




import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling




def 							_UpperCAmelCase  (UpperCamelCase_ : List[Any]      ,    UpperCamelCase_ : Dict       ):
				'''simple docstring'''
				_lowerCAmelCase    :    int										=      old_name

				if "patch_embed" in old_name:
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    List[str]										=      old_name.split("""."""       )

								if layer == "0":
												_lowerCAmelCase    :    List[Any]										=      old_name.replace("""0"""      ,    """convolution1"""       )
								elif layer == "1":
												_lowerCAmelCase    :    Union[str, Any]										=      old_name.replace("""1"""      ,    """batchnorm_before"""       )
								elif layer == "3":
												_lowerCAmelCase    :    List[str]										=      old_name.replace("""3"""      ,    """convolution2"""       )
								else:
												_lowerCAmelCase    :    Dict										=      old_name.replace("""4"""      ,    """batchnorm_after"""       )

				if "network" in old_name and re.search(R"""\d\.\d"""      ,    UpperCamelCase_       ):
								_lowerCAmelCase    :    Optional[Any]										=      R"""\b\d{2}\b"""
								if bool(re.search(UpperCamelCase_      ,    UpperCamelCase_       )       ):
												_lowerCAmelCase    :    Any										=      re.search(R"""\d\.\d\d."""      ,    UpperCamelCase_       ).group()
								else:
												_lowerCAmelCase    :    str										=      re.search(R"""\d\.\d."""      ,    UpperCamelCase_       ).group()
								if int(match[0]       ) < 6:
												_lowerCAmelCase    :    List[str]										=      old_name.replace(UpperCamelCase_      ,    """"""       )
												_lowerCAmelCase    :    Dict										=      trimmed_name.replace("""network"""      ,    match[0] + """.meta4D_layers.blocks.""" + match[2:-1]       )
												_lowerCAmelCase    :    Any										=      """intermediate_stages.""" + trimmed_name
								else:
												_lowerCAmelCase    :    Tuple										=      old_name.replace(UpperCamelCase_      ,    """"""       )
												if int(match[2]       ) < num_meta4D_last_stage:
																_lowerCAmelCase    :    Dict										=      trimmed_name.replace("""network"""      ,    """meta4D_layers.blocks.""" + match[2]       )
												else:
																_lowerCAmelCase    :    Any										=      str(int(match[2]       ) - num_meta4D_last_stage       )
																_lowerCAmelCase    :    int										=      trimmed_name.replace("""network"""      ,    """meta3D_layers.blocks.""" + layer_index       )
																if "norm1" in old_name:
																				_lowerCAmelCase    :    str										=      trimmed_name.replace("""norm1"""      ,    """layernorm1"""       )
																elif "norm2" in old_name:
																				_lowerCAmelCase    :    Any										=      trimmed_name.replace("""norm2"""      ,    """layernorm2"""       )
																elif "fc1" in old_name:
																				_lowerCAmelCase    :    Dict										=      trimmed_name.replace("""fc1"""      ,    """linear_in"""       )
																elif "fc2" in old_name:
																				_lowerCAmelCase    :    str										=      trimmed_name.replace("""fc2"""      ,    """linear_out"""       )

												_lowerCAmelCase    :    int										=      """last_stage.""" + trimmed_name

				elif "network" in old_name and re.search(R""".\d."""      ,    UpperCamelCase_       ):
								_lowerCAmelCase    :    Tuple										=      old_name.replace("""network"""      ,    """intermediate_stages"""       )

				if "fc" in new_name:
								_lowerCAmelCase    :    Any										=      new_name.replace("""fc"""      ,    """convolution"""       )
				elif ("norm1" in new_name) and ("layernorm1" not in new_name):
								_lowerCAmelCase    :    List[str]										=      new_name.replace("""norm1"""      ,    """batchnorm_before"""       )
				elif ("norm2" in new_name) and ("layernorm2" not in new_name):
								_lowerCAmelCase    :    Dict										=      new_name.replace("""norm2"""      ,    """batchnorm_after"""       )
				if "proj" in new_name:
								_lowerCAmelCase    :    Union[str, Any]										=      new_name.replace("""proj"""      ,    """projection"""       )
				if "dist_head" in new_name:
								_lowerCAmelCase    :    List[str]										=      new_name.replace("""dist_head"""      ,    """distillation_classifier"""       )
				elif "head" in new_name:
								_lowerCAmelCase    :    List[Any]										=      new_name.replace("""head"""      ,    """classifier"""       )
				elif "patch_embed" in new_name:
								_lowerCAmelCase    :    List[Any]										=      """efficientformer.""" + new_name
				elif new_name == "norm.weight" or new_name == "norm.bias":
								_lowerCAmelCase    :    Optional[Any]										=      new_name.replace("""norm"""      ,    """layernorm"""       )
								_lowerCAmelCase    :    Optional[int]										=      """efficientformer.""" + new_name
				else:
								_lowerCAmelCase    :    List[Any]										=      """efficientformer.encoder.""" + new_name

