


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							):
 SCREAMING_SNAKE_CASE__      =       ['image_processor', 'tokenizer']
 SCREAMING_SNAKE_CASE__      =       'Pix2StructImageProcessor'
 SCREAMING_SNAKE_CASE__      =       ('T5Tokenizer', 'T5TokenizerFast')




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:Optional[int] 							=       False
       super().__init__(_lowerCamelCase					,  _lowerCamelCase    )




 def __call__(		self					,  _lowerCamelCase=None					,  _lowerCamelCase = None					,  _lowerCamelCase = True					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = 2048					,  _lowerCamelCase = 0					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = False					,  _lowerCamelCase = False					,  _lowerCamelCase = False					,  _lowerCamelCase = False					,  _lowerCamelCase = False					,  _lowerCamelCase = True					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       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:
             a						:int 							=       self.tokenizer
             a						: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
             a						:Optional[Any] 							=       self.image_processor(
                 _lowerCamelCase					,  return_tensors=_lowerCamelCase					,  max_patches=_lowerCamelCase					,  **_lowerCamelCase    )
       else:
             # add pixel_values and bbox
             a						:Optional[int] 							=       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:
             a						:Any 							=       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:
                   a						:List[str] 							=       text_encoding.pop('''attention_mask'''    )
             if "input_ids" in text_encoding:
                   a						:List[Any] 							=       text_encoding.pop('''input_ids'''    )
       else:
             a						:str 							=       None

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

       return encoding_image_processor




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.batch_decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Union[str, Any] 							=       self.tokenizer.model_input_names
       a						:Dict 							=       self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )












import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""Speech2TextFeatureExtractor"""
			lowerCAmelCase_							     =						"""Speech2TextTokenizer"""





			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				super().__init__(__lowerCAmelCase       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		self.feature_extractor
				lowerCamelCase__								=		False





			def __call__(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				if self._in_target_context_manager:
					return self.current_processor(*__lowerCAmelCase       ,  **__lowerCAmelCase							)

				if "raw_speech" in kwargs:
					warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''							)
					lowerCamelCase__								=		kwargs.pop('''raw_speech'''							)
				else:
					lowerCamelCase__								=		kwargs.pop('''audio'''       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		kwargs.pop('''sampling_rate'''       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		kwargs.pop('''text'''       ,  __lowerCAmelCase							)
				if len(__lowerCAmelCase							) > 0:
					lowerCamelCase__								=		args[0]
					lowerCamelCase__								=		args[1:]

				if audio is None and text is None:
					raise ValueError('''You need to specify either an `audio` or `text` input to process.'''							)

				if audio is not None:
					lowerCamelCase__								=		self.feature_extractor(__lowerCAmelCase       ,  *__lowerCAmelCase       ,  sampling_rate=__lowerCAmelCase       ,  **__lowerCAmelCase							)
				if text is not None:
					lowerCamelCase__								=		self.tokenizer(__lowerCAmelCase       ,  **__lowerCAmelCase							)

				if text is None:
					return inputs
				elif audio is None:
					return encodings
				else:
					lowerCamelCase__								=		encodings['''input_ids''']
					return inputs





			def 		__lowerCamelCase				(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







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





			@contextmanager
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				warnings.warn(
				    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
				    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
				    '''your audio inputs, or in a separate call.'''							)
				lowerCamelCase__								=		True
				lowerCamelCase__								=		self.tokenizer
				yield
				lowerCamelCase__								=		self.feature_extractor
				lowerCamelCase__								=		False



