



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      (   _snake_case):







							'''simple docstring'''






							UpperCamelCase__         =					["""image_processor""", """tokenizer"""]
							UpperCamelCase__         =					"""LayoutLMv2ImageProcessor"""
							UpperCamelCase__         =					("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")


							def __init__(							self  ,  a_=None  ,  a_=None  ,  **a_		):
											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  ,  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."""		)

											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__				      =		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  ,  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(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  ,  *a_  ,  **a_		):
											return self.tokenizer.batch_decode(*snake_case_  ,  **snake_case_		)


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


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


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






def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				a__				      =		set()

				# Replace all the whitespace in our sentence
				a__				      =		input_str.replace(""" """ ,		""""""		)
				for alpha in input_str:
								if "a" <= alpha.lower() <= "z":
												frequency.add(alpha.lower()		)
				return len(__a		) == 26

def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				a__				      =		[False] * 26
				for char in input_str:
								if char.islower():
												a__				      =		True
								elif char.isupper():
												a__				      =		True
				return all(__a		)

def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				return len({char for char in input_str.lower() if char.isalpha()}		) == 26

def 			A_      (	):

				"""simple docstring"""
				from timeit import timeit

				a__				      =		"""from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
				print(timeit("""is_pangram()""" ,		setup=__a		)		)
				print(timeit("""is_pangram_faster()""" ,		setup=__a		)		)
				print(timeit("""is_pangram_fastest()""" ,		setup=__a		)		)
				# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
				# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()

