_dict_not_empty(results.memory_train_result       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tinier_bart'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      configs=[config]       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result       )
				self.check_results_dict_not_empty(results.memory_train_result       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				with tempfile.TemporaryDirectory() as tmp_dir:
						UpperCamelCase		=     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      save_to_csv=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      inference_time_csv_file=os.path.join(A_				,      'inf_time.csv'       )				,      train_memory_csv_file=os.path.join(A_				,      'train_mem.csv'       )				,      inference_memory_csv_file=os.path.join(A_				,      'inf_mem.csv'       )				,      train_time_csv_file=os.path.join(A_				,      'train_time.csv'       )				,      env_info_csv_file=os.path.join(A_				,      'env.csv'       )				,      multi_process=A_				,      )
						UpperCamelCase		=     PyTorchBenchmark(A_       )
						benchmark.run()
						self.assertTrue(Path(os.path.join(A_				,      'inf_time.csv'       )       ).exists()       )
						self.assertTrue(Path(os.path.join(A_				,      'train_time.csv'       )       ).exists()       )
						self.assertTrue(Path(os.path.join(A_				,      'inf_mem.csv'       )       ).exists()       )
						self.assertTrue(Path(os.path.join(A_				,      'train_mem.csv'       )       ).exists()       )
						self.assertTrue(Path(os.path.join(A_				,      'env.csv'       )       ).exists()       )



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'

				def _check_summary_is_not_empty(A_       ):
						self.assertTrue(hasattr(A_				,      'sequential'       )       )
						self.assertTrue(hasattr(A_				,      'cumulative'       )       )
						self.assertTrue(hasattr(A_				,      'current'       )       )
						self.assertTrue(hasattr(A_				,      'total'       )       )

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