
import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)


def       A       (					lowercase )   ->  Tuple:
		'''simple docstring'''

		UpperCamelCase		=     VideoMAEConfig()

		set_architecture_configs(lowercase    ,       lowercase )

		if "finetuned" not in model_name:
				UpperCamelCase		=     False

		if "finetuned" in model_name:
				UpperCamelCase		=     'huggingface/label-files'
				if "kinetics" in model_name:
						UpperCamelCase		=     400
						UpperCamelCase		=     'kinetics400-id2label.json'
				elif "ssv2" in model_name:
						UpperCamelCase		=     174
						UpperCamelCase		=     'something-something-v2-id2label.json'
				else:
						raise ValueError('Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.' )
				UpperCamelCase		=     json.load(open(hf_hub_download(lowercase    ,       lowercase    ,       repo_type='dataset' )    ,       'r' ) )
				UpperCamelCase		=     {int(lowercase ): v for k, v in idalabel.items()}
				UpperCamelCase		=     idalabel
				UpperCamelCase		=     {v: k for k, v in idalabel.items()}

		return config


def       A       (					lowercase    ,       lowercase )   ->  Optional[int]:
		'''simple docstring'''

		if "small" in model_name:
				UpperCamelCase		=     384
				UpperCamelCase		=     1_536
				UpperCamelCase		=     12
				UpperCamelCase		=     16
				UpperCamelCase		=     12
				UpperCamelCase		=     3
				UpperCamelCase		=     192
				UpperCamelCase		=     768
		elif "large" in model_name:
				UpperCamelCase		=     1_024
				UpperCamelCase		=     4_096
				UpperCamelCase		=     24
				UpperCamelCase		=     16
				UpperCamelCase		=     12
				UpperCamelCase		=     8
				UpperCamelCase		=     512
				UpperCamelCase		=     2_048
		elif "huge" in model_name:
				UpperCamelCase		=     1_280
				UpperCamelCase		=     5_120
				UpperCamelCase		=     32
				UpperCamelCase		=     16
				UpperCamelCase		=     12
				UpperCamelCase		=     8
				UpperCamelCase		=     640
				UpperCamelCase		=     2_560
		elif "base" not in model_name:
				raise ValueError('Model name should include either "small", "base", "large", or "huge"' )


def       A       (					lowercase )   ->  Any:
		'''simple docstring'''

		if "encoder." in name:
				UpperCamelCase		=     name.replace('encoder.'    ,       '' )
		if "cls_token" in name:
				UpperCamelCase		=     name.replace('cls_token'    ,       'videomae.embeddings.cls_token' )
		if "decoder_pos_embed" in name:
				UpperCamelCase		=     name.replace('decoder_pos_embed'    ,       'decoder.decoder_pos_embed' )
		if "pos_embed" in name and "decoder" not in name:
				UpperCamelCase		=     name.replace('pos_embed'    ,       'videomae.embeddings.position_embeddings' )
		if "patch_embed.proj" in name:
				UpperCamelCase		=     name.replace('patch_embed.proj'    ,       'videomae.embeddings.patch_embeddings.projection' )
		if "patch_embed.norm" in name:
				UpperCamelCase		=     name.replace('patch_embed.norm'    ,       'videomae.embeddings.norm' )
		if "decoder.blocks" in name:
				UpperCamelCase		=     name.replace('decoder.blocks'    ,       'decoder.decoder_layers' )
		if "blocks" in name:
				UpperCamelCase		=     name.replace('blocks'    ,       'videomae.encoder.layer' )
		if "attn.proj" in name:
				UpperCamelCase		=     name.replace('attn.proj'    ,       'attention.output.dense' )
		if "attn" in name and "bias" not in name:
				UpperCamelCase		=     name.replace('attn'    ,       'attention.self' )
		if "attn" in name:
				UpperCamelCase		=     name.replace('attn'    ,       'attention.attention' )
		if "norm1" in name:
				UpperCamelCase		=     name.replace('norm1'    ,       'layernorm_before' )
		if "norm2" in name:
				UpperCamelCase		=     name.replace('norm2'    ,       'layernorm_after' )
		if "mlp.fc1" in name:
				UpperCamelCase		=     name.replace('mlp.fc1'    ,       'intermediate.dense' )
		if "mlp.fc2" in name:
				UpperCamelCase		=     name.replace('mlp.fc2'    ,       'output.dense' )
		if "decoder_embed" in name:
				UpperCamelCase		=     name.replace('decoder_embed'    ,       'decoder.decoder_embed' )
		if "decoder_norm" in name:
				UpperCamelCase		=     name.replace('decoder_norm'    ,       'decoder.decoder_norm' )
		if "decoder_pred" in name:
				UpperCamelCase		=     name.replace('decoder_pred'    ,       'decoder.decoder_pred' )
		if "norm.weight" in name and "decoder" not in name and "fc" not in name:
				UpperCamelCase		=     name.replace('norm.weight'    ,       'videomae.layernorm.weight' )
		if "norm.bias" in name and "decoder" not in name and "fc" not in name:
				UpperCamelCase		=     name.replace('norm.bias'    ,       'videomae.layernorm.bias' )
		if "head" in name and "decoder" not in name:
				UpperCamelCase		=     name.replace('head'    ,       'classifier' )

