

"""simple docstring"""






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        _lowercase					(     __UpperCAmelCase     ):







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







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

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

      __magic_name__     =      audio_length_in_s * self.unet.config.sample_rate

      __magic_name__     =      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}.'''       )

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

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

      __magic_name__     =      randn_tensor(UpperCamelCase_ ,						generator=UpperCamelCase_ ,						device=self.device ,						dtype=UpperCamelCase_       )

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

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

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

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

      __magic_name__     =      audio[:, :, :original_sample_size]

      if not return_dict:
       return (audio,)

      return AudioPipelineOutput(audios=UpperCamelCase_       )




"""simple docstring"""






import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


__lowerCamelCase										=		[
    {"dataset": "wikipedia", "config_name": "20220301.de"},
    {"dataset": "wikipedia", "config_name": "20220301.en"},
    {"dataset": "wikipedia", "config_name": "20220301.fr"},
    {"dataset": "wikipedia", "config_name": "20220301.frr"},
    {"dataset": "wikipedia", "config_name": "20220301.it"},
    {"dataset": "wikipedia", "config_name": "20220301.simple"},
    {"dataset": "snli", "config_name": "plain_text"},
    {"dataset": "eli5", "config_name": "LFQA_reddit"},
    {"dataset": "wiki40b", "config_name": "en"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.compressed"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.no_index"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.multiset.no_index"},
    {"dataset": "natural_questions", "config_name": "default"},
]





def 	lowercase      (   __UpperCamelCase=True						) ->      List[Any]:
 if with_config:
  return [
      {
          "testcase_name": d["dataset"] + "/" + d["config_name"],
          "dataset": d["dataset"],
          "config_name": d["config_name"],
      }
      for d in DATASETS_ON_HF_GCP
  ]
 else:
  return [
      {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
  ]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__UpperCAmelCase     )     )
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       None
     _lowerCamelCase     =       None







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      with TemporaryDirectory() as tmp_dir:
       __magic_name__     =      dataset_module_factory(UpperCamelCase_ ,						cache_dir=UpperCamelCase_       )

       __magic_name__     =      import_main_class(dataset_module.module_path ,						dataset=UpperCamelCase_       )

       __magic_name__     =      builder_cls(
           cache_dir=UpperCamelCase_ ,						config_name=UpperCamelCase_ ,						hash=dataset_module.hash ,						)

       __magic_name__     =      '''/'''.join(
           [
               HF_GCP_BASE_URL,
               builder_instance._relative_data_dir(with_hash=UpperCamelCase_       ).replace(os.sep ,						'''/'''       ),
               config.DATASET_INFO_FILENAME,
           ]       )
       __magic_name__     =      cached_path(UpperCamelCase_ ,						cache_dir=UpperCamelCase_       )
       self.assertTrue(os.path.exists(UpperCamelCase_       )       )





@pytest.mark.integration
def 	lowercase      (   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      tmp_path_factory.mktemp('''test_hf_gcp'''						) / '''test_wikipedia_simple'''
 __magic_name__     =      dataset_module_factory('''wikipedia'''      ,   cache_dir=__UpperCamelCase						)
 __magic_name__     =      import_main_class(dataset_module.module_path						)
 __magic_name__     =      builder_cls(
     cache_dir=__UpperCamelCase      ,   config_name='''20220301.frr'''      ,   hash=dataset_module.hash      ,   )
 # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
 __magic_name__     =      None
 builder_instance.download_and_prepare()
 __magic_name__     =      builder_instance.as_dataset()
 assert ds





@pytest.mark.integration
def 	lowercase      (   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      dataset_module_factory('''wikipedia'''      ,   cache_dir=__UpperCamelCase						)
 __magic_name__     =      import_main_class(dataset_module.module_path      ,   dataset=__UpperCamelCase						)
 __magic_name__     =      builder_cls(
     cache_dir=__UpperCamelCase      ,   config_name='''20220301.frr'''      ,   hash=dataset_module.hash      ,   )
 __magic_name__     =      builder_instance.as_streaming_dataset()
 assert ds
 assert isinstance(__UpperCamelCase      ,   __UpperCamelCase						)
 assert "train" in ds
 assert isinstance(ds['''train''']      ,   __UpperCamelCase						)
 assert next(iter(ds['''train''']						)						)

