


import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests
def     __lowerCamelCase						(			UpperCAmelCase_						:		Any						):




      """simple docstring"""





      a						:int										=	{}

      a						:List[Any]										=	job['''started_at''']
      a						:List[Any]										=	job['''completed_at''']

      a						:List[str]										=	date_parser.parse(UpperCAmelCase_						)
      a						:Dict										=	date_parser.parse(UpperCAmelCase_						)

      a						:Optional[int]										=	round((end_datetime - start_datetime).total_seconds() / 60.0						)

      a						:List[str]										=	start
      a						:Optional[Any]										=	end
      a						:List[Any]										=	duration_in_min

      return job_info
def     __lowerCamelCase						(			UpperCAmelCase_						:		List[str]			,  UpperCAmelCase_						:		List[Any]=None						):




      """simple docstring"""





      a						:str										=	None
      if token is not None:
            a						:Optional[Any]										=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': F'''Bearer {token}'''}

      a						:Optional[int]										=	F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
      a						:str										=	requests.get(UpperCAmelCase_			,  headers=UpperCAmelCase_						).json()
      a						:Tuple										=	{}

      try:
            job_time.update({job['''name''']: extract_time_from_single_job(UpperCAmelCase_						) for job in result['''jobs''']}						)
            a						:Tuple										=	math.ceil((result['''total_count'''] - 100) / 100						)

            for i in range(UpperCAmelCase_						):
                  a						:Union[str, Any]										=	requests.get(url + F'''&page={i + 2}'''			,  headers=UpperCAmelCase_						).json()
                  job_time.update({job['''name''']: extract_time_from_single_job(UpperCAmelCase_						) for job in result['''jobs''']}						)

            return job_time
      except Exception:
            print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}'''						)

      return {}


if __name__ == "__main__":
     snake_case    :   Dict	    =					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
     snake_case    :   Union[str, Any]	    =					parser.parse_args()

     snake_case    :   Union[str, Any]	    =					get_job_time(args.workflow_run_id)
     snake_case    :   Optional[Any]	    =					dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

     for k, v in job_time.items():
          print(F"""{k}: {v["duration"]}""")









# Copyright 2023 The HuggingFace Inc. 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.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
     from datasets import load_dataset







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'microsoft/speecht5_tts'
 SCREAMING_SNAKE_CASE__				=	(
     'This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '
     'text to read (in English) and returns a waveform object containing the sound.'
 )
 SCREAMING_SNAKE_CASE__				=	'text_reader'
 SCREAMING_SNAKE_CASE__				=	SpeechTaProcessor
 SCREAMING_SNAKE_CASE__				=	SpeechTaForTextToSpeech
 SCREAMING_SNAKE_CASE__				=	SpeechTaHifiGan

 SCREAMING_SNAKE_CASE__				=	['text']
 SCREAMING_SNAKE_CASE__				=	['audio']




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       if self.post_processor is None:
             a						:List[Any]										=	'''microsoft/speecht5_hifigan'''
       super().setup()




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=None   ):
       a						:Tuple										=	self.pre_processor(text=_lowerCamelCase ,  return_tensors='''pt''' ,  truncation=_lowerCamelCase   )

       if speaker_embeddings is None:
             if not is_datasets_available():
                   raise ImportError('''Datasets needs to be installed if not passing speaker embeddings.'''   )

             a						:List[Any]										=	load_dataset('''Matthijs/cmu-arctic-xvectors''' ,  split='''validation'''   )
             a						:int										=	torch.tensor(embeddings_dataset[7305]['''xvector''']   ).unsqueeze(0   )

       return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       with torch.no_grad():
             return self.model.generate_speech(**_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       with torch.no_grad():
             return self.post_processor(_lowerCamelCase   ).cpu().detach()





