






'''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 						lowerCamelCase_				(      _UpperCamelCase ):
				a__					       =  ['''image_processor''', '''tokenizer''']
				a__					       =  '''Pix2StructImageProcessor'''
				a__					       =  ('''T5Tokenizer''', '''T5TokenizerFast''')







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      False
								super().__init__(_UpperCAmelCase       ,					_UpperCAmelCase     )







				def __call__(						self       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = False       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = 2_0_4_8       ,					__lowerCAmelCase = 0       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = False       ,					__lowerCAmelCase = False       ,					__lowerCAmelCase = False       ,					__lowerCAmelCase = False       ,					__lowerCAmelCase = False       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = None       ,					**__lowerCAmelCase       ,					):
								"""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:
												__magic_name__		:int        =      self.tokenizer
												__magic_name__		:List[str]        =      self.tokenizer(
												    text=_UpperCAmelCase       ,					add_special_tokens=_UpperCAmelCase       ,					padding=_UpperCAmelCase       ,					truncation=_UpperCAmelCase       ,					max_length=_UpperCAmelCase       ,					stride=_UpperCAmelCase       ,					pad_to_multiple_of=_UpperCAmelCase       ,					return_attention_mask=_UpperCAmelCase       ,					return_overflowing_tokens=_UpperCAmelCase       ,					return_special_tokens_mask=_UpperCAmelCase       ,					return_offsets_mapping=_UpperCAmelCase       ,					return_token_type_ids=_UpperCAmelCase       ,					return_length=_UpperCAmelCase       ,					verbose=_UpperCAmelCase       ,					return_tensors=_UpperCAmelCase       ,					**_UpperCAmelCase       ,					)
												return text_encoding

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

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

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

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

								return encoding_image_processor







				def 			A	(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								return self.tokenizer.batch_decode(*_UpperCAmelCase       ,					**_UpperCAmelCase     )







				def 			A	(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								return self.tokenizer.decode(*_UpperCAmelCase       ,					**_UpperCAmelCase     )







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      self.tokenizer.model_input_names
								__magic_name__		:Dict        =      self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )














from typing import List

from .keymap import KEYMAP, get_character

def      __lowercase					( snake_case ):







				"""simple docstring"""






				def decorator(snake_case ):
								__magic_name__		:int        =      getattr(snake_case,  '''handle_key''',  [] )
								handle += [key]
								setattr(snake_case,  '''handle_key''',  snake_case )
								return func

				return decorator

def      __lowercase					( *snake_case ):







				"""simple docstring"""






				def decorator(snake_case ):
								__magic_name__		:Union[str, Any]        =      getattr(snake_case,  '''handle_key''',  [] )
								handle += keys
								setattr(snake_case,  '''handle_key''',  snake_case )
								return func

				return decorator
class 						lowerCamelCase_				(      lowerCamelCase ):







				def __new__(						cls       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      super().__new__(cls       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )
								if not hasattr(__lowerCAmelCase       ,					'''key_handler'''     ):
												setattr(__lowerCAmelCase       ,					'''key_handler'''       ,					{}     )
								setattr(__lowerCAmelCase       ,					'''handle_input'''       ,					KeyHandler.handle_input     )

								for value in attrs.values():
												__magic_name__		:Dict        =      getattr(__lowerCAmelCase       ,					'''handle_key'''       ,					[]     )
												for key in handled_keys:
																__magic_name__		:List[Any]        =      value
								return new_cls







				@staticmethod
				def 			A	(						cls     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      get_character()
								if char != KEYMAP["undefined"]:
												__magic_name__		:Dict        =      ord(__lowerCAmelCase     )
								__magic_name__		:str        =      cls.key_handler.get(__lowerCAmelCase     )
								if handler:
												__magic_name__		:Optional[Any]        =      char
												return handler(cls     )
								else:
												return None







def      __lowercase					( cls ):







				"""simple docstring"""






				return KeyHandler(cls.__name__,  cls.__bases__,  cls.__dict__.copy() )






