


"""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 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Union[str, Any]										=				['image_processor', 'tokenizer']
		__UpperCAmelCase		:			int										=				'LayoutLMv3ImageProcessor'
		__UpperCAmelCase		:			List[str]										=				('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')

		def __init__(  self							, _a=None							, _a=None							, **_a      ):
				__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							, ):
				# verify input
				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      ):
				# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
				__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      ):
				return self.tokenizer.batch_decode(*_a							, **_a      )

		def 			__UpperCAmelCase       (  self							, *_a							, **_a      ):
				return self.tokenizer.decode(*_a							, **_a      )

		@property
		def 			__UpperCAmelCase       (  self      ):
				return ["input_ids", "bbox", "attention_mask", "pixel_values"]

		@property
		def 			__UpperCAmelCase       (  self      ):
				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      ):
				warnings.warn(
				    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							, _a							, )
				return self.image_processor










"""simple docstring"""




from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
				from .features import FeatureType
@dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]
		__UpperCAmelCase		:			Optional[str]										=				None
		# Automatically constructed
		__UpperCAmelCase		:			ClassVar[str]										=				"dict"
		__UpperCAmelCase		:			ClassVar[Any]										=				None
		__UpperCAmelCase		:			str										=				field(default='Translation'     ,			init=__SCREAMING_SNAKE_CASE     ,			repr=__SCREAMING_SNAKE_CASE       )

		def __call__(  self      ):
				return pa.struct({lang: pa.string() for lang in sorted(self.languages      )}      )





		def 			__UpperCAmelCase       (  self      ):
				from .features import Value

				return {k: Value('''string'''      ) for k in sorted(self.languages      )}







@dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			Optional[List]										=				None
		__UpperCAmelCase		:			Optional[int]										=				None
		__UpperCAmelCase		:			Optional[str]										=				None
		# Automatically constructed
		__UpperCAmelCase		:			ClassVar[str]										=				"dict"
		__UpperCAmelCase		:			ClassVar[Any]										=				None
		__UpperCAmelCase		:			str										=				field(default='TranslationVariableLanguages'     ,			init=__SCREAMING_SNAKE_CASE     ,			repr=__SCREAMING_SNAKE_CASE       )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			sorted(set(self.languages      )      ) if self.languages else None
				__a           =			len(self.languages      ) if self.languages else None

		def __call__(  self      ):
				return pa.struct({'''language''': pa.list_(pa.string()      ), '''translation''': pa.list_(pa.string()      )}      )

		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			set(self.languages      )
				if self.languages and set(_a      ) - lang_set:
						raise ValueError(
						    f'''Some languages in example ({', '.join(sorted(set(_a      ) - lang_set      )      )}) are not in valid set ({', '.join(_a      )}).'''      )

				# Convert dictionary into tuples, splitting out cases where there are
				# multiple translations for a single language.
				__a           =			[]
				for lang, text in translation_dict.items():
						if isinstance(_a							, _a      ):
								translation_tuples.append((lang, text)      )
						else:
								translation_tuples.extend([(lang, el) for el in text]      )

        # Ensure translations are in ascending order by language code.
				__a      ,     __a           =			zip(*sorted(_a      )      )

				return {"language": languages, "translation": translations}





		def 			__UpperCAmelCase       (  self      ):
				from .features import Sequence, Value

				return {
				    "language": Sequence(Value('''string'''      )      ),
				    "translation": Sequence(Value('''string'''      )      ),
				}






