
'''simple docstring'''




import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer





class  A				(							__snake_case						):
     __magic_name__						=		['''image_processor''', '''tokenizer''']
     __magic_name__						=		'''BlipImageProcessor'''
     __magic_name__						=		'''AutoTokenizer'''





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				str:






         """simple docstring"""






         super().__init__(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         # add QFormer tokenizer
         A       :   Tuple					=						qformer_tokenizer





     def __call__(							self						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				BatchFeature:






         """simple docstring"""






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

         A       :   Tuple					=						BatchFeature()

         if text is not None:
             A       :   Tuple					=						self.tokenizer(
                 text=SCREAMING_SNAKE_CASE						,			add_special_tokens=SCREAMING_SNAKE_CASE						,			padding=SCREAMING_SNAKE_CASE						,			truncation=SCREAMING_SNAKE_CASE						,			max_length=SCREAMING_SNAKE_CASE						,			stride=SCREAMING_SNAKE_CASE						,			pad_to_multiple_of=SCREAMING_SNAKE_CASE						,			return_attention_mask=SCREAMING_SNAKE_CASE						,			return_overflowing_tokens=SCREAMING_SNAKE_CASE						,			return_special_tokens_mask=SCREAMING_SNAKE_CASE						,			return_offsets_mapping=SCREAMING_SNAKE_CASE						,			return_token_type_ids=SCREAMING_SNAKE_CASE						,			return_length=SCREAMING_SNAKE_CASE						,			verbose=SCREAMING_SNAKE_CASE						,			return_tensors=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE						,			)
             encoding.update(SCREAMING_SNAKE_CASE	)
             A       :   List[str]					=						self.qformer_tokenizer(
                 text=SCREAMING_SNAKE_CASE						,			add_special_tokens=SCREAMING_SNAKE_CASE						,			padding=SCREAMING_SNAKE_CASE						,			truncation=SCREAMING_SNAKE_CASE						,			max_length=SCREAMING_SNAKE_CASE						,			stride=SCREAMING_SNAKE_CASE						,			pad_to_multiple_of=SCREAMING_SNAKE_CASE						,			return_attention_mask=SCREAMING_SNAKE_CASE						,			return_overflowing_tokens=SCREAMING_SNAKE_CASE						,			return_special_tokens_mask=SCREAMING_SNAKE_CASE						,			return_offsets_mapping=SCREAMING_SNAKE_CASE						,			return_token_type_ids=SCREAMING_SNAKE_CASE						,			return_length=SCREAMING_SNAKE_CASE						,			verbose=SCREAMING_SNAKE_CASE						,			return_tensors=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE						,			)
             A       :   Optional[Any]					=						qformer_text_encoding.pop('''input_ids'''	)
             A       :   List[str]					=						qformer_text_encoding.pop('''attention_mask'''	)

         if images is not None:
             A       :   List[Any]					=						self.image_processor(SCREAMING_SNAKE_CASE						,			return_tensors=SCREAMING_SNAKE_CASE	)
             encoding.update(SCREAMING_SNAKE_CASE	)

         return encoding





     def 				__lowerCAmelCase							(							self						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				Dict:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				Optional[int]:






         """simple docstring"""






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





     @property
     # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         if os.path.isfile(SCREAMING_SNAKE_CASE	):
             raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'	)
         os.makedirs(SCREAMING_SNAKE_CASE						,			exist_ok=SCREAMING_SNAKE_CASE	)
         A       :   int					=						os.path.join(SCREAMING_SNAKE_CASE						,			'''qformer_tokenizer'''	)
         self.qformer_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE	)
         return super().save_pretrained(SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     @classmethod
     def 				__lowerCAmelCase							(							cls						,			SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         A       :   int					=						AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE						,			subfolder='''qformer_tokenizer'''	)
         A       :   Union[str, Any]					=						cls._get_arguments_from_pretrained(SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         args.append(SCREAMING_SNAKE_CASE	)
         return cls(*SCREAMING_SNAKE_CASE	)






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


lowercase			:				Dict    		=			{
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				List[Any]    		=			['Speech2TextTokenizer']

try:
      if not is_speech_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				List[str]    		=			['Speech2TextFeatureExtractor']

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Dict    		=			[
          'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSpeech2TextForConditionalGeneration',
          'TFSpeech2TextModel',
          'TFSpeech2TextPreTrainedModel',
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Any    		=			[
          'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Speech2TextForConditionalGeneration',
          'Speech2TextModel',
          'Speech2TextPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
      from .processing_speech_to_text import SpeechaTextProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speech_to_text import SpeechaTextTokenizer

      try:
            if not is_speech_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_speech_to_text import (
                TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFSpeechaTextForConditionalGeneration,
                TFSpeechaTextModel,
                TFSpeechaTextPreTrainedModel,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speech_to_text import (
                SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechaTextForConditionalGeneration,
                SpeechaTextModel,
                SpeechaTextPreTrainedModel,
            )

else:
      import sys

      lowercase			:				List[Any]    		=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




