

'''simple docstring'''

import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict

import requests
from slack_sdk import WebClient


lowercase__         =      WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])




def 				UpperCamelCase(					UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[Any]						 =			test_results.split(' '     )

 UpperCAmelCase	:     Optional[Any]						 =			0
 UpperCAmelCase	:     Tuple						 =			0

 # When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
 # When it is too long, those signs are not present.
 UpperCAmelCase	:     Dict						 =			expressions[-2] if '=' in expressions[-1] else expressions[-1]

 for i, expression in enumerate(lowercase__     ):
  if "failed" in expression:
   failed += int(expressions[i - 1]     )
  if "passed" in expression:
   success += int(expressions[i - 1]     )

 return failed, success, time_spent




def 				UpperCamelCase(					UpperCAmelCase_     ):
 UpperCAmelCase	:     str						 =			{}
 UpperCAmelCase	:     Optional[int]						 =			None
 UpperCAmelCase	:     Dict						 =			False
 for line in failures_short_lines.split('\n'     ):
  if re.search(R'_ \[doctest\]'  ,	lowercase__     ):
   UpperCAmelCase	:     List[Any]						 =			True
   UpperCAmelCase	:     Union[str, Any]						 =			line.split(' '     )[2]
  elif in_error and not line.split(' '     )[0].isdigit():
   UpperCAmelCase	:     Dict						 =			line
   UpperCAmelCase	:     Optional[Any]						 =			False

 return failures




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					List[str]					,      lowercase_			:					Tuple					,      lowercase_			:					Union[str, Any]	)						->     Optional[int]:
      UpperCAmelCase	:     Tuple						 =			title

      UpperCAmelCase	:     Any						 =			doc_test_results['time_spent'].split(','	)[0]
      UpperCAmelCase	:     List[Any]						 =			doc_test_results['success']
      UpperCAmelCase	:     Any						 =			doc_test_results['failures']
      UpperCAmelCase	:     Any						 =			self.n_success + self.n_failures

      # Failures and success of the modeling tests
      UpperCAmelCase	:     int						 =			doc_test_results


     @property
     def        UpperCAmelCase_	(		self			:					List[str]	)						->     int:
      UpperCAmelCase	:     Tuple						 =			[self._time_spent]
      UpperCAmelCase	:     str						 =			0

      for time in time_spent:
       UpperCAmelCase	:     List[Any]						 =			time.split(':'	)

       # Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
       if len(lowercase_	) == 1:
        UpperCAmelCase	:     Union[str, Any]						 =			[0, 0, time_parts[0]]

       UpperCAmelCase	:     int						 =			int(time_parts[0]	), int(time_parts[1]	), float(time_parts[2]	)
       total_secs += hours * 3_600 + minutes * 60 + seconds

      UpperCAmelCase	:     Tuple						 =			total_secs // 3_600, (total_secs % 3_600) // 60, total_secs % 60
      return f"""{int(lowercase_	)}h{int(lowercase_	)}m{int(lowercase_	)}s"""


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     Any:
      return {"type": "header", "text": {"type": "plain_text", "text": self.title}}


     @property
     def        UpperCAmelCase_	(		self			:					int	)						->     List[Any]:
      return {
          "type": "section",
          "text": {
              "type": "plain_text",
              "text": f"""🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.""",
              "emoji": True,
          },
          "accessory": {
              "type": "button",
              "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
              "url": f"""https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
          },
      }


     @property
     def        UpperCAmelCase_	(		self			:					Tuple	)						->     int:
      return {
          "type": "section",
          "text": {
              "type": "plain_text",
              "text": (
                  f"""There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in"""
                  f""" {self.time}."""
              ),
              "emoji": True,
          },
          "accessory": {
              "type": "button",
              "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
              "url": f"""https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
          },
      }


     @property
     def        UpperCAmelCase_	(		self			:					Any	)						->     int:
      UpperCAmelCase	:     str						 =			40
      UpperCAmelCase	:     List[str]						 =			{k: v['failed'] for k, v in doc_test_results.items() if isinstance(lowercase_					,      lowercase_	)}

      UpperCAmelCase	:     Union[str, Any]						 =			''
      for category, failures in category_failures.items():
       if len(lowercase_	) == 0:
        continue

       if report != "":
        report += "\n\n"

       report += f"""*{category} failures*:""".ljust(line_length // 2	).rjust(line_length // 2	) + "\n"
       report += "`"
       report += "`\n`".join(lowercase_	)
       report += "`"

      return {
          "type": "section",
          "text": {
              "type": "mrkdwn",
              "text": f"""The following examples had failures:\n\n\n{report}\n""",
          },
      }


     @property
     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Any:
      UpperCAmelCase	:     List[Any]						 =			[self.header]

      if self.n_failures > 0:
       blocks.append(self.failures	)

      if self.n_failures > 0:
       blocks.extend([self.category_failures]	)

      if self.n_failures == 0:
       blocks.append(self.no_failures	)

      return json.dumps(lowercase_	)


