


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

import requests


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=None  ):


			'''simple docstring'''







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

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

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

						for i in range(_UpperCamelCase  ):
									__lowercase 					=   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 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=None  ):


			'''simple docstring'''







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

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

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

						for i in range(_UpperCamelCase  ):
									__lowercase 					=   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 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







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

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


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=None  ):


			'''simple docstring'''







			__lowercase 					=   []
			__lowercase 					=   []
			__lowercase 					=   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:
																					__lowercase 					=   line.decode('''UTF-8'''  ).strip()
																					if filename == "failures_line.txt":
																								try:
																											# `error_line` is the place where `error` occurs
																											__lowercase 					=   line[: line.index(''': '''  )]
																											__lowercase 					=   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
																								__lowercase 					=   line[len('''FAILED '''  ) :]
																								failed_tests.append(_UpperCamelCase  )
																					elif filename == "job_name.txt":
																								__lowercase 					=   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.'''  )

			__lowercase 					=   None
			if job_name and job_links:
						__lowercase 					=   job_links.get(_UpperCamelCase       ,			_UpperCamelCase  )

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

			return result


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=None  ):


			'''simple docstring'''







			__lowercase 					=   []

			__lowercase 					=   [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 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=None  ):


			'''simple docstring'''







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

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


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   test.split('''::'''  )[0]
			if test.startswith('''tests/models/'''  ):
						__lowercase 					=   test.split('''/'''  )[2]
			else:
						__lowercase 					=   None

			return test


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=None  ):


			'''simple docstring'''







			__lowercase 					=   [(x[0], x[1], get_model(x[2]  )) for x in logs]
			__lowercase 					=   [x for x in logs if x[2] is not None]
			__lowercase 					=   {x[2] for x in logs}

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

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


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   '''| no. | error | status |'''
			__lowercase 					=   '''|-:|:-|:-|'''
			__lowercase 					=   [header, sep]
			for error in reduced_by_error:
						__lowercase 					=   reduced_by_error[error]['''count''']
						__lowercase 					=   F'| {count} | {error[:1_00]} |  |'
						lines.append(_UpperCamelCase  )

			return "\n".join(_UpperCamelCase  )


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   '''| model | no. of errors | major error | count |'''
			__lowercase 					=   '''|-:|-:|-:|-:|'''
			__lowercase 					=   [header, sep]
			for model in reduced_by_model:
						__lowercase 					=   reduced_by_model[model]['''count''']
						__lowercase					, __lowercase 					=   list(reduced_by_model[model]['''errors'''].items()  )[0]
						__lowercase 					=   F'| {model} | {count} | {error[:60]} | {_count} |'
						lines.append(_UpperCamelCase  )

			return "\n".join(_UpperCamelCase  )


if __name__ == "__main__":
	a : List[Any]		=				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 : Tuple		=				parser.parse_args()

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

	a : str		=				get_job_links(args.workflow_run_id, token=args.token)
	a : Dict		=				{}
	# 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 : Dict		=				k.find(''' / ''')
				a : Optional[int]		=				k[index + len(''' / ''') :]
			a : Dict		=				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 : Optional[Any]		=				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 : Optional[Any]		=				get_all_errors(args.output_dir, job_links=job_links)

	# `e[1]` is the error
	a : Union[str, Any]		=				Counter()
	counter.update([e[1] for e in errors])

	# print the top 30 most common test errors
	a : Optional[int]		=				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)

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

	a : Optional[int]		=				make_github_table(reduced_by_error)
	a : List[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)






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				(DEISMultistepScheduler,)
					__UpperCAmelCase            =				(("num_inference_steps", 25),)






					def A      (				self ,       **snake_case_						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   {
								    '''num_train_timesteps''': 1_0_0_0,
								    '''beta_start''': 0.0_0_0_1,
								    '''beta_end''': 0.0_2,
								    '''beta_schedule''': '''linear''',
								    '''solver_order''': 2,
								}

								config.update(**snake_case_						)
								return config






					def A      (				self ,       snake_case_=0 ,       **snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)
								__lowercase 					=   kwargs.pop('''num_inference_steps''' ,       snake_case_						)
								__lowercase 					=   self.dummy_sample
								__lowercase 					=   0.1 * sample
								__lowercase 					=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
											__lowercase 					=   self.get_scheduler_config(**snake_case_						)
											__lowercase 					=   scheduler_class(**snake_case_						)
											scheduler.set_timesteps(snake_case_						)
											# copy over dummy past residuals
											__lowercase 					=   dummy_past_residuals[: scheduler.config.solver_order]

											with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(snake_case_						)
														__lowercase 					=   scheduler_class.from_pretrained(snake_case_						)
														new_scheduler.set_timesteps(snake_case_						)
														# copy over dummy past residuals
														__lowercase 					=   dummy_past_residuals[: new_scheduler.config.solver_order]

											__lowercase					, __lowercase 					=   sample, sample
											for t in range(snake_case_ ,       time_step + scheduler.config.solver_order + 1						):
														__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample
														__lowercase 					=   new_scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample

														assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								pass






					def A      (				self ,       snake_case_=0 ,       **snake_case_						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)
								__lowercase 					=   kwargs.pop('''num_inference_steps''' ,       snake_case_						)
								__lowercase 					=   self.dummy_sample
								__lowercase 					=   0.1 * sample
								__lowercase 					=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
											__lowercase 					=   self.get_scheduler_config()
											__lowercase 					=   scheduler_class(**snake_case_						)
											scheduler.set_timesteps(snake_case_						)

