






'''simple docstring'''







import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
			raise Exception('''requires gluonnlp == 0.8.3''')

if version.parse(mx.__version__) != version.parse('''1.5.0'''):
			raise Exception('''requires mxnet == 1.5.0''')

logging.set_verbosity_info()
_A     :      Union[str, Any]   					=logging.get_logger(__name__)

_A     :      Tuple   					='''The Nymphenburg Palace is a beautiful palace in Munich!'''





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	Union[str, Any]:
							lowerCamelCase__ :     Dict	  =	{
							    """attention_cell""": """multi_head""",
							    """num_layers""": 4,
							    """units""": 1024,
							    """hidden_size""": 768,
							    """max_length""": 512,
							    """num_heads""": 8,
							    """scaled""": True,
							    """dropout""": 0.1,
							    """use_residual""": True,
							    """embed_size""": 1024,
							    """embed_dropout""": 0.1,
							    """word_embed""": None,
							    """layer_norm_eps""": 1E-5,
							    """token_type_vocab_size""": 2,
							}

							lowerCamelCase__ :     Optional[int]	  =	bort_4_8_768_1024_hparams

							# Let's construct the original Bort model here
							# Taken from official BERT implementation, see:
							# https://github.com/alexa/bort/blob/master/bort/bort.py
							lowerCamelCase__ :     str	  =	BERTEncoder(
							    attention_cell=predefined_args["""attention_cell"""]    ,      num_layers=predefined_args["""num_layers"""]    ,      units=predefined_args["""units"""]    ,      hidden_size=predefined_args["""hidden_size"""]    ,      max_length=predefined_args["""max_length"""]    ,      num_heads=predefined_args["""num_heads"""]    ,      scaled=predefined_args["""scaled"""]    ,      dropout=predefined_args["""dropout"""]    ,      output_attention=UpperCamelCase    ,      output_all_encodings=UpperCamelCase    ,      use_residual=predefined_args["""use_residual"""]    ,      activation=predefined_args.get("""activation"""    ,      """gelu"""				)    ,      layer_norm_eps=predefined_args.get("""layer_norm_eps"""    ,      UpperCamelCase				)    ,      )

							# Vocab information needs to be fetched first
							# It's the same as RoBERTa, so RobertaTokenizer can be used later
							lowerCamelCase__ :     Tuple	  =	"""openwebtext_ccnews_stories_books_cased"""

							# Specify download folder to Gluonnlp's vocab
							lowerCamelCase__ :     List[Any]	  =	os.path.join(get_home_dir()    ,      """models"""				)
							lowerCamelCase__ :     str	  =	_load_vocab(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      cls=UpperCamelCase				)

							lowerCamelCase__ :     Union[str, Any]	  =	nlp.model.BERTModel(
							    UpperCamelCase    ,      len(UpperCamelCase				)    ,      units=predefined_args["""units"""]    ,      embed_size=predefined_args["""embed_size"""]    ,      embed_dropout=predefined_args["""embed_dropout"""]    ,      word_embed=predefined_args["""word_embed"""]    ,      use_pooler=UpperCamelCase    ,      use_token_type_embed=UpperCamelCase    ,      token_type_vocab_size=predefined_args["""token_type_vocab_size"""]    ,      use_classifier=UpperCamelCase    ,      use_decoder=UpperCamelCase    ,      )

							original_bort.load_parameters(UpperCamelCase    ,      cast_dtype=UpperCamelCase    ,      ignore_extra=UpperCamelCase				)
							lowerCamelCase__ :     str	  =	original_bort._collect_params_with_prefix()

							# Build our config 🤗
							lowerCamelCase__ :     Any	  =	{
							    """architectures""": ["""BertForMaskedLM"""],
							    """attention_probs_dropout_prob""": predefined_args["""dropout"""],
							    """hidden_act""": """gelu""",
							    """hidden_dropout_prob""": predefined_args["""dropout"""],
							    """hidden_size""": predefined_args["""embed_size"""],
							    """initializer_range""": 0.02,
							    """intermediate_size""": predefined_args["""hidden_size"""],
							    """layer_norm_eps""": predefined_args["""layer_norm_eps"""],
							    """max_position_embeddings""": predefined_args["""max_length"""],
							    """model_type""": """bort""",
							    """num_attention_heads""": predefined_args["""num_heads"""],
							    """num_hidden_layers""": predefined_args["""num_layers"""],
							    """pad_token_id""": 1,  # 2 = BERT, 1 = RoBERTa
							    """type_vocab_size""": 1,  # 2 = BERT, 1 = RoBERTa
							    """vocab_size""": len(UpperCamelCase				),
							}

							lowerCamelCase__ :     Optional[Any]	  =	BertConfig.from_dict(UpperCamelCase				)
							lowerCamelCase__ :     List[Any]	  =	BertForMaskedLM(UpperCamelCase				)
							hf_bort_model.eval()

