




'''simple docstring'''




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_0_0_0:
        UpperCAmelCase_				 =					"Too many failed tests, please see the full report in the Action results."
        UpperCAmelCase_				 =					len(err) + 1_0
        UpperCAmelCase_				 =					message[: 3_0_0_0 - 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],
            )






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

import sentencepiece as spm

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


_UpperCAmelCase		:  Any	=   logging.get_logger(__name__)

_UpperCAmelCase		:  List[Any]	=   {"vocab_file": "sentencepiece.bpe.model"}

_UpperCAmelCase		:  List[str]	=   {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

_UpperCAmelCase		:  Any	=   {
    "camembert-base": 512,
}

_UpperCAmelCase		:  List[Any]	=   "▁"




class       __lowerCAmelCase				(							lowerCAmelCase):
       _a           =  VOCAB_FILES_NAMES
       _a           =  PRETRAINED_VOCAB_FILES_MAP
       _a           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _a           =  ['''input_ids''', '''attention_mask''']






       def __init__(      self:    List[str]				,    _lowerCAmelCase:    Optional[int]				,    _lowerCAmelCase:    Dict="<s>"				,    _lowerCAmelCase:    Union[str, Any]="</s>"				,    _lowerCAmelCase:    Optional[int]="</s>"				,    _lowerCAmelCase:    List[Any]="<s>"				,    _lowerCAmelCase:    Tuple="<unk>"				,    _lowerCAmelCase:    Union[str, Any]="<pad>"				,    _lowerCAmelCase:    str="<mask>"				,    _lowerCAmelCase:    int=["<s>NOTUSED", "</s>NOTUSED"]				,    _lowerCAmelCase:    Optional[Dict[str, Any]] = None				,    **_lowerCAmelCase:    Any				,    ):
         # Mask token behave like a normal word, i.e. include the space before it
         lowercase			:Dict							=  AddedToken(_lowerCAmelCase				,    lstrip=_lowerCAmelCase				,    rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ) else mask_token

         lowercase			:Optional[int]							=  {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=_lowerCAmelCase				,    eos_token=_lowerCAmelCase				,    unk_token=_lowerCAmelCase				,    sep_token=_lowerCAmelCase				,    cls_token=_lowerCAmelCase				,    pad_token=_lowerCAmelCase				,    mask_token=_lowerCAmelCase				,    additional_special_tokens=_lowerCAmelCase				,    sp_model_kwargs=self.sp_model_kwargs				,    **_lowerCAmelCase				,    )
         lowercase			:Dict							=  spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(str(_lowerCAmelCase ) )
         lowercase			:Tuple							=  vocab_file
         # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
         # sentencepiece vocabulary (this is the case for <s> and </s>
         lowercase			:int							=  {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
         lowercase			:Tuple							=  len(self.fairseq_tokens_to_ids )
         lowercase			:int							=  len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
         lowercase			:Any							=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple				,    _lowerCAmelCase:    List[int]				,    _lowerCAmelCase:    Optional[List[int]] = None ):

         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         lowercase			:List[Any]							=  [self.cls_token_id]
         lowercase			:List[str]							=  [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    _lowerCAmelCase:    List[int]				,    _lowerCAmelCase:    Optional[List[int]] = None				,    _lowerCAmelCase:    bool = False ):
         if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_lowerCAmelCase				,    token_ids_a=_lowerCAmelCase				,    already_has_special_tokens=_lowerCAmelCase )

         if token_ids_a is None:
           return [1] + ([0] * len(_lowerCAmelCase )) + [1]
         return [1] + ([0] * len(_lowerCAmelCase )) + [1, 1] + ([0] * len(_lowerCAmelCase )) + [1]






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any]				,    _lowerCAmelCase:    List[int]				,    _lowerCAmelCase:    Optional[List[int]] = None ):
         lowercase			:Any							=  [self.sep_token_id]
         lowercase			:str							=  [self.cls_token_id]

         if token_ids_a is None:
           return len(cls + token_ids_a + sep ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase			:Any							=  {self.convert_ids_to_tokens(_lowerCAmelCase ): i for i in range(self.vocab_size )}
         vocab.update(self.added_tokens_encoder )
         return vocab






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    str ):
         return self.sp_model.encode(_lowerCAmelCase				,    out_type=_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    _lowerCAmelCase:    List[str] ):
         if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
         elif self.sp_model.PieceToId(_lowerCAmelCase ) == 0:
           # Convert sentence piece unk token to fairseq unk token index
           return self.unk_token_id
         return self.fairseq_offset + self.sp_model.PieceToId(_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    str				,    _lowerCAmelCase:    Tuple ):
         if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    List[str] ):
         lowercase			:Tuple							=  []
         lowercase			:Any							=  ""
         lowercase			:str							=  False
         for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
             if not prev_is_special:
               out_string += " "
             out_string += self.sp_model.decode(_lowerCAmelCase ) + token
             lowercase			:Optional[int]							=  True
             lowercase			:Any							=  []
           else:
             current_sub_tokens.append(_lowerCAmelCase )
             lowercase			:str							=  False
         out_string += self.sp_model.decode(_lowerCAmelCase )
         return out_string.strip()






       def __getstate__(      self:    Dict ):
         lowercase			:int							=  self.__dict__.copy()
         lowercase			:List[str]							=  None
         return state






       def __setstate__(      self:    Optional[Any]				,    _lowerCAmelCase:    Dict ):
         lowercase			:int							=  d

         # for backward compatibility
         if not hasattr(self				,    "sp_model_kwargs" ):
           lowercase			:Optional[int]							=  {}

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






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Optional[str] = None ):
         if not os.path.isdir(_lowerCAmelCase ):
           logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
           return
         lowercase			:Any							=  os.path.join(
             _lowerCAmelCase				,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

         if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
           copyfile(self.vocab_file				,    _lowerCAmelCase )
         elif not os.path.isfile(self.vocab_file ):
           with open(_lowerCAmelCase				,    "wb" ) as fi:
             lowercase			:Dict							=  self.sp_model.serialized_model_proto()
             fi.write(_lowerCAmelCase )

         return (out_vocab_file,)



