'''simple docstring'''





import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

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


if is_torch_available():
    import torch


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE_:     List[Any]  =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:     Union[str, Any]  ={'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE_:     List[str]  ={
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

SCREAMING_SNAKE_CASE_:     Union[str, Any]  ={
    'AI-Sweden/gpt-sw3-126m': 20_48,
    'AI-Sweden/gpt-sw3-350m': 20_48,
    'AI-Sweden/gpt-sw3-1.6b': 20_48,
    'AI-Sweden/gpt-sw3-6.7b': 20_48,
    'AI-Sweden/gpt-sw3-20b': 20_48,
}




class        __A      ( UpperCamelCase__	):
    a__       :						List[str]          =  VOCAB_FILES_NAMES
    a__       :						Any          =  PRETRAINED_VOCAB_FILES_MAP
    a__       :						int          =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__       :						Optional[int]          =  ["""input_ids""", """attention_mask"""]



    def __init__(self    :    Tuple   ,			__a    :    Dict   ,			__a    :    Union[str, Any]=False   ,			__a    :    int=False   ,			__a    :    Dict=False   ,			__a    :    Any=None   ,			__a    :    Tuple=None   ,			__a    :    List[str]=None   ,			__a    :    Optional[Any]=None   ,			__a    :    Optional[Dict[str, Any]] = None   ,			**__a    :    Optional[int]   ,			):
         UpperCAmelCase_           =   {} if sp_model_kwargs is None else sp_model_kwargs

         UpperCAmelCase_           =   kwargs.get("name_or_path" )
         if name_or_path is None:
              logger.warning(
                  "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
                  " you are testing the model, this can safely be ignored" )
              UpperCAmelCase_           =   "None"

         # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
         UpperCAmelCase_           =   "<|endoftext|>" if eos_token is None else eos_token
         UpperCAmelCase_           =   "<unk>" if unk_token is None else unk_token
         if "gpt-sw3-7b" in name_or_path:
              UpperCAmelCase_           =   unk_token if pad_token is None else pad_token
              UpperCAmelCase_           =   eos_token if bos_token is None else bos_token
         else:
              UpperCAmelCase_           =   "<pad>" if pad_token is None else pad_token
              UpperCAmelCase_           =   "<s>" if bos_token is None else bos_token

         super().__init__(
             do_lower_case=__a   ,			remove_space=__a   ,			keep_accents=__a   ,			bos_token=__a   ,			eos_token=__a   ,			unk_token=__a   ,			pad_token=__a   ,			sp_model_kwargs=self.sp_model_kwargs   ,			**__a   ,			)

         UpperCAmelCase_           =   do_lower_case
         UpperCAmelCase_           =   remove_space
         UpperCAmelCase_           =   keep_accents
         UpperCAmelCase_           =   vocab_file

         UpperCAmelCase_           =   spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(__a )

         # Used for whitespace normalization in input texts
         # fmt : off
         UpperCAmelCase_           =   {" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
         # fmt : on

         # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
         UpperCAmelCase_           =   re.compile(
             f"""[{"".join(map(__a   ,			list(range(0   ,			9 ) ) + list(range(11   ,			32 ) ) + list(range(127   ,			160 ) ) + [160, 173, 8203] ) )}]""" )



    def __getstate__(self    :    Optional[int] ):
         UpperCAmelCase_           =   self.__dict__.copy()
         UpperCAmelCase_           =   None
         return state



    def __setstate__(self    :    Optional[Any]   ,			__a    :    int ):
         UpperCAmelCase_           =   d

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

         UpperCAmelCase_           =   spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(self.vocab_file )



    @property
    # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
    def 					_lowercase						(self    :    int ):
         return len(self.sp_model )



    def 					_lowercase						(self    :    List[str]   ,			__a    :    str ):
         UpperCAmelCase_           =   self.non_printing_characters_re.sub(""   ,			__a )

