



"""simple docstring"""





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 						UpperCAmelCase  (		A						:    Any							):

		'''simple docstring'''


		_UpperCAmelCase							=		VideoMAEConfig()

		set_architecture_configs(a__  ,					a__							)

		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(a__  ,					a__  ,					repo_type='dataset'							)  ,					'r'							)							)
				_UpperCAmelCase							=		{int(a__							): v for k, v in idalabel.items()}
				_UpperCAmelCase							=		idalabel
				_UpperCAmelCase							=		{v: k for k, v in idalabel.items()}

		return config




def 						UpperCAmelCase  (		A						:    Optional[Any]  ,					A						:    List[Any]							):

		'''simple docstring'''


		if "small" in model_name:
				_UpperCAmelCase							=		384
				_UpperCAmelCase							=		1536
				_UpperCAmelCase							=		12
				_UpperCAmelCase							=		16
				_UpperCAmelCase							=		12
				_UpperCAmelCase							=		3
				_UpperCAmelCase							=		192
				_UpperCAmelCase							=		768
		elif "large" in model_name:
				_UpperCAmelCase							=		1024
				_UpperCAmelCase							=		4096
				_UpperCAmelCase							=		24
				_UpperCAmelCase							=		16
				_UpperCAmelCase							=		12
				_UpperCAmelCase							=		8
				_UpperCAmelCase							=		512
				_UpperCAmelCase							=		2048
		elif "huge" in model_name:
				_UpperCAmelCase							=		1280
				_UpperCAmelCase							=		5120
				_UpperCAmelCase							=		32
				_UpperCAmelCase							=		16
				_UpperCAmelCase							=		12
				_UpperCAmelCase							=		8
				_UpperCAmelCase							=		640
				_UpperCAmelCase							=		2560
		elif "base" not in model_name:
				raise ValueError('Model name should include either "small", "base", "large", or "huge"'							)




def 						UpperCAmelCase  (		A						:    Union[str, 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 						UpperCAmelCase  (		A						:    Optional[int]  ,					A						:    Optional[Any]							):

		'''simple docstring'''


		for key in orig_state_dict.copy().keys():
				_UpperCAmelCase							=		orig_state_dict.pop(a__							)

				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 						UpperCAmelCase  (		):

		'''simple docstring'''


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




def 						UpperCAmelCase  (		A						:    Optional[int]  ,					A						:    Optional[Any]  ,					A						:    Optional[int]  ,					A						:    List[str]							):

		'''simple docstring'''


		_UpperCAmelCase							=		get_videomae_config(a__							)

		if "finetuned" in model_name:
				_UpperCAmelCase							=		VideoMAEForVideoClassification(a__							)
		else:
				_UpperCAmelCase							=		VideoMAEForPreTraining(a__							)

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

		model.load_state_dict(a__							)
		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(a__  ,					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(a__							)

