
"""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       snake_case_       (    lowercase_       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							str   			=					['''image_processor''', '''tokenizer''']
	SCREAMING_SNAKE_CASE_:							Optional[Any]   			=					'''LayoutLMv3ImageProcessor'''
	SCREAMING_SNAKE_CASE_:							Tuple   			=					('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')


	def __init__(      self  ,					__a=None  ,					__a=None  ,					**__a						):


					"""simple docstring"""

					A__							 =       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  ,					)
									A__							 =       kwargs.pop('feature_extractor'						)

					A__							 =       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  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = True  ,					__a = False  ,					__a = None  ,					__a = None  ,					__a = 0  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = False  ,					__a = False  ,					__a = False  ,					__a = False  ,					__a = True  ,					__a = None  ,					**__a  ,					):


					"""simple docstring"""

					if self.image_processor.apply_ocr and (boxes is not None):
									raise ValueError(
									    'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'						)

					if self.image_processor.apply_ocr and (word_labels is not None):
									raise ValueError(
									    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'						)

					# first, apply the image processor
					A__							 =       self.image_processor(images=__a  ,					return_tensors=__a						)

					# second, apply the tokenizer
					if text is not None and self.image_processor.apply_ocr and text_pair is None:
									if isinstance(__a  ,					__a						):
													A__							 =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
									A__							 =       features['words']

					A__							 =       self.tokenizer(
					    text=text if text is not None else features['words']  ,					text_pair=text_pair if text_pair is not None else None  ,					boxes=boxes if boxes is not None else features['boxes']  ,					word_labels=__a  ,					add_special_tokens=__a  ,					padding=__a  ,					truncation=__a  ,					max_length=__a  ,					stride=__a  ,					pad_to_multiple_of=__a  ,					return_token_type_ids=__a  ,					return_attention_mask=__a  ,					return_overflowing_tokens=__a  ,					return_special_tokens_mask=__a  ,					return_offsets_mapping=__a  ,					return_length=__a  ,					verbose=__a  ,					return_tensors=__a  ,					**__a  ,					)

					# add pixel values
					A__							 =       features.pop('pixel_values'						)
					if return_overflowing_tokens is True:
									A__							 =       self.get_overflowing_images(__a  ,					encoded_inputs['overflow_to_sample_mapping']						)
					A__							 =       images

					return encoded_inputs


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       []
					for sample_idx in overflow_to_sample_mapping:
									images_with_overflow.append(images[sample_idx]						)

					if len(__a						) != len(__a						):
									raise ValueError(
									    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
									    f''' {len(__a						)} and {len(__a						)}'''						)

					return images_with_overflow


	def       _UpperCAmelCase						(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

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


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return ["input_ids", "bbox", "attention_mask", "pixel_values"]


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					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						):


					"""simple docstring"""

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










"""simple docstring"""






from typing import Union

import fire
import torch
from tqdm import tqdm



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = "cpu"       ,lowerCAmelCase__ = None				):
				A__							 =       torch.load(lowerCAmelCase__       ,map_location=lowerCAmelCase__				)
				for k, v in tqdm(state_dict.items()				):
								if not isinstance(lowerCAmelCase__       ,torch.Tensor				):
												raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'				)
								A__							 =       v.half()
				if save_path is None:  # overwrite src_path
								A__							 =       src_path
				torch.save(lowerCAmelCase__       ,lowerCAmelCase__				)


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