											# copy over dummy past residuals (must be after setting timesteps)
											__lowercase 					=   dummy_past_residuals[: scheduler.config.solver_order]

											with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(snake_case_						)
														__lowercase 					=   scheduler_class.from_pretrained(snake_case_						)
														# copy over dummy past residuals
														new_scheduler.set_timesteps(snake_case_						)

														# copy over dummy past residual (must be after setting timesteps)
														__lowercase 					=   dummy_past_residuals[: new_scheduler.config.solver_order]

											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample
											__lowercase 					=   new_scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample

											assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"






					def A      (				self ,       snake_case_=None ,       **snake_case_						)   ->			Optional[int]:







								'''simple docstring'''

								if scheduler is None:
											__lowercase 					=   self.scheduler_classes[0]
											__lowercase 					=   self.get_scheduler_config(**snake_case_						)
											__lowercase 					=   scheduler_class(**snake_case_						)

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config(**snake_case_						)
								__lowercase 					=   scheduler_class(**snake_case_						)

								__lowercase 					=   1_0
								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter
								scheduler.set_timesteps(snake_case_						)

								for i, t in enumerate(scheduler.timesteps						):
											__lowercase 					=   model(snake_case_ ,       snake_case_						)
											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_						).prev_sample

								return sample






					def A      (				self						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)

								__lowercase 					=   kwargs.pop('''num_inference_steps''' ,       snake_case_						)

								for scheduler_class in self.scheduler_classes:
											__lowercase 					=   self.get_scheduler_config()
											__lowercase 					=   scheduler_class(**snake_case_						)

											__lowercase 					=   self.dummy_sample
											__lowercase 					=   0.1 * sample

											if num_inference_steps is not None and hasattr(snake_case_ ,       '''set_timesteps'''						):
														scheduler.set_timesteps(snake_case_						)
											elif num_inference_steps is not None and not hasattr(snake_case_ ,       '''set_timesteps'''						):
														__lowercase 					=   num_inference_steps

											# copy over dummy past residuals (must be done after set_timesteps)
											__lowercase 					=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
											__lowercase 					=   dummy_past_residuals[: scheduler.config.solver_order]

											__lowercase 					=   scheduler.timesteps[5]
											__lowercase 					=   scheduler.timesteps[6]

											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample
											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample

											self.assertEqual(output_a.shape ,       sample.shape						)
											self.assertEqual(output_a.shape ,       output_a.shape						)






					def A      (				self						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   DEISMultistepScheduler(**self.get_scheduler_config()						)
								__lowercase 					=   self.full_loop(scheduler=snake_case_						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.2_3_9_1_6						) < 1e-3

								__lowercase 					=   DPMSolverSinglestepScheduler.from_config(scheduler.config						)
								__lowercase 					=   DPMSolverMultistepScheduler.from_config(scheduler.config						)
								__lowercase 					=   UniPCMultistepScheduler.from_config(scheduler.config						)
								__lowercase 					=   DEISMultistepScheduler.from_config(scheduler.config						)

								__lowercase 					=   self.full_loop(scheduler=snake_case_						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.2_3_9_1_6						) < 1e-3






					def A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

								for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=snake_case_						)






					def A      (				self						)   ->			Optional[int]:







								'''simple docstring'''

								self.check_over_configs(thresholding=snake_case_						)
								for order in [1, 2, 3]:
											for solver_type in ["logrho"]:
														for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample"]:
																				self.check_over_configs(
																				    thresholding=snake_case_ ,       prediction_type=snake_case_ ,       sample_max_value=snake_case_ ,       algorithm_type='''deis''' ,       solver_order=snake_case_ ,       solver_type=snake_case_ ,       )






					def A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

								for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=snake_case_						)






					def A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

								for algorithm_type in ["deis"]:
											for solver_type in ["logrho"]:
														for order in [1, 2, 3]:
																	for prediction_type in ["epsilon", "sample"]:
																				self.check_over_configs(
																				    solver_order=snake_case_ ,       solver_type=snake_case_ ,       prediction_type=snake_case_ ,       algorithm_type=snake_case_ ,       )
																				__lowercase 					=   self.full_loop(
																				    solver_order=snake_case_ ,       solver_type=snake_case_ ,       prediction_type=snake_case_ ,       algorithm_type=snake_case_ ,       )
																				assert not torch.isnan(snake_case_						).any(), "Samples have nan numbers"






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								self.check_over_configs(lower_order_final=snake_case_						)
								self.check_over_configs(lower_order_final=snake_case_						)






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
											self.check_over_forward(num_inference_steps=snake_case_ ,       time_step=0						)






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   self.full_loop()
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.2_3_9_1_6						) < 1e-3






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								__lowercase 					=   self.full_loop(prediction_type='''v_prediction'''						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.0_9_1						) < 1e-3






					def A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config(thresholding=snake_case_ ,       dynamic_thresholding_ratio=0						)
								__lowercase 					=   scheduler_class(**snake_case_						)

								__lowercase 					=   1_0
								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter.half()
								scheduler.set_timesteps(snake_case_						)

								for i, t in enumerate(scheduler.timesteps						):
											__lowercase 					=   model(snake_case_ ,       snake_case_						)
											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_						).prev_sample

								assert sample.dtype == torch.floataa


