






"""simple docstring"""





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				["image_processor", "tokenizer"]
					UpperCAmelCase__								=				"ViltImageProcessor"
					UpperCAmelCase__								=				("BertTokenizer", "BertTokenizerFast")

					def __init__(			self  :      List[Any]     ,		__snake_case  :      int=None     ,		__snake_case  :      str=None     ,		**__snake_case  :      Optional[Any]					)			->							Optional[Any]:
											__magic_name__:	str							=       None
											if "feature_extractor" in kwargs:
																	warnings.warn(
																	    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																	    """ instead."""     ,		__snake_case     ,		)
																	__magic_name__:	Optional[Any]							=       kwargs.pop("""feature_extractor"""					)

											__magic_name__:	Any							=       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__(__snake_case     ,		__snake_case					)
											__magic_name__:	List[Any]							=       self.image_processor

					def __call__(			self  :      List[str]     ,		__snake_case  :      Dict     ,		__snake_case  :      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,		__snake_case  :      bool = True     ,		__snake_case  :      Union[bool, str, PaddingStrategy] = False     ,		__snake_case  :      Union[bool, str, TruncationStrategy] = None     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      int = 0     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      Optional[bool] = None     ,		__snake_case  :      Optional[bool] = None     ,		__snake_case  :      bool = False     ,		__snake_case  :      bool = False     ,		__snake_case  :      bool = False     ,		__snake_case  :      bool = False     ,		__snake_case  :      bool = True     ,		__snake_case  :      Optional[Union[str, TensorType]] = None     ,		**__snake_case  :      str     ,		)			->							BatchEncoding:
											__magic_name__:	Any							=       self.tokenizer(
											    text=__snake_case     ,		add_special_tokens=__snake_case     ,		padding=__snake_case     ,		truncation=__snake_case     ,		max_length=__snake_case     ,		stride=__snake_case     ,		pad_to_multiple_of=__snake_case     ,		return_token_type_ids=__snake_case     ,		return_attention_mask=__snake_case     ,		return_overflowing_tokens=__snake_case     ,		return_special_tokens_mask=__snake_case     ,		return_offsets_mapping=__snake_case     ,		return_length=__snake_case     ,		verbose=__snake_case     ,		return_tensors=__snake_case     ,		**__snake_case     ,		)
											# add pixel_values + pixel_mask
											__magic_name__:	Tuple							=       self.image_processor(__snake_case     ,		return_tensors=__snake_case					)
											encoding.update(__snake_case					)

											return encoding

					def   lowerCamelCase__   (			self  :      Optional[int]     ,		*__snake_case  :      int     ,		**__snake_case  :      Tuple					)			->							Dict:
											return self.tokenizer.batch_decode(*__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      List[Any]     ,		*__snake_case  :      Optional[Any]     ,		**__snake_case  :      Any					)			->							int:
											return self.tokenizer.decode(*__snake_case     ,		**__snake_case					)

					@property
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							List[Any]:
											__magic_name__:	Any							=       self.tokenizer.model_input_names
											__magic_name__:	Tuple							=       self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)

					@property
					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							List[Any]:
											warnings.warn(
											    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,		__snake_case     ,		)
											return self.image_processor_class






					@property
					def   lowerCamelCase__   (			self  :      Dict					)			->							Tuple:
											warnings.warn(
											    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,		__snake_case     ,		)
											return self.image_processor











"""simple docstring"""





from typing import Union

import fire
import torch
from tqdm import tqdm



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    str = "cpu"      ,   __UpperCAmelCase      :    Union[str, None] = None  )    ->							None:
						__magic_name__:	List[Any]							=       torch.load(__UpperCAmelCase      ,   map_location=__UpperCAmelCase  )
						for k, v in tqdm(state_dict.items()  ):
												if not isinstance(__UpperCAmelCase      ,   torch.Tensor  ):
																		raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""  )
												__magic_name__:	Any							=       v.half()
						if save_path is None:  # overwrite src_path
												__magic_name__:	List[str]							=       src_path
						torch.save(__UpperCAmelCase      ,   __UpperCAmelCase  )


if __name__ == "__main__":
							fire.Fire(convert)



