"""simple docstring"""




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_      (  __lowerCAmelCase			):
					UpperCamelCase_ :	int			=    ['''image_processor''', '''tokenizer''']
					UpperCamelCase_ :	str			=    '''Pix2StructImageProcessor'''
					UpperCamelCase_ :	Tuple			=    ('''T5Tokenizer''', '''T5TokenizerFast''')




					def __init__(	self						:     Any  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     Dict     ):
							lowerCAmelCase__        =   False
							super().__init__(lowerCAmelCase_  ,						lowerCAmelCase_     )




					def __call__(	self						:     int  ,						snake_case__						:     Optional[Any]=None  ,						snake_case__						:     Any = None  ,						snake_case__						:     List[str] = True  ,						snake_case__						:     List[Any] = False  ,						snake_case__						:     List[str] = None  ,						snake_case__						:     List[Any] = None  ,						snake_case__						:     Union[str, Any] = 2048  ,						snake_case__						:     List[Any] = 0  ,						snake_case__						:     List[str] = None  ,						snake_case__						:     int = None  ,						snake_case__						:     Optional[int] = False  ,						snake_case__						:     List[Any] = False  ,						snake_case__						:     Tuple = False  ,						snake_case__						:     List[str] = False  ,						snake_case__						:     Union[str, Any] = False  ,						snake_case__						:     Dict = True  ,						snake_case__						:     Any = None  ,						**snake_case__						:     Optional[Any]  ,						):
							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__        =   self.tokenizer
									lowerCAmelCase__        =   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__        =   self.image_processor(
									    lowerCAmelCase_  ,						return_tensors=lowerCAmelCase_  ,						max_patches=lowerCAmelCase_  ,						**lowerCAmelCase_     )
							else:
									# add pixel_values and bbox
									lowerCAmelCase__        =   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__        =   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__        =   text_encoding.pop("""attention_mask"""     )
									if "input_ids" in text_encoding:
											lowerCAmelCase__        =   text_encoding.pop("""input_ids"""     )
							else:
									lowerCAmelCase__        =   None

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

							return encoding_image_processor




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						*snake_case__						:     int  ,						**snake_case__						:     str     ):
							return self.tokenizer.batch_decode(*lowerCAmelCase_  ,						**lowerCAmelCase_     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						*snake_case__						:     str  ,						**snake_case__						:     Optional[int]     ):
							return self.tokenizer.decode(*lowerCAmelCase_  ,						**lowerCAmelCase_     )




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   self.tokenizer.model_input_names
							lowerCAmelCase__        =   self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )





"""simple docstring"""




import pprint

import requests

__lowerCAmelCase    :							Union[str, Any]													=						"https://zenquotes.io/api"







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		return requests.get(API_ENDPOINT_URL + """/today"""     ).json()







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		return requests.get(API_ENDPOINT_URL + """/random"""     ).json()


if __name__ == "__main__":
			__lowerCAmelCase    :							Union[str, Any]													=						random_quotes()
			pprint.pprint(response)




