





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

import numpy as np

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

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[int]					="EncodecFeatureExtractor"
 SCREAMING_SNAKE_CASE_	:					List[Any]					=("T5Tokenizer", "T5TokenizerFast")






 def __init__(       self       :							Optional[int]							,	__A       :							Optional[Any]							,	__A       :							List[Any]	):
        super().__init__(__A							,	__A	)
        __UpperCamelCase       =  self.feature_extractor
        __UpperCamelCase       =  False






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							Dict=None							,	__A       :							Dict=None							,	__A       :							Union[str, Any]=True	):
        return self.tokenizer.get_decoder_prompt_ids(task=__A							,	language=__A							,	no_timestamps=__A	)






 def __call__(       self       :							Union[str, Any]							,	*__A       :							List[Any]							,	**__A       :							List[Any]	):
        # For backward compatibility
        if self._in_target_context_manager:
               return self.current_processor(*__A							,	**__A	)

        __UpperCamelCase       =  kwargs.pop('audio'							,	__A	)
        __UpperCamelCase       =  kwargs.pop('sampling_rate'							,	__A	)
        __UpperCamelCase       =  kwargs.pop('text'							,	__A	)
        if len(__A	) > 0:
               __UpperCamelCase       =  args[0]
               __UpperCamelCase       =  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:
               __UpperCamelCase       =  self.tokenizer(__A							,	**__A	)

        if audio is not None:
               __UpperCamelCase       =  self.feature_extractor(__A							,	*__A							,	sampling_rate=__A							,	**__A	)

        if audio is None:
               return inputs

        elif text is None:
               return audio_inputs

        else:
               __UpperCamelCase       =  audio_inputs['input_values']
               if "padding_mask" in audio_inputs:
                      __UpperCamelCase       =  audio_inputs['padding_mask']
               return inputs






 def        _lowerCamelCase					(       self       :							Optional[Any]							,	*__A       :							str							,	**__A       :							Dict	):
        __UpperCamelCase       =  kwargs.pop('audio'							,	__A	)
        __UpperCamelCase       =  kwargs.pop('padding_mask'							,	__A	)

        if len(__A	) > 0:
               __UpperCamelCase       =  args[0]
               __UpperCamelCase       =  args[1:]

        if audio_values is not None:
               return self._decode_audio(__A							,	padding_mask=__A	)
        else:
               return self.tokenizer.batch_decode(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							str							,	*__A       :							List[str]							,	**__A       :							List[Any]	):
        return self.tokenizer.decode(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							List[Any]							,	__A       :							Optional = None	):
        __UpperCamelCase       =  to_numpy(__A	)
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  audio_values.shape

        if padding_mask is None:
               return list(__A	)

        __UpperCamelCase       =  to_numpy(__A	)

        # 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)
        __UpperCamelCase       =  seq_len - padding_mask.shape[-1]
        __UpperCamelCase       =  1 - self.feature_extractor.padding_value
        __UpperCamelCase       =  np.pad(__A							,	((0, 0), (0, difference))							,	'constant'							,	constant_values=__A	)

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

        return audio_values







'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
    if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import (
        VersatileDiffusionDualGuidedPipeline,
        VersatileDiffusionImageVariationPipeline,
        VersatileDiffusionPipeline,
        VersatileDiffusionTextToImagePipeline,
    )
else:
    from .modeling_text_unet import UNetFlatConditionModel
    from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
    from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
    from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
    from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
