
'''simple docstring'''

import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

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


if is_torch_available():
		import torch

		from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel






class  A  :





	def __init__(       self      :							Optional[int]   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							int=13   ,				__magic_name__      :							List[Any]=7   ,				__magic_name__      :							Any=True   ,				__magic_name__      :							Optional[int]=True   ,				__magic_name__      :							Tuple=False   ,				__magic_name__      :							List[Any]=True   ,				__magic_name__      :							List[Any]=99   ,				__magic_name__      :							List[Any]=32   ,				__magic_name__      :							str=5   ,				__magic_name__      :							int=4   ,				__magic_name__      :							Dict=37   ,				__magic_name__      :							Tuple="gelu"   ,				__magic_name__      :							Union[str, Any]=0.1   ,				__magic_name__      :							Optional[int]=0.1   ,				__magic_name__      :							List[str]=512   ,				__magic_name__      :							Dict=16   ,				__magic_name__      :							Any=2   ,				__magic_name__      :							Any=0.02   ,				__magic_name__      :							Tuple=3   ,				__magic_name__      :							List[str]=4   ,				__magic_name__      :							Union[str, Any]=None   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				parent
				lowerCAmelCase__										=				batch_size
				lowerCAmelCase__										=				seq_length
				lowerCAmelCase__										=				is_training
				lowerCAmelCase__										=				use_input_mask
				lowerCAmelCase__										=				use_token_type_ids
				lowerCAmelCase__										=				use_labels
				lowerCAmelCase__										=				vocab_size
				lowerCAmelCase__										=				hidden_size
				lowerCAmelCase__										=				num_hidden_layers
				lowerCAmelCase__										=				num_attention_heads
				lowerCAmelCase__										=				intermediate_size
				lowerCAmelCase__										=				hidden_act
				lowerCAmelCase__										=				hidden_dropout_prob
				lowerCAmelCase__										=				attention_probs_dropout_prob
				lowerCAmelCase__										=				max_position_embeddings
				lowerCAmelCase__										=				type_vocab_size
				lowerCAmelCase__										=				type_sequence_label_size
				lowerCAmelCase__										=				initializer_range
				lowerCAmelCase__										=				num_labels
				lowerCAmelCase__										=				num_choices
				lowerCAmelCase__										=				scope





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]				):


				"""simple docstring"""

				lowerCAmelCase__										=				ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size				)

				lowerCAmelCase__										=				None
				if self.use_input_mask:
							lowerCAmelCase__										=				random_attention_mask([self.batch_size, self.seq_length]				)

				lowerCAmelCase__										=				None
				if self.use_token_type_ids:
							lowerCAmelCase__										=				ids_tensor([self.batch_size, self.seq_length]   ,				self.type_vocab_size				)

				lowerCAmelCase__										=				None
				lowerCAmelCase__										=				None
				lowerCAmelCase__										=				None
				if self.use_labels:
							lowerCAmelCase__										=				ids_tensor([self.batch_size]   ,				self.type_sequence_label_size				)
							lowerCAmelCase__										=				ids_tensor([self.batch_size, self.seq_length]   ,				self.num_labels				)
							lowerCAmelCase__										=				ids_tensor([self.batch_size]   ,				self.num_choices				)

				lowerCAmelCase__										=				self.get_config()