     @staticmethod
     def        UpperCAmelCase_	(		)						->     Union[str, Any]:
      UpperCAmelCase	:     List[Any]						 =			[
          {
              'type': 'section',
              'text': {
                  'type': 'plain_text',
                  'text': 'There was an issue running the tests.',
              },
              'accessory': {
                  'type': 'button',
                  'text': {'type': 'plain_text', 'text': 'Check Action results', 'emoji': True},
                  'url': f"""https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
              },
          }
      ]

      print('Sending the following payload'	)
      print(json.dumps({'blocks': json.loads(lowercase_	)}	)	)

      client.chat_postMessage(
          channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']					,      text='There was an issue running the tests.'					,      blocks=lowercase_					,      )


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     int:
      print('Sending the following payload'	)
      print(json.dumps({'blocks': json.loads(self.payload	)}	)	)

      UpperCAmelCase	:     List[Any]						 =			f"""{self.n_failures} failures out of {self.n_tests} tests,""" if self.n_failures else 'All tests passed.'

      UpperCAmelCase	:     Dict						 =			client.chat_postMessage(
          channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']					,      blocks=self.payload					,      text=lowercase_					,      )


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					Any					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Dict					,      lowercase_			:					Union[str, Any]	)						->     Dict:
      UpperCAmelCase	:     Union[str, Any]						 =			''
      for key, value in failures.items():
       UpperCAmelCase	:     Tuple						 =			value[:200] + ' [Truncated]' if len(lowercase_	) > 250 else value
       failures_text += f"""*{key}*\n_{value}_\n\n"""

      UpperCAmelCase	:     Optional[Any]						 =			job_name
      UpperCAmelCase	:     str						 =			{'type': 'section', 'text': {'type': 'mrkdwn', 'text': text}}

      if job_link is not None:
       UpperCAmelCase	:     Optional[int]						 =			{
           'type': 'button',
           'text': {'type': 'plain_text', 'text': 'GitHub Action job', 'emoji': True},
           'url': job_link,
       }

      return [
          {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
          content,
          {"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
      ]




     def        UpperCAmelCase_	(		self			:					int	)						->     int:
      if self.thread_ts is None:
       raise ValueError('Can only post reply if a post has been made.'	)

      UpperCAmelCase	:     str						 =			self.doc_test_results.pop('job_link'	)
      self.doc_test_results.pop('failures'	)
      self.doc_test_results.pop('success'	)
      self.doc_test_results.pop('time_spent'	)

      UpperCAmelCase	:     int						 =			sorted(self.doc_test_results.items()					,      key=lambda lowercase_	: t[0]	)
      for job, job_result in sorted_dict:
       if len(job_result['failures']	):
        UpperCAmelCase	:     List[str]						 =			f"""*Num failures* :{len(job_result["failed"]	)} \n"""
        UpperCAmelCase	:     Any						 =			job_result['failures']
        UpperCAmelCase	:     Any						 =			self.get_reply_blocks(lowercase_					,      lowercase_					,      lowercase_					,      text=lowercase_	)

        print('Sending the following reply'	)
        print(json.dumps({'blocks': blocks}	)	)

        client.chat_postMessage(
            channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']					,      text=f"""Results for {job}"""					,      blocks=lowercase_					,      thread_ts=self.thread_ts['ts']					,      )

        time.sleep(1	)




def 				UpperCamelCase(					):
 UpperCAmelCase	:     Optional[Any]						 =			os.environ['GITHUB_RUN_ID']
 UpperCAmelCase	:     Tuple						 =			F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"""
 UpperCAmelCase	:     Optional[Any]						 =			requests.get(lowercase__     ).json()
 UpperCAmelCase	:     Union[str, Any]						 =			{}

 try:
  jobs.update({job['name']: job['html_url'] for job in result['jobs']}     )
  UpperCAmelCase	:     List[Any]						 =			math.ceil((result['total_count'] - 1_00) / 1_00     )

  for i in range(lowercase__     ):
   UpperCAmelCase	:     Tuple						 =			requests.get(url + F"""&page={i + 2}"""     ).json()
   jobs.update({job['name']: job['html_url'] for job in result['jobs']}     )

  return jobs
 except Exception as e:
  print('Unknown error, could not fetch links.'  ,	lowercase__     )

 return {}




def 				UpperCamelCase(					UpperCAmelCase_     ):
 UpperCAmelCase	:     List[Any]						 =			{}

 if os.path.exists(lowercase__     ):
  UpperCAmelCase	:     List[Any]						 =			os.listdir(lowercase__     )
  for file in files:
   try:
    with open(os.path.join(lowercase__  ,	lowercase__     )  ,	encoding='utf-8'     ) as f:
     UpperCAmelCase	:     Optional[int]						 =			f.read()
   except UnicodeDecodeError as e:
    raise ValueError(F"""Could not open {os.path.join(lowercase__  ,	lowercase__     )}."""     ) from e

 return _artifact




def 				UpperCamelCase(					):




 class 	A_			:






      '''simple docstring'''



      def __init__(		self			:					int					,      lowercase_			:					List[Any]	)						->     Optional[Any]:
       UpperCAmelCase	:     Optional[Any]						 =			name
       UpperCAmelCase	:     Dict						 =			[]


      def __str__(		self			:					List[Any]	)						->     Optional[int]:
       return self.name




      def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					Dict	)						->     Union[str, Any]:
       self.paths.append({'name': self.name, 'path': path}	)