							# Parameter mapping table (Gluonnlp to Transformers)
							# * denotes layer index
							#
							# | Gluon Parameter                                                | Transformers Parameter
							# | -------------------------------------------------------------- | ----------------------
							# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
							# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
							# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
							# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
							# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
							# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
							# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
							# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
							# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
							# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
							# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
							# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
							# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
							# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
							# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
							# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
							# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
							# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
							# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
							# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

							# Helper function to convert MXNET Arrays to PyTorch
							def to_torch(UpperCamelCase				) -> nn.Parameter:
														return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()				)				)

							# Check param shapes and map new HF param back
							def check_and_map_params(UpperCamelCase    ,      UpperCamelCase				):
														lowerCamelCase__ :     Optional[int]	  =	hf_param.shape

														lowerCamelCase__ :     List[str]	  =	to_torch(params[gluon_param]				)
														lowerCamelCase__ :     List[Any]	  =	gluon_param.shape

														assert (
														    shape_hf == shape_gluon
														), f'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'''

														return gluon_param

							lowerCamelCase__ :     Any	  =	check_and_map_params(
							    hf_bort_model.bert.embeddings.word_embeddings.weight    ,      """word_embed.0.weight"""				)
							lowerCamelCase__ :     Tuple	  =	check_and_map_params(
							    hf_bort_model.bert.embeddings.position_embeddings.weight    ,      """encoder.position_weight"""				)
							lowerCamelCase__ :     List[str]	  =	check_and_map_params(
							    hf_bort_model.bert.embeddings.LayerNorm.bias    ,      """encoder.layer_norm.beta"""				)
							lowerCamelCase__ :     List[str]	  =	check_and_map_params(
							    hf_bort_model.bert.embeddings.LayerNorm.weight    ,      """encoder.layer_norm.gamma"""				)

							# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
							lowerCamelCase__ :     List[Any]	  =	torch.zeros_like(
							    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data				)

							for i in range(hf_bort_config.num_hidden_layers				):
														lowerCamelCase__ :     BertLayer	  =	hf_bort_model.bert.encoder.layer[i]

														# self attention
														lowerCamelCase__ :     BertSelfAttention	  =	layer.attention.self

														lowerCamelCase__ :     Union[str, Any]	  =	check_and_map_params(
														    self_attn.key.bias.data    ,      f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''				)

														lowerCamelCase__ :     Dict	  =	check_and_map_params(
														    self_attn.key.weight.data    ,      f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''				)
														lowerCamelCase__ :     Dict	  =	check_and_map_params(
														    self_attn.query.bias.data    ,      f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''				)
														lowerCamelCase__ :     str	  =	check_and_map_params(
														    self_attn.query.weight.data    ,      f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''				)
														lowerCamelCase__ :     List[str]	  =	check_and_map_params(
														    self_attn.value.bias.data    ,      f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''				)
														lowerCamelCase__ :     Dict	  =	check_and_map_params(
														    self_attn.value.weight.data    ,      f'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''				)

														# self attention output
														lowerCamelCase__ :     BertSelfOutput	  =	layer.attention.output

														lowerCamelCase__ :     Dict	  =	check_and_map_params(
														    self_output.dense.bias    ,      f'''encoder.transformer_cells.{i}.proj.bias'''				)
														lowerCamelCase__ :     List[str]	  =	check_and_map_params(
														    self_output.dense.weight    ,      f'''encoder.transformer_cells.{i}.proj.weight'''				)
														lowerCamelCase__ :     List[str]	  =	check_and_map_params(
														    self_output.LayerNorm.bias    ,      f'''encoder.transformer_cells.{i}.layer_norm.beta'''				)
														lowerCamelCase__ :     Tuple	  =	check_and_map_params(
														    self_output.LayerNorm.weight    ,      f'''encoder.transformer_cells.{i}.layer_norm.gamma'''				)

