

# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

A_			:					List[Any]    			=     2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
A_			:					Tuple    			=     {
    # fairseq:
    'wmt19-ru-en': {'length_penalty': 1.1},
    'wmt19-en-ru': {'length_penalty': 1.15},
    'wmt19-en-de': {'length_penalty': 1.0},
    'wmt19-de-en': {'length_penalty': 1.1},
    # allenai:
    'wmt16-en-de-dist-12-1': {'length_penalty': 0.6},
    'wmt16-en-de-dist-6-1': {'length_penalty': 0.6},
    'wmt16-en-de-12-1': {'length_penalty': 0.8},
    'wmt19-de-en-6-6-base': {'length_penalty': 0.6},
    'wmt19-de-en-6-6-big': {'length_penalty': 0.6},
}

# this remaps the different models to their organization names
A_			:					Optional[int]    			=     {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
	A_			:					Optional[int]    			=     'facebook'
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
	A_			:					Optional[int]    			=     'allenai'

def 		UpperCamelCase   (lowercase_:					int   )	-> Tuple:
	# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
	# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
	A__				:       int	  =						dict((re.sub(r"""@@$"""       , """"""       , lowercase_   ), v) if k.endswith("""@@"""   ) else (re.sub(r"""$"""       , """</w>"""       , lowercase_   ), v) for k, v in d.items()   )
	A__				:       str	  =						"""<s> <pad> </s> <unk>""".split()
	# restore the special tokens
	for k in keep_keys:
		del da[f"""{k}</w>"""]
		A__				:       Any	  =						d[k]  # restore
	return da

def 		UpperCamelCase   (lowercase_:					Tuple       , lowercase_:					Tuple   )	-> Optional[int]:
	# prep
	assert os.path.exists(lowercase_   )
	os.makedirs(lowercase_       , exist_ok=lowercase_   )
	print(f"""Writing results to {pytorch_dump_folder_path}"""   )

	# handle various types of models

	A__				:       Dict	  =						basename(lowercase_   )
	A__				:       int	  =						dirname(lowercase_   )

	A__				:       List[str]	  =						fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
	A__				:       Union[str, Any]	  =						cls.hub_models()
	A__				:       Optional[Any]	  =						{"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
	A__				:       Union[str, Any]	  =						"""."""
	# note: since the model dump is old, fairseq has upgraded its model some
	# time later, and it does a whole lot of rewrites and splits on the saved
	# weights, therefore we can't use torch.load() directly on the model file.
	# see: upgrade_state_dict(state_dict) in fairseq_model.py
	print(f"""using checkpoint {checkpoint_file}"""   )
	A__				:       Tuple	  =						hub_utils.from_pretrained(
	    lowercase_       , lowercase_       , lowercase_       , archive_map=lowercase_       , **lowercase_   )

	A__				:       Any	  =						vars(chkpt["""args"""]["""model"""]   )

	A__				:       Optional[Any]	  =						args["""source_lang"""]
	A__				:       Optional[Any]	  =						args["""target_lang"""]

	A__				:       Dict	  =						dirname(lowercase_   )
	A__				:       Optional[Any]	  =						basename(lowercase_   )

	# dicts
	A__				:       Optional[int]	  =						os.path.join(lowercase_       , f"""dict.{src_lang}.txt"""   )
	A__				:       int	  =						os.path.join(lowercase_       , f"""dict.{tgt_lang}.txt"""   )

	A__				:       Dict	  =						Dictionary.load(lowercase_   )
	A__				:       List[str]	  =						rewrite_dict_keys(src_dict.indices   )
	A__				:       Any	  =						len(lowercase_   )
	A__				:       str	  =						os.path.join(lowercase_       , """vocab-src.json"""   )
	print(f"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"""   )
	with open(lowercase_       , """w"""       , encoding="""utf-8"""   ) as f:
		f.write(json.dumps(lowercase_       , ensure_ascii=lowercase_       , indent=lowercase_   )   )

