


"""simple docstring"""




import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
				from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder

def        lowercase							(		lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     Tuple=None    ,					lowerCAmelCase__ :     Optional[Any]=None    ,					lowerCAmelCase__ :     Union[str, Any]=None    ,					lowerCAmelCase__ :     Optional[Any]=None    ,					lowerCAmelCase__ :     str=None    ,					) ->      List[str]:
		if attention_mask is None:
				__a           =			input_ids.ne(config.pad_token_id			)
		if decoder_attention_mask is None:
				__a           =			decoder_input_ids.ne(config.pad_token_id			)
		if head_mask is None:
				__a           =			torch.ones(config.encoder_layers    ,					config.encoder_attention_heads    ,					device=lowerCAmelCase__			)
		if decoder_head_mask is None:
				__a           =			torch.ones(config.decoder_layers    ,					config.decoder_attention_heads    ,					device=lowerCAmelCase__			)
		if cross_attn_head_mask is None:
				__a           =			torch.ones(config.decoder_layers    ,					config.decoder_attention_heads    ,					device=lowerCAmelCase__			)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": attention_mask,
		    "head_mask": head_mask,
		    "decoder_head_mask": decoder_head_mask,
		    "cross_attn_head_mask": cross_attn_head_mask,
		}
class 					__lowerCAmelCase					:


		'''simple docstring'''



		def __init__(  self							, _a							, _a=13							, _a=7							, _a=True							, _a=False							, _a=99							, _a=16							, _a=2							, _a=4							, _a=4							, _a="relu"							, _a=0.1							, _a=0.1							, _a=0.0							, _a=0.0							, _a=20							, _a=2							, _a=1							, _a=0							, ):
				__a           =			parent
				__a           =			batch_size
				__a           =			seq_length
				__a           =			is_training
				__a           =			use_labels
				__a           =			vocab_size
				__a           =			hidden_size
				__a           =			num_hidden_layers
				__a           =			num_attention_heads
				__a           =			intermediate_size
				__a           =			hidden_act
				__a           =			hidden_dropout_prob
				__a           =			attention_probs_dropout_prob
				__a           =			encoder_layerdrop
				__a           =			decoder_layerdrop
				__a           =			max_position_embeddings
				__a           =			eos_token_id
				__a           =			pad_token_id
				__a           =			bos_token_id

		def 			__UpperCAmelCase       (  self      ):
				__a           =			ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size      )
				__a           =			self.eos_token_id  # Eos Token
				__a           =			ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size      )

				# we need to clamp the input ids here to avoid having pad token in between
				# this is because for M2M100 the position_ids are prepared such that
				# all pad tokens have pos id = 2 and rest are between 2..seq_length
				# and the seq_length here is seq_length - num_pad_tokens
				# but when using past, there is no way of knowing if the past input ids had
				# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
				# position_ids being off by num_pad_tokens in past input
				__a           =			input_ids.clamp(self.pad_token_id + 1      )
				__a           =			decoder_input_ids.clamp(self.pad_token_id + 1      )

				__a           =			self.get_config()
				__a           =			prepare_mam_aaa_inputs_dict(_a							, _a							, _a      )
				return config, inputs_dict

		def 			__UpperCAmelCase       (  self      ):
				return MaMaaaConfig(
				    vocab_size=self.vocab_size							, d_model=self.hidden_size							, encoder_layers=self.num_hidden_layers							, decoder_layers=self.num_hidden_layers							, encoder_attention_heads=self.num_attention_heads							, decoder_attention_heads=self.num_attention_heads							, encoder_ffn_dim=self.intermediate_size							, decoder_ffn_dim=self.intermediate_size							, dropout=self.hidden_dropout_prob							, attention_dropout=self.attention_probs_dropout_prob							, encoder_layerdrop=self.encoder_layerdrop							, decoder_layerdrop=self.decoder_layerdrop							, max_position_embeddings=self.max_position_embeddings							, eos_token_id=self.eos_token_id							, bos_token_id=self.bos_token_id							, pad_token_id=self.pad_token_id							, )

		def 			__UpperCAmelCase       (  self      ):
				__a      ,     __a           =			self.prepare_config_and_inputs()
				return config, inputs_dict

		def 			__UpperCAmelCase       (  self							, _a							, _a      ):
				__a           =			MaMaaaModel(config=_a      ).get_decoder().to(_a      ).eval()
				__a           =			inputs_dict['''input_ids''']
				__a           =			inputs_dict['''attention_mask''']
				__a           =			inputs_dict['''head_mask''']

