





import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict

import requests
from slack_sdk import WebClient


_a 						=      WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])





def lowerCAmelCase__(__snake_case							)				->      List[Any]:







	'''simple docstring'''







	lowerCamelCase__								=		test_results.split(''' '''							)

	lowerCamelCase__								=		0
	lowerCamelCase__								=		0

	# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
	# When it is too long, those signs are not present.
	lowerCamelCase__								=		expressions[-2] if '''=''' in expressions[-1] else expressions[-1]

	for i, expression in enumerate(__a							):
		if "failed" in expression:
			failed += int(expressions[i - 1]							)
		if "passed" in expression:
			success += int(expressions[i - 1]							)

	return failed, success, time_spent





def lowerCAmelCase__(__snake_case							)				->      Union[str, Any]:







	'''simple docstring'''







	lowerCamelCase__								=		{}
	lowerCamelCase__								=		None
	lowerCamelCase__								=		False
	for line in failures_short_lines.split('''\n'''							):
		if re.search(R'''_ \[doctest\]'''       ,__a							):
			lowerCamelCase__								=		True
			lowerCamelCase__								=		line.split(''' '''							)[2]
		elif in_error and not line.split(''' '''							)[0].isdigit():
			lowerCamelCase__								=		line
			lowerCamelCase__								=		False

	return failures






class 					__A    :

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		title

				lowerCamelCase__								=		doc_test_results['''time_spent'''].split(''','''							)[0]
				lowerCamelCase__								=		doc_test_results['''success''']
				lowerCamelCase__								=		doc_test_results['''failures''']
				lowerCamelCase__								=		self.n_success + self.n_failures

				# Failures and success of the modeling tests
				lowerCamelCase__								=		doc_test_results





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		[self._time_spent]
				lowerCamelCase__								=		0

				for time in time_spent:
					lowerCamelCase__								=		time.split(''':'''							)

					# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
					if len(_a							) == 1:
						lowerCamelCase__								=		[0, 0, time_parts[0]]

					lowerCamelCase__								=		int(time_parts[0]							), int(time_parts[1]							), float(time_parts[2]							)
					total_secs += hours * 3_6_0_0 + minutes * 6_0 + seconds

				lowerCamelCase__								=		total_secs // 3_6_0_0, (total_secs % 3_6_0_0) // 6_0, total_secs % 6_0
				return F'{int(_a							)}h{int(_a							)}m{int(_a							)}s'





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return {"type": "header", "text": {"type": "plain_text", "text": self.title}}





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return {
				    "type": "section",
				    "text": {
				        "type": "plain_text",
				        "text": F'🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.',
				        "emoji": True,
				    },
				    "accessory": {
				        "type": "button",
				        "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
				        "url": F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
				    },
				}





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return {
				    "type": "section",
				    "text": {
				        "type": "plain_text",
				        "text": (
				            F'There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'
				            F' {self.time}.'
				        ),
				        "emoji": True,
				    },
				    "accessory": {
				        "type": "button",
				        "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
				        "url": F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
				    },
				}





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		4_0
				lowerCamelCase__								=		{k: v['''failed'''] for k, v in doc_test_results.items() if isinstance(_a       ,  _a							)}

				lowerCamelCase__								=		''''''
				for category, failures in category_failures.items():
					if len(_a							) == 0:
						continue

					if report != "":
						report += "\n\n"

					report += F'*{category} failures*:'.ljust(line_length // 2							).rjust(line_length // 2							) + "\n"
					report += "`"
					report += "`\n`".join(_a							)
					report += "`"

				return {
				    "type": "section",
				    "text": {
				        "type": "mrkdwn",
				        "text": F'The following examples had failures:\n\n\n{report}\n',
				    },
				}





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		[self.header]

				if self.n_failures > 0:
					blocks.append(self.failures							)

				if self.n_failures > 0:
					blocks.extend([self.category_failures]							)

				if self.n_failures == 0:
					blocks.append(self.no_failures							)

				return json.dumps(_a							)





			@staticmethod
			def 		__lowerCamelCase				(    ):



