

'''simple docstring'''




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

from tabulate import DataRow, TableFormat, tabulate


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


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

_a					:			List[Any]							=    {'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					:			Tuple							=    0
for log in Path().glob('*.log'):
  _a					:			int							=    0
  with open(log, 'r') as f:
    for line in f:
      _a					:			Optional[Any]							=    json.loads(line)
      if line.get('nodeid', '') != "":
        _a					:			Union[str, Any]							=    line['nodeid']
        if line.get('duration', None) is not None:
          _a					:			Any							=    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					:			Dict							=    []
  log.unlink()

_a					:			Optional[Any]							=    ''
_a					:			Any							=    []
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					:			int							=    []
      _a					:			Dict							=    {}
      for test in failed_tests:
        _a					:			Any							=    test[0].split('::')
        _a					:			Tuple							=    data[0].split('/')[-1]
        if data[0] not in filesafailed:
          _a					:			List[str]							=    [data[1:]]
        else:
          filesafailed[data[0]] += [data[1:]]
        failed_table.append(data)

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

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

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

  _a					:			List[str]							=    WebClient(token=os.environ['SLACK_API_TOKEN'])
  if message != "No failed tests! 🤗":
    _a					:			List[str]							=    {
        'type': 'section',
        'text': {
            'type': 'mrkdwn',
            'text': message,
        },
    }
    payload.append(md_report)
    _a					:			Any							=    {
        '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					:			List[str]							=    {
        '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					:			Optional[int]							=    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					:			Optional[int]							=    ''
      for i, row in enumerate(test_failures):
        if row[0] != test_class:
          _a					:			Union[str, Any]							=    row[0]
        else:
          _a					:			str							=    ''

      _a					:			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 os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow



@require_torch
class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						'ylacombe/bark-small'
    UpperCAmelCase    				=						tempfile.mkdtemp()
    UpperCAmelCase    				=						'en_speaker_1'
    UpperCAmelCase    				=						'This is a test string'
    UpperCAmelCase    				=						'speaker_embeddings_path.json'
    UpperCAmelCase    				=						'speaker_embeddings'
  def     snake_case_      (  self ,			**a_  )				->					List[str]:


    """simple docstring"""
    return AutoTokenizer.from_pretrained(self.checkpoint ,			**a_  )
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    shutil.rmtree(self.tmpdirname  )
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						BarkProcessor(tokenizer=a_  )

    processor.save_pretrained(self.tmpdirname  )
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(self.tmpdirname  )

    self.assertEqual(processor.tokenizer.get_vocab() ,			tokenizer.get_vocab()  )
  @slow
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        pretrained_processor_name_or_path=self.checkpoint ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			)
    processor.save_pretrained(
        self.tmpdirname ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			speaker_embeddings_directory=self.speaker_embeddings_directory ,			)

    UpperCAmelCase    				=						self.get_tokenizer(bos_token='(BOS)' ,			eos_token='(EOS)'  )

    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        self.tmpdirname ,			self.speaker_embeddings_dict_path ,			bos_token='(BOS)' ,			eos_token='(EOS)' ,			)

    self.assertEqual(processor.tokenizer.get_vocab() ,			tokenizer_add_kwargs.get_vocab()  )
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        pretrained_processor_name_or_path=self.checkpoint ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			)

    UpperCAmelCase    				=						3_5
    UpperCAmelCase    				=						2
    UpperCAmelCase    				=						8

    UpperCAmelCase    				=						{
        'semantic_prompt': np.ones(a_  ),
        'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)  ),
        'fine_prompt': np.ones((nb_codebooks_total, seq_len)  ),
    }

    # test providing already loaded voice_preset
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=a_  )

    UpperCAmelCase    				=						inputs['history_prompt']
    for key in voice_preset:
      self.assertListEqual(voice_preset[key].tolist() ,			processed_voice_preset.get(a_ ,			np.array([]  )  ).tolist()  )

    # test loading voice preset from npz file
    UpperCAmelCase    				=						os.path.join(self.tmpdirname ,			'file.npz'  )
    np.savez(a_ ,			**a_  )
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=a_  )
    UpperCAmelCase    				=						inputs['history_prompt']

    for key in voice_preset:
      self.assertListEqual(voice_preset[key].tolist() ,			processed_voice_preset.get(a_ ,			np.array([]  )  ).tolist()  )

    # test loading voice preset from the hub
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=self.voice_preset  )





  def     snake_case_      (  self  )				->					List[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						BarkProcessor(tokenizer=a_  )

    UpperCAmelCase    				=						processor(text=self.input_string  )

    UpperCAmelCase    				=						tokenizer(
        self.input_string ,			padding='max_length' ,			max_length=2_5_6 ,			add_special_tokens=a_ ,			return_attention_mask=a_ ,			return_token_type_ids=a_ ,			)

    for key in encoded_tok.keys():
      self.assertListEqual(encoded_tok[key] ,			encoded_processor[key].squeeze().tolist()  )