				# first forward pass
				__a           =			model(_a							, attention_mask=_a							, head_mask=_a							, use_cache=_a      )

				__a      ,     __a           =			outputs.to_tuple()

				# create hypothetical multiple next token and extent to next_input_ids
				__a           =			ids_tensor((self.batch_size, 3)							, config.vocab_size      )
				__a           =			ids_tensor((self.batch_size, 3)							, 2      )

				# append to next input_ids and
				__a           =			torch.cat([input_ids, next_tokens]							, dim=-1      )
				__a           =			torch.cat([attention_mask, next_attn_mask]							, dim=-1      )

				__a           =			model(_a							, attention_mask=_a      )['''last_hidden_state''']
				__a           =			model(_a							, attention_mask=_a							, past_key_values=_a      )[
				    '''last_hidden_state'''
				]

				# select random slice
				__a           =			ids_tensor((1,)							, output_from_past.shape[-1]      ).item()
				__a           =			output_from_no_past[:, -3:, random_slice_idx].detach()
				__a           =			output_from_past[:, :, random_slice_idx].detach()

				self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]      )

				# test that outputs are equal for slice
				self.parent.assertTrue(torch.allclose(_a							, _a							, atol=1E-2      )      )





		def 			__UpperCAmelCase       (  self							, _a							, _a      ):
				__a           =			MaMaaaModel(config=_a      ).to(_a      ).eval()
				__a           =			model(**_a      )

				__a           =			outputs.encoder_last_hidden_state
				__a           =			outputs.last_hidden_state

				with tempfile.TemporaryDirectory() as tmpdirname:
						__a           =			model.get_encoder()
						encoder.save_pretrained(_a      )
						__a           =			MaMaaaEncoder.from_pretrained(_a      ).to(_a      )

				__a           =			encoder(inputs_dict['''input_ids''']							, attention_mask=inputs_dict['''attention_mask''']      )[
				    0
				]

				self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3      )

				with tempfile.TemporaryDirectory() as tmpdirname:
						__a           =			model.get_decoder()
						decoder.save_pretrained(_a      )
						__a           =			MaMaaaDecoder.from_pretrained(_a      ).to(_a      )

				__a           =			decoder(
				    input_ids=inputs_dict['''decoder_input_ids''']							, attention_mask=inputs_dict['''decoder_attention_mask''']							, encoder_hidden_states=_a							, encoder_attention_mask=inputs_dict['''attention_mask''']							, )[0]

				self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3      )







@require_torch
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Any										=				(
		    (
		        MaMaaaModel,
		        MaMaaaForConditionalGeneration,
		    )
		    if is_torch_available()
		    else ()
		)
		__UpperCAmelCase		:			Dict										=				(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
		__UpperCAmelCase		:			Optional[int]										=				(
		    {
		        'conversational': MaMaaaForConditionalGeneration,
		        'feature-extraction': MaMaaaModel,
		        'summarization': MaMaaaForConditionalGeneration,
		        'text2text-generation': MaMaaaForConditionalGeneration,
		        'translation': MaMaaaForConditionalGeneration,
		    }
		    if is_torch_available()
		    else {}
		)
		__UpperCAmelCase		:			int										=				True
		__UpperCAmelCase		:			Optional[int]										=				True
		__UpperCAmelCase		:			Dict										=				False
		__UpperCAmelCase		:			int										=				False

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a      ):
				if pipeline_test_casse_name == "TranslationPipelineTests":
						# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
						# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
						return True

				return False

		def 			__UpperCAmelCase       (  self      ):
				__a           =			MaMaaaModelTester(self      )
				__a           =			ConfigTester(self							, config_class=_a      )

		def 			__UpperCAmelCase       (  self      ):
				self.config_tester.run_common_tests()

		def 			__UpperCAmelCase       (  self      ):
				__a      ,     __a           =			self.model_tester.prepare_config_and_inputs()
				for model_class in self.all_model_classes:
						__a           =			model_class(_a      )

						with tempfile.TemporaryDirectory() as tmpdirname:
								model.save_pretrained(_a      )
								__a      ,     __a           =			model_class.from_pretrained(_a							, output_loading_info=_a      )
						self.assertEqual(info['''missing_keys''']							, []      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_decoder_model_past_large_inputs(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_encoder_decoder_model_standalone(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a      ,     __a           =			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
						__a           =			model_class(_a      )
						model.to(_a      )
						model.eval()