				return new_name




def 							_UpperCAmelCase  (UpperCamelCase_ : List[Any]      ,    UpperCamelCase_ : List[str]       ):
				'''simple docstring'''


				for key in checkpoint.copy().keys():
								_lowerCAmelCase    :    Any										=      checkpoint.pop(UpperCamelCase_       )
								_lowerCAmelCase    :    str										=      val

				return checkpoint




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    Optional[Any]										=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
				_lowerCAmelCase    :    Union[str, Any]										=      Image.open(requests.get(UpperCamelCase_      ,    stream=UpperCamelCase_       ).raw       )

				return image




def 							_UpperCAmelCase  (UpperCamelCase_ : Path      ,    UpperCamelCase_ : Path      ,    UpperCamelCase_ : Path      ,    UpperCamelCase_ : bool       ):
				'''simple docstring'''
				_lowerCAmelCase    :    int										=      torch.load(UpperCamelCase_      ,    map_location="""cpu"""       )["""model"""]
				_lowerCAmelCase    :    str										=      EfficientFormerConfig.from_json_file(UpperCamelCase_       )
				_lowerCAmelCase    :    Optional[int]										=      EfficientFormerForImageClassificationWithTeacher(UpperCamelCase_       )
				_lowerCAmelCase    :    Tuple										=      """_""".join(checkpoint_path.split("""/"""       )[-1].split("""."""       )[0].split("""_"""       )[:-1]       )

				_lowerCAmelCase    :    Optional[int]										=      config.depths[-1] - config.num_metaad_blocks + 1
				_lowerCAmelCase    :    List[str]										=      convert_torch_checkpoint(UpperCamelCase_      ,    UpperCamelCase_       )

				model.load_state_dict(UpperCamelCase_       )
				model.eval()

				_lowerCAmelCase    :    int										=      {
				    """bilinear""": PILImageResampling.BILINEAR,
				    """bicubic""": PILImageResampling.BICUBIC,
				    """nearest""": PILImageResampling.NEAREST,
				}

				# prepare image
				_lowerCAmelCase    :    List[Any]										=      prepare_img()
				_lowerCAmelCase    :    Optional[Any]										=      256
				_lowerCAmelCase    :    Union[str, Any]										=      224
				_lowerCAmelCase    :    Optional[int]										=      EfficientFormerImageProcessor(
				    size={"""shortest_edge""": image_size}      ,    crop_size={"""height""": crop_size, """width""": crop_size}      ,    resample=pillow_resamplings["""bicubic"""]      ,    )
				_lowerCAmelCase    :    Optional[Any]										=      processor(images=UpperCamelCase_      ,    return_tensors="""pt"""       ).pixel_values

				# original processing pipeline
				_lowerCAmelCase    :    List[Any]										=      Compose(
				    [
				        Resize(UpperCamelCase_      ,    interpolation=pillow_resamplings["""bicubic"""]       ),
				        CenterCrop(UpperCamelCase_       ),
				        ToTensor(),
				        Normalize(UpperCamelCase_      ,    UpperCamelCase_       ),
				    ]       )
				_lowerCAmelCase    :    str										=      image_transforms(UpperCamelCase_       ).unsqueeze(0       )

				assert torch.allclose(UpperCamelCase_      ,    UpperCamelCase_       )

				_lowerCAmelCase    :    Optional[int]										=      model(UpperCamelCase_       )
				_lowerCAmelCase    :    Tuple										=      outputs.logits