		_UpperCAmelCase							=		model(**a__							)
		_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([-0.9291, -0.4061, -0.9307]							)
		elif model_name == "videomae-small-finetuned-ssv2":
				_UpperCAmelCase							=		torch.Size([1, 174]							)
				_UpperCAmelCase							=		torch.tensor([0.2671, -0.4689, -0.8235]							)
		elif model_name == "videomae-base":
				_UpperCAmelCase							=		torch.Size([1, 1408, 1536]							)
				_UpperCAmelCase							=		torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]]							)
		elif model_name == "videomae-base-short":
				_UpperCAmelCase							=		torch.Size([1, 1408, 1536]							)
				_UpperCAmelCase							=		torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]							)
				# we verified the loss both for normalized and unnormalized targets for this one
				_UpperCAmelCase							=		torch.tensor([0.5142]							) if config.norm_pix_loss else torch.tensor([0.6469]							)
		elif model_name == "videomae-large":
				_UpperCAmelCase							=		torch.Size([1, 1408, 1536]							)
				_UpperCAmelCase							=		torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]]							)
		elif model_name == "videomae-large-finetuned-kinetics":
				_UpperCAmelCase							=		torch.Size([1, 400]							)
				_UpperCAmelCase							=		torch.tensor([0.0771, 0.0011, -0.3625]							)
		elif model_name == "videomae-huge-finetuned-kinetics":
				_UpperCAmelCase							=		torch.Size([1, 400]							)
				_UpperCAmelCase							=		torch.tensor([0.2433, 0.1632, -0.4894]							)
		elif model_name == "videomae-base-short-finetuned-kinetics":
				_UpperCAmelCase							=		torch.Size([1, 400]							)
				_UpperCAmelCase							=		torch.tensor([0.6588, 0.0990, -0.2493]							)
		elif model_name == "videomae-base-finetuned-kinetics":
				_UpperCAmelCase							=		torch.Size([1, 400]							)
				_UpperCAmelCase							=		torch.tensor([0.3669, -0.0688, -0.2421]							)
		elif model_name == "videomae-base-short-ssv2":
				_UpperCAmelCase							=		torch.Size([1, 1408, 1536]							)
				_UpperCAmelCase							=		torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]]							)
		elif model_name == "videomae-base-short-finetuned-ssv2":
				_UpperCAmelCase							=		torch.Size([1, 174]							)
				_UpperCAmelCase							=		torch.tensor([-0.0537, -0.1539, -0.3266]							)
		elif model_name == "videomae-base-ssv2":
				_UpperCAmelCase							=		torch.Size([1, 1408, 1536]							)
				_UpperCAmelCase							=		torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]]							)
		elif model_name == "videomae-base-finetuned-ssv2":
				_UpperCAmelCase							=		torch.Size([1, 174]							)
				_UpperCAmelCase							=		torch.tensor([0.1961, -0.8337, -0.6389]							)
		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]  ,					a__  ,					atol=1e-4							)
		else:
				print('Logits:'  ,					logits[0, :3, :3]							)
				assert torch.allclose(logits[0, :3, :3]  ,					a__  ,					atol=1e-4							)
		print('Logits ok!'							)

		# verify loss, if applicable
		if model_name == "videomae-base-short":
				_UpperCAmelCase							=		outputs.loss
				assert torch.allclose(a__  ,					a__  ,					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(a__							)
				model.save_pretrained(a__							)

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


if __name__ == "__main__":
					lowercase					=       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.'''
					)

					lowercase					=       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_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
		import tensorflow as tf

		from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments

@require_tf
class 						__a (							unittest.TestCase     ):
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			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(_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       eager_mode=_SCREAMING_SNAKE_CASE     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
									_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		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     'sgugger/tiny-distilbert-classification'
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    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     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
									_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[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
									_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[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       eager_mode=_SCREAMING_SNAKE_CASE     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE     ,       [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[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE     ,       [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[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
									_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		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE     ,       [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											=     'patrickvonplaten/t5-tiny-random'
									_UpperCAmelCase											=     AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE     ,       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		)
		@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'		)		) == 0     ,       'Cannot do xla on CPU.'		)
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       use_xla=_SCREAMING_SNAKE_CASE     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
									_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'
									with tempfile.TemporaryDirectory() as tmp_dir:
																_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
																    models=[MODEL_ID]     ,       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'		)     ,       inference_memory_csv_file=os.path.join(_SCREAMING_SNAKE_CASE     ,       'inf_mem.csv'		)     ,       env_info_csv_file=os.path.join(_SCREAMING_SNAKE_CASE     ,       'env.csv'		)     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
																_UpperCAmelCase											=     TensorFlowBenchmark(_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     ,       'inf_mem.csv'		)		).exists()		)
																self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE     ,       'env.csv'		)		).exists()		)






		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'

									def _check_summary_is_not_empty(_SCREAMING_SNAKE_CASE		):
																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:
																_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
																    models=[MODEL_ID]     ,       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     ,       eager_mode=_SCREAMING_SNAKE_CASE     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
																_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     benchmark.run()
																_check_summary_is_not_empty(result.inference_summary		)
																self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE     ,       'log.txt'		)		).exists()		)