														# intermediate
														lowerCamelCase__ :     BertIntermediate	  =	layer.intermediate

														lowerCamelCase__ :     List[str]	  =	check_and_map_params(
														    intermediate.dense.bias    ,      f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''				)
														lowerCamelCase__ :     int	  =	check_and_map_params(
														    intermediate.dense.weight    ,      f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''				)

														# output
														lowerCamelCase__ :     BertOutput	  =	layer.output

														lowerCamelCase__ :     int	  =	check_and_map_params(
														    bert_output.dense.bias    ,      f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''				)
														lowerCamelCase__ :     Any	  =	check_and_map_params(
														    bert_output.dense.weight    ,      f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''				)
														lowerCamelCase__ :     Union[str, Any]	  =	check_and_map_params(
														    bert_output.LayerNorm.bias    ,      f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''				)
														lowerCamelCase__ :     Optional[int]	  =	check_and_map_params(
														    bert_output.LayerNorm.weight    ,      f'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma'''				)

							# Save space and energy 🎄
							hf_bort_model.half()

							# Compare output of both models
							lowerCamelCase__ :     List[str]	  =	RobertaTokenizer.from_pretrained("""roberta-base"""				)

							lowerCamelCase__ :     int	  =	tokenizer.encode_plus(UpperCamelCase				)["""input_ids"""]

							# Get gluon output
							lowerCamelCase__ :     List[str]	  =	mx.nd.array([input_ids]				)
							lowerCamelCase__ :     Dict	  =	original_bort(inputs=UpperCamelCase    ,      token_types=[]				)

							# Get Transformer output (save and reload model again)
							hf_bort_model.save_pretrained(UpperCamelCase				)
							lowerCamelCase__ :     Tuple	  =	BertModel.from_pretrained(UpperCamelCase				)
							hf_bort_model.eval()

							lowerCamelCase__ :     Dict	  =	tokenizer.encode_plus(UpperCamelCase    ,      return_tensors="""pt"""				)
							lowerCamelCase__ :     List[str]	  =	hf_bort_model(**UpperCamelCase				)[0]

							lowerCamelCase__ :     str	  =	output_gluon[0].asnumpy()
							lowerCamelCase__ :     Dict	  =	output_hf[0].detach().numpy()

							lowerCamelCase__ :     List[str]	  =	np.max(np.abs(hf_layer - gluon_layer				)				).item()
							lowerCamelCase__ :     Union[str, Any]	  =	np.allclose(UpperCamelCase    ,      UpperCamelCase    ,      atol=1E-3				)

							if success:
														print("""✔️ Both model do output the same tensors"""				)
							else:
														print("""❌ Both model do **NOT** output the same tensors"""				)
														print("""Absolute difference is:"""    ,      UpperCamelCase				)


if __name__ == "__main__":
			_A     :      List[str]   					=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			_A     :      List[str]   					=parser.parse_args()
			convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''







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 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Tuple:
							lowerCamelCase__ :     int	  =	test_results.split(""" """				)

							lowerCamelCase__ :     Optional[int]	  =	0
							lowerCamelCase__ :     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__ :     Union[str, Any]	  =	expressions[-2] if """=""" in expressions[-1] else expressions[-1]

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

							return failed, success, time_spent





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

							return failures

class _lowercase     :




							def __init__(							self:    Tuple      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Dict					):
														lowerCamelCase__ :     Union[str, Any]	  =	title

														lowerCamelCase__ :     Tuple	  =	doc_test_results["""time_spent"""].split(""","""					)[0]
														lowerCamelCase__ :     Union[str, Any]	  =	doc_test_results["""success"""]
														lowerCamelCase__ :     List[Any]	  =	doc_test_results["""failures"""]
														lowerCamelCase__ :     List[str]	  =	self.n_success + self.n_failures

														# Failures and success of the modeling tests
														lowerCamelCase__ :     str	  =	doc_test_results




							@property
							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     Union[str, Any]	  =	[self._time_spent]
														lowerCamelCase__ :     Tuple	  =	0

														for time in time_spent:
																					lowerCamelCase__ :     Tuple	  =	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(UpperCamelCase__					) == 1:
																												lowerCamelCase__ :     Tuple	  =	[0, 0, time_parts[0]]

																					lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     List[Any]	  =	int(time_parts[0]					), int(time_parts[1]					), float(time_parts[2]					)
																					total_secs += hours * 3_600 + minutes * 60 + seconds

