"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


lowerCAmelCase__  :  Tuple						     =	logging.get_logger(__name__)

lowerCAmelCase__  :  Tuple						     =	{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__  :  Tuple						     =	{
    'vocab_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'
        ),
        'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt',
        'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-openqa': (
            'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-reader': (
            'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-openqa': (
            'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-reader': (
            'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase__  :  int						     =	{
    'google/realm-cc-news-pretrained-embedder': 512,
    'google/realm-cc-news-pretrained-encoder': 512,
    'google/realm-cc-news-pretrained-scorer': 512,
    'google/realm-cc-news-pretrained-openqa': 512,
    'google/realm-orqa-nq-openqa': 512,
    'google/realm-orqa-nq-reader': 512,
    'google/realm-orqa-wq-openqa': 512,
    'google/realm-orqa-wq-reader': 512,
}

lowerCAmelCase__  :  Optional[int]						     =	{
    'google/realm-cc-news-pretrained-embedder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-encoder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-scorer': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-reader': {'do_lower_case': True},
    'google/realm-orqa-wq-openqa': {'do_lower_case': True},
    'google/realm-orqa-wq-reader': {'do_lower_case': True},
}
class 				snake_case       ( __lowerCamelCase				):




					"""simple docstring"""

					snake_case__				    = VOCAB_FILES_NAMES
					snake_case__				    = PRETRAINED_VOCAB_FILES_MAP
					snake_case__				    = PRETRAINED_INIT_CONFIGURATION
					snake_case__				    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					snake_case__				    = RealmTokenizer







					def __init__(				self   :	int    ,lowerCamelCase__   :	Tuple=None    ,lowerCamelCase__   :	List[str]=None    ,lowerCamelCase__   :	List[str]=True    ,lowerCamelCase__   :	Any="[UNK]"    ,lowerCamelCase__   :	List[Any]="[SEP]"    ,lowerCamelCase__   :	Tuple="[PAD]"    ,lowerCamelCase__   :	Dict="[CLS]"    ,lowerCamelCase__   :	Optional[int]="[MASK]"    ,lowerCamelCase__   :	List[Any]=True    ,lowerCamelCase__   :	str=None    ,**lowerCamelCase__   :	Any    ,):
									super().__init__(
									    UpperCamelCase_    ,tokenizer_file=UpperCamelCase_    ,do_lower_case=UpperCamelCase_    ,unk_token=UpperCamelCase_    ,sep_token=UpperCamelCase_    ,pad_token=UpperCamelCase_    ,cls_token=UpperCamelCase_    ,mask_token=UpperCamelCase_    ,tokenize_chinese_chars=UpperCamelCase_    ,strip_accents=UpperCamelCase_    ,**UpperCamelCase_    ,)

									UpperCAmelCase__             =							json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
									if (
									    normalizer_state.get('lowercase'    ,UpperCamelCase_			) != do_lower_case
									    or normalizer_state.get('strip_accents'    ,UpperCamelCase_			) != strip_accents
									    or normalizer_state.get('handle_chinese_chars'    ,UpperCamelCase_			) != tokenize_chinese_chars
									):
													UpperCAmelCase__             =							getattr(UpperCamelCase_    ,normalizer_state.pop('type'			)			)
													UpperCAmelCase__             =							do_lower_case
													UpperCAmelCase__             =							strip_accents
													UpperCAmelCase__             =							tokenize_chinese_chars
													UpperCAmelCase__             =							normalizer_class(**UpperCamelCase_			)

									UpperCAmelCase__             =							do_lower_case







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	int    ,**lowerCamelCase__   :	Union[str, Any]			):
									UpperCAmelCase__             =							PaddingStrategy.MAX_LENGTH

									UpperCAmelCase__             =							text
									UpperCAmelCase__             =							kwargs.pop('text_pair'    ,UpperCamelCase_			)
									UpperCAmelCase__             =							kwargs.pop('return_tensors'    ,UpperCamelCase_			)

									UpperCAmelCase__             =							{
									    'input_ids': [],
									    'attention_mask': [],
									    'token_type_ids': [],
									}

									for idx, candidate_text in enumerate(UpperCamelCase_			):
													if batch_text_pair is not None:
																	UpperCAmelCase__             =							batch_text_pair[idx]
													else:
																	UpperCAmelCase__             =							None

													UpperCAmelCase__             =							super().__call__(UpperCamelCase_    ,UpperCamelCase_    ,return_tensors=UpperCamelCase_    ,**UpperCamelCase_			)

													UpperCAmelCase__             =							encoded_candidates.get('input_ids'			)
													UpperCAmelCase__             =							encoded_candidates.get('attention_mask'			)
													UpperCAmelCase__             =							encoded_candidates.get('token_type_ids'			)

													if encoded_input_ids is not None:
																	output_data["input_ids"].append(UpperCamelCase_			)
													if encoded_attention_mask is not None:
																	output_data["attention_mask"].append(UpperCamelCase_			)
													if encoded_token_type_ids is not None:
																	output_data["token_type_ids"].append(UpperCamelCase_			)

									UpperCAmelCase__             =							{key: item for key, item in output_data.items() if len(UpperCamelCase_			) != 0}

									return BatchEncoding(UpperCamelCase_    ,tensor_type=UpperCamelCase_			)







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	Optional[Any]=None			):
									UpperCAmelCase__             =							[self.cls_token_id] + token_ids_a + [self.sep_token_id]

									if token_ids_a:
													output += token_ids_a + [self.sep_token_id]

									return output







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]    ,lowerCamelCase__   :	List[int]    ,lowerCamelCase__   :	Optional[List[int]] = None			):
									UpperCAmelCase__             =							[self.sep_token_id]
									UpperCAmelCase__             =							[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			) * [0] + len(token_ids_a + sep			) * [1]







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	Optional[str] = None			):
									UpperCAmelCase__             =							self._tokenizer.model.save(UpperCamelCase_    ,name=UpperCamelCase_			)
									return tuple(UpperCamelCase_			)









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_000:
              UpperCAmelCase_									=	'Too many failed tests, please see the full report in the Action results.'
              UpperCAmelCase_									=	len(err) + 10
              UpperCAmelCase_									=	message[: 3_000 - 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],
                     )

