




'''simple docstring'''


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

import requests
def 						lowerCamelCase__   (				a    ,	a=None							):
						__snake_case						 =   None
						if token is not None:
												__snake_case						 =   {'Accept': 'application/vnd.github+json', 'Authorization': f'Bearer {token}'}

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

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

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

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

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

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

						__snake_case						 =   requests.get(a    ,	headers=a    ,	allow_redirects=a							)
						__snake_case						 =   result.headers['Location']
						__snake_case						 =   requests.get(a    ,	allow_redirects=a							)
						__snake_case						 =   os.path.join(a    ,	f'{artifact_name}.zip'							)
						with open(a    ,	'wb'							) as fp:
												fp.write(response.content							)
def 						lowerCamelCase__   (				a    ,	a=None							):
						__snake_case						 =   []
						__snake_case						 =   []
						__snake_case						 =   None

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

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

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

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

						return result
def 						lowerCamelCase__   (				a    ,	a=None							):
						__snake_case						 =   []

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

						return errors
def 						lowerCamelCase__   (				a    ,	a=None							):
						__snake_case						 =   Counter()
						counter.update([x[1] for x in logs]							)
						__snake_case						 =   counter.most_common()
						__snake_case						 =   {}
						for error, count in counts:
												if error_filter is None or error not in error_filter:
																		__snake_case						 =   {'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

						__snake_case						 =   dict(sorted(r.items()    ,	key=lambda a							: item[1]["count"]    ,	reverse=a							)							)
						return r
def 						lowerCamelCase__   (				a							):
						__snake_case						 =   test.split('::'							)[0]
						if test.startswith('tests/models/'							):
												__snake_case						 =   test.split('/'							)[2]
						else:
												__snake_case						 =   None

						return test
def 						lowerCamelCase__   (				a    ,	a=None							):
						__snake_case						 =   [(x[0], x[1], get_model(x[2]							)) for x in logs]
						__snake_case						 =   [x for x in logs if x[2] is not None]
						__snake_case						 =   {x[2] for x in logs}

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

						__snake_case						 =   dict(sorted(r.items()    ,	key=lambda a							: item[1]["count"]    ,	reverse=a							)							)
						return r
def 						lowerCamelCase__   (				a							):
						__snake_case						 =   '| no. | error | status |'
						__snake_case						 =   '|-:|:-|:-|'
						__snake_case						 =   [header, sep]
						for error in reduced_by_error:
												__snake_case						 =   reduced_by_error[error]['count']
												__snake_case						 =   f'| {count} | {error[:100]} |  |'
												lines.append(a							)

						return "\n".join(a							)
def 						lowerCamelCase__   (				a							):
						__snake_case						 =   '| model | no. of errors | major error | count |'
						__snake_case						 =   '|-:|-:|-:|-:|'
						__snake_case						 =   [header, sep]
						for model in reduced_by_model:
												__snake_case						 =   reduced_by_model[model]['count']
												__snake_case ,       __snake_case						 =   list(reduced_by_model[model]['errors'].items()							)[0]
												__snake_case						 =   f'| {model} | {count} | {error[:60]} | {_count} |'
												lines.append(a							)

						return "\n".join(a							)


if __name__ == "__main__":
	_lowercase  =			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.""")
	_lowercase  =			parser.parse_args()

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

	_lowercase  =			get_job_links(args.workflow_run_id, token=args.token)
	_lowercase  =			{}
	# 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:
				_lowercase  =			k.find(""" / """)
				_lowercase  =			k[index + len(""" / """) :]
			_lowercase  =			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)

	_lowercase  =			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)

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

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

	# print the top 30 most common test errors
	_lowercase  =			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)

	_lowercase  =			reduce_by_error(errors)
	_lowercase  =			reduce_by_model(errors)

	_lowercase  =			make_github_table(reduced_by_error)
	_lowercase  =			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)







'''simple docstring'''


import numpy
class 						a_				:




	def __init__(       self     :  Any       ,		__lowerCAmelCase     :  numpy.ndarray       ,		__lowerCAmelCase     :  numpy.ndarray      ):
							__snake_case						 =   input_array

							# Random initial weights are assigned where first argument is the
							# number of nodes in previous layer and second argument is the
							# number of nodes in the next layer.

