



'''simple docstring'''





import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
  import os

  # The slow tests are often failing with OOM error on GPU
  # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
  # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
  UpperCamelCase__  :			int          =					'platform'
  import jax
  import jax.numpy as jnp
  import numpy as np

  from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel


@require_flax
class 				_lowerCAmelCase     :


 """simple docstring"""

 lowerCamelCase		       =			PegasusConfig
 lowerCamelCase		       =			{}
 lowerCamelCase		       =			'''gelu'''







 def __init__(	self ,  _lowerCamelCase ,  _lowerCamelCase=13 ,  _lowerCamelCase=7 ,  _lowerCamelCase=True ,  _lowerCamelCase=False ,  _lowerCamelCase=99 ,  _lowerCamelCase=32 ,  _lowerCamelCase=5 ,  _lowerCamelCase=4 ,  _lowerCamelCase=37 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=20 ,  _lowerCamelCase=2 ,  _lowerCamelCase=1 ,  _lowerCamelCase=0 ,  )  ->  Dict:
    A_   :						Optional[int]		= parent
    A_   :						Union[str, Any]		= batch_size
    A_   :						str		= seq_length
    A_   :						Union[str, Any]		= is_training
    A_   :						Optional[int]		= use_labels
    A_   :						List[str]		= vocab_size
    A_   :						int		= hidden_size
    A_   :						List[Any]		= num_hidden_layers
    A_   :						Any		= num_attention_heads
    A_   :						Union[str, Any]		= intermediate_size

    A_   :						Optional[int]		= hidden_dropout_prob
    A_   :						Optional[Any]		= attention_probs_dropout_prob
    A_   :						List[str]		= max_position_embeddings
    A_   :						Optional[Any]		= eos_token_id
    A_   :						List[Any]		= pad_token_id
    A_   :						Tuple		= bos_token_id







 def 	UpperCAmelCase_    (	self   )  ->  Tuple:
    A_   :						List[str]		= ids_tensor([self.batch_size, self.seq_length - 1] ,  self.vocab_size   ).clip(3 ,  self.vocab_size   )
    A_   :						Dict		= np.expand_dims(np.array([self.eos_token_id] * self.batch_size   ) ,  1   )
    A_   :						Optional[int]		= np.concatenate([input_ids, eos_tensor] ,  axis=1   )

    A_   :						List[Any]		= ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size   )

    A_   :						Dict		= self.config_cls(
        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 ,  max_position_embeddings=self.max_position_embeddings ,  eos_token_ids=[2] ,  bos_token_id=self.bos_token_id ,  pad_token_id=self.pad_token_id ,  decoder_start_token_id=self.pad_token_id ,  **self.config_updates ,  )
    A_   :						int		= prepare_pegasus_inputs_dict(a_ ,  a_ ,  a_   )
    return config, inputs_dict







 def 	UpperCAmelCase_    (	self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   )  ->  int:
    A_   :						Optional[Any]		= 20
    A_   :						int		= model_class_name(a_   )

    A_   :						List[Any]		= model.encode(inputs_dict["""input_ids"""]   )

    A_   :						Any		= (
        inputs_dict['''decoder_input_ids'''],
        inputs_dict['''decoder_attention_mask'''],
    )

    A_   :						Optional[Any]		= model.init_cache(decoder_input_ids.shape[0] ,  a_ ,  a_   )
    A_   :						str		= jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,  dtype="""i4"""   )

    A_   :						List[Any]		= jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :] ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,  )
    A_   :						Tuple		= model.decode(
        decoder_input_ids[:, :-1] ,  a_ ,  decoder_attention_mask=a_ ,  past_key_values=a_ ,  decoder_position_ids=a_ ,  )

    A_   :						List[str]		= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,  dtype="""i4"""   )
    A_   :						List[Any]		= model.decode(
        decoder_input_ids[:, -1:] ,  a_ ,  decoder_attention_mask=a_ ,  past_key_values=outputs_cache.past_key_values ,  decoder_position_ids=a_ ,  )

    A_   :						int		= model.decode(a_ ,  a_   )

    A_   :						str		= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
    self.parent.assertTrue(diff < 1e-3 ,  msg=F"Max diff is {diff}"   )







 def 	UpperCAmelCase_    (	self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   )  ->  str:
    A_   :						Optional[Any]		= 20
    A_   :						str		= model_class_name(a_   )