						__a           =			copy.deepcopy(self._prepare_for_class(_a							, _a      )      )

						if not self.is_encoder_decoder:
								__a           =			inputs['''input_ids''']
								del inputs["input_ids"]
						else:
								__a           =			inputs['''input_ids''']
								__a           =			inputs.get('''decoder_input_ids'''							, _a      )
								del inputs["input_ids"]
								inputs.pop('''decoder_input_ids'''							, _a      )

						__a           =			model.get_input_embeddings()
						if not self.is_encoder_decoder:
								__a           =			wte(_a      )
						else:
								__a           =			wte(_a      )
								__a           =			wte(_a      )

						with torch.no_grad():
								model(**_a      )[0]





		def 			__UpperCAmelCase       (  self      ):
				__a      ,     __a           =			self.model_tester.prepare_config_and_inputs()
				__a           =			input_dict['''input_ids''']
				__a           =			input_ids.ne(1      ).to(_a      )
				__a           =			MaMaaaForConditionalGeneration(_a      ).eval().to(_a      )
				if torch_device == "cuda":
						model.half()
				model.generate(_a							, attention_mask=_a      )
				model.generate(num_beams=4							, do_sample=_a							, early_stopping=_a							, num_return_sequences=3      )







def        lowercase							(		lowerCAmelCase__ :     List[str]			) ->      Tuple:
		return torch.tensor(lowerCAmelCase__    ,					dtype=torch.long    ,					device=lowerCAmelCase__			)


lowercase_			     =  1e-4




@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		@cached_property
		def 			__UpperCAmelCase       (  self      ):
				return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			MaMaaaModel.from_pretrained('''facebook/m2m100_418M'''      ).to(_a      )
				__a           =			_long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]]      )
				__a           =			_long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]]      )
				__a           =			prepare_mam_aaa_inputs_dict(model.config							, _a							, _a      )
				with torch.no_grad():
						__a           =			model(**_a      )[0]
				__a           =			torch.Size((1, 11, 1_024)      )
				self.assertEqual(output.shape							, _a      )
				# change to expected output here
				__a           =			torch.tensor(
				    [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]							, device=_a      )
				self.assertTrue(torch.allclose(output[:, :3, :3]							, _a							, atol=_a      )      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''      ).to(_a      )

				# change to intended input
				__a           =			_long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]]      )
				__a           =			_long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]]      )
				__a           =			prepare_mam_aaa_inputs_dict(model.config							, _a							, _a      )
				with torch.no_grad():
						__a           =			model(**_a      )[0]
				__a           =			torch.Size((1, 11, model.config.vocab_size)      )
				self.assertEqual(output.shape							, _a      )
				# change to expected output here
				__a           =			torch.tensor(
				    [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]							, device=_a      )
				self.assertTrue(torch.allclose(output[:, :3, :3]							, _a							, atol=_a      )      )





		def 			__UpperCAmelCase       (  self      ):
				__a           =			MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''      ).to(_a      )
				__a           =			MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''							, src_lang='''fr'''							, tgt_lang='''en'''      )

				__a           =			[
				    '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
				    '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
				    '''Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'''
				    ''' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'''
				    ''' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.''',
				]

				# The below article tests that we don't add any hypotheses outside of the top n_beams
				__a           =			tokenizer(_a							, padding=_a							, return_tensors='''pt'''      )

				__a           =			model.generate(
				    input_ids=dct['''input_ids'''].to(_a      )							, attention_mask=dct['''attention_mask'''].to(_a      )							, num_beams=5							, forced_bos_token_id=tokenizer.get_lang_id('''en'''      )							, )

				__a           =			[
				    '''The NSA case highlights the total absence of intelligence debate''',
				    '''I think there are two levels of response from the French government.''',
				    '''When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'''
				    ''' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'''
				    ''' communications in France.''',
				]

				__a           =			tokenizer.batch_decode(
				    hypotheses_batch.tolist()							, clean_up_tokenization_spaces=_a							, skip_special_tokens=_a      )
				assert generated == expected_en








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()

