"""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


lowerCAmelCase__  :  Any						     =	logging.get_logger(__name__)
class 				snake_case       ( __lowerCamelCase				):




					"""simple docstring"""

					snake_case__				    = 'AutoTokenizer'
					snake_case__				    = ['tokenizer']

					snake_case__				    = {
					    'semantic_prompt': 1,
					    'coarse_prompt': 2,
					    'fine_prompt': 2,
					}







					def __init__(				self   :	str    ,lowerCamelCase__   :	Dict    ,lowerCamelCase__   :	Optional[Any]=None			):
									super().__init__(UpperCamelCase_			)

									UpperCAmelCase__             =							speaker_embeddings







					@classmethod
					def 		__lowerCAmelCase						(				cls   :	List[str]    ,lowerCamelCase__   :	List[str]    ,lowerCamelCase__   :	Tuple="speaker_embeddings_path.json"    ,**lowerCamelCase__   :	Dict			):

									if speaker_embeddings_dict_path is not None:
													UpperCAmelCase__             =							get_file_from_repo(
													    UpperCamelCase_    ,UpperCamelCase_    ,subfolder=kwargs.pop('subfolder'    ,UpperCamelCase_			)    ,cache_dir=kwargs.pop('cache_dir'    ,UpperCamelCase_			)    ,force_download=kwargs.pop('force_download'    ,UpperCamelCase_			)    ,proxies=kwargs.pop('proxies'    ,UpperCamelCase_			)    ,resume_download=kwargs.pop('resume_download'    ,UpperCamelCase_			)    ,local_files_only=kwargs.pop('local_files_only'    ,UpperCamelCase_			)    ,use_auth_token=kwargs.pop('use_auth_token'    ,UpperCamelCase_			)    ,revision=kwargs.pop('revision'    ,UpperCamelCase_			)    ,)
													if speaker_embeddings_path is None:
																	logger.warning(
																	    f'''`{os.path.join(UpperCamelCase_    ,UpperCamelCase_			)}` 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__             =							None
													else:
																	with open(UpperCamelCase_			) as speaker_embeddings_json:
																					UpperCAmelCase__             =							json.load(UpperCamelCase_			)
									else:
													UpperCAmelCase__             =							None

									UpperCAmelCase__             =							AutoTokenizer.from_pretrained(UpperCamelCase_    ,**UpperCamelCase_			)

									return cls(tokenizer=UpperCamelCase_    ,speaker_embeddings=UpperCamelCase_			)







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	int    ,lowerCamelCase__   :	int="speaker_embeddings_path.json"    ,lowerCamelCase__   :	Optional[Any]="speaker_embeddings"    ,lowerCamelCase__   :	bool = False    ,**lowerCamelCase__   :	Union[str, Any]    ,):
									if self.speaker_embeddings is not None:
													os.makedirs(os.path.join(UpperCamelCase_    ,UpperCamelCase_    ,'v2'			)    ,exist_ok=UpperCamelCase_			)

													UpperCAmelCase__             =							{}

													UpperCAmelCase__             =							save_directory

													for prompt_key in self.speaker_embeddings:
																	if prompt_key != "repo_or_path":
																					UpperCAmelCase__             =							self._load_voice_preset(UpperCamelCase_			)

																					UpperCAmelCase__             =							{}
																					for key in self.speaker_embeddings[prompt_key]:
																									np.save(
																									    os.path.join(
																									        embeddings_dict['repo_or_path']    ,UpperCamelCase_    ,f'''{prompt_key}_{key}'''			)    ,voice_preset[key]    ,allow_pickle=UpperCamelCase_    ,)
																									UpperCAmelCase__             =							os.path.join(UpperCamelCase_    ,f'''{prompt_key}_{key}.npy'''			)

																					UpperCAmelCase__             =							tmp_dict

													with open(os.path.join(UpperCamelCase_    ,UpperCamelCase_			)    ,'w'			) as fp:
																	json.dump(UpperCamelCase_    ,UpperCamelCase_			)

									super().save_pretrained(UpperCamelCase_    ,UpperCamelCase_    ,**UpperCamelCase_			)







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	str = None    ,**lowerCamelCase__   :	List[Any]			):
									UpperCAmelCase__             =							self.speaker_embeddings[voice_preset]

									UpperCAmelCase__             =							{}
									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__             =							get_file_from_repo(
													    self.speaker_embeddings.get('repo_or_path'    ,'/'			)    ,voice_preset_paths[key]    ,subfolder=kwargs.pop('subfolder'    ,UpperCamelCase_			)    ,cache_dir=kwargs.pop('cache_dir'    ,UpperCamelCase_			)    ,force_download=kwargs.pop('force_download'    ,UpperCamelCase_			)    ,proxies=kwargs.pop('proxies'    ,UpperCamelCase_			)    ,resume_download=kwargs.pop('resume_download'    ,UpperCamelCase_			)    ,local_files_only=kwargs.pop('local_files_only'    ,UpperCamelCase_			)    ,use_auth_token=kwargs.pop('use_auth_token'    ,UpperCamelCase_			)    ,revision=kwargs.pop('revision'    ,UpperCamelCase_			)    ,)
													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__             =							np.load(UpperCamelCase_			)