    A_   :						int		= model.encode(inputs_dict["""input_ids"""]   )

    A_   :						List[str]		= (
        inputs_dict['''decoder_input_ids'''],
        inputs_dict['''decoder_attention_mask'''],
    )

    A_   :						List[Any]		= jnp.concatenate(
        [
            decoder_attention_mask,
            jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])   ),
        ] ,  axis=-1 ,  )

    A_   :						Dict		= model.init_cache(decoder_input_ids.shape[0] ,  a_ ,  a_   )
    A_   :						Optional[int]		= jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :] ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,  )

    A_   :						List[Any]		= model.decode(
        decoder_input_ids[:, :-1] ,  a_ ,  decoder_attention_mask=a_ ,  past_key_values=a_ ,  decoder_position_ids=a_ ,  )
    A_   :						Tuple		= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,  dtype="""i4"""   )
    A_   :						Any		= model.decode(
        decoder_input_ids[:, -1:] ,  a_ ,  past_key_values=outputs_cache.past_key_values ,  decoder_attention_mask=a_ ,  decoder_position_ids=a_ ,  )

    A_   :						List[Any]		= model.decode(a_ ,  a_ ,  decoder_attention_mask=a_   )

    A_   :						Union[str, Any]		= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
    self.parent.assertTrue(diff < 1e-3 ,  msg=F"Max diff is {diff}"   )



def   UpperCAmelCase			(  a_	,						a_	,						a_	,						a_=None	,						a_=None	,						)					->    Any:



   """simple docstring"""






   if attention_mask is None:
      A_   :						List[str]		= np.not_equal(_UpperCAmelCase	,						config.pad_token_id						).astype(np.inta						)
   if decoder_attention_mask is None:
      A_   :						int		= np.concatenate(
          [
              np.ones(decoder_input_ids[:, :1].shape	,						dtype=np.inta						),
              np.not_equal(decoder_input_ids[:, 1:]	,						config.pad_token_id						).astype(np.inta						),
          ]	,						axis=-1	,						)
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": decoder_attention_mask,
   }







@require_flax
class 				_lowerCAmelCase     (   __UpperCamelCase,				unittest.TestCase				):


 """simple docstring"""

 lowerCamelCase		       =			(
     (
         FlaxPegasusForConditionalGeneration,
         FlaxPegasusModel,
     )
     if is_flax_available()
     else ()
 )
 lowerCamelCase		       =			(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
 lowerCamelCase		       =			True
 lowerCamelCase		       =			False
 lowerCamelCase		       =			False
 lowerCamelCase		       =			False







 def 	UpperCAmelCase_    (	self   )  ->  List[str]:
    A_   :						Union[str, Any]		= FlaxPegasusModelTester(self   )
    A_   :						Optional[int]		= ConfigTester(self ,  config_class=a_   )







 def 	UpperCAmelCase_    (	self   )  ->  List[str]:
    self.config_tester.run_common_tests()







 def 	UpperCAmelCase_    (	self   )  ->  Dict:
    A_   :						Union[str, Any]		= self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(a_ ,  a_ ,  a_   )







 def 	UpperCAmelCase_    (	self   )  ->  Optional[int]:
    A_   :						List[Any]		= self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward_with_attn_mask(a_ ,  a_ ,  a_   )







 def 	UpperCAmelCase_    (	self   )  ->  Optional[int]:
    A_   :						Optional[Any]		= self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__   ):
          A_   :						Optional[int]		= self._prepare_for_class(a_ ,  a_   )
          A_   :						Optional[int]		= model_class(a_   )

          @jax.jit
          def encode_jitted(_lowerCamelCase ,  _lowerCamelCase=None ,  **_lowerCamelCase   ):
             return model.encode(input_ids=a_ ,  attention_mask=a_   )

          with self.subTest("""JIT Enabled"""   ):
             A_   :						str		= encode_jitted(**a_   ).to_tuple()

          with self.subTest("""JIT Disabled"""   ):
             with jax.disable_jit():
                A_   :						Tuple		= encode_jitted(**a_   ).to_tuple()

          self.assertEqual(len(a_   ) ,  len(a_   )   )
          for jitted_output, output in zip(a_ ,  a_   ):
             self.assertEqual(jitted_output.shape ,  output.shape   )







 def 	UpperCAmelCase_    (	self   )  ->  Any:
    A_   :						Optional[Any]		= self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__   ):
          A_   :						Union[str, Any]		= model_class(a_   )
          A_   :						Dict		= model.encode(inputs_dict["""input_ids"""] ,  inputs_dict["""attention_mask"""]   )

          A_   :						Any		= {
              '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
              '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
              '''encoder_outputs''': encoder_outputs,
          }

          @jax.jit
          def decode_jitted(_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
             return model.decode(
                 decoder_input_ids=a_ ,  decoder_attention_mask=a_ ,  encoder_outputs=a_ ,  )

          with self.subTest("""JIT Enabled"""   ):
             A_   :						Union[str, Any]		= decode_jitted(**a_   ).to_tuple()

          with self.subTest("""JIT Disabled"""   ):
             with jax.disable_jit():
                A_   :						int		= decode_jitted(**a_   ).to_tuple()

          self.assertEqual(len(a_   ) ,  len(a_   )   )
          for jitted_output, output in zip(a_ ,  a_   ):
             self.assertEqual(jitted_output.shape ,  output.shape   )







 @slow
 def 	UpperCAmelCase_    (	self   )  ->  Tuple:





    for model_class_name in self.all_model_classes:
       A_   :						int		= model_class_name.from_pretrained("""google/pegasus-large""" ,  from_pt=a_   )
       A_   :						Dict		= np.ones((1, 1)   )
       A_   :						Union[str, Any]		= model(a_   )
       self.assertIsNotNone(a_   )







 @slow
 def 	UpperCAmelCase_    (	self   )  ->  List[Any]:
    A_   :						Union[str, Any]		= FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""   )
    A_   :						List[Any]		= PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""   )

    A_   :						Union[str, Any]		= [
        ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
        ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
    ]

    A_   :						str		= [
        '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
        '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
    ]

    A_   :						int		= tokenizer(a_ ,  return_tensors="""np""" ,  truncation=a_ ,  max_length=512 ,  padding=a_   )
    A_   :						int		= model.generate(**a_ ,  num_beams=2   ).sequences
    A_   :						List[Any]		= tokenizer.batch_decode(a_ ,  skip_special_tokens=a_   )
    assert tgt_text == decoded





import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests

def        a						(      _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					Any=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[int]													=	None
		if token is not None:
				__UpperCAmelCase    :     Optional[Any]													=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'Bearer {token}'}

		__UpperCAmelCase    :     List[str]													=	f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
		__UpperCAmelCase    :     Union[str, Any]													=	requests.get(_UpperCAmelCase ,   headers=_UpperCAmelCase						).json()
		__UpperCAmelCase    :     Union[str, Any]													=	{}

		try:
				job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}						)
				__UpperCAmelCase    :     List[Any]													=	math.ceil((result['''total_count'''] - 1_00) / 1_00						)

				for i in range(_UpperCAmelCase						):
						__UpperCAmelCase    :     Optional[Any]													=	requests.get(url + f'&page={i + 2}' ,   headers=_UpperCAmelCase						).json()
						job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}						)

				return job_links
		except Exception:
				print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}'						)

		return {}

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					List[str]=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	None
		if token is not None:
				__UpperCAmelCase    :     Tuple													=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'Bearer {token}'}

		__UpperCAmelCase    :     int													=	f'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
		__UpperCAmelCase    :     str													=	requests.get(_UpperCAmelCase ,   headers=_UpperCAmelCase						).json()
		__UpperCAmelCase    :     List[Any]													=	{}

		try:
				artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']}						)
				__UpperCAmelCase    :     str													=	math.ceil((result['''total_count'''] - 1_00) / 1_00						)

				for i in range(_UpperCAmelCase						):
						__UpperCAmelCase    :     Dict													=	requests.get(url + f'&page={i + 2}' ,   headers=_UpperCAmelCase						).json()
						artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']}						)

				return artifacts
		except Exception:
				print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}'						)

		return {}

def        a						(      _UpperCAmelCase							:					Optional[Any] ,   _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     str													=	None
		if token is not None:
				__UpperCAmelCase    :     str													=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'Bearer {token}'}