							# Random initial weights are assigned.
							# self.input_array.shape[1] is used to represent number of nodes in input layer.
							# First hidden layer consists of 4 nodes.
							__snake_case						 =   numpy.random.rand(
							    self.input_array.shape[1]       ,		4      )

							# Random initial values for the first hidden layer.
							# First hidden layer has 4 nodes.
							# Second hidden layer has 3 nodes.
							__snake_case						 =   numpy.random.rand(
							    4       ,		3      )

							# Random initial values for the second hidden layer.
							# Second hidden layer has 3 nodes.
							# Output layer has 1 node.
							__snake_case						 =   numpy.random.rand(3       ,		1      )

							# Real output values provided.
							__snake_case						 =   output_array

							# Predicted output values by the neural network.
							# Predicted_output array initially consists of zeroes.
							__snake_case						 =   numpy.zeros(output_array.shape      )




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   sigmoid(
							    numpy.dot(self.input_array       ,		self.input_layer_and_first_hidden_layer_weights      )      )

							# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
							# connecting the first hidden set of nodes with the second hidden set of nodes.
							__snake_case						 =   sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,		self.first_hidden_layer_and_second_hidden_layer_weights       ,		)      )

							# layer_between_second_hidden_layer_and_output is the layer connecting
							# second hidden layer with the output node.
							__snake_case						 =   sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer       ,		self.second_hidden_layer_and_output_layer_weights       ,		)      )

							return self.layer_between_second_hidden_layer_and_output




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   numpy.dot(
							    self.layer_between_first_hidden_layer_and_second_hidden_layer.T       ,		2
							    * (self.output_array - self.predicted_output)
							    * sigmoid_derivative(self.predicted_output      )       ,		)
							__snake_case						 =   numpy.dot(
							    self.layer_between_input_and_first_hidden_layer.T       ,		numpy.dot(
							        2
							        * (self.output_array - self.predicted_output)
							        * sigmoid_derivative(self.predicted_output      )       ,		self.second_hidden_layer_and_output_layer_weights.T       ,		)
							    * sigmoid_derivative(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer      )       ,		)
							__snake_case						 =   numpy.dot(
							    self.input_array.T       ,		numpy.dot(
							        numpy.dot(
							            2
							            * (self.output_array - self.predicted_output)
							            * sigmoid_derivative(self.predicted_output      )       ,		self.second_hidden_layer_and_output_layer_weights.T       ,		)
							        * sigmoid_derivative(
							            self.layer_between_first_hidden_layer_and_second_hidden_layer      )       ,		self.first_hidden_layer_and_second_hidden_layer_weights.T       ,		)
							    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer      )       ,		)

							self.input_layer_and_first_hidden_layer_weights += (
							    updated_input_layer_and_first_hidden_layer_weights
							)
							self.first_hidden_layer_and_second_hidden_layer_weights += (
							    updated_first_hidden_layer_and_second_hidden_layer_weights
							)
							self.second_hidden_layer_and_output_layer_weights += (
							    updated_second_hidden_layer_and_output_layer_weights
							)




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  numpy.ndarray       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  bool      ):
							for iteration in range(1       ,		iterations + 1      ):
													__snake_case						 =   self.feedforward()
													self.back_propagation()
													if give_loss:
																			__snake_case						 =   numpy.mean(numpy.square(output - self.feedforward()      )      )
																			print(F'Iteration {iteration} Loss: {loss}'      )




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  numpy.ndarray      ):
							__snake_case						 =   input_arr

							__snake_case						 =   sigmoid(
							    numpy.dot(self.array       ,		self.input_layer_and_first_hidden_layer_weights      )      )

							__snake_case						 =   sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,		self.first_hidden_layer_and_second_hidden_layer_weights       ,		)      )

							__snake_case						 =   sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer       ,		self.second_hidden_layer_and_output_layer_weights       ,		)      )

							return int(self.layer_between_second_hidden_layer_and_output > 0.6      )





def 						lowerCamelCase__   (				a							):
						return 1 / (1 + numpy.exp(-value							))
def 						lowerCamelCase__   (				a							):
						return (value) * (1 - (value))
def 						lowerCamelCase__   (				):
						__snake_case						 =   numpy.array(
						    (
						        [0, 0, 0],
						        [0, 0, 1],
						        [0, 1, 0],
						        [0, 1, 1],
						        [1, 0, 0],
						        [1, 0, 1],
						        [1, 1, 0],
						        [1, 1, 1],
						    )    ,	dtype=numpy.floataa    ,	)

						# True output values for the given input values.
						__snake_case						 =   numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])    ,	dtype=numpy.floataa							)

						# Calling neural network class.
						__snake_case						 =   TwoHiddenLayerNeuralNetwork(
						    input_array=a    ,	output_array=a							)

						# Calling training function.
						# Set give_loss to True if you want to see loss in every iteration.
						neural_network.train(output=a    ,	iterations=10    ,	give_loss=a							)

						return neural_network.predict(numpy.array(([1, 1, 1])    ,	dtype=numpy.floataa							)							)


if __name__ == "__main__":
	example()