				return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				return OpenLlamaConfig(
				    vocab_size=self.vocab_size   ,				hidden_size=self.hidden_size   ,				num_hidden_layers=self.num_hidden_layers   ,				num_attention_heads=self.num_attention_heads   ,				intermediate_size=self.intermediate_size   ,				hidden_act=self.hidden_act   ,				hidden_dropout_prob=self.hidden_dropout_prob   ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,				max_position_embeddings=self.max_position_embeddings   ,				type_vocab_size=self.type_vocab_size   ,				is_decoder=__magic_name__   ,				initializer_range=self.initializer_range   ,				use_stable_embedding=__magic_name__   ,				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							Tuple   ,				__magic_name__      :							List[Any]   ,				__magic_name__      :							Union[str, Any]   ,				__magic_name__      :							str   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				OpenLlamaModel(config=__magic_name__				)
				model.to(__magic_name__				)
				model.eval()
				lowerCAmelCase__										=				model(__magic_name__   ,				attention_mask=__magic_name__				)
				lowerCAmelCase__										=				model(__magic_name__				)
				self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							List[Any]   ,				__magic_name__      :							Any   ,				__magic_name__      :							Tuple   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							Tuple   ,				__magic_name__      :							Dict   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							Dict   ,				__magic_name__      :							Optional[Any]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				True
				lowerCAmelCase__										=				OpenLlamaModel(__magic_name__				)
				model.to(__magic_name__				)
				model.eval()
				lowerCAmelCase__										=				model(
				    __magic_name__   ,				attention_mask=__magic_name__   ,				encoder_hidden_states=__magic_name__   ,				encoder_attention_mask=__magic_name__   ,				)
				lowerCAmelCase__										=				model(
				    __magic_name__   ,				attention_mask=__magic_name__   ,				encoder_hidden_states=__magic_name__   ,				)
				lowerCAmelCase__										=				model(__magic_name__   ,				attention_mask=__magic_name__				)
				self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]   ,				__magic_name__      :							str   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							str   ,				__magic_name__      :							List[str]   ,				__magic_name__      :							int   ,				__magic_name__      :							str   ,				__magic_name__      :							Dict   ,				__magic_name__      :							Union[str, Any]   ,				__magic_name__      :							Optional[Any]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				OpenLlamaForCausalLM(config=__magic_name__				)
				model.to(__magic_name__				)
				model.eval()
				lowerCAmelCase__										=				model(__magic_name__   ,				attention_mask=__magic_name__   ,				labels=__magic_name__				)
				self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							Tuple   ,				__magic_name__      :							Dict   ,				__magic_name__      :							int   ,				__magic_name__      :							int   ,				__magic_name__      :							str   ,				__magic_name__      :							List[str]   ,				__magic_name__      :							Dict   ,				__magic_name__      :							Union[str, Any]   ,				__magic_name__      :							Any   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				True
				lowerCAmelCase__										=				True
				lowerCAmelCase__										=				OpenLlamaForCausalLM(config=__magic_name__				)
				model.to(__magic_name__				)
				model.eval()

				# first forward pass
				lowerCAmelCase__										=				model(
				    __magic_name__   ,				attention_mask=__magic_name__   ,				encoder_hidden_states=__magic_name__   ,				encoder_attention_mask=__magic_name__   ,				use_cache=__magic_name__   ,				)
				lowerCAmelCase__										=				outputs.past_key_values

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

				# append to next input_ids and
				lowerCAmelCase__										=				torch.cat([input_ids, next_tokens]   ,				dim=-1				)
				lowerCAmelCase__										=				torch.cat([input_mask, next_mask]   ,				dim=-1				)

				lowerCAmelCase__										=				model(
				    __magic_name__   ,				attention_mask=__magic_name__   ,				encoder_hidden_states=__magic_name__   ,				encoder_attention_mask=__magic_name__   ,				output_hidden_states=__magic_name__   ,				)["hidden_states"][0]
				lowerCAmelCase__										=				model(
				    __magic_name__   ,				attention_mask=__magic_name__   ,				encoder_hidden_states=__magic_name__   ,				encoder_attention_mask=__magic_name__   ,				past_key_values=__magic_name__   ,				output_hidden_states=__magic_name__   ,				)["hidden_states"][0]

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







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.prepare_config_and_inputs()
				(
				    (
				    lowerCAmelCase__
				)						,(
				    lowerCAmelCase__
				)						,(
				    lowerCAmelCase__
				)						,(
				    lowerCAmelCase__
				)						,(
				    lowerCAmelCase__
				)						,(
				    lowerCAmelCase__
				)						,(
				    lowerCAmelCase__
				)						,
				)										=				config_and_inputs
				lowerCAmelCase__										=				{"input_ids": input_ids, "attention_mask": input_mask}
				return config, inputs_dict






@require_torch
class  A  (						SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   unittest.TestCase     ):
	snake_case__   :Dict   =  (
	    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
	)
	snake_case__   :Tuple   =  (OpenLlamaForCausalLM,) if is_torch_available() else ()
	snake_case__   :List[str]   =  (
	    {
	        'feature-extraction': OpenLlamaModel,
	        'text-classification': OpenLlamaForSequenceClassification,
	        'text-generation': OpenLlamaForCausalLM,
	        'zero-shot': OpenLlamaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	snake_case__   :List[str]   =  False
	snake_case__   :Any   =  False





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				OpenLlamaModelTester(self				)
				lowerCAmelCase__										=				ConfigTester(self   ,				config_class=__magic_name__   ,				hidden_size=37				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int				):


				"""simple docstring"""

				self.config_tester.run_common_tests()