	# detect whether this is a do_lower_case situation, which can be derived by checking whether we
	# have at least one uppercase letter in the source vocab
	A__				:       Optional[Any]	  =						True
	for k in src_vocab.keys():
		if not k.islower():
			A__				:       Tuple	  =						False
			break

	A__				:       List[str]	  =						Dictionary.load(lowercase_   )
	A__				:       Union[str, Any]	  =						rewrite_dict_keys(tgt_dict.indices   )
	A__				:       str	  =						len(lowercase_   )
	A__				:       int	  =						os.path.join(lowercase_       , """vocab-tgt.json"""   )
	print(f"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"""   )
	with open(lowercase_       , """w"""       , encoding="""utf-8"""   ) as f:
		f.write(json.dumps(lowercase_       , ensure_ascii=lowercase_       , indent=lowercase_   )   )

	# merges_file (bpecodes)
	A__				:       Dict	  =						os.path.join(lowercase_       , VOCAB_FILES_NAMES["""merges_file"""]   )
	for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
		A__				:       Any	  =						os.path.join(lowercase_       , lowercase_   )
		if os.path.exists(lowercase_   ):
			break
	with open(lowercase_       , encoding="""utf-8"""   ) as fin:
		A__				:       Any	  =						fin.read()
	A__				:       List[str]	  =						re.sub(r""" \d+$"""       , """"""       , lowercase_       , 0       , re.M   )  # remove frequency number
	print(f"""Generating {merges_file}"""   )
	with open(lowercase_       , """w"""       , encoding="""utf-8"""   ) as fout:
		fout.write(lowercase_   )

	# model config
	A__				:       Optional[Any]	  =						os.path.join(lowercase_       , """config.json"""   )

	# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
	# may have to modify the tokenizer if a different type is used by a future model
	assert args["bpe"] == "fastbpe", f"""need to extend tokenizer to support bpe={args['bpe']}"""
	assert args["tokenizer"] == "moses", f"""need to extend tokenizer to support bpe={args['tokenizer']}"""

	A__				:       List[str]	  =						{
	    """architectures""": ["""FSMTForConditionalGeneration"""],
	    """model_type""": """fsmt""",
	    """activation_dropout""": args["""activation_dropout"""],
	    """activation_function""": """relu""",
	    """attention_dropout""": args["""attention_dropout"""],
	    """d_model""": args["""decoder_embed_dim"""],
	    """dropout""": args["""dropout"""],
	    """init_std""": 0.02,
	    """max_position_embeddings""": args["""max_source_positions"""],
	    """num_hidden_layers""": args["""encoder_layers"""],
	    """src_vocab_size""": src_vocab_size,
	    """tgt_vocab_size""": tgt_vocab_size,
	    """langs""": [src_lang, tgt_lang],
	    """encoder_attention_heads""": args["""encoder_attention_heads"""],
	    """encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
	    """encoder_layerdrop""": args["""encoder_layerdrop"""],
	    """encoder_layers""": args["""encoder_layers"""],
	    """decoder_attention_heads""": args["""decoder_attention_heads"""],
	    """decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
	    """decoder_layerdrop""": args["""decoder_layerdrop"""],
	    """decoder_layers""": args["""decoder_layers"""],
	    """bos_token_id""": 0,
	    """pad_token_id""": 1,
	    """eos_token_id""": 2,
	    """is_encoder_decoder""": True,
	    """scale_embedding""": not args["""no_scale_embedding"""],
	    """tie_word_embeddings""": args["""share_all_embeddings"""],
	}

	# good hparam defaults to start with
	A__				:       Tuple	  =						5
	A__				:       List[str]	  =						False
	if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
		A__				:       int	  =						best_score_hparams[model_dir]["""length_penalty"""]
	else:
		A__				:       List[Any]	  =						1.0

	print(f"""Generating {fsmt_model_config_file}"""   )
	with open(lowercase_       , """w"""       , encoding="""utf-8"""   ) as f:
		f.write(json.dumps(lowercase_       , ensure_ascii=lowercase_       , indent=lowercase_   )   )

