
"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_a      :			int					=							{
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			Any					=							["""BlipImageProcessor"""]


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			Dict					=							[
        """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """BlipModel""",
        """BlipPreTrainedModel""",
        """BlipForConditionalGeneration""",
        """BlipForQuestionAnswering""",
        """BlipVisionModel""",
        """BlipTextModel""",
        """BlipForImageTextRetrieval""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			int					=							[
        """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFBlipModel""",
        """TFBlipPreTrainedModel""",
        """TFBlipForConditionalGeneration""",
        """TFBlipForQuestionAnswering""",
        """TFBlipVisionModel""",
        """TFBlipTextModel""",
        """TFBlipForImageTextRetrieval""",
    ]

if TYPE_CHECKING:
    from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
    from .processing_blip import BlipProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_blip import BlipImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_blip import (
            BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            BlipForConditionalGeneration,
            BlipForImageTextRetrieval,
            BlipForQuestionAnswering,
            BlipModel,
            BlipPreTrainedModel,
            BlipTextModel,
            BlipVisionModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_blip import (
            TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFBlipForConditionalGeneration,
            TFBlipForImageTextRetrieval,
            TFBlipForQuestionAnswering,
            TFBlipModel,
            TFBlipPreTrainedModel,
            TFBlipTextModel,
            TFBlipVisionModel,
        )

else:
    import sys

    _a      :			int					=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





"""simple docstring"""
from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: int  =					"""EncodecFeatureExtractor"""
    __lowercase							: str  =					("""T5Tokenizer""", """T5TokenizerFast""")
    def __init__(    self						,    snake_case_						,    snake_case_     ):
      super().__init__(snake_case_						,    snake_case_     )
      _snake_case     :						Dict						=   self.feature_extractor
      _snake_case     :						Any						=   False
    def 					lowerCamelCase__	(    self						,    snake_case_=None						,    snake_case_=None						,    snake_case_=True     ):
      return self.tokenizer.get_decoder_prompt_ids(task=snake_case_						,    language=snake_case_						,    no_timestamps=snake_case_     )
    def __call__(    self						,    *snake_case_						,    **snake_case_     ):
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*snake_case_						,    **snake_case_     )

      _snake_case     :						str						=   kwargs.pop("audio"						,    snake_case_     )
      _snake_case     :						Optional[int]						=   kwargs.pop("sampling_rate"						,    snake_case_     )
      _snake_case     :						Optional[Any]						=   kwargs.pop("text"						,    snake_case_     )
      if len(snake_case_     ) > 0:
        _snake_case     :						Any						=   args[0]
        _snake_case     :						Union[str, Any]						=   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 text is not None:
        _snake_case     :						Any						=   self.tokenizer(snake_case_						,    **snake_case_     )

      if audio is not None:
        _snake_case     :						Any						=   self.feature_extractor(snake_case_						,    *snake_case_						,    sampling_rate=snake_case_						,    **snake_case_     )

      if audio is None:
        return inputs

      elif text is None:
        return audio_inputs

      else:
        _snake_case     :						str						=   audio_inputs["input_values"]
        if "padding_mask" in audio_inputs:
          _snake_case     :						List[str]						=   audio_inputs["padding_mask"]
        return inputs
    def 					lowerCamelCase__	(    self						,    *snake_case_						,    **snake_case_     ):
      _snake_case     :						Tuple						=   kwargs.pop("audio"						,    snake_case_     )
      _snake_case     :						List[str]						=   kwargs.pop("padding_mask"						,    snake_case_     )

      if len(snake_case_     ) > 0:
        _snake_case     :						Tuple						=   args[0]
        _snake_case     :						Dict						=   args[1:]

      if audio_values is not None:
        return self._decode_audio(snake_case_						,    padding_mask=snake_case_     )
      else:
        return self.tokenizer.batch_decode(*snake_case_						,    **snake_case_     )
    def 					lowerCamelCase__	(    self						,    *snake_case_						,    **snake_case_     ):
      return self.tokenizer.decode(*snake_case_						,    **snake_case_     )


    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_ = None     ):
      _snake_case     :						Optional[int]						=   to_numpy(snake_case_     )
      _snake_case			,					_snake_case			,					_snake_case     :						Tuple						=   audio_values.shape

      if padding_mask is None:
        return list(snake_case_     )

      _snake_case     :						Optional[int]						=   to_numpy(snake_case_     )

      # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
      # token (so that the generated audio values are **not** treated as padded tokens)
      _snake_case     :						Any						=   seq_len - padding_mask.shape[-1]
      _snake_case     :						Optional[Any]						=   1 - self.feature_extractor.padding_value
      _snake_case     :						Optional[int]						=   np.pad(snake_case_						,    ((0, 0), (0, difference))						,    "constant"						,    constant_values=snake_case_     )

      _snake_case     :						Any						=   audio_values.tolist()
      for i in range(snake_case_     ):
        _snake_case     :						Tuple						=   np.asarray(audio_values[i]     )[
            padding_mask[i][None, :] != self.feature_extractor.padding_value
        ]
        _snake_case     :						Tuple						=   sliced_audio.reshape(snake_case_						,    -1     )

      return audio_values

