



"""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      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	int 							=     ["""image_processor""", """tokenizer"""]
		lowerCAmelCase_       :	List[Any] 							=     """LayoutLMv3ImageProcessor"""
		lowerCAmelCase_       :	Dict 							=     ("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")



		def __init__( self  :    Optional[int]      ,							snake_case  :    Union[str, Any]=None      ,							snake_case  :    Optional[Any]=None      ,							**snake_case  :    str				)			->						Dict:






									'''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.'      ,							snake_case      ,							)
																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__(snake_case      ,							snake_case				)



		def __call__( self  :    List[Any]      ,							snake_case  :    List[Any]      ,							snake_case  :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,							snake_case  :    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,							snake_case  :    Union[List[List[int]], List[List[List[int]]]] = None      ,							snake_case  :    Optional[Union[List[int], List[List[int]]]] = 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      ,							)			->						Dict:






									'''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=snake_case      ,							return_tensors=snake_case				)

									# second, apply the tokenizer
									if text is not None and self.image_processor.apply_ocr and text_pair is None:
																if isinstance(snake_case      ,							snake_case				):
																							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=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
									A         =					features.pop('pixel_values'				)
									if return_overflowing_tokens is True:
																A         =					self.get_overflowing_images(snake_case      ,							encoded_inputs['overflow_to_sample_mapping']				)
									A         =					images

									return encoded_inputs



		def 					A_		( self  :    Any      ,							snake_case  :    List[str]      ,							snake_case  :    int				)			->						Dict:






									'''simple docstring'''

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

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

									return images_with_overflow



		def 					A_		( self  :    Union[str, Any]      ,							*snake_case  :    Optional[Any]      ,							**snake_case  :    List[str]				)			->						Optional[int]:






									'''simple docstring'''

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



		def 					A_		( self  :    Tuple      ,							*snake_case  :    List[str]      ,							**snake_case  :    Optional[Any]				)			->						Optional[int]:






									'''simple docstring'''

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



		@property
		def 					A_		( self  :    int				)			->						List[str]:






									'''simple docstring'''

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



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






									'''simple docstring'''

									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 					A_		( self  :    Dict				)			->						List[str]:






									'''simple docstring'''

									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 ...configuration_utils import PretrainedConfig
from ...utils import logging


A    =			logging.get_logger(__name__)

A    =			{
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	List[Any] 							=     """fnet"""



		def __init__( self  :    Tuple      ,							snake_case  :    Optional[int]=32_000      ,							snake_case  :    Optional[Any]=768      ,							snake_case  :    Union[str, Any]=12      ,							snake_case  :    List[str]=3_072      ,							snake_case  :    str="gelu_new"      ,							snake_case  :    Any=0.1      ,							snake_case  :    List[str]=512      ,							snake_case  :    Any=4      ,							snake_case  :    str=0.02      ,							snake_case  :    Union[str, Any]=1E-1_2      ,							snake_case  :    Optional[Any]=False      ,							snake_case  :    List[Any]=512      ,							snake_case  :    int=3      ,							snake_case  :    Any=1      ,							snake_case  :    Union[str, Any]=2      ,							**snake_case  :    Union[str, Any]      ,							)			->						Dict:






									'''simple docstring'''

									super().__init__(pad_token_id=snake_case      ,							bos_token_id=snake_case      ,							eos_token_id=snake_case      ,							**snake_case				)

									A         =					vocab_size
									A         =					max_position_embeddings
									A         =					hidden_size
									A         =					num_hidden_layers
									A         =					intermediate_size
									A         =					hidden_act
									A         =					hidden_dropout_prob
									A         =					initializer_range
									A         =					type_vocab_size
									A         =					layer_norm_eps
									A         =					use_tpu_fourier_optimizations
									A         =					tpu_short_seq_length



