





"""simple docstring"""



import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests







def 		__lowercase     (   snake_case_  :  List[str]						)   ->Any:



    '''simple docstring'''







    __A    :						Optional[Any]						 =     {}

    __A    :						List[Any]						 =     job['''started_at''']
    __A    :						Optional[int]						 =     job['''completed_at''']

    __A    :						Optional[Any]						 =     date_parser.parse(snake_case_						)
    __A    :						Any						 =     date_parser.parse(snake_case_						)

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

    __A    :						Dict						 =     start
    __A    :						List[Any]						 =     end
    __A    :						int						 =     duration_in_min

    return job_info







def 		__lowercase     (   snake_case_  :  List[Any]				,snake_case_  :  Tuple=None						)   ->Union[str, Any]:



    '''simple docstring'''







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

    __A    :						Any						 =     F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
    __A    :						Any						 =     requests.get(snake_case_				,headers=snake_case_						).json()
    __A    :						List[Any]						 =     {}

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

        for i in range(snake_case_						):
            __A    :						str						 =     requests.get(url + F"""&page={i + 2}"""				,headers=snake_case_						).json()
            job_time.update({job['''name''']: extract_time_from_single_job(snake_case_						) 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__":
       a_ =			argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
       a_ =			parser.parse_args()

       a_ =			get_job_time(args.workflow_run_id)
       a_ =			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']}''')











"""simple docstring"""



# 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					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """microsoft/speecht5_tts"""
 _lowerCamelCase        =     (
     """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."""
 )
 _lowerCamelCase        =     """text_reader"""
 _lowerCamelCase        =     SpeechTaProcessor
 _lowerCamelCase        =     SpeechTaForTextToSpeech
 _lowerCamelCase        =     SpeechTaHifiGan

 _lowerCamelCase        =     ["""text"""]
 _lowerCamelCase        =     ["""audio"""]

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.post_processor is None:
         __A    :						List[str]						 =     '''microsoft/speecht5_hifigan'''
     super().setup()

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase=None  ):



     '''simple docstring'''




     __A    :						int						 =     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 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     with torch.no_grad():
         return self.model.generate_speech(**__lowerCamelCase  )







 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     with torch.no_grad():
         return self.post_processor(__lowerCamelCase  ).cpu().detach()




