



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__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     =["""image_processor""", """tokenizer"""]
		snake_case_     ="""Pix2StructImageProcessor"""
		snake_case_     =("""T5Tokenizer""", """T5TokenizerFast""")



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					False
						super().__init__(__lowerCamelCase		,__lowerCamelCase			)



		def __call__(self		,__lowerCamelCase=None		,__lowerCamelCase = None		,__lowerCamelCase = True		,__lowerCamelCase = False		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = 20_48		,__lowerCamelCase = 0		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = False		,__lowerCamelCase = False		,__lowerCamelCase = False		,__lowerCamelCase = False		,__lowerCamelCase = False		,__lowerCamelCase = True		,__lowerCamelCase = None		,**__lowerCamelCase		,)					->      BatchEncoding:

						"""simple docstring"""






						if images is None and text is None:
										raise ValueError('''You have to specify either images or text.'''			)

						# Get only text
						if images is None and not self.image_processor.is_vqa:
										lowerCAmelCase__					:    str												=					self.tokenizer
										lowerCAmelCase__					:    int												=					self.tokenizer(
										    text=__lowerCamelCase		,add_special_tokens=__lowerCamelCase		,padding=__lowerCamelCase		,truncation=__lowerCamelCase		,max_length=__lowerCamelCase		,stride=__lowerCamelCase		,pad_to_multiple_of=__lowerCamelCase		,return_attention_mask=__lowerCamelCase		,return_overflowing_tokens=__lowerCamelCase		,return_special_tokens_mask=__lowerCamelCase		,return_offsets_mapping=__lowerCamelCase		,return_token_type_ids=__lowerCamelCase		,return_length=__lowerCamelCase		,verbose=__lowerCamelCase		,return_tensors=__lowerCamelCase		,**__lowerCamelCase		,)
										return text_encoding

						if not self.image_processor.is_vqa:
										# add pixel_values
										lowerCAmelCase__					:    Optional[Any]												=					self.image_processor(
										    __lowerCamelCase		,return_tensors=__lowerCamelCase		,max_patches=__lowerCamelCase		,**__lowerCamelCase			)
						else:
										# add pixel_values and bbox
										lowerCAmelCase__					:    Optional[Any]												=					self.image_processor(
										    __lowerCamelCase		,return_tensors=__lowerCamelCase		,max_patches=__lowerCamelCase		,header_text=__lowerCamelCase		,**__lowerCamelCase			)

						if text is not None and not self.image_processor.is_vqa:
										lowerCAmelCase__					:    str												=					self.tokenizer(
										    text=__lowerCamelCase		,add_special_tokens=__lowerCamelCase		,padding=__lowerCamelCase		,truncation=__lowerCamelCase		,max_length=__lowerCamelCase		,stride=__lowerCamelCase		,pad_to_multiple_of=__lowerCamelCase		,return_attention_mask=__lowerCamelCase		,return_overflowing_tokens=__lowerCamelCase		,return_special_tokens_mask=__lowerCamelCase		,return_offsets_mapping=__lowerCamelCase		,return_token_type_ids=__lowerCamelCase		,return_length=__lowerCamelCase		,verbose=__lowerCamelCase		,return_tensors=__lowerCamelCase		,**__lowerCamelCase		,)

										if "attention_mask" in text_encoding:
														lowerCAmelCase__					:    Dict												=					text_encoding.pop('''attention_mask'''			)
										if "input_ids" in text_encoding:
														lowerCAmelCase__					:    List[str]												=					text_encoding.pop('''input_ids'''			)
						else:
										lowerCAmelCase__					:    List[Any]												=					None

						if text_encoding is not None:
										encoding_image_processor.update(__lowerCamelCase			)

						return encoding_image_processor



		def 		lowerCAmelCase__				(self		,*__lowerCamelCase		,**__lowerCamelCase			)					->      Dict:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self		,*__lowerCamelCase		,**__lowerCamelCase			)					->      Optional[Any]:

						"""simple docstring"""






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



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					self.tokenizer.model_input_names
						lowerCAmelCase__					:    Union[str, Any]												=					self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)






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





				'''simple docstring'''

				lowerCAmelCase__					:    Any												=					set()

				# Replace all the whitespace in our sentence
				lowerCAmelCase__					:    List[Any]												=					input_str.replace(''' '''	,'''''')
				for alpha in input_str:
								if "a" <= alpha.lower() <= "z":
												frequency.add(alpha.lower())
				return len(lowerCamelCase_) == 26






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





				'''simple docstring'''

				lowerCAmelCase__					:    List[str]												=					[False] * 26
				for char in input_str:
								if char.islower():
												lowerCAmelCase__					:    Union[str, Any]												=					True
								elif char.isupper():
												lowerCAmelCase__					:    str												=					True
				return all(lowerCamelCase_)






def      lowerCAmelCase__		(   lowerCamelCase_		:		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      lowerCAmelCase__		(   ):





				'''simple docstring'''

				from timeit import timeit

				lowerCAmelCase__					:    Optional[Any]												=					'''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
				print(timeit('''is_pangram()'''	,setup=lowerCamelCase_))
				print(timeit('''is_pangram_faster()'''	,setup=lowerCamelCase_))
				print(timeit('''is_pangram_fastest()'''	,setup=lowerCamelCase_))
				# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
				# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							benchmark()

