





"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase_       =		"""▁"""

lowerCamelCase_       =		{"""vocab_file""": """spiece.model"""}

lowerCamelCase_       =		{
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

lowerCamelCase_       =		{
    """google/pegasus-xsum""": 5_1_2,
}


lowerCamelCase_       =		logging.get_logger(__name__)






class  _SCREAMING_SNAKE_CASE(       __A  ):
       SCREAMING_SNAKE_CASE_       :				List[Any]     						=  VOCAB_FILES_NAMES

       SCREAMING_SNAKE_CASE_       :				List[str]     						=  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_       :				Tuple     						=  PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_       :				Dict     						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_       :				Tuple     						=  ['input_ids', 'attention_mask']




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__="<pad>"		,SCREAMING_SNAKE_CASE__="</s>"		,SCREAMING_SNAKE_CASE__="<unk>"		,SCREAMING_SNAKE_CASE__="<mask_2>"		,SCREAMING_SNAKE_CASE__="<mask_1>"		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=1_03		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__		,)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  offset
             if additional_special_tokens is not None:
                   if not isinstance(__lowercase		,__lowercase							):
                         raise TypeError(
                             f'''additional_special_tokens should be of type {type(__lowercase							)}, but is'''
                             f''' {type(__lowercase							)}'''							)

                   __SCREAMING_SNAKE_CASE       :Tuple		    =  (
                       ([mask_token_sent] + additional_special_tokens)
                       if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                       else additional_special_tokens
                   )
                   # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                   additional_special_tokens_extended += [
                       f'''<unk_{i}>''' for i in range(len(__lowercase							)		,self.offset - 1							)
                   ]

                   if len(set(__lowercase							)							) != len(__lowercase							):
                         raise ValueError(
                             '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                             f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''							)
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  additional_special_tokens_extended
             else:
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  [mask_token_sent] if mask_token_sent is not None else []
                   additional_special_tokens += [f'''<unk_{i}>''' for i in range(2		,self.offset							)]

             __SCREAMING_SNAKE_CASE       :Dict		    =  {} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 eos_token=__lowercase		,unk_token=__lowercase		,mask_token=__lowercase		,pad_token=__lowercase		,mask_token_sent=__lowercase		,offset=__lowercase		,additional_special_tokens=__lowercase		,sp_model_kwargs=self.sp_model_kwargs		,**__lowercase		,)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  mask_token_sent
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  vocab_file
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.Load(__lowercase							)

             # add special tokens to encoder dict
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  {
                 0: self.pad_token,
                 1: self.eos_token,
             }

             if self.mask_token_sent is not None:
                   self.encoder.update(
                       {
                           2: self.mask_token_sent,
                           3: self.mask_token,
                       }							)

             if self.offset > 0:
                   # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
                   # mask_token_sent is already added to list -> so start at 1
                   self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1		,self.offset - 1							)}							)

             __SCREAMING_SNAKE_CASE       :str		    =  {v: k for k, v in self.encoder.items()}




       @property
       def    _UpperCamelCase					(						self							)      ->				List[Any]:





             """simple docstring"""






             return len(self.sp_model							) + self.offset




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  {self.convert_ids_to_tokens(__lowercase							): i for i in range(self.vocab_size							)}
             vocab.update(self.added_tokens_encoder							)
             return vocab




       def __getstate__(						self							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self.__dict__.copy()
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  None
             return state




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  d

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

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.Load(self.vocab_file							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[Any]:





             """simple docstring"""






             if token in self.decoder:
                   return self.decoder[token]
             elif token in self.added_tokens_decoder:
                   return self.added_tokens_decoder[token]
             __SCREAMING_SNAKE_CASE       :Any		    =  self.sp_model.piece_to_id(__lowercase							)
             return sp_id + self.offset




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Any:





             """simple docstring"""






             if index in self.encoder:
                   return self.encoder[index]
             elif index in self.added_tokens_encoder:
                   return self.added_tokens_encoder[index]
             else:
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.sp_model.IdToPiece(index - self.offset							)
             return token




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  []
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  ''''''
             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(__lowercase							) + token
                         __SCREAMING_SNAKE_CASE       :Dict		    =  []
                   else:
                         current_sub_tokens.append(__lowercase							)
             out_string += self.sp_model.decode(__lowercase							)
             return out_string.strip()




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__=False							)      ->				Tuple:





             """simple docstring"""






             return 1




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  set(self.all_special_ids							)  # call it once instead of inside list comp
             all_special_ids.remove(self.unk_token_id							)  # <unk> is only sometimes special

             return [1 if x in all_special_ids else 0 for x in seq]




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = False							)      ->				Optional[int]:





             """simple docstring"""






             if already_has_special_tokens:
                   return self._special_token_mask(__lowercase							)
             elif token_ids_a is None:
                   return self._special_token_mask(__lowercase							) + [1]
             else:
                   return self._special_token_mask(token_ids_a + token_ids_a							) + [1]




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=None							)      ->				Dict:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)      ->				str:





             """simple docstring"""






             if not os.path.isdir(__lowercase							):
                   logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
                   return
             __SCREAMING_SNAKE_CASE       :int		    =  os.path.join(
                 __lowercase		,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

             if os.path.abspath(self.vocab_file							) != os.path.abspath(__lowercase							) and os.path.isfile(self.vocab_file							):
                   copyfile(self.vocab_file		,__lowercase							)
             elif not os.path.isfile(self.vocab_file							):
                   with open(__lowercase		,'''wb'''							) as fi:
                         __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.sp_model.serialized_model_proto()
                         fi.write(__lowercase							)

             return (out_vocab_file,)







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

from tabulate import DataRow, TableFormat, tabulate


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


lowerCamelCase_       =		[]
lowerCamelCase_       =		[]

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

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

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

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

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

      lowerCamelCase_       =		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:
    lowerCamelCase_       =		"Too many failed tests, please see the full report in the Action results."
    lowerCamelCase_       =		len(err) + 1_0
    lowerCamelCase_       =		message[: 3_0_0_0 - offset] + f'\n...\n```\n{err}'
  print(f'### {message}')
else:
  lowerCamelCase_       =		"No failed tests! 🤗"
  print(f'## {message}')
  payload.append(no_error_payload)

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

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

      lowerCamelCase_       =		{
          "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],
      )