														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     List[Any]	  =	total_secs // 3_600, (total_secs % 3_600) // 60, total_secs % 60
														return F'''{int(UpperCamelCase__					)}h{int(UpperCamelCase__					)}m{int(UpperCamelCase__					)}s'''




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




							@property
							def 		lowerCamelCase_    (							self:    Any					):
														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 		lowerCamelCase_    (							self:    Any					):
														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 		lowerCamelCase_    (							self:    Optional[Any]					):
														lowerCamelCase__ :     Any	  =	40
														lowerCamelCase__ :     List[str]	  =	{k: v["""failed"""] for k, v in doc_test_results.items() if isinstance(UpperCamelCase__      ,							UpperCamelCase__					)}

														lowerCamelCase__ :     List[Any]	  =	""""""
														for category, failures in category_failures.items():
																					if len(UpperCamelCase__					) == 0:
																												continue

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

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

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




							@property
							def 		lowerCamelCase_    (							self:    Any					):
														lowerCamelCase__ :     Union[str, Any]	  =	[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(UpperCamelCase__					)




							@staticmethod
							def 		lowerCamelCase_    (							):
														lowerCamelCase__ :     List[Any]	  =	[
														    {
														        """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(UpperCamelCase__					)}					)					)

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




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

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

														lowerCamelCase__ :     List[str]	  =	client.chat_postMessage(
														    channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""]      ,							blocks=self.payload      ,							text=UpperCamelCase__      ,							)




							def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Union[str, Any]					):
														lowerCamelCase__ :     Optional[Any]	  =	""""""
														for key, value in failures.items():
																					lowerCamelCase__ :     int	  =	value[:200] + """ [Truncated]""" if len(UpperCamelCase__					) > 250 else value
																					failures_text += F'''*{key}*\n_{value}_\n\n'''

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

														if job_link is not None:
																					lowerCamelCase__ :     Union[str, Any]	  =	{
																					    """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 		lowerCamelCase_    (							self:    Tuple					):
														if self.thread_ts is None:
																					raise ValueError("""Can only post reply if a post has been made."""					)

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

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

																												time.sleep(1					)





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

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

														for i in range(UpperCamelCase				):
																					lowerCamelCase__ :     List[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."""    ,      UpperCamelCase				)

							return {}





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Any:
							lowerCamelCase__ :     int	  =	{}

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

							return _artifact





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[Any]:

							class _lowercase     :




														def __init__(							self:    Tuple      ,							UpperCamelCase__:    str					):
																					lowerCamelCase__ :     Any	  =	name
																					lowerCamelCase__ :     Union[str, Any]	  =	[]




														def __str__(							self:    int					):
																					return self.name




														def 		lowerCamelCase_    (							self:    Tuple      ,							UpperCamelCase__:    str					):
																					self.paths.append({"""name""": self.name, """path""": path}					)



							lowerCamelCase__ :     Dict[str, Artifact]	  =	{}

							lowerCamelCase__ :     List[str]	  =	filter(os.path.isdir    ,      os.listdir()				)
							for directory in directories:
														lowerCamelCase__ :     Union[str, Any]	  =	directory
														if artifact_name not in _available_artifacts:
																					lowerCamelCase__ :     Optional[int]	  =	Artifact(UpperCamelCase				)

																					_available_artifacts[artifact_name].add_path(UpperCamelCase				)

							return _available_artifacts


if __name__ == "__main__":
			_A     :      Any   					=get_job_links()
			_A     :      str   					=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     :      Union[str, Any]   					={
			    v: {
			        '''failed''': [],
			        '''failures''': {},
			    }
			    for v in docs.values()
			}

			# Link to the GitHub Action job
			_A     :      Union[str, Any]   					=github_actions_job_links.get('''run_doctests''')

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

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

												if "::" in line:
															_A					,				_A     :      Any   					=line.split('''::''')
												else:
															_A					,				_A     :      Tuple   					=line, line

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

																		_A     :      str   					=all_failures[test] if test in all_failures else '''N/A'''
																		_A     :      Tuple   					=failure
																		break

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