         # Normalize whitespaces
         UpperCAmelCase_           =   "".join([char if char not in self.whitespaces else " " for char in text] )

         # NFC Unicode normalization
         UpperCAmelCase_           =   unicodedata.normalize("NFC"   ,			__a )
         return text



    def 					_lowercase						(self    :    int   ,			__a    :    str   ,			**__a    :    str ):
         UpperCAmelCase_           =   self.preprocess_text(__a )
         return self.sp_model.encode(__a   ,			out_type=__a )



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    str ):
         return self.sp_model.PieceToId(__a )



    def 					_lowercase						(self    :    List[Any]   ,			__a    :    int ):
         return self.sp_model.IdToPiece(__a )



    @staticmethod
    def 					_lowercase						(__a    :    str ):
         return out_string



    def 					_lowercase						(self    :    Dict   ,			__a    :    List[str] ):
         UpperCAmelCase_           =   []
         UpperCAmelCase_           =   ""
         UpperCAmelCase_           =   False
         for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                   # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                   if not prev_is_special:
                        out_string += " "

                   out_string += self.sp_model.decode(__a ) + token
                   UpperCAmelCase_           =   True
                   UpperCAmelCase_           =   []
              else:
                   current_sub_tokens.append(__a )
                   UpperCAmelCase_           =   False
         out_string += self.sp_model.decode(__a )

         return out_string



    def 					_lowercase						(self    :    List[str] ):
         UpperCAmelCase_           =   {self.convert_ids_to_tokens(__a ): i for i in range(self.vocab_size )}
         vocab.update(self.added_tokens_encoder )
         return vocab



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

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

         return (out_vocab_file,)



    def 					_lowercase						(self    :    str   ,			__a    :    Union[str, List[str]]   ,			__a    :    Union[str, bool] = False ):

         if isinstance(__a   ,			__a ):
              UpperCAmelCase_           =   self.preprocess_text(__a )
              UpperCAmelCase_           =   self.sp_model.encode(__a )
         else:
              UpperCAmelCase_           =   [self.preprocess_text(__a ) for t in text]
              UpperCAmelCase_           =   self.sp_model.encode(__a )

         if return_tensors is True or return_tensors == "pt":
              UpperCAmelCase_           =   torch.tensor(__a )

         return token_ids



    def 					_lowercase						(self    :    List[Any]   ,			__a    :    Union[int, List[int]] ):
         return self.sp_model.decode(__a )






    def 					_lowercase						(self    :    int   ,			__a    :    "Conversation" ):
         UpperCAmelCase_           =   [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
         UpperCAmelCase_           =   (
             f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(__a ) + f"""{self.bos_token}Bot:"""
         )
         return self.encode(text=__a )






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


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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    test_results.split(' '							)

	lowercase       =    0
	lowercase       =    0

	# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
	# When it is too long, those signs are not present.
	lowercase       =    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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    {}
	lowercase       =    None
	lowercase       =    False
	for line in failures_short_lines.split('\n'							):
		if re.search(r'_ \[doctest\]' ,							__SCREAMING_SNAKE_CASE							):
			lowercase       =    True
			lowercase       =    line.split(' '							)[2]
		elif in_error and not line.split(' '							)[0].isdigit():
			lowercase       =    line
			lowercase       =    False

	return failures







class        A_   :




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case       ):
		lowercase       =    title

		lowercase       =    doc_test_results['time_spent'].split(','       )[0]
		lowercase       =    doc_test_results['success']
		lowercase       =    doc_test_results['failures']
		lowercase       =    self.n_success + self.n_failures

		# Failures and success of the modeling tests
		lowercase       =    doc_test_results





	@property
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    [self._time_spent]
		lowercase       =    0

		for time in time_spent:
			lowercase       =    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(snake_case       ) == 1:
				lowercase       =    [0, 0, time_parts[0]]

			lowercase  ,					lowercase  ,					lowercase       =    int(time_parts[0]       ), int(time_parts[1]       ), float(time_parts[2]       )
			total_secs += hours * 3600 + minutes * 60 + seconds

		lowercase  ,					lowercase  ,					lowercase       =    total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
		return F'''{int(snake_case       )}h{int(snake_case       )}m{int(snake_case       )}s'''





