



"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


A:     Any										=	TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow("", "|", "|"),
    datarow=DataRow("", "|", "|"),
    padding=1,
    with_header_hide=None,
)


A:     Any										=	[]
A:     Union[str, Any]										=	[]

A:     Optional[int]										=	{"type": "section", "text": {"type": "plain_text", "text": "No failed tests! 🤗", "emoji": True}}

A:     List[str]										=	[
    {
        "type": "header",
        "text": {
            "type": "plain_text",
            "text": f"""🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results""",
            "emoji": True,
        },
    }
]

A:     int										=	0
for log in Path().glob("*.log"):
      A:     Union[str, Any]										=	0
      with open(log, "r") as f:
            for line in f:
                  A:     Union[str, Any]										=	json.loads(line)
                  if line.get("nodeid", "") != "":
                        A:     Union[str, Any]										=	line["nodeid"]
                        if line.get("duration", None) is not None:
                              A:     str										=	f"""{line['duration']:.4f}"""
                              if line.get("outcome", "") == "failed":
                                    section_num_failed += 1
                                    failed.append([test, duration, log.name.split("_")[0]])
                                    total_num_failed += 1
      group_info.append([str(log), section_num_failed, failed])
      A:     Any										=	[]
      log.unlink()

A:     Union[str, Any]										=	""
A:     List[str]										=	[]
if total_num_failed > 0:
      for name, num_failed, failed_tests in group_info:
            if num_failed > 0:
                  if num_failed == 1:
                        message += f"*{name[1:]}: {num_failed} failed test*\n"
                  else:
                        message += f"*{name[1:]}: {num_failed} failed tests*\n"
                  A:     Optional[int]										=	[]
                  A:     Tuple										=	{}
                  for test in failed_tests:
                        A:     List[Any]										=	test[0].split("::")
                        A:     str										=	data[0].split("/")[-1]
                        if data[0] not in filesafailed:
                              A:     Dict										=	[data[1:]]
                        else:
                              filesafailed[data[0]] += [data[1:]]
                        failed_table.append(data)

                  A:     int										=	[test[0] for test in failed_table]
                  A:     List[Any]										=	list(set(files))
                  # Count number of instances in failed_tests
                  A:     Optional[int]										=	[]
                  for file in individual_files:
                        table.append([file, len(filesafailed[file])])

                  A:     Union[str, Any]										=	tabulate(
                      table,
                      headers=["Test Location", "Num Failed"],
                      tablefmt=hf_table_format,
                      stralign="right",
                  )
                  message += f"\n```\n{failed_table}\n```"
                  all_filesafailed.append(filesafailed)
      if len(message) > 3_0_0_0:
            A:     Optional[Any]										=	"Too many failed tests, please see the full report in the Action results."
            A:     str										=	len(err) + 1_0
            A:     List[Any]										=	message[: 3_0_0_0 - offset] + f"""\n...\n```\n{err}"""
      print(f"""### {message}""")
else:
      A:     Tuple										=	"No failed tests! 🤗"
      print(f"""## {message}""")
      payload.append(no_error_payload)

if os.environ.get("TEST_TYPE", "") != "":
      from slack_sdk import WebClient

      A:     Optional[Any]										=	WebClient(token=os.environ["SLACK_API_TOKEN"])
      if message != "No failed tests! 🤗":
            A:     Optional[Any]										=	{
                "type": "section",
                "text": {
                    "type": "mrkdwn",
                    "text": message,
                },
            }
            payload.append(md_report)
            A:     List[str]										=	{
                "type": "section",
                "text": {
                    "type": "mrkdwn",
                    "text": "*For more details:*",
                },
                "accessory": {
                    "type": "button",
                    "text": {
                        "type": "plain_text",
                        "text": "Check Action results",
                        "emoji": True,
                    },
                    "url": f"""https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}""",
                },
            }
            payload.append(action_button)
            A:     int										=	{
                "type": "context",
                "elements": [
                    {
                        "type": "plain_text",
                        "text": f"""Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}""",
                    }
                ],
            }
            payload.append(date_report)
      A:     str										=	client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
      A:     Dict										=	response.data["ts"]
      for failed_file in all_filesafailed:
            for test_location, test_failures in failed_file.items():
                  # Keep only the first instance of the test name
                  A:     List[str]										=	""
                  for i, row in enumerate(test_failures):
                        if row[0] != test_class:
                              A:     Optional[int]										=	row[0]
                        else:
                              A:     str										=	""

