



'''simple docstring'''





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

from tabulate import DataRow, TableFormat, tabulate


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


lowercase_		       =  []
lowercase_		       =  []

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

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

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

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

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

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

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

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

      lowercase_		       =  {
          "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 pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  "▁"

lowercase_		       =  {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase_		       =  {
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

lowercase_		       =  {
    "facebook/m2m100_418M": 1_024,
}

# fmt: off
lowercase_		       =  {
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Union[str, Any]          =       VOCAB_FILES_NAMES
 __lowerCamelCase    :       Tuple          =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :       Tuple          =       PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :       Dict          =       ['input_ids', 'attention_mask']

 __lowerCamelCase    :       List[int]          =       []
 __lowerCamelCase    :       List[int]          =       []



 def __init__(self ,   A ,   A ,   A=None ,   A=None ,   A="<s>" ,   A="</s>" ,   A="</s>" ,   A="<pad>" ,   A="<unk>" ,   A="m2m100" ,   A = None ,   A=8 ,   **A ,   )    ->     None:


      """simple docstring"""



      _a      =  {} if sp_model_kwargs is None else sp_model_kwargs

      _a      =  language_codes
      _a      =  FAIRSEQ_LANGUAGE_CODES[language_codes]
      _a      =  {lang_code: f'''__{lang_code}__''' for lang_code in fairseq_language_code}

      _a      =  kwargs.get('''additional_special_tokens''' ,   []       )
      kwargs["additional_special_tokens"] += [
          self.get_lang_token(A       )
          for lang_code in fairseq_language_code
          if self.get_lang_token(A       ) not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          src_lang=A ,   tgt_lang=A ,   bos_token=A ,   eos_token=A ,   sep_token=A ,   unk_token=A ,   pad_token=A ,   language_codes=A ,   sp_model_kwargs=self.sp_model_kwargs ,   num_madeup_words=A ,   **A ,   )

      _a      =  vocab_file
      _a      =  load_json(A       )
      _a      =  {v: k for k, v in self.encoder.items()}
      _a      =  spm_file
      _a      =  load_spm(A ,   self.sp_model_kwargs       )

      _a      =  len(self.encoder       )

      _a      =  {
          self.get_lang_token(A       ): self.encoder_size + i for i, lang_code in enumerate(A       )
      }
      _a      =  {lang_code: self.encoder_size + i for i, lang_code in enumerate(A       )}
      _a      =  {v: k for k, v in self.lang_token_to_id.items()}

      _a      =  src_lang if src_lang is not None else '''en'''
      _a      =  tgt_lang
      _a      =  self.get_lang_id(self._src_lang       )
      self.set_src_lang_special_tokens(self._src_lang       )

      _a      =  num_madeup_words



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return len(self.encoder       ) + len(self.lang_token_to_id       )



 @property
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      return self._src_lang



 @src_lang.setter
 def 					a__  (self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  new_src_lang
      self.set_src_lang_special_tokens(self._src_lang       )



 def 					a__  (self ,   A       )    ->     List[str]:


      """simple docstring"""



      return self.sp_model.encode(A ,   out_type=A       )



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


      """simple docstring"""



      if token in self.lang_token_to_id:
           return self.lang_token_to_id[token]
      return self.encoder.get(A ,   self.encoder[self.unk_token]       )



 def 					a__  (self ,   A       )    ->     str:


      """simple docstring"""



      if index in self.id_to_lang_token:
           return self.id_to_lang_token[index]
      return self.decoder.get(A ,   self.unk_token       )



 def 					a__  (self ,   A       )    ->     Dict:


      """simple docstring"""



      _a      =  []
      _a      =  ''''''
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                out_string += self.sp_model.decode(A       ) + token
                _a      =  []
           else:
                current_sub_tokens.append(A       )
      out_string += self.sp_model.decode(A       )
      return out_string.strip()



 def 					a__  (self ,   A ,   A = None ,   A = False       )    ->     List[int]:


      """simple docstring"""