				'''simple docstring'''







				lowerCamelCase__								=		[
				    {
				        '''type''': '''section''',
				        '''text''': {
				            '''type''': '''plain_text''',
				            '''text''': '''There was an issue running the tests.''',
				        },
				        '''accessory''': {
				            '''type''': '''button''',
				            '''text''': {'''type''': '''plain_text''', '''text''': '''Check Action results''', '''emoji''': True},
				            '''url''': F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
				        },
				    }
				]

				print('''Sending the following payload'''							)
				print(json.dumps({'''blocks''': json.loads(_a							)}							)							)

				client.chat_postMessage(
				    channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']       ,  text='''There was an issue running the tests.'''       ,  blocks=_a       ,  )





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				print('''Sending the following payload'''							)
				print(json.dumps({'''blocks''': json.loads(self.payload							)}							)							)

				lowerCamelCase__								=		F'{self.n_failures} failures out of {self.n_tests} tests,' if self.n_failures else '''All tests passed.'''

				lowerCamelCase__								=		client.chat_postMessage(
				    channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']       ,  blocks=self.payload       ,  text=_a       ,  )





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		''''''
				for key, value in failures.items():
					lowerCamelCase__								=		value[:2_0_0] + ''' [Truncated]''' if len(_a							) > 2_5_0 else value
					failures_text += F'*{key}*\n_{value}_\n\n'

				lowerCamelCase__								=		job_name
				lowerCamelCase__								=		{'''type''': '''section''', '''text''': {'''type''': '''mrkdwn''', '''text''': text}}

				if job_link is not None:
					lowerCamelCase__								=		{
					    '''type''': '''button''',
					    '''text''': {'''type''': '''plain_text''', '''text''': '''GitHub Action job''', '''emoji''': True},
					    '''url''': job_link,
					}

				return [
				    {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
				    content,
				    {"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
				]





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				if self.thread_ts is None:
					raise ValueError('''Can only post reply if a post has been made.'''							)

				lowerCamelCase__								=		self.doc_test_results.pop('''job_link'''							)
				self.doc_test_results.pop('''failures'''							)
				self.doc_test_results.pop('''success'''							)
				self.doc_test_results.pop('''time_spent'''							)

				lowerCamelCase__								=		sorted(self.doc_test_results.items()       ,  key=lambda __lowerCAmelCase							: t[0]							)
				for job, job_result in sorted_dict:
					if len(job_result['''failures''']							):
						lowerCamelCase__								=		F'*Num failures* :{len(job_result["failed"]							)} \n'
						lowerCamelCase__								=		job_result['''failures''']
						lowerCamelCase__								=		self.get_reply_blocks(_a       ,  _a       ,  _a       ,  text=_a							)

						print('''Sending the following reply'''							)
						print(json.dumps({'''blocks''': blocks}							)							)

						client.chat_postMessage(
						    channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']       ,  text=F'Results for {job}'       ,  blocks=_a       ,  thread_ts=self.thread_ts['''ts''']       ,  )

						time.sleep(1							)





def lowerCAmelCase__()				->      List[str]:







	'''simple docstring'''







	lowerCamelCase__								=		os.environ['''GITHUB_RUN_ID''']
	lowerCamelCase__								=		F'https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'
	lowerCamelCase__								=		requests.get(__a							).json()
	lowerCamelCase__								=		{}

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

		for i in range(__a							):
			lowerCamelCase__								=		requests.get(url + F'&page={i + 2}'							).json()
			jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}							)

		return jobs
	except Exception as e:
		print('''Unknown error, could not fetch links.'''       ,__a							)

	return {}





def lowerCAmelCase__(__snake_case							)				->      Tuple:







	'''simple docstring'''







	lowerCamelCase__								=		{}

	if os.path.exists(__a							):
		lowerCamelCase__								=		os.listdir(__a							)
		for file in files:
			try:
				with open(os.path.join(__a       ,__a							)       ,encoding='''utf-8'''							) as f:
					lowerCamelCase__								=		f.read()
			except UnicodeDecodeError as e:
				raise ValueError(F'Could not open {os.path.join(__a       ,__a							)}.'							) from e

	return _artifact





def lowerCAmelCase__()				->      Any:







	'''simple docstring'''







	class 					__A    :

				'''simple docstring'''







				def __init__(    self       ,  __lowerCAmelCase							):



					'''simple docstring'''







					lowerCamelCase__								=		name
					lowerCamelCase__								=		[]





				def __str__(    self							):



					'''simple docstring'''







					return self.name





				def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



					'''simple docstring'''







					self.paths.append({'''name''': self.name, '''path''': path}							)



	lowerCamelCase__								=		{}

	lowerCamelCase__								=		filter(os.path.isdir       ,os.listdir()							)
	for directory in directories:
		lowerCamelCase__								=		directory
		if artifact_name not in _available_artifacts:
			lowerCamelCase__								=		Artifact(__a							)

			_available_artifacts[artifact_name].add_path(__a							)

	return _available_artifacts


if __name__ == "__main__":
			_a 						=      get_job_links()
			_a 						=      retrieve_available_artifacts()

			_a 						=      collections.OrderedDict(
			    [
			        ("*.py", "API Examples"),
			        ("*.md", "MD Examples"),
			    ]
			)

			# This dict will contain all the information relative to each doc test category:
			# - failed: list of failed tests
			# - failures: dict in the format 'test': 'error_message'
			_a 						=      {
			    v: {
			        "failed": [],
			        "failures": {},
			    }
			    for v in docs.values()
			}

			# Link to the GitHub Action job
			_a 						=      github_actions_job_links.get("run_doctests")

			_a 						=      available_artifacts["doc_tests_gpu_test_reports"].paths[0]
			_a 						=      retrieve_artifact(artifact_path["name"])
			if "stats" in artifact:
						_a						,		_a						,		_a 						=      handle_test_results(artifact["stats"])
						_a 						=      failed
						_a 						=      success
						_a 						=      time_spent[1:-1] + ", "

						_a 						=      extract_first_line_failure(artifact["failures_short"])
						for line in artifact["summary_short"].split("\n"):
									if re.search("FAILED", line):
												_a 						=      line.replace("FAILED ", "")
												_a 						=      line.split()[0].replace("\n", "")

												if "::" in line:
															_a						,		_a 						=      line.split("::")
												else:
															_a						,		_a 						=      line, line

												for file_regex in docs.keys():
															if fnmatch(file_path, file_regex):
																		_a 						=      docs[file_regex]
																		doc_test_results[category]["failed"].append(test)

																		_a 						=      all_failures[test] if test in all_failures else "N/A"
																		_a 						=      failure
																		break

			_a 						=      Message("🤗 Results of the doc tests.", doc_test_results)
			message.post()
			message.post_reply()






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						List[Any]	    =    (DDPMScheduler,)


							def 				__lowercase				(			self       ,							**_a       )    ->    Any:
												_a	:    List[Any]         =		{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''variance_type''': '''fixed_small''',
												    '''clip_sample''': True,
												}

												config.update(**_a       )
												return config


							def 				__lowercase				(			self       )    ->    Any:
												for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=_a       )


							def 				__lowercase				(			self       )    ->    List[Any]:
												for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,							[0.002, 0.02, 0.2, 2]       ):
																	self.check_over_configs(beta_start=_a       ,							beta_end=_a       )


							def 				__lowercase				(			self       )    ->    List[str]:
												for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=_a       )


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												for variance in ["fixed_small", "fixed_large", "other"]:
																	self.check_over_configs(variance_type=_a       )


							def 				__lowercase				(			self       )    ->    str:
												for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=_a       )


							def 				__lowercase				(			self       )    ->    Dict:
												self.check_over_configs(thresholding=_a       )
												for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=_a       ,							prediction_type=_a       ,							sample_max_value=_a       ,							)


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(prediction_type=_a       )


							def 				__lowercase				(			self       )    ->    int:
												for t in [0, 5_0_0, 9_9_9]:
																	self.check_over_forward(time_step=_a       )


							def 				__lowercase				(			self       )    ->    int:
												_a	:    int         =		self.scheduler_classes[0]
												_a	:    List[Any]         =		self.get_scheduler_config()
												_a	:    Dict         =		scheduler_class(**_a       )

												assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ) - 0.0_0979       )       ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ) - 0.02       )       ) < 1e-5


							def 				__lowercase				(			self       )    ->    Tuple:
												_a	:    int         =		self.scheduler_classes[0]
												_a	:    int         =		self.get_scheduler_config()
												_a	:    int         =		scheduler_class(**_a       )

												_a	:    Optional[int]         =		len(_a       )