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
							lowerCAmelCase__										=				type
							self.model_tester.create_and_check_model(*__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase__										=				3
				lowerCAmelCase__										=				input_dict["input_ids"]
				lowerCAmelCase__										=				input_ids.ne(1				).to(__magic_name__				)
				lowerCAmelCase__										=				ids_tensor([self.model_tester.batch_size]   ,				self.model_tester.type_sequence_label_size				)
				lowerCAmelCase__										=				OpenLlamaForSequenceClassification(__magic_name__				)
				model.to(__magic_name__				)
				model.eval()
				lowerCAmelCase__										=				model(__magic_name__   ,				attention_mask=__magic_name__   ,				labels=__magic_name__				)
				self.assertEqual(result.logits.shape   ,				(self.model_tester.batch_size, self.model_tester.num_labels)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase__										=				3
				lowerCAmelCase__										=				"single_label_classification"
				lowerCAmelCase__										=				input_dict["input_ids"]
				lowerCAmelCase__										=				input_ids.ne(1				).to(__magic_name__				)
				lowerCAmelCase__										=				ids_tensor([self.model_tester.batch_size]   ,				self.model_tester.type_sequence_label_size				)
				lowerCAmelCase__										=				OpenLlamaForSequenceClassification(__magic_name__				)
				model.to(__magic_name__				)
				model.eval()
				lowerCAmelCase__										=				model(__magic_name__   ,				attention_mask=__magic_name__   ,				labels=__magic_name__				)
				self.assertEqual(result.logits.shape   ,				(self.model_tester.batch_size, self.model_tester.num_labels)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase__										=				3
				lowerCAmelCase__										=				"multi_label_classification"
				lowerCAmelCase__										=				input_dict["input_ids"]
				lowerCAmelCase__										=				input_ids.ne(1				).to(__magic_name__				)
				lowerCAmelCase__										=				ids_tensor(
				    [self.model_tester.batch_size, config.num_labels]   ,				self.model_tester.type_sequence_label_size				).to(torch.float				)
				lowerCAmelCase__										=				OpenLlamaForSequenceClassification(__magic_name__				)
				model.to(__magic_name__				)
				model.eval()
				lowerCAmelCase__										=				model(__magic_name__   ,				attention_mask=__magic_name__   ,				labels=__magic_name__				)
				self.assertEqual(result.logits.shape   ,				(self.model_tester.batch_size, self.model_tester.num_labels)				)





	@unittest.skip("Open-Llama buffers include complex numbers, which breaks this test"				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				pass







	@parameterized.expand([("linear",), ("dynamic",)]				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							Optional[Any]				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase__										=				ids_tensor([1, 10]   ,				config.vocab_size				)
				lowerCAmelCase__										=				ids_tensor([1, int(config.max_position_embeddings * 1.5				)]   ,				config.vocab_size				)

				set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
				lowerCAmelCase__										=				OpenLlamaModel(__magic_name__				)
				original_model.to(__magic_name__				)
				original_model.eval()
				lowerCAmelCase__										=				original_model(__magic_name__				).last_hidden_state
				lowerCAmelCase__										=				original_model(__magic_name__				).last_hidden_state

				set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
				lowerCAmelCase__										=				{"type": scaling_type, "factor": 10.0}
				lowerCAmelCase__										=				OpenLlamaModel(__magic_name__				)
				scaled_model.to(__magic_name__				)
				scaled_model.eval()
				lowerCAmelCase__										=				scaled_model(__magic_name__				).last_hidden_state
				lowerCAmelCase__										=				scaled_model(__magic_name__				).last_hidden_state

				# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
				# maximum sequence length, so the outputs for the short input should match.
				if scaling_type == "dynamic":
							self.assertTrue(torch.allclose(__magic_name__   ,				__magic_name__   ,				atol=1E-5				)				)
				else:
							self.assertFalse(torch.allclose(__magic_name__   ,				__magic_name__   ,				atol=1E-5				)				)

				# The output should be different for long inputs
				self.assertFalse(torch.allclose(__magic_name__   ,				__magic_name__   ,				atol=1E-5				)				)











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

import requests





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Tuple:
	a									=				None
	if token is not None:
		a									=				{"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

	a									=				f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
	a									=				requests.get(__lowerCamelCase				,     headers=__lowerCamelCase       ).json()
	a									=				{}

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

		for i in range(__lowerCamelCase       ):
			a									=				requests.get(url + f'&page={i + 2}'				,     headers=__lowerCamelCase       ).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						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Dict:
	a									=				None
	if token is not None:
		a									=				{"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

	a									=				f'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
	a									=				requests.get(__lowerCamelCase				,     headers=__lowerCamelCase       ).json()
	a									=				{}

	try:
		artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}       )
		a									=				math.ceil((result["""total_count"""] - 100) / 100       )

		for i in range(__lowerCamelCase       ):
			a									=				requests.get(url + f'&page={i + 2}'				,     headers=__lowerCamelCase       ).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						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Optional[int]:
	a									=				None
	if token is not None:
		a									=				{"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