	# tokenizer config
	A__				:       Dict	  =						os.path.join(lowercase_       , lowercase_   )

	A__				:       str	  =						{
	    """langs""": [src_lang, tgt_lang],
	    """model_max_length""": 1024,
	    """do_lower_case""": do_lower_case,
	}

	print(f"""Generating {fsmt_tokenizer_config_file}"""   )
	with open(lowercase_       , """w"""       , encoding="""utf-8"""   ) as f:
		f.write(json.dumps(lowercase_       , ensure_ascii=lowercase_       , indent=lowercase_   )   )

	# model
	A__				:       int	  =						chkpt["""models"""][0]
	A__				:       Dict	  =						model.state_dict()

	# rename keys to start with 'model.'
	A__				:       Union[str, Any]	  =						OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items()   )

	# remove unneeded keys
	A__				:       List[str]	  =						[
	    """model.model""",
	    """model.encoder.version""",
	    """model.decoder.version""",
	    """model.encoder_embed_tokens.weight""",
	    """model.decoder_embed_tokens.weight""",
	    """model.encoder.embed_positions._float_tensor""",
	    """model.decoder.embed_positions._float_tensor""",
	]
	for k in ignore_keys:
		model_state_dict.pop(lowercase_       , lowercase_   )

	A__				:       str	  =						FSMTConfig.from_pretrained(lowercase_   )
	A__				:       Dict	  =						FSMTForConditionalGeneration(lowercase_   )

	# check that it loads ok
	model_new.load_state_dict(lowercase_       , strict=lowercase_   )

	# save
	A__				:       int	  =						os.path.join(lowercase_       , lowercase_   )
	print(f"""Generating {pytorch_weights_dump_path}"""   )
	torch.save(lowercase_       , lowercase_   )

	print("""Conversion is done!"""   )
	print("""\nLast step is to upload the files to s3"""   )
	print(f"""cd {data_root}"""   )
	print(f"""transformers-cli upload {model_dir}"""   )


if __name__ == "__main__":
	A_			:					Union[str, Any]    			=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--fsmt_checkpoint_path',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
	        ' bpecodes, etc.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	A_			:					Tuple    			=     parser.parse_args()
	convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)






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


A_			:					str    			=     WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])

def 		UpperCamelCase   (lowercase_:					Union[str, Any]   )	-> Dict:
	A__				:       Union[str, Any]	  =						test_results.split(""" """   )

	A__				:       Union[str, Any]	  =						0
	A__				:       Union[str, 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.
	A__				:       List[str]	  =						expressions[-2] if """=""" in expressions[-1] else expressions[-1]

	for i, expression in enumerate(lowercase_   ):
		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   (lowercase_:					Any   )	-> Optional[int]:
	A__				:       Dict	  =						{}
	A__				:       Union[str, Any]	  =						None
	A__				:       List[str]	  =						False
	for line in failures_short_lines.split("""\n"""   ):
		if re.search(r"""_ \[doctest\]"""       , lowercase_   ):
			A__				:       Tuple	  =						True
			A__				:       Dict	  =						line.split(""" """   )[2]
		elif in_error and not line.split(""" """   )[0].isdigit():
			A__				:       Union[str, Any]	  =						line
			A__				:       List[str]	  =						False

	return failures



class        _a		:


					'''simple docstring'''






					def __init__(					self       ,			A__       ,			A__     ):
						A__				:       Optional[Any]	  =						title

						A__				:       Tuple	  =						doc_test_results["""time_spent"""].split(""","""     )[0]
						A__				:       str	  =						doc_test_results["""success"""]
						A__				:       Optional[int]	  =						doc_test_results["""failures"""]
						A__				:       int	  =						self.n_success + self.n_failures

						# Failures and success of the modeling tests
						A__				:       Optional[int]	  =						doc_test_results






					@property
					def     __A     (					self     ):
						A__				:       Tuple	  =						[self._time_spent]
						A__				:       Tuple	  =						0

						for time in time_spent:
							A__				:       Dict	  =						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(A__     ) == 1:
								A__				:       Dict	  =						[0, 0, time_parts[0]]