 UpperCAmelCase	:     Dict[str, Artifact]						 =			{}

 UpperCAmelCase	:     Optional[Any]						 =			filter(os.path.isdir  ,	os.listdir()     )
 for directory in directories:
  UpperCAmelCase	:     Dict						 =			directory
  if artifact_name not in _available_artifacts:
   UpperCAmelCase	:     List[str]						 =			Artifact(lowercase__     )

   _available_artifacts[artifact_name].add_path(lowercase__     )

 return _available_artifacts


if __name__ == "__main__":
 lowercase__         =      get_job_links()
 lowercase__         =      retrieve_available_artifacts()

 lowercase__         =      collections.OrderedDict(
     [
         ("*.py", "API Examples"),
         ("*.md", "MD Examples"),
     ]
 )

 # This dict will contain all the information relative to each doc test category:
 # - failed: list of failed tests
 # - failures: dict in the format 'test': 'error_message'
 lowercase__         =      {
     v: {
         "failed": [],
         "failures": {},
     }
     for v in docs.values()
 }

 # Link to the GitHub Action job
 lowercase__         =      github_actions_job_links.get("run_doctests")

 lowercase__         =      available_artifacts["doc_tests_gpu_test_reports"].paths[0]
 lowercase__         =      retrieve_artifact(artifact_path["name"])
 if "stats" in artifact:
  lowercase__   ,  lowercase__   ,  lowercase__         =      handle_test_results(artifact["stats"])
  lowercase__         =      failed
  lowercase__         =      success
  lowercase__         =      time_spent[1:-1] + ", "

  lowercase__         =      extract_first_line_failure(artifact["failures_short"])
  for line in artifact["summary_short"].split("\n"):
   if re.search("FAILED", line):
    lowercase__         =      line.replace("FAILED ", "")
    lowercase__         =      line.split()[0].replace("\n", "")

    if "::" in line:
     lowercase__   ,  lowercase__         =      line.split("::")
    else:
     lowercase__   ,  lowercase__         =      line, line

    for file_regex in docs.keys():
     if fnmatch(file_path, file_regex):
      lowercase__         =      docs[file_regex]
      doc_test_results[category]["failed"].append(test)

      lowercase__         =      all_failures[test] if test in all_failures else "N/A"
      lowercase__         =      failure
      break

 lowercase__         =      Message("🤗 Results of the doc tests.", doc_test_results)
 message.post()
 message.post_reply()







'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase__         =      logging.get_logger("transformers.models.speecht5")




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 hf_model.apply_weight_norm()

 UpperCAmelCase	:     Dict						 =			checkpoint['input_conv.weight_g']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.weight_v']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.bias']

 for i in range(len(config.upsample_rates     )     ):
  UpperCAmelCase	:     Any						 =			checkpoint[F"""upsamples.{i}.1.weight_g"""]
  UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""upsamples.{i}.1.weight_v"""]
  UpperCAmelCase	:     str						 =			checkpoint[F"""upsamples.{i}.1.bias"""]

 for i in range(len(config.upsample_rates     ) * len(config.resblock_kernel_sizes     )     ):
  for j in range(len(config.resblock_dilation_sizes     )     ):
   UpperCAmelCase	:     Any						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   UpperCAmelCase	:     Dict						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

   UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCAmelCase	:     int						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCAmelCase	:     Dict						 =			checkpoint['output_conv.1.weight_g']
 UpperCAmelCase	:     str						 =			checkpoint['output_conv.1.weight_v']
 UpperCAmelCase	:     Union[str, Any]						 =			checkpoint['output_conv.1.bias']

 hf_model.remove_weight_norm()




@torch.no_grad()
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_=None  ,	):
 if config_path is not None:
  UpperCAmelCase	:     Any						 =			SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase_     )
 else:
  UpperCAmelCase	:     Optional[Any]						 =			SpeechTaHifiGanConfig()

 UpperCAmelCase	:     List[Any]						 =			SpeechTaHifiGan(UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			torch.load(UpperCAmelCase_     )
 load_weights(orig_checkpoint['model']['generator']  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			np.load(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			stats[0].reshape(-1     )
 UpperCAmelCase	:     List[str]						 =			stats[1].reshape(-1     )
 UpperCAmelCase	:     Union[str, Any]						 =			torch.from_numpy(UpperCAmelCase_     ).float()
 UpperCAmelCase	:     List[str]						 =			torch.from_numpy(UpperCAmelCase_     ).float()

 model.save_pretrained(UpperCAmelCase_     )

 if repo_id:
  print('Pushing to the hub...'     )
  model.push_to_hub(UpperCAmelCase_     )


if __name__ == "__main__":
 lowercase__         =      argparse.ArgumentParser()
 parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
 parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 parser.add_argument(
     "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
 )
 parser.add_argument(
     "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
 )

 lowercase__         =      parser.parse_args()
 convert_hifigan_checkpoint(
     args.checkpoint_path,
     args.stats_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )




