





import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


snake_case_							=			logging.get_logger(__name__)
@dataclass
class        snake_case_		:
  lowerCamelCase    :str						=     field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys())})
  lowerCamelCase    :str						=     field(
      metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."})
  lowerCamelCase    :int						=     field(
      default=128						,					metadata={
          "help": (
              "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          )
      }						,					)
  lowerCamelCase    :bool						=     field(
      default=UpperCAmelCase__						,					metadata={"help": "Overwrite the cached training and evaluation sets"})







  def 			__lowercase   (		self   )				->   Dict:
    lowerCamelCase   : Dict								=self.task_name.lower()

class        snake_case_		(			UpperCAmelCase__):
  lowerCamelCase    :Union[str, Any]						=     "train"
  lowerCamelCase    :Any						=     "dev"
  lowerCamelCase    :Union[str, Any]						=     "test"

class        snake_case_		(			UpperCAmelCase__):
  lowerCamelCase    :GlueDataTrainingArguments
  lowerCamelCase    :str
  lowerCamelCase    :List[InputFeatures]







  def __init__(		self			,     __lowercase			,     __lowercase			,     __lowercase = None			,     __lowercase = Split.train			,     __lowercase = None			,     )				->   List[Any]:
    warnings.warn(
        '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
        '''library. You can have a look at this example script for pointers: '''
        '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''			,     lowerCamelCase__			,     )
    lowerCamelCase   : Optional[int]								=args
    lowerCamelCase   : Any								=glue_processors[args.task_name]()
    lowerCamelCase   : Union[str, Any]								=glue_output_modes[args.task_name]
    if isinstance(lowerCamelCase__			,     lowerCamelCase__   ):
      try:
        lowerCamelCase   : Union[str, Any]								=Split[mode]
      except KeyError:
        raise KeyError('''mode is not a valid split name'''   )
        # Load data features from cache or dataset file
    lowerCamelCase   : Union[str, Any]								=os.path.join(
        cache_dir if cache_dir is not None else args.data_dir			,     F"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"			,     )
    lowerCamelCase   : List[Any]								=self.processor.get_labels()
    if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
        "RobertaTokenizer",
        "RobertaTokenizerFast",
        "XLMRobertaTokenizer",
        "BartTokenizer",
        "BartTokenizerFast",
    ):
      # HACK(label indices are swapped in RoBERTa pretrained model)
      lowerCamelCase   : Union[str, Any]								=label_list[2], label_list[1]
    lowerCamelCase   : Optional[int]								=label_list

    # Make sure only the first process in distributed training processes the dataset,
    # and the others will use the cache.
    lowerCamelCase   : Dict								=cached_features_file + ".lock"
    with FileLock(lowerCamelCase__   ):
      if os.path.exists(lowerCamelCase__   ) and not args.overwrite_cache:
        lowerCamelCase   : Tuple								=time.time()
        lowerCamelCase   : Dict								=torch.load(lowerCamelCase__   )
        logger.info(
            F"Loading features from cached file {cached_features_file} [took %.3f s]"			,     time.time() - start   )
      else:
        logger.info(F"Creating features from dataset file at {args.data_dir}"   )

        if mode == Split.dev:
          lowerCamelCase   : List[Any]								=self.processor.get_dev_examples(args.data_dir   )
        elif mode == Split.test:
          lowerCamelCase   : Optional[int]								=self.processor.get_test_examples(args.data_dir   )
        else:
          lowerCamelCase   : Tuple								=self.processor.get_train_examples(args.data_dir   )
        if limit_length is not None:
          lowerCamelCase   : str								=examples[:limit_length]
        lowerCamelCase   : Optional[int]								=glue_convert_examples_to_features(
            lowerCamelCase__			,     lowerCamelCase__			,     max_length=args.max_seq_length			,     label_list=lowerCamelCase__			,     output_mode=self.output_mode			,     )
        lowerCamelCase   : Any								=time.time()
        torch.save(self.features			,     lowerCamelCase__   )
        # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
        logger.info(
            F"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"   )







  def __len__(		self   )				->   Optional[int]:
    return len(self.features   )







  def __getitem__(		self			,     __lowercase   )				->   InputFeatures:
    return self.features[i]







  def 			__lowercase   (		self   )				->   Tuple:
    return self.label_list








import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict

import requests
from slack_sdk import WebClient


snake_case_							=			WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     int:
		lowerCamelCase   : Tuple								=test_results.split(''' '''		)

		lowerCamelCase   : List[str]								=0
		lowerCamelCase   : Optional[Any]								=0

		# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
		# When it is too long, those signs are not present.
		lowerCamelCase   : Optional[int]								=expressions[-2] if '''=''' in expressions[-1] else expressions[-1]

		for i, expression in enumerate(SCREAMING_SNAKE_CASE_		):
				if "failed" in expression:
						failed += int(expressions[i - 1]		)
				if "passed" in expression:
						success += int(expressions[i - 1]		)

		return failed, success, time_spent


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     int:
		lowerCamelCase   : Union[str, Any]								={}
		lowerCamelCase   : Tuple								=None
		lowerCamelCase   : List[str]								=False
		for line in failures_short_lines.split('''\n'''		):
				if re.search(R'''_ \[doctest\]'''							,       SCREAMING_SNAKE_CASE_		):
						lowerCamelCase   : Optional[int]								=True
						lowerCamelCase   : Any								=line.split(''' '''		)[2]
				elif in_error and not line.split(''' '''		)[0].isdigit():
						lowerCamelCase   : Tuple								=line
						lowerCamelCase   : Optional[Any]								=False

		return failures
class        snake_case_		:







		def __init__(		self			,     __lowercase			,     __lowercase   )				->   Any:
				lowerCamelCase   : Optional[int]								=title

				lowerCamelCase   : Optional[int]								=doc_test_results['''time_spent'''].split(''','''   )[0]
				lowerCamelCase   : List[str]								=doc_test_results['''success''']
				lowerCamelCase   : Tuple								=doc_test_results['''failures''']
				lowerCamelCase   : List[Any]								=self.n_success + self.n_failures

				# Failures and success of the modeling tests
				lowerCamelCase   : Union[str, Any]								=doc_test_results







		@property
		def 			__lowercase   (		self   )				->   str:
				lowerCamelCase   : List[str]								=[self._time_spent]
				lowerCamelCase   : List[str]								=0

				for time in time_spent:
						lowerCamelCase   : List[str]								=time.split(''':'''   )

						# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
						if len(__lowercase   ) == 1:
								lowerCamelCase   : Any								=[0, 0, time_parts[0]]

						lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase   : Optional[int]								=int(time_parts[0]   ), int(time_parts[1]   ), float(time_parts[2]   )
						total_secs += hours * 3_6_0_0 + minutes * 6_0 + seconds

				lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase   : List[str]								=total_secs // 3_6_0_0, (total_secs % 3_6_0_0) // 6_0, total_secs % 6_0
				return F"{int(__lowercase   )}h{int(__lowercase   )}m{int(__lowercase   )}s"







		@property
		def 			__lowercase   (		self   )				->   Dict:
				return {"type": "header", "text": {"type": "plain_text", "text": self.title}}







		@property
		def 			__lowercase   (		self   )				->   Dict:
				return {
				    "type": "section",
				    "text": {
				        "type": "plain_text",
				        "text": F"🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.",
				        "emoji": True,
				    },
				    "accessory": {
				        "type": "button",
				        "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
				        "url": F"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}",
				    },
				}







		@property
		def 			__lowercase   (		self   )				->   Dict:
				return {
				    "type": "section",
				    "text": {
				        "type": "plain_text",
				        "text": (
				            F"There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in"
				            F" {self.time}."
				        ),
				        "emoji": True,
				    },
				    "accessory": {
				        "type": "button",
				        "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
				        "url": F"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}",
				    },
				}







		@property
		def 			__lowercase   (		self   )				->   Dict:
				lowerCamelCase   : int								=4_0
				lowerCamelCase   : str								={k: v['''failed'''] for k, v in doc_test_results.items() if isinstance(__lowercase			,     __lowercase   )}

				lowerCamelCase   : List[str]								=''''''
				for category, failures in category_failures.items():
						if len(__lowercase   ) == 0:
								continue

						if report != "":
								report += "\n\n"

						report += F"*{category} failures*:".ljust(line_length // 2   ).rjust(line_length // 2   ) + "\n"
						report += "`"
						report += "`\n`".join(__lowercase   )
						report += "`"

				return {
				    "type": "section",
				    "text": {
				        "type": "mrkdwn",
				        "text": F"The following examples had failures:\n\n\n{report}\n",
				    },
				}







		@property
		def 			__lowercase   (		self   )				->   str:
				lowerCamelCase   : Dict								=[self.header]

				if self.n_failures > 0:
						blocks.append(self.failures   )

				if self.n_failures > 0:
						blocks.extend([self.category_failures]   )

				if self.n_failures == 0:
						blocks.append(self.no_failures   )

				return json.dumps(__lowercase   )







		@staticmethod
		def 			__lowercase   (		)				->   Optional[Any]:
				lowerCamelCase   : Tuple								=[
				    {
				        '''type''': '''section''',
				        '''text''': {
				            '''type''': '''plain_text''',
				            '''text''': '''There was an issue running the tests.''',
				        },
				        '''accessory''': {
				            '''type''': '''button''',
				            '''text''': {'''type''': '''plain_text''', '''text''': '''Check Action results''', '''emoji''': True},
				            '''url''': F"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}",
				        },
				    }
				]

				print('''Sending the following payload'''   )
				print(json.dumps({'''blocks''': json.loads(__lowercase   )}   )   )

				client.chat_postMessage(
				    channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']			,     text='''There was an issue running the tests.'''			,     blocks=__lowercase			,     )







		def 			__lowercase   (		self   )				->   Optional[int]:
				print('''Sending the following payload'''   )
				print(json.dumps({'''blocks''': json.loads(self.payload   )}   )   )

				lowerCamelCase   : int								=F"{self.n_failures} failures out of {self.n_tests} tests," if self.n_failures else '''All tests passed.'''

				lowerCamelCase   : Any								=client.chat_postMessage(
				    channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']			,     blocks=self.payload			,     text=__lowercase			,     )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )				->   Optional[int]:
				lowerCamelCase   : Union[str, Any]								=''''''
				for key, value in failures.items():
						lowerCamelCase   : Any								=value[:2_0_0] + ''' [Truncated]''' if len(__lowercase   ) > 2_5_0 else value
						failures_text += F"*{key}*\n_{value}_\n\n"

				lowerCamelCase   : Union[str, Any]								=job_name
				lowerCamelCase   : List[str]								={'''type''': '''section''', '''text''': {'''type''': '''mrkdwn''', '''text''': text}}

				if job_link is not None:
						lowerCamelCase   : Optional[int]								={
						    '''type''': '''button''',
						    '''text''': {'''type''': '''plain_text''', '''text''': '''GitHub Action job''', '''emoji''': True},
						    '''url''': job_link,
						}

				return [
				    {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
				    content,
				    {"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
				]







		def 			__lowercase   (		self   )				->   Tuple:
				if self.thread_ts is None:
						raise ValueError('''Can only post reply if a post has been made.'''   )

				lowerCamelCase   : str								=self.doc_test_results.pop('''job_link'''   )
				self.doc_test_results.pop('''failures'''   )
				self.doc_test_results.pop('''success'''   )
				self.doc_test_results.pop('''time_spent'''   )

				lowerCamelCase   : Union[str, Any]								=sorted(self.doc_test_results.items()			,     key=lambda __lowercase   : t[0]   )
				for job, job_result in sorted_dict:
						if len(job_result['''failures''']   ):
								lowerCamelCase   : int								=F"*Num failures* :{len(job_result['failed']   )} \n"
								lowerCamelCase   : Optional[Any]								=job_result['''failures''']
								lowerCamelCase   : List[str]								=self.get_reply_blocks(__lowercase			,     __lowercase			,     __lowercase			,     text=__lowercase   )

								print('''Sending the following reply'''   )
								print(json.dumps({'''blocks''': blocks}   )   )

								client.chat_postMessage(
								    channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']			,     text=F"Results for {job}"			,     blocks=__lowercase			,     thread_ts=self.thread_ts['''ts''']			,     )

								time.sleep(1   )


def 					A__	(       )   ->     Union[str, Any]:
		lowerCamelCase   : Optional[Any]								=os.environ['''GITHUB_RUN_ID''']
		lowerCamelCase   : int								=F"https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"
		lowerCamelCase   : Tuple								=requests.get(SCREAMING_SNAKE_CASE_		).json()
		lowerCamelCase   : Union[str, Any]								={}

		try:
				jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}		)
				lowerCamelCase   : Any								=math.ceil((result['''total_count'''] - 1_0_0) / 1_0_0		)

				for i in range(SCREAMING_SNAKE_CASE_		):
						lowerCamelCase   : Optional[Any]								=requests.get(url + F"&page={i + 2}"		).json()
						jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}		)

				return jobs
		except Exception as e:
				print('''Unknown error, could not fetch links.'''							,       SCREAMING_SNAKE_CASE_		)

		return {}


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     Union[str, Any]:
		lowerCamelCase   : List[str]								={}

		if os.path.exists(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase   : Optional[Any]								=os.listdir(SCREAMING_SNAKE_CASE_		)
				for file in files:
						try:
								with open(os.path.join(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)							,       encoding='''utf-8'''		) as f:
										lowerCamelCase   : List[str]								=f.read()
						except UnicodeDecodeError as e:
								raise ValueError(F"Could not open {os.path.join(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)}."		) from e

		return _artifact


def 					A__	(       )   ->     Any:
		class        snake_case_		:







				def __init__(		self			,     __lowercase   )				->   Optional[Any]:
						lowerCamelCase   : Optional[Any]								=name
						lowerCamelCase   : List[str]								=[]







				def __str__(		self   )				->   Any:
						return self.name







				def 			__lowercase   (		self			,     __lowercase   )				->   Dict:
						self.paths.append({'''name''': self.name, '''path''': path}   )

		lowerCamelCase   : Dict[str, Artifact]								={}

		lowerCamelCase   : List[Any]								=filter(os.path.isdir							,       os.listdir()		)
		for directory in directories:
				lowerCamelCase   : Tuple								=directory
				if artifact_name not in _available_artifacts:
						lowerCamelCase   : Any								=Artifact(SCREAMING_SNAKE_CASE_		)

						_available_artifacts[artifact_name].add_path(SCREAMING_SNAKE_CASE_		)

		return _available_artifacts


if __name__ == "__main__":
				snake_case_							=			get_job_links()
				snake_case_							=			retrieve_available_artifacts()

				snake_case_							=			collections.OrderedDict(
				    [
				        ('''*.py''', '''API Examples'''),
				        ('''*.md''', '''MD Examples'''),
				    ]
				)

				# This dict will contain all the information relative to each doc test category:
				# - failed: list of failed tests
				# - failures: dict in the format 'test': 'error_message'
				snake_case_							=			{
				    v: {
				        '''failed''': [],
				        '''failures''': {},
				    }
				    for v in docs.values()
				}

				# Link to the GitHub Action job
				snake_case_							=			github_actions_job_links.get('''run_doctests''')

				snake_case_							=			available_artifacts['''doc_tests_gpu_test_reports'''].paths[0]
				snake_case_							=			retrieve_artifact(artifact_path['''name'''])
				if "stats" in artifact:
								snake_case_,       snake_case_,       snake_case_							=			handle_test_results(artifact['''stats'''])
								snake_case_							=			failed
								snake_case_							=			success
								snake_case_							=			time_spent[1:-1] + ''', '''

								snake_case_							=			extract_first_line_failure(artifact['''failures_short'''])
								for line in artifact["summary_short"].split('''\n'''):
												if re.search('''FAILED''', line):
																snake_case_							=			line.replace('''FAILED ''', '''''')
																snake_case_							=			line.split()[0].replace('''\n''', '''''')

																if "::" in line:
																				snake_case_,       snake_case_							=			line.split('''::''')
																else:
																				snake_case_,       snake_case_							=			line, line

																for file_regex in docs.keys():
																				if fnmatch(file_path, file_regex):
																								snake_case_							=			docs[file_regex]
																								doc_test_results[category]["failed"].append(test)

																								snake_case_							=			all_failures[test] if test in all_failures else '''N/A'''
																								snake_case_							=			failure
																								break

				snake_case_							=			Message('''🤗 Results of the doc tests.''', doc_test_results)
				message.post()
				message.post_reply()

