






'''simple docstring'''



import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


_lowercase      :      Tuple            =					logging.get_logger(__name__)


def 				lowerCamelCase__				(	A       :      Optional[Any]=None				,       A       :      Optional[Any]=None					):




						'''simple docstring'''






						return field(default_factory=lambda: default				,       metadata=lowercase__					)






@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				List[str]  		=	list_field(
				    default=[]						,    metadata={
				        "help": (
				            "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
				            " of all available models"
				        )
				    }						,    )

				__magic_name__    :				List[int]  		=	list_field(
				    default=[8]						,    metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}	)

				__magic_name__    :				List[int]  		=	list_field(
				    default=[8, 32, 128, 512]						,    metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}						,    )

				__magic_name__    :				bool  		=	field(
				    default=__UpperCamelCase						,    metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}						,    )
				__magic_name__    :				bool  		=	field(
				    default=__UpperCamelCase						,    metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}						,    )
				__magic_name__    :				bool  		=	field(
				    default=__UpperCamelCase						,    metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}	)
				__magic_name__    :				bool  		=	field(default=__UpperCamelCase						,    metadata={"help": "Use FP16 to accelerate inference."}	)
				__magic_name__    :				bool  		=	field(default=__UpperCamelCase						,    metadata={"help": "Benchmark training of model"}	)
				__magic_name__    :				bool  		=	field(default=__UpperCamelCase						,    metadata={"help": "Verbose memory tracing"}	)
				__magic_name__    :				bool  		=	field(
				    default=__UpperCamelCase						,    metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}						,    )
				__magic_name__    :				bool  		=	field(
				    default=__UpperCamelCase						,    metadata={
				        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
				    }						,    )
				__magic_name__    :				bool  		=	field(default=__UpperCamelCase						,    metadata={"help": "Trace memory line by line"}	)
				__magic_name__    :				bool  		=	field(default=__UpperCamelCase						,    metadata={"help": "Save result to a CSV file"}	)
				__magic_name__    :				bool  		=	field(default=__UpperCamelCase						,    metadata={"help": "Save all print statements in a log file"}	)
				__magic_name__    :				bool  		=	field(default=__UpperCamelCase						,    metadata={"help": "Whether to print environment information"}	)
				__magic_name__    :				bool  		=	field(
				    default=__UpperCamelCase						,    metadata={
				        "help": (
				            "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
				            " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
				            " for debugging / testing and on TPU."
				        )
				    }						,    )
				__magic_name__    :				str  		=	field(
				    default=f'inference_time_{round(time()	)}.csv'						,    metadata={"help": "CSV filename used if saving time results to csv."}						,    )
				__magic_name__    :				str  		=	field(
				    default=f'inference_memory_{round(time()	)}.csv'						,    metadata={"help": "CSV filename used if saving memory results to csv."}						,    )
				__magic_name__    :				str  		=	field(
				    default=f'train_time_{round(time()	)}.csv'						,    metadata={"help": "CSV filename used if saving time results to csv for training."}						,    )
				__magic_name__    :				str  		=	field(
				    default=f'train_memory_{round(time()	)}.csv'						,    metadata={"help": "CSV filename used if saving memory results to csv for training."}						,    )
				__magic_name__    :				str  		=	field(
				    default=f'env_info_{round(time()	)}.csv'						,    metadata={"help": "CSV filename used if saving environment information."}						,    )
				__magic_name__    :				str  		=	field(
				    default=f'log_{round(time()	)}.csv'						,    metadata={"help": "Log filename used if print statements are saved in log."}						,    )
				__magic_name__    :				int  		=	field(default=3						,    metadata={"help": "Times an experiment will be run."}	)
				__magic_name__    :				bool  		=	field(
				    default=__UpperCamelCase						,    metadata={
				        "help": (
				            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
				            " model weights."
				        )
				    }						,    )




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

										"""simple docstring"""




										warnings.warn(
										    F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
										    ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
										    ''' to benchmark Transformer models.'''				,   lowerCAmelCase				,   )




				def 					a__(		self      :			Dict				)->					Optional[int]:

										"""simple docstring"""




										return json.dumps(dataclasses.asdict(self				)				,   indent=2				)




				@property
				def 					a__(		self      :			Union[str, Any]				)->					List[Any]:

										"""simple docstring"""




										if len(self.models				) <= 0:
																raise ValueError(
																    '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
																    ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''				)
										return self.models





				@property
				def 					a__(		self      :			Union[str, Any]				)->					Dict:

										"""simple docstring"""




										if not self.multi_process:
																return False
										elif self.is_tpu:
																logger.info('''Multiprocessing is currently not possible on TPU.'''				)
																return False
										else:
																return True









'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				List[Any]  		=	["image_processor", "tokenizer"]
				__magic_name__    :				Tuple  		=	"ViTImageProcessor"
				__magic_name__    :				int  		=	("CLIPTokenizer", "CLIPTokenizerFast")




				def __init__(		self      :			List[str]				,   lowerCAmelCase      :			Tuple=None				,   lowerCAmelCase      :			List[str]=None				,   **lowerCAmelCase      :			Optional[int]				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''				,   lowerCAmelCase				,   )
																UpperCAmelCase					    =     kwargs.pop('''feature_extractor'''				)

										UpperCAmelCase					    =     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__(lowerCAmelCase				,   lowerCAmelCase				)




				def __call__(		self      :			int				,   lowerCAmelCase      :			Dict=None				,   lowerCAmelCase      :			Optional[int]=None				,   lowerCAmelCase      :			Union[str, Any]=None				,   lowerCAmelCase      :			int=None				,   **lowerCAmelCase      :			Tuple				)->					Optional[int]:

										"""simple docstring"""




										if text is None and visual_prompt is None and images is None:
																raise ValueError('''You have to specify either text, visual prompt or images.'''				)

										if text is not None and visual_prompt is not None:
																raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''				)

										if text is not None:
																UpperCAmelCase					    =     self.tokenizer(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if visual_prompt is not None:
																UpperCAmelCase					    =     self.image_processor(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if images is not None:
																UpperCAmelCase					    =     self.image_processor(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if visual_prompt is not None and images is not None:
																UpperCAmelCase					    =     {
																    '''pixel_values''': image_features.pixel_values,
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										elif text is not None and images is not None:
																UpperCAmelCase					    =     image_features.pixel_values
																return encoding
										elif text is not None:
																return encoding
										elif visual_prompt is not None:
																UpperCAmelCase					    =     {
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										else:
																return BatchEncoding(data=dict(**lowerCAmelCase				)				,   tensor_type=lowerCAmelCase				)




				def 					a__(		self      :			Optional[int]				,   *lowerCAmelCase      :			List[str]				,   **lowerCAmelCase      :			Dict				)->					Tuple:

										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			List[Any]				,   *lowerCAmelCase      :			str				,   **lowerCAmelCase      :			List[Any]				)->					Optional[Any]:

										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase				,   **lowerCAmelCase				)




				@property
				def 					a__(		self      :			Any				)->					Optional[int]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,   lowerCAmelCase				,   )
										return self.image_processor_class





				@property
				def 					a__(		self      :			str				)->					List[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,   lowerCAmelCase				,   )
										return self.image_processor