	@property
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return {"type": "header", "text": {"type": "plain_text", "text": self.title}}





	@property
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		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 	SCREAMING_SNAKE_CASE__						(       self       ):
		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 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    40
		lowercase       =    {k: v['failed'] for k, v in doc_test_results.items() if isinstance(snake_case		,    snake_case       )}

		lowercase       =    ''
		for category, failures in category_failures.items():
			if len(snake_case       ) == 0:
				continue

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

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

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





	@property
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    [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(snake_case       )





	@staticmethod
	def 	SCREAMING_SNAKE_CASE__						(       ):
		lowercase       =    [
		    {
		        '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(snake_case       )}       )       )

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





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

		lowercase       =    F'''{self.n_failures} failures out of {self.n_tests} tests,''' if self.n_failures else 'All tests passed.'

		lowercase       =    client.chat_postMessage(
		    channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']		,    blocks=self.payload		,    text=snake_case		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    ''
		for key, value in failures.items():
			lowercase       =    value[:200] + ' [Truncated]' if len(snake_case       ) > 250 else value
			failures_text += F'''*{key}*\n_{value}_\n\n'''

		lowercase       =    job_name
		lowercase       =    {'type': 'section', 'text': {'type': 'mrkdwn', 'text': text}}

		if job_link is not None:
			lowercase       =    {
			    '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 	SCREAMING_SNAKE_CASE__						(       self       ):
		if self.thread_ts is None:
			raise ValueError('Can only post reply if a post has been made.'       )

		lowercase       =    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'       )

		lowercase       =    sorted(self.doc_test_results.items()		,    key=lambda snake_case       : t[0]       )
		for job, job_result in sorted_dict:
			if len(job_result['failures']       ):
				lowercase       =    F'''*Num failures* :{len(job_result['failed']       )} \n'''
				lowercase       =    job_result['failures']
				lowercase       =    self.get_reply_blocks(snake_case		,    snake_case		,    snake_case		,    text=snake_case       )

				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=snake_case		,    thread_ts=self.thread_ts['ts']		,    )

				time.sleep(1       )






def 							UpperCAmelCase_		(							):
	lowercase       =    os.environ['GITHUB_RUN_ID']
	lowercase       =    F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'''
	lowercase       =    requests.get(__SCREAMING_SNAKE_CASE							).json()
	lowercase       =    {}

	try:
		jobs.update({job['name']: job['html_url'] for job in result['jobs']}							)
		lowercase       =    math.ceil((result['total_count'] - 100) / 100							)

		for i in range(__SCREAMING_SNAKE_CASE							):
			lowercase       =    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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    {}

	if os.path.exists(__SCREAMING_SNAKE_CASE							):
		lowercase       =    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:
					lowercase       =    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 							UpperCAmelCase_		(							):







	class        A_   :




		'''simple docstring'''


		def __init__(       self		,    snake_case       ):
			lowercase       =    name
			lowercase       =    []





		def __str__(       self       ):
			return self.name





		def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
			self.paths.append({'name': self.name, 'path': path}       )




	lowercase       =    {}

	lowercase       =    filter(os.path.isdir ,							os.listdir()							)
	for directory in directories:
		lowercase       =    directory
		if artifact_name not in _available_artifacts:
			lowercase       =    Artifact(__SCREAMING_SNAKE_CASE							)

			_available_artifacts[artifact_name].add_path(__SCREAMING_SNAKE_CASE							)

	return _available_artifacts


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

	UpperCAmelCase	      = 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'
	UpperCAmelCase	      = {
	    v: {
	        '''failed''': [],
	        '''failures''': {},
	    }
	    for v in docs.values()
	}

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

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

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

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

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

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

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

