





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

from tabulate import DataRow, TableFormat, tabulate


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


_lowerCamelCase :    List[Any]  							= []
_lowerCamelCase :    Dict  							= []

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

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

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

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

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

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

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

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

						_lowerCamelCase :    List[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],
						)













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


_lowerCamelCase :    int  							= logging.get_logger(__name__)

_lowerCamelCase :    Optional[Any]  							= '''▁'''

_lowerCamelCase :    Dict  							= {'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowerCamelCase :    int  							= {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

_lowerCamelCase :    Optional[Any]  							= {
    '''xlm-roberta-base''': 5_1_2,
    '''xlm-roberta-large''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_1_2,
}


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   VOCAB_FILES_NAMES
							UpperCAmelCase_      						=   PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase_      						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase_      						=   ["input_ids", "attention_mask"]






							def __init__( self     :					Dict,			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Optional[int]="<s>",			_UpperCAmelCase     :					Optional[int]="</s>",			_UpperCAmelCase     :					Dict="</s>",			_UpperCAmelCase     :					List[Any]="<s>",			_UpperCAmelCase     :					Union[str, Any]="<unk>",			_UpperCAmelCase     :					List[Any]="<pad>",			_UpperCAmelCase     :					str="<mask>",			_UpperCAmelCase     :					Optional[Dict[str, Any]] = None,			**_UpperCAmelCase     :					List[Any],			)				->			None:





														"""simple docstring"""
														# Mask token behave like a normal word, i.e. include the space before it
														SCREAMING_SNAKE_CASE__       :      int     =						AddedToken(_UpperCAmelCase,			lstrip=_UpperCAmelCase,			rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase,			_UpperCAmelCase						) else mask_token

														SCREAMING_SNAKE_CASE__       :      List[str]     =						{} if sp_model_kwargs is None else sp_model_kwargs

														super().__init__(
														    bos_token=_UpperCAmelCase,			eos_token=_UpperCAmelCase,			unk_token=_UpperCAmelCase,			sep_token=_UpperCAmelCase,			cls_token=_UpperCAmelCase,			pad_token=_UpperCAmelCase,			mask_token=_UpperCAmelCase,			sp_model_kwargs=self.sp_model_kwargs,			**_UpperCAmelCase,			)

														SCREAMING_SNAKE_CASE__       :      str     =						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
														self.sp_model.Load(str(_UpperCAmelCase						)						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						vocab_file

														# Original fairseq vocab and spm vocab must be "aligned":
														# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
														# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
														# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
														# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

														# Mimic fairseq token-to-id alignment for the first 4 token
														SCREAMING_SNAKE_CASE__       :      List[str]     =						{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

														# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
														SCREAMING_SNAKE_CASE__       :      Dict     =						1

														SCREAMING_SNAKE_CASE__       :      int     =						len(self.sp_model						) + self.fairseq_offset
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}






							def __getstate__( self     :					str						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.__dict__.copy()
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						None
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.sp_model.serialized_model_proto()
														return state






							def __setstate__( self     :					int,			_UpperCAmelCase     :					List[Any]						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						d

														# for backward compatibility
														if not hasattr(self,			"sp_model_kwargs"						):
																					SCREAMING_SNAKE_CASE__       :      Dict     =						{}

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
														self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					List[int],			_UpperCAmelCase     :					Optional[List[int]] = None						)				->			List[int]:





														"""simple docstring"""

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






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					List[int],			_UpperCAmelCase     :					Optional[List[int]] = None,			_UpperCAmelCase     :					bool = False						)				->			List[int]:





														"""simple docstring"""

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

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






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					List[int],			_UpperCAmelCase     :					Optional[List[int]] = None						)				->			List[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						[self.sep_token_id]
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[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  A_     ( self     :					List[str]						)				->			List[str]:





														"""simple docstring"""
														return len(self.sp_model						) + self.fairseq_offset + 1  # Add the <mask> token






							def  A_     ( self     :					List[Any]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						{self.convert_ids_to_tokens(_UpperCAmelCase						): i for i in range(self.vocab_size						)}
														vocab.update(self.added_tokens_encoder						)
														return vocab






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					str						)				->			List[str]:





														"""simple docstring"""
														return self.sp_model.encode(_UpperCAmelCase,			out_type=_UpperCAmelCase						)






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					Union[str, Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														if token in self.fairseq_tokens_to_ids:
																					return self.fairseq_tokens_to_ids[token]
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.sp_model.PieceToId(_UpperCAmelCase						)

														# Need to return unknown token if the SP model returned 0
														return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					List[str]						)				->			List[str]:





														"""simple docstring"""
														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  A_     ( self     :					Any,			_UpperCAmelCase     :					int						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						"".join(_UpperCAmelCase						).replace(_UpperCAmelCase,			" "						).strip()
														return out_string






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Optional[str] = None						)				->			Tuple[str]:





														"""simple docstring"""
														if not os.path.isdir(_UpperCAmelCase						):
																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
																					return
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						os.path.join(
														    _UpperCAmelCase,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

														return (out_vocab_file,)






