



from typing import List, Optional, Tuple, Union

import torch

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


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





class 		UpperCAmelCase						(						__A				):





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





   @torch.no_grad()
   def __call__(	self       ,     _lowerCAmelCase = 1       ,     _lowerCAmelCase = 100       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = True       ,     ):





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

         _lowerCAmelCase 			=  audio_length_in_s * self.unet.config.sample_rate

         _lowerCAmelCase 			=  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}.'''			)

         _lowerCAmelCase 			=  int(_lowerCAmelCase			)
         if sample_size % down_scale_factor != 0:
               _lowerCAmelCase 			=  (
                   (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.'''			)
         _lowerCAmelCase 			=  int(_lowerCAmelCase			)

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

         _lowerCAmelCase 			=  randn_tensor(_lowerCAmelCase       ,     generator=_lowerCAmelCase       ,     device=self.device       ,     dtype=_lowerCAmelCase			)

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

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

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

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

         _lowerCAmelCase 			=  audio[:, :, :original_sample_size]

         if not return_dict:
               return (audio,)

         return AudioPipelineOutput(audios=_lowerCAmelCase			)




# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_									=							{
    "configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAntConfig"],
    "tokenization_cpmant": ["CpmAntTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "CpmAntForCausalLM",
        "CpmAntModel",
        "CpmAntPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
    from .tokenization_cpmant import CpmAntTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_cpmant import (
            CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CpmAntForCausalLM,
            CpmAntModel,
            CpmAntPreTrainedModel,
        )


else:
    import sys

    UpperCAmelCase_									=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)