


'''simple docstring'''
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      lowerCamelCase_  (      __a ):






      def __init__(   self							:       Dict		,		_A							:       List[str]		,		_A							:       int					):


          '''simple docstring'''


          super().__init__()
          self.register_modules(unet=_A		,		scheduler=_A					)






      @torch.no_grad()
      def __call__(   self							:       List[Any]		,		_A							:       int = 1		,		_A							:       int = 100		,		_A							:       Optional[Union[torch.Generator, List[torch.Generator]]] = None		,		_A							:       Optional[float] = None		,		_A							:       bool = True		,		):


          '''simple docstring'''


          if audio_length_in_s is None:
              UpperCAmelCase__     :						List[str]						=							self.unet.config.sample_size / self.unet.config.sample_rate

          UpperCAmelCase__     :						Union[str, Any]						=							audio_length_in_s * self.unet.config.sample_rate

          UpperCAmelCase__     :						List[Any]						=							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__     :						List[Any]						=							int(_A					)
          if sample_size % down_scale_factor != 0:
              UpperCAmelCase__     :						int						=							(
                  (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__     :						Dict						=							int(_A					)

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

          UpperCAmelCase__     :						Optional[int]						=							randn_tensor(_A		,		generator=_A		,		device=self.device		,		dtype=_A					)

          # set step values
          self.scheduler.set_timesteps(_A		,		device=audio.device					)
          UpperCAmelCase__     :						List[str]						=							self.scheduler.timesteps.to(_A					)

          for t in self.progress_bar(self.scheduler.timesteps					):
              # 1. predict noise model_output
              UpperCAmelCase__     :						Optional[int]						=							self.unet(_A		,		_A					).sample

              # 2. compute previous image: x_t -> t_t-1
              UpperCAmelCase__     :						List[Any]						=							self.scheduler.step(_A		,		_A		,		_A					).prev_sample

          UpperCAmelCase__     :						Any						=							audio.clamp(-1		,		1					).float().cpu().numpy()

          UpperCAmelCase__     :						Any						=							audio[:, :, :original_sample_size]

          if not return_dict:
              return (audio,)

          return AudioPipelineOutput(audios=_A					)







import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links






def 				_UpperCAmelCase			(    A				,							A=7     ):




   '''simple docstring'''

   UpperCAmelCase__						=None
   if token is not None:
      UpperCAmelCase__						={"Accept": "application/vnd.github+json", "Authorization": F"""Bearer {token}"""}

   # The id of a workflow (not of a workflow run)
   UpperCAmelCase__						="636036"

   UpperCAmelCase__						=F"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
   # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
   url += F"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

   UpperCAmelCase__						=requests.get(A				,							headers=A     ).json()

   return result["workflow_runs"]






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=get_daily_ci_runs(A     )
   UpperCAmelCase__						=None
   for workflow_run in workflow_runs:
      if workflow_run["status"] == "completed":
         UpperCAmelCase__						=workflow_run["id"]
         break

   return workflow_run_id






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=get_last_daily_ci_runs(A     )
   if workflow_run_id is not None:
      UpperCAmelCase__						=get_artifacts_links(worflow_run_id=A				,							token=A     )
      for artifact_name in artifact_names:
         if artifact_name in artifacts_links:
            UpperCAmelCase__						=artifacts_links[artifact_name]
            download_artifact(
                artifact_name=A				,							artifact_url=A				,							output_dir=A				,							token=A     )






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   get_last_daily_ci_artifacts(A				,							A				,							A     )

   UpperCAmelCase__						={}
   for artifact_name in artifact_names:
      UpperCAmelCase__						=os.path.join(A				,							F"""{artifact_name}.zip"""     )
      if os.path.isfile(A     ):
         UpperCAmelCase__						={}
         with zipfile.ZipFile(A     ) as z:
            for filename in z.namelist():
               if not os.path.isdir(A     ):
                  # read the file
                  with z.open(A     ) as f:
                     UpperCAmelCase__						=f.read().decode("UTF-8"     )

   return results