									return voice_preset_dict







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	Optional[dict] = None			):
									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   :	Any    ,lowerCamelCase__   :	Optional[int]=None    ,lowerCamelCase__   :	Optional[Any]=None    ,lowerCamelCase__   :	List[Any]="pt"    ,lowerCamelCase__   :	str=256    ,lowerCamelCase__   :	List[Any]=False    ,lowerCamelCase__   :	Union[str, Any]=True    ,lowerCamelCase__   :	Optional[Any]=False    ,**lowerCamelCase__   :	Dict    ,):
									if voice_preset is not None and not isinstance(UpperCamelCase_    ,UpperCamelCase_			):
													if (
													    isinstance(UpperCamelCase_    ,UpperCamelCase_			)
													    and self.speaker_embeddings is not None
													    and voice_preset in self.speaker_embeddings
													):
																	UpperCAmelCase__             =							self._load_voice_preset(UpperCamelCase_			)

													else:
																	if isinstance(UpperCamelCase_    ,UpperCamelCase_			) and not voice_preset.endswith('.npz'			):
																					UpperCAmelCase__             =							voice_preset + '.npz'

																	UpperCAmelCase__             =							np.load(UpperCamelCase_			)

									if voice_preset is not None:
													self._validate_voice_preset_dict(UpperCamelCase_    ,**UpperCamelCase_			)
													UpperCAmelCase__             =							BatchFeature(data=UpperCamelCase_    ,tensor_type=UpperCamelCase_			)

									UpperCAmelCase__             =							self.tokenizer(
									    UpperCamelCase_    ,return_tensors=UpperCamelCase_    ,padding='max_length'    ,max_length=UpperCamelCase_    ,return_attention_mask=UpperCamelCase_    ,return_token_type_ids=UpperCamelCase_    ,add_special_tokens=UpperCamelCase_    ,**UpperCamelCase_    ,)

									if voice_preset is not None:
													UpperCAmelCase__             =							voice_preset

									return encoded_text









import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


UpperCAmelCase_									=	TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow('', '|', '|'),
    datarow=DataRow('', '|', '|'),
    padding=1,
    with_header_hide=None,
)


UpperCAmelCase_									=	[]
UpperCAmelCase_									=	[]

UpperCAmelCase_									=	{'type': 'section', 'text': {'type': 'plain_text', 'text': 'No failed tests! 🤗', 'emoji': True}}

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

UpperCAmelCase_									=	0
for log in Path().glob('*.log'):
       UpperCAmelCase_									=	0
       with open(log, 'r') as f:
              for line in f:
                     UpperCAmelCase_									=	json.loads(line)
                     if line.get('nodeid', '') != "":
                            UpperCAmelCase_									=	line['nodeid']
                            if line.get('duration', None) is not None:
                                   UpperCAmelCase_									=	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])
       UpperCAmelCase_									=	[]
       log.unlink()

UpperCAmelCase_									=	''
UpperCAmelCase_									=	[]
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"
                     UpperCAmelCase_									=	[]
                     UpperCAmelCase_									=	{}
                     for test in failed_tests:
                            UpperCAmelCase_									=	test[0].split('::')
                            UpperCAmelCase_									=	data[0].split('/')[-1]
                            if data[0] not in filesafailed:
                                   UpperCAmelCase_									=	[data[1:]]
                            else:
                                   filesafailed[data[0]] += [data[1:]]
                            failed_table.append(data)

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

                     UpperCAmelCase_									=	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_000:
              UpperCAmelCase_									=	'Too many failed tests, please see the full report in the Action results.'
              UpperCAmelCase_									=	len(err) + 10
              UpperCAmelCase_									=	message[: 3_000 - offset] + f"""\n...\n```\n{err}"""
       print(f"""### {message}""")
else:
       UpperCAmelCase_									=	'No failed tests! 🤗'
       print(f"""## {message}""")
       payload.append(no_error_payload)

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

       UpperCAmelCase_									=	WebClient(token=os.environ['SLACK_API_TOKEN'])
       if message != "No failed tests! 🤗":
              UpperCAmelCase_									=	{
                  'type': 'section',
                  'text': {
                      'type': 'mrkdwn',
                      'text': message,
                  },
              }
              payload.append(md_report)
              UpperCAmelCase_									=	{
                  '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)
              UpperCAmelCase_									=	{
                  'type': 'context',
                  'elements': [
                      {
                          'type': 'plain_text',
                          'text': f"""Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}""",
                      }
                  ],
              }
              payload.append(date_report)
       UpperCAmelCase_									=	client.chat_postMessage(channel='#accelerate-ci-daily', text=message, blocks=payload)
       UpperCAmelCase_									=	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
                     UpperCAmelCase_									=	''
                     for i, row in enumerate(test_failures):
                            if row[0] != test_class:
                                   UpperCAmelCase_									=	row[0]
                            else:
                                   UpperCAmelCase_									=	''

                     UpperCAmelCase_									=	{
                         '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],
                     )