                  A:     Union[str, Any]										=	{
                      "type": "section",
                      "text": {
                          "type": "mrkdwn",
                          "text": f"""Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```""",
                      },
                  }

                  client.chat_postMessage(
                      channel="#accelerate-ci-daily",
                      thread_ts=ts,
                      blocks=[payload],
                  )





"""simple docstring"""
import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


A:     List[Any]										=	logging.get_logger(__name__)



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     Optional[int]		=		'AutoTokenizer'
   __lowerCAmelCase						:     str		=		['tokenizer']

   __lowerCAmelCase						:     Any		=		{
       'semantic_prompt': 1,
       'coarse_prompt': 2,
       'fine_prompt': 2,
   }

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None		)						->   int:





    '''simple docstring'''




    super().__init__(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			speaker_embeddings

   @classmethod
   def 			SCREAMING_SNAKE_CASE  (     cls   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings_path.json"   ,  **_SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    if speaker_embeddings_dict_path is not None:
     UpperCAmelCase			:      Any			=			get_file_from_repo(
         _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  subfolder=kwargs.pop("""subfolder"""   ,  _SCREAMING_SNAKE_CASE		)   ,  cache_dir=kwargs.pop("""cache_dir"""   ,  _SCREAMING_SNAKE_CASE		)   ,  force_download=kwargs.pop("""force_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  proxies=kwargs.pop("""proxies"""   ,  _SCREAMING_SNAKE_CASE		)   ,  resume_download=kwargs.pop("""resume_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  local_files_only=kwargs.pop("""local_files_only"""   ,  _SCREAMING_SNAKE_CASE		)   ,  use_auth_token=kwargs.pop("""use_auth_token"""   ,  _SCREAMING_SNAKE_CASE		)   ,  revision=kwargs.pop("""revision"""   ,  _SCREAMING_SNAKE_CASE		)   ,  )
     if speaker_embeddings_path is None:
      logger.warning(
          F"`{os.path.join(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."		)
      UpperCAmelCase			:      Optional[int]			=			None
     else:
      with open(_SCREAMING_SNAKE_CASE		) as speaker_embeddings_json:
       UpperCAmelCase			:      List[str]			=			json.load(_SCREAMING_SNAKE_CASE		)
    else:
     UpperCAmelCase			:      List[str]			=			None

    UpperCAmelCase			:      List[Any]			=			AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

    return cls(tokenizer=_SCREAMING_SNAKE_CASE   ,  speaker_embeddings=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings_path.json"   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings"   ,  _SCREAMING_SNAKE_CASE = False   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Union[str, Any]:





    '''simple docstring'''




    if self.speaker_embeddings is not None:
     os.makedirs(os.path.join(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  """v2"""		)   ,  exist_ok=_SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      str			=			{}

     UpperCAmelCase			:      Union[str, Any]			=			save_directory

     for prompt_key in self.speaker_embeddings:
      if prompt_key != "repo_or_path":
       UpperCAmelCase			:      Optional[Any]			=			self._load_voice_preset(_SCREAMING_SNAKE_CASE		)

       UpperCAmelCase			:      Any			=			{}
       for key in self.speaker_embeddings[prompt_key]:
        np.save(
            os.path.join(
                embeddings_dict["""repo_or_path"""]   ,  _SCREAMING_SNAKE_CASE   ,  F"{prompt_key}_{key}"		)   ,  voice_preset[key]   ,  allow_pickle=_SCREAMING_SNAKE_CASE   ,  )
        UpperCAmelCase			:      Optional[int]			=			os.path.join(_SCREAMING_SNAKE_CASE   ,  F"{prompt_key}_{key}.npy"		)

