


"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :   Tuple			    =			{
    "configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperOnnxConfig"],
    "feature_extraction_whisper": ["WhisperFeatureExtractor"],
    "processing_whisper": ["WhisperProcessor"],
    "tokenization_whisper": ["WhisperTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   Tuple			    =			["WhisperTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   List[Any]			    =			[
	    "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "WhisperForConditionalGeneration",
	    "WhisperModel",
	    "WhisperPreTrainedModel",
	    "WhisperForAudioClassification",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   Union[str, Any]			    =			[
	    "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFWhisperForConditionalGeneration",
	    "TFWhisperModel",
	    "TFWhisperPreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   List[str]			    =			[
	    "FlaxWhisperForConditionalGeneration",
	    "FlaxWhisperModel",
	    "FlaxWhisperPreTrainedModel",
	    "FlaxWhisperForAudioClassification",
	]


if TYPE_CHECKING:
	from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
	from .feature_extraction_whisper import WhisperFeatureExtractor
	from .processing_whisper import WhisperProcessor
	from .tokenization_whisper import WhisperTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_whisper_fast import WhisperTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_whisper import (
		    WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    WhisperForAudioClassification,
		    WhisperForConditionalGeneration,
		    WhisperModel,
		    WhisperPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_whisper import (
		    TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFWhisperForConditionalGeneration,
		    TFWhisperModel,
		    TFWhisperPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_whisper import (
		    FlaxWhisperForAudioClassification,
		    FlaxWhisperForConditionalGeneration,
		    FlaxWhisperModel,
		    FlaxWhisperPreTrainedModel,
		)

else:
	import sys

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











"""simple docstring"""




import warnings
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  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Dict	    =   ["""image_processor""", """tokenizer"""]
      lowercase_      : Union[str, Any]	    =   """ViltImageProcessor"""
      lowercase_      : Any	    =   ("""BertTokenizer""", """BertTokenizerFast""")

      def __init__(     self					,      snake_case_=None					,      snake_case_=None					,      **snake_case_  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  None
        if "feature_extractor" in kwargs:
          warnings.warn(
              'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
              ' instead.'					,      snake_case_					,      )
          A_  :       Dict										=  kwargs.pop('feature_extractor'  )

        A_  :       Dict										=  image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
          raise ValueError('You need to specify an `image_processor`.'  )
        if tokenizer is None:
          raise ValueError('You need to specify a `tokenizer`.'  )

        super().__init__(snake_case_					,      snake_case_  )
        A_  :       List[str]										=  self.image_processor

      def __call__(     self					,      snake_case_					,      snake_case_ = None					,      snake_case_ = True					,      snake_case_ = False					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = 0					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = True					,      snake_case_ = None					,      **snake_case_					,      ):

        """simple docstring"""

        A_  :       str										=  self.tokenizer(
            text=snake_case_					,      add_special_tokens=snake_case_					,      padding=snake_case_					,      truncation=snake_case_					,      max_length=snake_case_					,      stride=snake_case_					,      pad_to_multiple_of=snake_case_					,      return_token_type_ids=snake_case_					,      return_attention_mask=snake_case_					,      return_overflowing_tokens=snake_case_					,      return_special_tokens_mask=snake_case_					,      return_offsets_mapping=snake_case_					,      return_length=snake_case_					,      verbose=snake_case_					,      return_tensors=snake_case_					,      **snake_case_					,      )
        # add pixel_values + pixel_mask
        A_  :       Optional[int]										=  self.image_processor(snake_case_					,      return_tensors=snake_case_  )
        encoding.update(snake_case_  )

        return encoding

      def 					lowerCamelCase_			(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

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

      def 					lowerCamelCase_			(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

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

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Any										=  self.tokenizer.model_input_names
        A_  :       Any										=  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					,      snake_case_					,      )
        return self.image_processor_class

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					,      snake_case_					,      )
        return self.image_processor