




'''simple docstring'''



import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


UpperCamelCase_       =							logging.get_logger(__name__)




def    lowercase__(       __UpperCamelCase: List[str]	,__UpperCamelCase: Optional[int]  ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					:	Any   =		set()
      SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		[]

      def parse_line(__UpperCamelCase: Optional[Any]  ):
            for line in fp:
                  if isinstance(_lowerCAmelCase	,_lowerCAmelCase  ):
                        SCREAMING_SNAKE_CASE					:	Dict   =		line.decode('UTF-8'  )
                  if "warnings summary (final)" in line:
                        continue
                  # This means we are outside the body of a warning
                  elif not line.startswith(' '  ):
                        # process a single warning and move it to `selected_warnings`.
                        if len(_lowerCAmelCase  ) > 0:
                              SCREAMING_SNAKE_CASE					:	Dict   =		"\n".join(_lowerCAmelCase  )
                              # Only keep the warnings specified in `targets`
                              if any(f": {x}: " in warning for x in targets  ):
                                    selected_warnings.add(_lowerCAmelCase  )
                              buffer.clear()
                        continue
                  else:
                        SCREAMING_SNAKE_CASE					:	List[Any]   =		line.strip()
                        buffer.append(_lowerCAmelCase  )

      if from_gh:
            for filename in os.listdir(_lowerCAmelCase  ):
                  SCREAMING_SNAKE_CASE					:	Optional[Any]   =		os.path.join(_lowerCAmelCase	,_lowerCAmelCase  )
                  if not os.path.isdir(_lowerCAmelCase  ):
                        # read the file
                        if filename != "warnings.txt":
                              continue
                        with open(_lowerCAmelCase  ) as fp:
                              parse_line(_lowerCAmelCase  )
      else:
            try:
                  with zipfile.ZipFile(_lowerCAmelCase  ) as z:
                        for filename in z.namelist():
                              if not os.path.isdir(_lowerCAmelCase  ):
                                    # read the file
                                    if filename != "warnings.txt":
                                          continue
                                    with z.open(_lowerCAmelCase  ) as fp:
                                          parse_line(_lowerCAmelCase  )
            except Exception:
                  logger.warning(
                      f"{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."  )

      return selected_warnings




def    lowercase__(       __UpperCamelCase: Any	,__UpperCamelCase: Union[str, Any]  ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					:	List[str]   =		set()

      SCREAMING_SNAKE_CASE					:	Dict   =		[os.path.join(_lowerCAmelCase	,_lowerCAmelCase  ) for p in os.listdir(_lowerCAmelCase  ) if (p.endswith('.zip'  ) or from_gh)]
      for p in paths:
            selected_warnings.update(extract_warnings_from_single_artifact(_lowerCAmelCase	,_lowerCAmelCase  )  )

      return selected_warnings


if __name__ == "__main__":




      def    lowercase__(       __UpperCamelCase: Optional[int]  ):

            """simple docstring"""






            return values.split(','  )

      UpperCamelCase_       =							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
      parser.add_argument(
          "--output_dir",
          type=str,
          required=True,
          help="Where to store the downloaded artifacts and other result files.",
      )
      parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
      # optional parameters
      parser.add_argument(
          "--targets",
          default="DeprecationWarning,UserWarning,FutureWarning",
          type=list_str,
          help="Comma-separated list of target warning(s) which we want to extract.",
      )
      parser.add_argument(
          "--from_gh",
          action="store_true",
          help="If running from a GitHub action workflow and collecting warnings from its artifacts.",
      )

      UpperCamelCase_       =							parser.parse_args()

      UpperCamelCase_       =							args.from_gh
      if from_gh:
            # The artifacts have to be downloaded using `actions/download-artifact@v3`
            pass
      else:
            os.makedirs(args.output_dir, exist_ok=True)

            # get download links
            UpperCamelCase_       =							get_artifacts_links(args.workflow_run_id, token=args.token)
            with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
                  json.dump(artifacts, fp, ensure_ascii=False, indent=4)

            # download artifacts
            for idx, (name, url) in enumerate(artifacts.items()):
                  print(name)
                  print(url)
                  print("=" * 8_0)
                  download_artifact(name, url, args.output_dir, args.token)
                  # Be gentle to GitHub
                  time.sleep(1)

    # extract warnings from artifacts
      UpperCamelCase_       =							extract_warnings(args.output_dir, args.targets)
      UpperCamelCase_       =							sorted(selected_warnings)
      with open(os.path.join(args.output_dir, "selected_warnings.json"), "w", encoding="UTF-8") as fp:
            json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)









from typing import List, Optional, Tuple, Union

import torch

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


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



class 					A__   (       __snake_case			):





      def __init__( self       ,							A_       ,							A_   ):
       '''simple docstring'''




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





      @torch.no_grad()
      def __call__( self       ,							A_ = 1       ,							A_ = 100       ,							A_ = None       ,							A_ = None       ,							A_ = True       ,							):
       '''simple docstring'''




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

       UpperCamelCase	:					Optional[Any]       	=   audio_length_in_s * self.unet.config.sample_rate

       UpperCamelCase	:					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	:					Union[str, Any]       	=   int(A_   )
       if sample_size % down_scale_factor != 0:
        UpperCamelCase	:					List[str]       	=   (
            (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	:					Any       	=   int(A_   )

       UpperCamelCase	:					Union[str, Any]       	=   next(iter(self.unet.parameters()   )   ).dtype
       UpperCamelCase	:					Optional[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[Any]       	=   randn_tensor(A_       ,							generator=A_       ,							device=self.device       ,							dtype=A_   )

       # set step values
       self.scheduler.set_timesteps(A_       ,							device=audio.device   )
       UpperCamelCase	:					Optional[int]       	=   self.scheduler.timesteps.to(A_   )

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

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

       UpperCamelCase	:					Optional[Any]       	=   audio.clamp(-1       ,							1   ).float().cpu().numpy()

       UpperCamelCase	:					Dict       	=   audio[:, :, :original_sample_size]

       if not return_dict:
        return (audio,)

       return AudioPipelineOutput(audios=A_   )