				_lowerCAmelCase    :    int										=      (1, 1000)

				if "l1" in model_name:
								_lowerCAmelCase    :    Optional[int]										=      torch.Tensor(
								    [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]       )
								assert torch.allclose(logits[0, :10]      ,    UpperCamelCase_      ,    atol=1E-3       )
								assert logits.shape == expected_shape
				elif "l3" in model_name:
								_lowerCAmelCase    :    str										=      torch.Tensor(
								    [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]       )
								assert torch.allclose(logits[0, :10]      ,    UpperCamelCase_      ,    atol=1E-3       )
								assert logits.shape == expected_shape
				elif "l7" in model_name:
								_lowerCAmelCase    :    List[str]										=      torch.Tensor(
								    [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]       )
								assert logits.shape == expected_shape
				else:
								raise ValueError(
								    F"Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"       )

				# Save Checkpoints
				Path(UpperCamelCase_       ).mkdir(exist_ok=UpperCamelCase_       )
				model.save_pretrained(UpperCamelCase_       )
				print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"       )
				processor.save_pretrained(UpperCamelCase_       )
				print(F"Processor successfuly saved at {pytorch_dump_path}"       )

				if push_to_hub:
								print("""Pushing model to the hub..."""       )

								model.push_to_hub(
								    repo_id=F"Bearnardd/{pytorch_dump_path}"      ,    commit_message="""Add model"""      ,    use_temp_dir=UpperCamelCase_      ,    )
								processor.push_to_hub(
								    repo_id=F"Bearnardd/{pytorch_dump_path}"      ,    commit_message="""Add image processor"""      ,    use_temp_dir=UpperCamelCase_      ,    )


if __name__ == "__main__":
					_lowerCamelCase      :  str      						=						argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--pytorch_model_path",
					    default=None,
					    type=str,
					    required=True,
					    help="Path to EfficientFormer pytorch checkpoint.",
					)
					parser.add_argument(
					    "--config_file",
					    default=None,
					    type=str,
					    required=True,
					    help="The json file for EfficientFormer model config.",
					)
					parser.add_argument(
					    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
					)

					parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
					parser.add_argument(
					    "--no-push_to_hub",
					    dest="push_to_hub",
					    action="store_false",
					    help="Do not push model and image processor to the hub",
					)
					parser.set_defaults(push_to_hub=True)

					_lowerCamelCase      :  int      						=						parser.parse_args()
					convert_efficientformer_checkpoint(
					    checkpoint_path=args.pytorch_model_path,
					    efficientformer_config_file=args.config_file,
					    pytorch_dump_path=args.pytorch_dump_path,
					    push_to_hub=args.push_to_hub,
					)







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        __snake_case  (unittest.TestCase		):



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		List[str]      )  ->    List[str]:





								'''simple docstring'''




								for model_result in results.values():
												for batch_size, sequence_length in zip(model_result["""bs"""]		,	model_result["""ss"""]      ):
																_lowerCAmelCase    :    List[Any]										=      model_result["""result"""][batch_size][sequence_length]
																self.assertIsNotNone(_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Dict										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    str										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[int]										=      benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result      )
								self.check_results_dict_not_empty(results.memory_inference_result      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      """sgugger/tiny-distilbert-classification"""
								_lowerCAmelCase    :    Tuple										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	only_pretrain_model=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result      )
								self.check_results_dict_not_empty(results.memory_inference_result      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	torchscript=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      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       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Optional[Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	fpaa=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result      )
								self.check_results_dict_not_empty(results.memory_inference_result      )



				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    int:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    Union[str, Any]										=      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       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Dict										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	fpaa=_UpperCAmelCase		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result      )
								self.check_results_dict_not_empty(results.memory_train_result      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    List[str]:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      """sshleifer/tinier_bart"""
								_lowerCAmelCase    :    Tuple										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Tuple										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    Any										=      benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result      )
								self.check_results_dict_not_empty(results.memory_inference_result      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Optional[int]										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Optional[Any]										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    Optional[Any]										=      benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result      )
								self.check_results_dict_not_empty(results.memory_train_result      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      """sshleifer/tinier_bart"""
								_lowerCAmelCase    :    Dict										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Optional[Any]										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    int										=      benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result      )
								self.check_results_dict_not_empty(results.memory_train_result      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    int:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      """sshleifer/tiny-gpt2"""

								def _check_summary_is_not_empty(_UpperCAmelCase      :		int      ):
												self.assertTrue(hasattr(_UpperCAmelCase		,	"""sequential"""      )      )
												self.assertTrue(hasattr(_UpperCAmelCase		,	"""cumulative"""      )      )
												self.assertTrue(hasattr(_UpperCAmelCase		,	"""current"""      )      )
												self.assertTrue(hasattr(_UpperCAmelCase		,	"""total"""      )      )

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

