
import copy
import re





class 				UpperCAmelCase :




	'''simple docstring'''





	snake_case_          =	"hp"
	snake_case_          =	{}
	snake_case_          =	None






	@classmethod
	def 							UpperCamelCase_    (			cls   :					Optional[Any]				,A   :					List[str]				,A   :					List[str]					):
					__A       =					prefix
					__A       =					defaults
					cls.build_naming_info()






	@staticmethod
	def 							UpperCamelCase_    (			A   :					str				,A   :					Optional[int]					):
					if len(A					) == 0:
									return ""
					__A       =					None
					if any(char.isdigit() for char in word					):
									raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number'''					)
					if word in info["short_word"]:
									return info["short_word"][word]
					for prefix_len in range(1				,len(A					) + 1					):
									__A       =					word[:prefix_len]
									if prefix in info["reverse_short_word"]:
													continue
									else:
													__A       =					prefix
													break

					if short_word is None:
									# Paranoid fallback
									def int_to_alphabetic(A   :					Union[str, Any]					):
													__A       =					""
													while integer != 0:
																	__A       =					chr(ord("A"					) + integer % 10					) + s
																	integer //= 10
													return s

									__A       =					0
									while True:
													__A       =					word + "#" + int_to_alphabetic(A					)
													if sword in info["reverse_short_word"]:
																	continue
													else:
																	__A       =					sword
																	break

					__A       =					short_word
					__A       =					word
					return short_word






	@staticmethod
	def 							UpperCamelCase_    (			A   :					Tuple				,A   :					str					):
					__A       =					param_name.split("_"					)

					__A       =					[TrialShortNamer.shortname_for_word(A				,A					) for word in words]

					# We try to create a separatorless short name, but if there is a collision we have to fallback
					# to a separated short name
					__A       =					["", "_"]

					for separator in separators:
									__A       =					separator.join(A					)
									if shortname not in info["reverse_short_param"]:
													__A       =					shortname
													__A       =					param_name
													return shortname

					return param_name






	@staticmethod
	def 							UpperCamelCase_    (			A   :					Union[str, Any]				,A   :					List[Any]					):
					__A       =					TrialShortNamer.shortname_for_key(A				,A					)
					__A       =					short_name
					__A       =					param_name






	@classmethod
	def 							UpperCamelCase_    (			cls   :					List[Any]					):
					if cls.NAMING_INFO is not None:
									return

					__A       =					{
					    "short_word": {},
					    "reverse_short_word": {},
					    "short_param": {},
					    "reverse_short_param": {},
					}

					__A       =					list(cls.DEFAULTS.keys()					)

					for k in field_keys:
									cls.add_new_param_name(A				,A					)

					__A       =					info






	@classmethod
	def 							UpperCamelCase_    (			cls   :					Union[str, Any]				,A   :					int					):
					cls.build_naming_info()
					assert cls.PREFIX is not None
					__A       =					[copy.copy(cls.PREFIX					)]

					for k, v in params.items():
									if k not in cls.DEFAULTS:
													raise Exception(f'''You should provide a default value for the param name {k} with value {v}'''					)
									if v == cls.DEFAULTS[k]:
													# The default value is not added to the name
													continue

									__A       =					cls.NAMING_INFO["short_param"][k]

									if isinstance(A				,A					):
													__A       =					1 if v else 0

									__A       =					"" if isinstance(A				,(int, float)					) else "-"
									__A       =					f'''{key}{sep}{v}'''
									name.append(A					)

					return "_".join(A					)







	@classmethod
	def 							UpperCamelCase_    (			cls   :					List[str]				,A   :					Tuple					):
					__A       =					repr[len(cls.PREFIX					) + 1 :]
					if repr == "":
									__A       =					[]
					else:
									__A       =					repr.split("_"					)

					__A       =					{}

					for value in values:
									if "-" in value:
													__A     ,				__A       =					value.split("-"					)
									else:
													__A       =					re.sub("[0-9.]"				,""				,A					)
													__A       =					float(re.sub("[^0-9.]"				,""				,A					)					)

									__A       =					cls.NAMING_INFO["reverse_short_param"][p_k]

									__A       =					p_v

					for k in cls.DEFAULTS:
									if k not in parameters:
													__A       =					cls.DEFAULTS[k]

					return parameters


"""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			(					lowercase      ):
      UpperCamelCase							:   Tuple           =     ["""image_processor""", """tokenizer"""]
      UpperCamelCase							:   List[str]           =     """LayoutLMv2ImageProcessor"""
      UpperCamelCase							:   Optional[int]           =     ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")



      def __init__(   self     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	**UpperCamelCase_      ):
          if "feature_extractor" in kwargs:
              warnings.warn(
                  'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                  ' instead.'     ,	UpperCamelCase_     ,	)
              UpperCAmelCase__	:      Tuple     							=					kwargs.pop('feature_extractor'      )

          UpperCAmelCase__	:      Optional[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__(UpperCamelCase_     ,	UpperCamelCase_      )



      def __call__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = 0     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = None     ,	**UpperCamelCase_     ,	):
          # 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.'      )

          if return_overflowing_tokens is True and return_offsets_mapping is False:
              raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'      )

          # first, apply the image processor
          UpperCAmelCase__	:      List[Any]     							=					self.image_processor(images=UpperCamelCase_     ,	return_tensors=UpperCamelCase_      )

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

          UpperCAmelCase__	:      List[Any]     							=					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=UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_     ,	padding=UpperCamelCase_     ,	truncation=UpperCamelCase_     ,	max_length=UpperCamelCase_     ,	stride=UpperCamelCase_     ,	pad_to_multiple_of=UpperCamelCase_     ,	return_token_type_ids=UpperCamelCase_     ,	return_attention_mask=UpperCamelCase_     ,	return_overflowing_tokens=UpperCamelCase_     ,	return_special_tokens_mask=UpperCamelCase_     ,	return_offsets_mapping=UpperCamelCase_     ,	return_length=UpperCamelCase_     ,	verbose=UpperCamelCase_     ,	return_tensors=UpperCamelCase_     ,	**UpperCamelCase_     ,	)

          # add pixel values
          UpperCAmelCase__	:      List[Any]     							=					features.pop('pixel_values'      )
          if return_overflowing_tokens is True:
              UpperCAmelCase__	:      int     							=					self.get_overflowing_images(UpperCamelCase_     ,	encoded_inputs['overflow_to_sample_mapping']      )
          UpperCAmelCase__	:      Dict     							=					images

          return encoded_inputs



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          UpperCAmelCase__	:      Dict     							=					[]
          for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx]      )

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

          return images_with_overflow



      def  __snake_case							(   self     ,	*UpperCamelCase_     ,	**UpperCamelCase_      ):
          return self.tokenizer.batch_decode(*UpperCamelCase_     ,	**UpperCamelCase_      )



      def  __snake_case							(   self     ,	*UpperCamelCase_     ,	**UpperCamelCase_      ):
          return self.tokenizer.decode(*UpperCamelCase_     ,	**UpperCamelCase_      )



      @property
      def  __snake_case							(   self      ):
          return ["input_ids", "bbox", "attention_mask", "image"]



      @property
      def  __snake_case							(   self      ):
          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,	UpperCamelCase_     ,	)
          return self.image_processor_class



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

