


'''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							(						__A   ):
	A_           =       ["image_processor", "tokenizer"]
	A_           =       "LayoutLMv2ImageProcessor"
	A_           =       ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")




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

						'''simple docstring'''





						if "feature_extractor" in kwargs:
											warnings.warn(
											    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
											    ' instead.'		,							lowerCAmelCase_		,							)
											__a     :			List[Any]          =    kwargs.pop('feature_extractor'				)

						__a     :			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__(lowerCAmelCase_		,							lowerCAmelCase_				)




	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.'				)

						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
						__a     :			Optional[Any]          =    self.image_processor(images=lowerCAmelCase_		,							return_tensors=lowerCAmelCase_				)

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

						__a     :			Tuple          =    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=lowerCAmelCase_		,							add_special_tokens=lowerCAmelCase_		,							padding=lowerCAmelCase_		,							truncation=lowerCAmelCase_		,							max_length=lowerCAmelCase_		,							stride=lowerCAmelCase_		,							pad_to_multiple_of=lowerCAmelCase_		,							return_token_type_ids=lowerCAmelCase_		,							return_attention_mask=lowerCAmelCase_		,							return_overflowing_tokens=lowerCAmelCase_		,							return_special_tokens_mask=lowerCAmelCase_		,							return_offsets_mapping=lowerCAmelCase_		,							return_length=lowerCAmelCase_		,							verbose=lowerCAmelCase_		,							return_tensors=lowerCAmelCase_		,							**lowerCAmelCase_		,							)

						# add pixel values
						__a     :			int          =    features.pop('pixel_values'				)
						if return_overflowing_tokens is True:
											__a     :			Union[str, Any]          =    self.get_overflowing_images(lowerCAmelCase_		,							encoded_inputs['overflow_to_sample_mapping']				)
						__a     :			Optional[Any]          =    images

						return encoded_inputs




	def  __UpperCAmelCase				(  self		,							__a		,							__a				):

						'''simple docstring'''





						__a     :			List[Any]          =    []
						for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]				)

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

						return images_with_overflow




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

						'''simple docstring'''





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




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

						'''simple docstring'''





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




	@property
	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





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




	@property
	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





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











"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


lowerCamelCase_							      = {
    '''<''': operator.lt,
    '''<=''': operator.le,
    '''==''': operator.eq,
    '''!=''': operator.ne,
    '''>=''': operator.ge,
    '''>''': operator.gt,
}

def 							snake_case (  A__      ,A__      ,A__      ,A__      ,A__      ,A__		):
			if got_ver is None or want_ver is None:
						raise ValueError(
						    F"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
						    F""" reinstalling {pkg}."""		)
			if not ops[op](version.parse(A__		)      ,version.parse(A__		)		):
						raise ImportError(
						    F"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"""		)

def 							snake_case (  A__      ,A__ = None		):
			UpperCAmelCase_	:					int     	=   F"""\n{hint}""" if hint is not None else ""

			# non-versioned check
			if re.match(r"^[\w_\-\d]+$"      ,A__		):
						UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   requirement, None, None
			else:
						UpperCAmelCase_	:					Optional[int]     	=   re.findall(r"^([^!=<>\s]+)([\s!=<>]{1,2}.+)"      ,A__		)
						if not match:
									raise ValueError(
									    "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"
									    F""" got {requirement}"""		)
						UpperCAmelCase_	,      UpperCAmelCase_	:					List[str]     	=   match[0]
						UpperCAmelCase_	:					Optional[Any]     	=   want_full.split(","		)  # there could be multiple requirements
						UpperCAmelCase_	:					int     	=   {}
						for w in want_range:
									UpperCAmelCase_	:					str     	=   re.findall(r"^([\s!=<>]{1,2})(.+)"      ,A__		)
									if not match:
												raise ValueError(
												    "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"
												    F""" but got {requirement}"""		)
									UpperCAmelCase_	,      UpperCAmelCase_	:					Any     	=   match[0]
									UpperCAmelCase_	:					Union[str, Any]     	=   want_ver
									if op not in ops:
												raise ValueError(F"""{requirement}: need one of {list(ops.keys()		)}, but got {op}"""		)

    # special case
			if pkg == "python":
						UpperCAmelCase_	:					List[Any]     	=   ".".join([str(A__		) for x in sys.version_info[:3]]		)
						for op, want_ver in wanted.items():
									_compare_versions(A__      ,A__      ,A__      ,A__      ,A__      ,A__		)
						return

			# check if any version is installed
			try:
						UpperCAmelCase_	:					str     	=   importlib.metadata.version(A__		)
			except importlib.metadata.PackageNotFoundError:
						raise importlib.metadata.PackageNotFoundError(
						    F"""The '{requirement}' distribution was not found and is required by this application. {hint}"""		)

			# check that the right version is installed if version number or a range was provided
			if want_ver is not None:
						for op, want_ver in wanted.items():
									_compare_versions(A__      ,A__      ,A__      ,A__      ,A__      ,A__		)




def 							snake_case (  A__		):
			UpperCAmelCase_	:					Any     	=   "Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"
			return require_version(A__      ,A__		)