												_a	:    Optional[Any]         =		self.dummy_model()
												_a	:    str         =		self.dummy_sample_deter
												_a	:    Union[str, Any]         =		torch.manual_seed(0       )

												for t in reversed(range(_a       )       ):
																	# 1. predict noise residual
																	_a	:    str         =		model(_a       ,							_a       )

																	# 2. predict previous mean of sample x_t-1
																	_a	:    Optional[int]         =		scheduler.step(_a       ,							_a       ,							_a       ,							generator=_a       ).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	_a	:    List[Any]         =		pred_prev_sample

												_a	:    str         =		torch.sum(torch.abs(_a       )       )
												_a	:    Optional[Any]         =		torch.mean(torch.abs(_a       )       )

												assert abs(result_sum.item() - 258.9606       ) < 1e-2
												assert abs(result_mean.item() - 0.3372       ) < 1e-3


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Optional[int]         =		self.scheduler_classes[0]
												_a	:    Optional[Any]         =		self.get_scheduler_config(prediction_type='''v_prediction'''       )
												_a	:    Union[str, Any]         =		scheduler_class(**_a       )

												_a	:    Dict         =		len(_a       )

												_a	:    int         =		self.dummy_model()
												_a	:    Tuple         =		self.dummy_sample_deter
												_a	:    List[Any]         =		torch.manual_seed(0       )

												for t in reversed(range(_a       )       ):
																	# 1. predict noise residual
																	_a	:    Dict         =		model(_a       ,							_a       )

																	# 2. predict previous mean of sample x_t-1
																	_a	:    int         =		scheduler.step(_a       ,							_a       ,							_a       ,							generator=_a       ).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	_a	:    str         =		pred_prev_sample

												_a	:    str         =		torch.sum(torch.abs(_a       )       )
												_a	:    Tuple         =		torch.mean(torch.abs(_a       )       )

												assert abs(result_sum.item() - 202.0296       ) < 1e-2
												assert abs(result_mean.item() - 0.2631       ) < 1e-3


							def 				__lowercase				(			self       )    ->    Dict:
												_a	:    Union[str, Any]         =		self.scheduler_classes[0]
												_a	:    Tuple         =		self.get_scheduler_config()
												_a	:    Any         =		scheduler_class(**_a       )

												_a	:    Optional[Any]         =		[1_0_0, 8_7, 5_0, 1, 0]

												scheduler.set_timesteps(timesteps=_a       )

												_a	:    Optional[int]         =		scheduler.timesteps

												for i, timestep in enumerate(_a       ):
																	if i == len(_a       ) - 1:
																						_a	:    Dict         =		-1
																	else:
																						_a	:    Tuple         =		timesteps[i + 1]

																	_a	:    Optional[Any]         =		scheduler.previous_timestep(_a       )
																	_a	:    Optional[Any]         =		prev_t.item()

																	self.assertEqual(_a       ,							_a       )


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Dict         =		self.scheduler_classes[0]
												_a	:    List[str]         =		self.get_scheduler_config()
												_a	:    Tuple         =		scheduler_class(**_a       )

												_a	:    str         =		[1_0_0, 8_7, 5_0, 5_1, 0]

												with self.assertRaises(_a       ,							msg='''`custom_timesteps` must be in descending order.'''       ):
																	scheduler.set_timesteps(timesteps=_a       )


							def 				__lowercase				(			self       )    ->    str:
												_a	:    List[str]         =		self.scheduler_classes[0]
												_a	:    List[str]         =		self.get_scheduler_config()
												_a	:    Dict         =		scheduler_class(**_a       )

												_a	:    Union[str, Any]         =		[1_0_0, 8_7, 5_0, 1, 0]
												_a	:    Optional[Any]         =		len(_a       )

												with self.assertRaises(_a       ,							msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''       ):
																	scheduler.set_timesteps(num_inference_steps=_a       ,							timesteps=_a       )




							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Dict         =		self.scheduler_classes[0]
												_a	:    Union[str, Any]         =		self.get_scheduler_config()
												_a	:    int         =		scheduler_class(**_a       )

												_a	:    str         =		[scheduler.config.num_train_timesteps]

												with self.assertRaises(
												    _a       ,							msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''       ,							):
																	scheduler.set_timesteps(timesteps=_a       )