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

						A__  ,  A__  ,  A__				:       List[str]	  =						total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
						return F"""{int(A__     )}h{int(A__     )}m{int(A__     )}s"""






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






					@property
					def     __A     (					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     __A     (					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     __A     (					self     ):
						A__				:       Tuple	  =						40
						A__				:       Dict	  =						{k: v["""failed"""] for k, v in doc_test_results.items() if isinstance(A__       ,			A__     )}

						A__				:       str	  =						""""""
						for category, failures in category_failures.items():
							if len(A__     ) == 0:
								continue

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

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

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






					@property
					def     __A     (					self     ):
						A__				:       Tuple	  =						[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(A__     )






					@staticmethod
					def     __A     (					):
						A__				:       List[str]	  =						[
						    {
						        """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(A__     )}     )     )

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






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

						A__				:       Any	  =						F"""{self.n_failures} failures out of {self.n_tests} tests,""" if self.n_failures else """All tests passed."""

						A__				:       Any	  =						client.chat_postMessage(
						    channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""]       ,			blocks=self.payload       ,			text=A__       ,			)






					def     __A     (					self       ,			A__       ,			A__       ,			A__       ,			A__     ):
						A__				:       Tuple	  =						""""""
						for key, value in failures.items():
							A__				:       Any	  =						value[:200] + """ [Truncated]""" if len(A__     ) > 250 else value
							failures_text += F"""*{key}*\n_{value}_\n\n"""

						A__				:       Optional[Any]	  =						job_name
						A__				:       Union[str, Any]	  =						{"""type""": """section""", """text""": {"""type""": """mrkdwn""", """text""": text}}

						if job_link is not None:
							A__				:       Dict	  =						{
							    """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     __A     (					self     ):
						if self.thread_ts is None:
							raise ValueError("""Can only post reply if a post has been made."""     )

						A__				:       List[Any]	  =						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"""     )

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

								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=A__       ,			thread_ts=self.thread_ts["""ts"""]       ,			)

								time.sleep(1     )



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

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

		for i in range(lowercase_   ):
			A__				:       str	  =						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."""       , lowercase_   )

	return {}

def 		UpperCamelCase   (lowercase_:					str   )	-> Any:
	A__				:       List[Any]	  =						{}

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

	return _artifact

def 		UpperCamelCase   ()	-> Union[str, Any]:



	class        _a		:


						'''simple docstring'''






						def __init__(					self       ,			A__     ):
							A__				:       str	  =						name
							A__				:       Optional[int]	  =						[]






						def __str__(					self     ):
							return self.name






						def     __A     (					self       ,			A__     ):
							self.paths.append({"""name""": self.name, """path""": path}     )



	A__				:       Dict[str, Artifact]	  =						{}

	A__				:       int	  =						filter(os.path.isdir       , os.listdir()   )
	for directory in directories:
		A__				:       Dict	  =						directory
		if artifact_name not in _available_artifacts:
			A__				:       int	  =						Artifact(lowercase_   )

			_available_artifacts[artifact_name].add_path(lowercase_   )

	return _available_artifacts


if __name__ == "__main__":
	A_			:					str    			=     get_job_links()
	A_			:					Dict    			=     retrieve_available_artifacts()

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

	# Link to the GitHub Action job
	A_			:					Optional[Any]    			=     github_actions_job_links.get('run_doctests')

	A_			:					str    			=     available_artifacts['doc_tests_gpu_test_reports'].paths[0]
	A_			:					List[Any]    			=     retrieve_artifact(artifact_path['name'])
	if "stats" in artifact:
		A_			,     A_			,     A_			:					Any    			=     handle_test_results(artifact['stats'])
		A_			:					Union[str, Any]    			=     failed
		A_			:					int    			=     success
		A_			:					Optional[Any]    			=     time_spent[1:-1] + ', '

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

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

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

						A_			:					Optional[int]    			=     all_failures[test] if test in all_failures else 'N/A'
						A_			:					List[str]    			=     failure
						break

	A_			:					Optional[Any]    			=     Message('🤗 Results of the doc tests.', doc_test_results)
	message.post()
	message.post_reply()