		__UpperCAmelCase    :     int													=	requests.get(_UpperCAmelCase ,   headers=_UpperCAmelCase ,   allow_redirects=_UpperCAmelCase						)
		__UpperCAmelCase    :     Optional[int]													=	result.headers['''Location''']
		__UpperCAmelCase    :     Optional[int]													=	requests.get(_UpperCAmelCase ,   allow_redirects=_UpperCAmelCase						)
		__UpperCAmelCase    :     int													=	os.path.join(_UpperCAmelCase ,   f'{artifact_name}.zip'						)
		with open(_UpperCAmelCase ,   '''wb'''						) as fp:
				fp.write(response.content						)

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					List[str]=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	[]
		__UpperCAmelCase    :     int													=	[]
		__UpperCAmelCase    :     List[Any]													=	None

		with zipfile.ZipFile(_UpperCAmelCase						) as z:
				for filename in z.namelist():
						if not os.path.isdir(_UpperCAmelCase						):
								# read the file
								if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
										with z.open(_UpperCAmelCase						) as f:
												for line in f:
														__UpperCAmelCase    :     Tuple													=	line.decode('''UTF-8'''						).strip()
														if filename == "failures_line.txt":
																try:
																		# `error_line` is the place where `error` occurs
																		__UpperCAmelCase    :     int													=	line[: line.index(''': '''						)]
																		__UpperCAmelCase    :     Any													=	line[line.index(''': '''						) + len(''': '''						) :]
																		errors.append([error_line, error]						)
																except Exception:
																		# skip un-related lines
																		pass
														elif filename == "summary_short.txt" and line.startswith('''FAILED '''						):
																# `test` is the test method that failed
																__UpperCAmelCase    :     str													=	line[len('''FAILED '''						) :]
																failed_tests.append(_UpperCAmelCase						)
														elif filename == "job_name.txt":
																__UpperCAmelCase    :     Tuple													=	line

		if len(_UpperCAmelCase						) != len(_UpperCAmelCase						):
				raise ValueError(
				    f'`errors` and `failed_tests` should have the same number of elements. Got {len(_UpperCAmelCase						)} for `errors` '
				    f'and {len(_UpperCAmelCase						)} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'
				    ''' problem.'''						)

		__UpperCAmelCase    :     str													=	None
		if job_name and job_links:
				__UpperCAmelCase    :     Any													=	job_links.get(_UpperCAmelCase ,   _UpperCAmelCase						)

		# A list with elements of the form (line of error, error, failed test)
		__UpperCAmelCase    :     Dict													=	[x + [y] + [job_link] for x, y in zip(_UpperCAmelCase ,   _UpperCAmelCase						)]

		return result

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					Tuple=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     str													=	[]

		__UpperCAmelCase    :     str													=	[os.path.join(_UpperCAmelCase ,   _UpperCAmelCase						) for p in os.listdir(_UpperCAmelCase						) if p.endswith('''.zip'''						)]
		for p in paths:
				errors.extend(get_errors_from_single_artifact(_UpperCAmelCase ,   job_links=_UpperCAmelCase						)						)

		return errors

def        a						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					Optional[Any]=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	Counter()
		counter.update([x[1] for x in logs]						)
		__UpperCAmelCase    :     Dict													=	counter.most_common()
		__UpperCAmelCase    :     int													=	{}
		for error, count in counts:
				if error_filter is None or error not in error_filter:
						__UpperCAmelCase    :     Tuple													=	{'''count''': count, '''failed_tests''': [(x[2], x[0]) for x in logs if x[1] == error]}

		__UpperCAmelCase    :     Union[str, Any]													=	dict(sorted(r.items() ,   key=lambda _UpperCAmelCase						: item[1]["count"] ,   reverse=_UpperCAmelCase						)						)
		return r

def        a						(      _UpperCAmelCase							:					Dict						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	test.split('''::'''						)[0]
		if test.startswith('''tests/models/'''						):
				__UpperCAmelCase    :     Optional[int]													=	test.split('''/'''						)[2]
		else:
				__UpperCAmelCase    :     Optional[Any]													=	None

		return test

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					int=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	[(x[0], x[1], get_model(x[2]						)) for x in logs]
		__UpperCAmelCase    :     int													=	[x for x in logs if x[2] is not None]
		__UpperCAmelCase    :     Tuple													=	{x[2] for x in logs}

