
from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


lowerCamelCase_						     =					logging.get_logger(__name__)  # pylint: disable=invalid-name




class      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       super().__init__()
       self.register_modules(unet=SCREAMING_SNAKE_CASE_	,      scheduler=SCREAMING_SNAKE_CASE_						)







  @torch.no_grad()
  def __call__(self	,      SCREAMING_SNAKE_CASE_ = 1	,      SCREAMING_SNAKE_CASE_ = 1_00	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = True	,      ):

       if audio_length_in_s is None:
            UpperCamelCase__				=    self.unet.config.sample_size / self.unet.config.sample_rate

       UpperCamelCase__				=    audio_length_in_s * self.unet.config.sample_rate

       UpperCamelCase__				=    2 ** len(self.unet.up_blocks						)
       if sample_size < 3 * down_scale_factor:
            raise ValueError(
                F"{audio_length_in_s} is too small. Make sure it's bigger or equal to"
                F" {3 * down_scale_factor / self.unet.config.sample_rate}."						)

       UpperCamelCase__				=    int(SCREAMING_SNAKE_CASE_						)
       if sample_size % down_scale_factor != 0:
            UpperCamelCase__				=    (
                (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
            ) * down_scale_factor
            logger.info(
                F"{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"
                F" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"
                """ process."""						)
       UpperCamelCase__				=    int(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    next(iter(self.unet.parameters()						)						).dtype
       UpperCamelCase__				=    (batch_size, self.unet.config.in_channels, sample_size)
       if isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						) and len(SCREAMING_SNAKE_CASE_						) != batch_size:
            raise ValueError(
                F"You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_						)}, but requested an effective batch"
                F" size of {batch_size}. Make sure the batch size matches the length of the generators."						)

       UpperCamelCase__				=    randn_tensor(SCREAMING_SNAKE_CASE_	,      generator=SCREAMING_SNAKE_CASE_	,      device=self.device	,      dtype=SCREAMING_SNAKE_CASE_						)

       # set step values
       self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	,      device=audio.device						)
       UpperCamelCase__				=    self.scheduler.timesteps.to(SCREAMING_SNAKE_CASE_						)

       for t in self.progress_bar(self.scheduler.timesteps						):
            # 1. predict noise model_output
            UpperCamelCase__				=    self.unet(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						).sample

            # 2. compute previous image: x_t -> t_t-1
            UpperCamelCase__				=    self.scheduler.step(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						).prev_sample

       UpperCamelCase__				=    audio.clamp(-1	,      1						).float().cpu().numpy()

       UpperCamelCase__				=    audio[:, :, :original_sample_size]

       if not return_dict:
            return (audio,)

       return AudioPipelineOutput(audios=SCREAMING_SNAKE_CASE_						)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase_						     =					{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_						     =					[
        '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''CanineForMultipleChoice''',
        '''CanineForQuestionAnswering''',
        '''CanineForSequenceClassification''',
        '''CanineForTokenClassification''',
        '''CanineLayer''',
        '''CanineModel''',
        '''CaninePreTrainedModel''',
        '''load_tf_weights_in_canine''',
    ]


if TYPE_CHECKING:
    from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
    from .tokenization_canine import CanineTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_canine import (
            CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
            CanineForMultipleChoice,
            CanineForQuestionAnswering,
            CanineForSequenceClassification,
            CanineForTokenClassification,
            CanineLayer,
            CanineModel,
            CaninePreTrainedModel,
            load_tf_weights_in_canine,
        )


else:
    import sys

    lowerCamelCase_						     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





