






'''simple docstring'''

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   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self		,						__A				) ->     Optional[int]:




								for model_result in results.values():
														for batch_size, sequence_length in zip(model_result['bs']		,						model_result['ss']				):
																				_lowerCAmelCase										=model_result['result'][batch_size][sequence_length]
																				self.assertIsNotNone(__A				)






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_lowerCAmelCase										='sshleifer/tiny-gpt2'
								_lowerCAmelCase										=PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,						training=__A		,						inference=__A		,						sequence_lengths=[8]		,						batch_sizes=[1]		,						multi_process=__A		,						)
								_lowerCAmelCase										=PyTorchBenchmark(__A				)
								_lowerCAmelCase										=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]:
								_lowerCAmelCase										='sgugger/tiny-distilbert-classification'
								_lowerCAmelCase										=PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,						training=__A		,						inference=__A		,						sequence_lengths=[8]		,						batch_sizes=[1]		,						multi_process=__A		,						only_pretrain_model=__A		,						)
								_lowerCAmelCase										=PyTorchBenchmark(__A				)
								_lowerCAmelCase										=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]:
								_lowerCAmelCase										='sshleifer/tiny-gpt2'
								_lowerCAmelCase										=PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,						training=__A		,						inference=__A		,						torchscript=__A		,						sequence_lengths=[8]		,						batch_sizes=[1]		,						multi_process=__A		,						)
								_lowerCAmelCase										=PyTorchBenchmark(__A				)
								_lowerCAmelCase										=benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result				)
								self.check_results_dict_not_empty(results.memory_inference_result				)






		@unittest.skipIf(torch_device == 'cpu'		,						'Cant do half precision'				)
		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_lowerCAmelCase										='sshleifer/tiny-gpt2'
								_lowerCAmelCase										=PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,						training=__A		,						inference=__A		,						fpaa=__A		,						sequence_lengths=[8]		,						batch_sizes=[1]		,						multi_process=__A		,						)
								_lowerCAmelCase										=PyTorchBenchmark(__A				)
								_lowerCAmelCase										=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:
								_lowerCAmelCase										='sshleifer/tiny-gpt2'
								_lowerCAmelCase										=AutoConfig.from_pretrained(__A				)
								# set architectures equal to `None`
								_lowerCAmelCase										=None
								_lowerCAmelCase										=PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,						training=__A		,						inference=__A		,						sequence_lengths=[8]		,						batch_sizes=[1]		,						multi_process=__A		,						)
								_lowerCAmelCase										=PyTorchBenchmark(__A		,						configs=[config]				)
								_lowerCAmelCase										=benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result				)
								self.check_results_dict_not_empty(results.memory_inference_result				)






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






		@unittest.skipIf(torch_device == 'cpu'		,						'Can\'t do half precision'				)
		def 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										='sshleifer/tiny-gpt2'
								_lowerCAmelCase										=PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,						training=__A		,						inference=__A		,						sequence_lengths=[8]		,						batch_sizes=[1]		,						fpaa=__A		,						multi_process=__A		,						)
								_lowerCAmelCase										=PyTorchBenchmark(__A				)
								_lowerCAmelCase										=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:
								_lowerCAmelCase										='sshleifer/tiny-gpt2'
								_lowerCAmelCase										=AutoConfig.from_pretrained(__A				)
								_lowerCAmelCase										=PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,						training=__A		,						inference=__A		,						sequence_lengths=[8]		,						batch_sizes=[1]		,						multi_process=__A		,						)
								_lowerCAmelCase										=PyTorchBenchmark(__A		,						configs=[config]				)
								_lowerCAmelCase										=benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result				)
								self.check_results_dict_not_empty(results.memory_inference_result				)






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_lowerCAmelCase										='sshleifer/tinier_bart'
								_lowerCAmelCase										=AutoConfig.from_pretrained(__A				)
								_lowerCAmelCase										=PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,						training=__A		,						inference=__A		,						sequence_lengths=[8]		,						batch_sizes=[1]		,						multi_process=__A		,						)
								_lowerCAmelCase										=PyTorchBenchmark(__A		,						configs=[config]				)
								_lowerCAmelCase										=benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result				)
								self.check_results_dict_not_empty(results.memory_inference_result				)






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






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






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								_lowerCAmelCase										='sshleifer/tiny-gpt2'
								with tempfile.TemporaryDirectory() as tmp_dir:
														_lowerCAmelCase										=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		,						)
														_lowerCAmelCase										=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				) ->     Tuple:
								_lowerCAmelCase										='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:
														_lowerCAmelCase										=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		,						)
														_lowerCAmelCase										=PyTorchBenchmark(__A				)
														_lowerCAmelCase										=benchmark.run()
														_check_summary_is_not_empty(result.inference_summary				)
														_check_summary_is_not_empty(result.train_summary				)
														self.assertTrue(Path(os.path.join(__A		,						'log.txt'				)				).exists()				)






'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Any       =							['image_processor', 'tokenizer']
		lowercase   :      Any       =							'CLIPImageProcessor'
		lowercase   :      int       =							('CLIPTokenizer', 'CLIPTokenizerFast')






		def __init__(   self		,						__A=None		,						__A=None		,						**__A				) ->     str:
								_lowerCAmelCase										=None
								if "feature_extractor" in kwargs:
														warnings.warn(
														    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
														    ' instead.'		,						__A		,						)
														_lowerCAmelCase										=kwargs.pop('feature_extractor'				)

								_lowerCAmelCase										=image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
														raise ValueError('You need to specify an `image_processor`.'				)
								if tokenizer is None:
														raise ValueError('You need to specify a `tokenizer`.'				)

								super().__init__(__A		,						__A				)






		def __call__(   self		,						__A=None		,						__A=None		,						__A=None		,						**__A				) ->     Optional[int]:

								if text is None and images is None:
														raise ValueError('You have to specify either text or images. Both cannot be none.'				)

								if text is not None:
														_lowerCAmelCase										=self.tokenizer(__A		,						return_tensors=__A		,						**__A				)

								if images is not None:
														_lowerCAmelCase										=self.image_processor(__A		,						return_tensors=__A		,						**__A				)

								if text is not None and images is not None:
														_lowerCAmelCase										=image_features.pixel_values
														return encoding
								elif text is not None:
														return encoding
								else:
														return BatchEncoding(data=dict(**__A				)		,						tensor_type=__A				)






		def 		UpperCamelCase__       (   self		,						*__A		,						**__A				) ->     Any:
								return self.tokenizer.batch_decode(*__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						*__A		,						**__A				) ->     Optional[int]:
								return self.tokenizer.decode(*__A		,						**__A				)






		@property
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=self.tokenizer.model_input_names
								_lowerCAmelCase										=self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								warnings.warn(
								    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,						__A		,						)
								return self.image_processor_class






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								warnings.warn(
								    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,						__A		,						)
								return self.image_processor