		__UpperCAmelCase    :     List[str]													=	{}
		for test in tests:
				__UpperCAmelCase    :     List[str]													=	Counter()
				# count by errors in `test`
				counter.update([x[1] for x in logs if x[2] == test]						)
				__UpperCAmelCase    :     Tuple													=	counter.most_common()
				__UpperCAmelCase    :     Any													=	{error: count for error, count in counts if (error_filter is None or error not in error_filter)}
				__UpperCAmelCase    :     List[Any]													=	sum(error_counts.values()						)
				if n_errors > 0:
						__UpperCAmelCase    :     Tuple													=	{'''count''': n_errors, '''errors''': error_counts}

		__UpperCAmelCase    :     Optional[int]													=	dict(sorted(r.items() ,   key=lambda _UpperCAmelCase						: item[1]["count"] ,   reverse=_UpperCAmelCase						)						)
		return r

def        a						(      _UpperCAmelCase							:					Union[str, Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	'''| no. | error | status |'''
		__UpperCAmelCase    :     List[str]													=	'''|-:|:-|:-|'''
		__UpperCAmelCase    :     Union[str, Any]													=	[header, sep]
		for error in reduced_by_error:
				__UpperCAmelCase    :     Tuple													=	reduced_by_error[error]['''count''']
				__UpperCAmelCase    :     int													=	f'| {count} | {error[:1_00]} |  |'
				lines.append(_UpperCAmelCase						)

		return "\n".join(_UpperCAmelCase						)

def        a						(      _UpperCAmelCase							:					Dict						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	'''| model | no. of errors | major error | count |'''
		__UpperCAmelCase    :     List[Any]													=	'''|-:|-:|-:|-:|'''
		__UpperCAmelCase    :     Tuple													=	[header, sep]
		for model in reduced_by_model:
				__UpperCAmelCase    :     List[Any]													=	reduced_by_model[model]['''count''']
				__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	list(reduced_by_model[model]['''errors'''].items()						)[0]
				__UpperCAmelCase    :     Dict													=	f'| {model} | {count} | {error[:60]} | {_count} |'
				lines.append(_UpperCAmelCase						)

		return "\n".join(_UpperCAmelCase						)


if __name__ == "__main__":
				__A   			=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
				parser.add_argument(
				    "--output_dir",
				    type=str,
				    required=True,
				    help="Where to store the downloaded artifacts and other result files.",
				)
				parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
				__A   			=parser.parse_args()

				os.makedirs(args.output_dir, exist_ok=True)

				__A   			=get_job_links(args.workflow_run_id, token=args.token)
				__A   			={}
				# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
				# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
				if _job_links:
								for k, v in _job_links.items():
												# This is how GitHub actions combine job names.
												if " / " in k:
																__A   			=k.find(" / ")
																__A   			=k[index + len(" / ") :]
												__A   			=v
				with open(os.path.join(args.output_dir, "job_links.json"), "w", encoding="UTF-8") as fp:
								json.dump(job_links, fp, ensure_ascii=False, indent=4)

				__A   			=get_artifacts_links(args.workflow_run_id, token=args.token)
				with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
								json.dump(artifacts, fp, ensure_ascii=False, indent=4)

				for idx, (name, url) in enumerate(artifacts.items()):
								download_artifact(name, url, args.output_dir, args.token)
								# Be gentle to GitHub
								time.sleep(1)

				__A   			=get_all_errors(args.output_dir, job_links=job_links)

				# `e[1]` is the error
				__A   			=Counter()
				counter.update([e[1] for e in errors])

				# print the top 30 most common test errors
				__A   			=counter.most_common(3_0)
				for item in most_common:
								print(item)

				with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp:
								json.dump(errors, fp, ensure_ascii=False, indent=4)

				__A   			=reduce_by_error(errors)
				__A   			=reduce_by_model(errors)

				__A   			=make_github_table(reduced_by_error)
				__A   			=make_github_table_per_model(reduced_by_model)

				with open(os.path.join(args.output_dir, "reduced_by_error.txt"), "w", encoding="UTF-8") as fp:
								fp.write(sa)
				with open(os.path.join(args.output_dir, "reduced_by_model.txt"), "w", encoding="UTF-8") as fp:
								fp.write(sa)