		return name


def       A       (					lowercase    ,       lowercase )   ->  List[str]:
		'''simple docstring'''

		for key in orig_state_dict.copy().keys():
				UpperCamelCase		=     orig_state_dict.pop(lowercase )

				if key.startswith('encoder.' ):
						UpperCamelCase		=     key.replace('encoder.'    ,       '' )

				if "qkv" in key:
						UpperCamelCase		=     key.split('.' )
						if key.startswith('decoder.blocks' ):
								UpperCamelCase		=     config.decoder_hidden_size
								UpperCamelCase		=     int(key_split[2] )
								UpperCamelCase		=     'decoder.decoder_layers.'
								if "weight" in key:
										UpperCamelCase		=     val[:dim, :]
										UpperCamelCase		=     val[dim : dim * 2, :]
										UpperCamelCase		=     val[-dim:, :]
						else:
								UpperCamelCase		=     config.hidden_size
								UpperCamelCase		=     int(key_split[1] )
								UpperCamelCase		=     'videomae.encoder.layer.'
								if "weight" in key:
										UpperCamelCase		=     val[:dim, :]
										UpperCamelCase		=     val[dim : dim * 2, :]
										UpperCamelCase		=     val[-dim:, :]
				else:
						UpperCamelCase		=     val

		return orig_state_dict


def       A       (					)   ->  Optional[Any]:
		'''simple docstring'''

		UpperCamelCase		=     hf_hub_download(
		    repo_id='hf-internal-testing/spaghetti-video'    ,       filename='eating_spaghetti.npy'    ,       repo_type='dataset' )
		UpperCamelCase		=     np.load(lowercase )
		return list(lowercase )


def       A       (					lowercase    ,       lowercase    ,       lowercase    ,       lowercase )   ->  Optional[int]:
		'''simple docstring'''

		UpperCamelCase		=     get_videomae_config(lowercase )

		if "finetuned" in model_name:
				UpperCamelCase		=     VideoMAEForVideoClassification(lowercase )
		else:
				UpperCamelCase		=     VideoMAEForPreTraining(lowercase )

		# download original checkpoint, hosted on Google Drive
		UpperCamelCase		=     'pytorch_model.bin'
		gdown.cached_download(lowercase    ,       lowercase    ,       quiet=lowercase )
		UpperCamelCase		=     torch.load(lowercase    ,       map_location='cpu' )
		if "model" in files:
				UpperCamelCase		=     files['model']
		else:
				UpperCamelCase		=     files['module']
		UpperCamelCase		=     convert_state_dict(lowercase    ,       lowercase )

		model.load_state_dict(lowercase )
		model.eval()

		# verify model on basic input
		UpperCamelCase		=     VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]    ,       image_std=[0.5, 0.5, 0.5] )
		UpperCamelCase		=     prepare_video()
		UpperCamelCase		=     image_processor(lowercase    ,       return_tensors='pt' )

		if "finetuned" not in model_name:
				UpperCamelCase		=     hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'    ,       filename='bool_masked_pos.pt' )
				UpperCamelCase		=     torch.load(lowercase )

		UpperCamelCase		=     model(**lowercase )
		UpperCamelCase		=     outputs.logits

		UpperCamelCase		=     [
		    'videomae-small-finetuned-kinetics',
		    'videomae-small-finetuned-ssv2',
		    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
		    'videomae-base-short',
		    'videomae-base-short-finetuned-kinetics',
		    'videomae-base',
		    'videomae-base-finetuned-kinetics',
		    'videomae-large',
		    'videomae-large-finetuned-kinetics',
		    'videomae-huge-finetuned-kinetics',
		    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
		    'videomae-base-short-ssv2',
		    'videomae-base-short-finetuned-ssv2',
		    'videomae-base-ssv2',
		    'videomae-base-finetuned-ssv2',
		]

		# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
		if model_name == "videomae-small-finetuned-kinetics":
				UpperCamelCase		=     torch.Size([1, 400] )
				UpperCamelCase		=     torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7] )
		elif model_name == "videomae-small-finetuned-ssv2":
				UpperCamelCase		=     torch.Size([1, 174] )
				UpperCamelCase		=     torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5] )
		elif model_name == "videomae-base":
				UpperCamelCase		=     torch.Size([1, 1_408, 1_536] )
				UpperCamelCase		=     torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]] )
		elif model_name == "videomae-base-short":
				UpperCamelCase		=     torch.Size([1, 1_408, 1_536] )
				UpperCamelCase		=     torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]] )
				# we verified the loss both for normalized and unnormalized targets for this one
				UpperCamelCase		=     torch.tensor([0.5_1_4_2] ) if config.norm_pix_loss else torch.tensor([0.6_4_6_9] )
		elif model_name == "videomae-large":
				UpperCamelCase		=     torch.Size([1, 1_408, 1_536] )
				UpperCamelCase		=     torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]] )
		elif model_name == "videomae-large-finetuned-kinetics":
				UpperCamelCase		=     torch.Size([1, 400] )
				UpperCamelCase		=     torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5] )
		elif model_name == "videomae-huge-finetuned-kinetics":
				UpperCamelCase		=     torch.Size([1, 400] )
				UpperCamelCase		=     torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4] )
		elif model_name == "videomae-base-short-finetuned-kinetics":
				UpperCamelCase		=     torch.Size([1, 400] )
				UpperCamelCase		=     torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3] )
		elif model_name == "videomae-base-finetuned-kinetics":
				UpperCamelCase		=     torch.Size([1, 400] )
				UpperCamelCase		=     torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1] )
		elif model_name == "videomae-base-short-ssv2":
				UpperCamelCase		=     torch.Size([1, 1_408, 1_536] )
				UpperCamelCase		=     torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]] )
		elif model_name == "videomae-base-short-finetuned-ssv2":
				UpperCamelCase		=     torch.Size([1, 174] )
				UpperCamelCase		=     torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6] )
		elif model_name == "videomae-base-ssv2":
				UpperCamelCase		=     torch.Size([1, 1_408, 1_536] )
				UpperCamelCase		=     torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]] )
		elif model_name == "videomae-base-finetuned-ssv2":
				UpperCamelCase		=     torch.Size([1, 174] )
				UpperCamelCase		=     torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9] )
		else:
				raise ValueError(f'''Model name not supported. Should be one of {model_names}''' )

		# verify logits
		assert logits.shape == expected_shape
		if "finetuned" in model_name:
				assert torch.allclose(logits[0, :3]    ,       lowercase    ,       atol=1e-4 )
		else:
				print('Logits:'    ,       logits[0, :3, :3] )
				assert torch.allclose(logits[0, :3, :3]    ,       lowercase    ,       atol=1e-4 )
		print('Logits ok!' )

		# verify loss, if applicable
		if model_name == "videomae-base-short":
				UpperCamelCase		=     outputs.loss
				assert torch.allclose(lowercase    ,       lowercase    ,       atol=1e-4 )
				print('Loss ok!' )

		if pytorch_dump_folder_path is not None:
				print(f'''Saving model and image processor to {pytorch_dump_folder_path}''' )
				image_processor.save_pretrained(lowercase )
				model.save_pretrained(lowercase )

		if push_to_hub:
				print('Pushing to the hub...' )
				model.push_to_hub(lowercase    ,       organization='nielsr' )


if __name__ == "__main__":
					_UpperCAmelCase					: Optional[Any]      =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--checkpoint_url",
					    default="https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4",
					    type=str,
					    help=(
					        "URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"
					        " download link."
					    ),
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path",
					    default="/Users/nielsrogge/Documents/VideoMAE/Test",
					    type=str,
					    help="Path to the output PyTorch model directory.",
					)
					parser.add_argument("--model_name", default="videomae-base", type=str, help="Name of the model.")
					parser.add_argument(
					    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
					)

					_UpperCAmelCase					: Union[str, Any]      =       parser.parse_args()
					convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, 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 				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()       )