      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=A ,   token_ids_a=A ,   already_has_special_tokens=A       )

      _a      =  [1] * len(self.prefix_tokens       )
      _a      =  [1] * len(self.suffix_tokens       )
      if token_ids_a is None:
           return prefix_ones + ([0] * len(A       )) + suffix_ones
      return prefix_ones + ([0] * len(A       )) + ([0] * len(A       )) + suffix_ones



 def 					a__  (self ,   A ,   A = None       )    ->     List[int]:


      """simple docstring"""



      if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + self.suffix_tokens
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a      =  {self.convert_ids_to_tokens(A       ): i for i in range(self.vocab_size       )}
      vocab.update(self.added_tokens_encoder       )
      return vocab



 def __getstate__(self       )    ->     Dict:


      """simple docstring"""



      _a      =  self.__dict__.copy()
      _a      =  None
      return state



 def __setstate__(self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  d

      # for backward compatibility
      if not hasattr(self ,   '''sp_model_kwargs'''       ):
           _a      =  {}

      _a      =  load_spm(self.spm_file ,   self.sp_model_kwargs       )



 def 					a__  (self ,   A ,   A = None       )    ->     Tuple[str]:


      """simple docstring"""



      _a      =  Path(A       )
      if not save_dir.is_dir():
           raise OSError(f'''{save_directory} should be a directory'''       )
      _a      =  save_dir / (
          (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
      )
      _a      =  save_dir / (
          (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
      )

      save_json(self.encoder ,   A       )

      if os.path.abspath(self.spm_file       ) != os.path.abspath(A       ) and os.path.isfile(self.spm_file       ):
           copyfile(self.spm_file ,   A       )
      elif not os.path.isfile(self.spm_file       ):
           with open(A ,   '''wb'''       ) as fi:
                _a      =  self.sp_model.serialized_model_proto()
                fi.write(A       )

      return (str(A       ), str(A       ))



 def 					a__  (self ,   A ,   A = "en" ,   A = None ,   A = "ro" ,   **A ,   )    ->     BatchEncoding:


      """simple docstring"""



      _a      =  src_lang
      _a      =  tgt_lang
      self.set_src_lang_special_tokens(self.src_lang       )
      return super().prepare_seqaseq_batch(A ,   A ,   **A       )



 def 					a__  (self ,   A ,   A ,   A ,   **A       )    ->     Union[str, Any]:


      """simple docstring"""



      if src_lang is None or tgt_lang is None:
           raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''       )
      _a      =  src_lang
      _a      =  self(A ,   add_special_tokens=A ,   **A       )
      _a      =  self.get_lang_id(A       )
      _a      =  tgt_lang_id
      return inputs



 def 					a__  (self       )    ->     Optional[Any]:


      """simple docstring"""



      self.set_src_lang_special_tokens(self.src_lang       )



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      self.set_tgt_lang_special_tokens(self.tgt_lang       )



 def 					a__  (self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  self.get_lang_token(A       )
      _a      =  self.lang_token_to_id[lang_token]
      _a      =  [self.cur_lang_id]
      _a      =  [self.eos_token_id]



 def 					a__  (self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  self.get_lang_token(A       )
      _a      =  self.lang_token_to_id[lang_token]
      _a      =  [self.cur_lang_id]
      _a      =  [self.eos_token_id]



 def 					a__  (self ,   A       )    ->     str:


      """simple docstring"""



      return self.lang_code_to_token[lang]



 def 					a__  (self ,   A       )    ->     int:


      """simple docstring"""



      _a      =  self.get_lang_token(A       )
      return self.lang_token_to_id[lang_token]





def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  sentencepiece.SentencePieceProcessor(**__A)
     spm.Load(str(__A))
     return spm




def lowerCAmelCase  (__A):



     """simple docstring"""



     with open(__A  ,	'''r''') as f:
          return json.load(__A)




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     with open(__A  ,	'''w''') as f:
          json.dump(__A  ,	__A  ,	indent=2)