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





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Tuple:
	a									=				[]
	a									=				[]
	a									=				None

	with zipfile.ZipFile(__lowerCamelCase       ) as z:
		for filename in z.namelist():
			if not os.path.isdir(__lowerCamelCase       ):
				# read the file
				if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
					with z.open(__lowerCamelCase       ) as f:
						for line in f:
							a									=				line.decode("""UTF-8"""       ).strip()
							if filename == "failures_line.txt":
								try:
									# `error_line` is the place where `error` occurs
									a									=				line[: line.index(""": """       )]
									a									=				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
								a									=				line[len("""FAILED """       ) :]
								failed_tests.append(__lowerCamelCase       )
							elif filename == "job_name.txt":
								a									=				line

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

	a									=				None
	if job_name and job_links:
		a									=				job_links.get(__lowerCamelCase				,     __lowerCamelCase       )

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

	return result





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Dict:
	a									=				[]

	a									=				[os.path.join(__lowerCamelCase				,     __lowerCamelCase       ) for p in os.listdir(__lowerCamelCase       ) if p.endswith(""".zip"""       )]
	for p in paths:
		errors.extend(get_errors_from_single_artifact(__lowerCamelCase				,     job_links=__lowerCamelCase       )       )

	return errors





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Tuple:
	a									=				Counter()
	counter.update([x[1] for x in logs]       )
	a									=				counter.most_common()
	a									=				{}
	for error, count in counts:
		if error_filter is None or error not in error_filter:
			a									=				{"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

	a									=				dict(sorted(r.items()				,     key=lambda __lowerCamelCase       : item[1]["count"]				,     reverse=__lowerCamelCase       )       )
	return r





def 						__A						(      __lowerCamelCase       ) ->			List[str]:
	a									=				test.split("""::"""       )[0]
	if test.startswith("""tests/models/"""       ):
		a									=				test.split("""/"""       )[2]
	else:
		a									=				None

	return test





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Any:
	a									=				[(x[0], x[1], get_model(x[2]       )) for x in logs]
	a									=				[x for x in logs if x[2] is not None]
	a									=				{x[2] for x in logs}

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

	a									=				dict(sorted(r.items()				,     key=lambda __lowerCamelCase       : item[1]["count"]				,     reverse=__lowerCamelCase       )       )
	return r





def 						__A						(      __lowerCamelCase       ) ->			Optional[int]:
	a									=				"""| no. | error | status |"""
	a									=				"""|-:|:-|:-|"""
	a									=				[header, sep]
	for error in reduced_by_error:
		a									=				reduced_by_error[error]["""count"""]
		a									=				f'| {count} | {error[:100]} |  |'
		lines.append(__lowerCamelCase       )

	return "\n".join(__lowerCamelCase       )





def 						__A						(      __lowerCamelCase       ) ->			int:
	a									=				"""| model | no. of errors | major error | count |"""
	a									=				"""|-:|-:|-:|-:|"""
	a									=				[header, sep]
	for model in reduced_by_model:
		a									=				reduced_by_model[model]["""count"""]
		a							,					a									=				list(reduced_by_model[model]["""errors"""].items()       )[0]
		a									=				f'| {model} | {count} | {error[:60]} | {_count} |'
		lines.append(__lowerCamelCase       )

	return "\n".join(__lowerCamelCase       )


if __name__ == "__main__":
		__UpperCamelCase   :			Dict          = 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.")
		__UpperCamelCase   :			Any          = parser.parse_args()

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

		__UpperCamelCase   :			Optional[Any]          = get_job_links(args.workflow_run_id, token=args.token)
		__UpperCamelCase   :			str          = {}
		# 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:
								__UpperCamelCase   :			List[str]          = k.find(" / ")
								__UpperCamelCase   :			List[Any]          = k[index + len(" / ") :]
						__UpperCamelCase   :			List[str]          = 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)

		__UpperCamelCase   :			Tuple          = 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)

		__UpperCamelCase   :			int          = get_all_errors(args.output_dir, job_links=job_links)

		# `e[1]` is the error
		__UpperCamelCase   :			Optional[Any]          = Counter()
		counter.update([e[1] for e in errors])

		# print the top 30 most common test errors
		__UpperCamelCase   :			Any          = counter.most_common(30)
		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)

		__UpperCamelCase   :			Union[str, Any]          = reduce_by_error(errors)
		__UpperCamelCase   :			Dict          = reduce_by_model(errors)

		__UpperCamelCase   :			Union[str, Any]          = make_github_table(reduced_by_error)
		__UpperCamelCase   :			Union[str, Any]          = 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)