       UpperCAmelCase			:      Tuple			=			tmp_dict

     with open(os.path.join(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)   ,  """w"""		) as fp:
      json.dump(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    super().save_pretrained(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE = None   ,  **_SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.speaker_embeddings[voice_preset]

    UpperCAmelCase			:      List[Any]			=			{}
    for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
     if key not in voice_preset_paths:
      raise ValueError(
          F"Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."		)

     UpperCAmelCase			:      List[str]			=			get_file_from_repo(
         self.speaker_embeddings.get("""repo_or_path"""   ,  """/"""		)   ,  voice_preset_paths[key]   ,  subfolder=kwargs.pop("""subfolder"""   ,  _SCREAMING_SNAKE_CASE		)   ,  cache_dir=kwargs.pop("""cache_dir"""   ,  _SCREAMING_SNAKE_CASE		)   ,  force_download=kwargs.pop("""force_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  proxies=kwargs.pop("""proxies"""   ,  _SCREAMING_SNAKE_CASE		)   ,  resume_download=kwargs.pop("""resume_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  local_files_only=kwargs.pop("""local_files_only"""   ,  _SCREAMING_SNAKE_CASE		)   ,  use_auth_token=kwargs.pop("""use_auth_token"""   ,  _SCREAMING_SNAKE_CASE		)   ,  revision=kwargs.pop("""revision"""   ,  _SCREAMING_SNAKE_CASE		)   ,  )
     if path is None:
      raise ValueError(
          F"`{os.path.join(self.speaker_embeddings.get('repo_or_path'   ,  '/'		)   ,  voice_preset_paths[key]		)}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings."		)

     UpperCAmelCase			:      List[str]			=			np.load(_SCREAMING_SNAKE_CASE		)

    return voice_preset_dict

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE = None		)						->   List[str]:





    '''simple docstring'''




    for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
     if key not in voice_preset:
      raise ValueError(F"Voice preset unrecognized, missing {key} as a key."		)

     if not isinstance(voice_preset[key]   ,  np.ndarray		):
      raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key]		)}D ndarray."		)

     if len(voice_preset[key].shape		) != self.preset_shape[key]:
      raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key]		)}D ndarray."		)




   def __call__(     self   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE="pt"   ,  _SCREAMING_SNAKE_CASE=256   ,  _SCREAMING_SNAKE_CASE=False   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=False   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Optional[int]:





    '''simple docstring'''




    if voice_preset is not None and not isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		):
     if (
         isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
         and self.speaker_embeddings is not None
         and voice_preset in self.speaker_embeddings
     ):
      UpperCAmelCase			:      Dict			=			self._load_voice_preset(_SCREAMING_SNAKE_CASE		)

     else:
      if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) and not voice_preset.endswith(""".npz"""		):
       UpperCAmelCase			:      Tuple			=			voice_preset + """.npz"""

      UpperCAmelCase			:      Union[str, Any]			=			np.load(_SCREAMING_SNAKE_CASE		)

    if voice_preset is not None:
     self._validate_voice_preset_dict(_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      List[Any]			=			BatchFeature(data=_SCREAMING_SNAKE_CASE   ,  tensor_type=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[str]			=			self.tokenizer(
        _SCREAMING_SNAKE_CASE   ,  return_tensors=_SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  max_length=_SCREAMING_SNAKE_CASE   ,  return_attention_mask=_SCREAMING_SNAKE_CASE   ,  return_token_type_ids=_SCREAMING_SNAKE_CASE   ,  add_special_tokens=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )

    if voice_preset is not None:
     UpperCAmelCase			:      List[Any]			=			voice_preset

    return encoded_text
