



"""simple docstring"""







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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			Any						    = test_results.split(" " )

						snake_case   :			int						    = 0
						snake_case   :			List[str]						    = 0

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

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

						return failed, success, time_spent

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = {}
						snake_case   :			Any						    = None
						snake_case   :			Optional[int]						    = False
						for line in failures_short_lines.split("\n" ):
												if re.search(r"_ \[doctest\]"     ,		lowerCamelCase_ ):
																		snake_case   :			List[str]						    = True
																		snake_case   :			Union[str, Any]						    = line.split(" " )[2]
												elif in_error and not line.split(" " )[0].isdigit():
																		snake_case   :			Optional[int]						    = line
																		snake_case   :			List[str]						    = False

						return failures

class       _a  :



			def __init__(			self							:				Union[str, Any]   ,	_lowercase							:				str   ,	_lowercase							:				Dict		)							->					List[Any]:
									snake_case   :			Union[str, Any]						    = title

									snake_case   :			List[str]						    = doc_test_results["time_spent"].split(","		)[0]
									snake_case   :			Tuple						    = doc_test_results["success"]
									snake_case   :			List[str]						    = doc_test_results["failures"]
									snake_case   :			Any						    = self.n_success + self.n_failures

									# Failures and success of the modeling tests
									snake_case   :			int						    = doc_test_results



			@property
			def 				__lowercase					(			self							:				List[str]		)							->					str:
									snake_case   :			Any						    = [self._time_spent]
									snake_case   :			Dict						    = 0

									for time in time_spent:
															snake_case   :			List[Any]						    = 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(_lowercase		) == 1:
																					snake_case   :			Tuple						    = [0, 0, time_parts[0]]

															snake_case    ,			snake_case    ,			snake_case   :			Optional[int]						    = int(time_parts[0]		), int(time_parts[1]		), float(time_parts[2]		)
															total_secs += hours * 3600 + minutes * 60 + seconds

									snake_case    ,			snake_case    ,			snake_case   :			Any						    = total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
									return F'''{int(_lowercase		)}h{int(_lowercase		)}m{int(_lowercase		)}s'''



			@property
			def 				__lowercase					(			self							:				Optional[Any]		)							->					Dict:
									return {"type": "header", "text": {"type": "plain_text", "text": self.title}}



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Dict:
									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 				__lowercase					(			self							:				List[str]		)							->					Dict:
									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 				__lowercase					(			self							:				Union[str, Any]		)							->					Dict:
									snake_case   :			int						    = 40
									snake_case   :			str						    = {k: v["failed"] for k, v in doc_test_results.items() if isinstance(_lowercase   ,	_lowercase		)}

									snake_case   :			str						    = ""
									for category, failures in category_failures.items():
															if len(_lowercase		) == 0:
																					continue

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

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

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



			@property
			def 				__lowercase					(			self							:				List[Any]		)							->					str:
									snake_case   :			Optional[Any]						    = [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(_lowercase		)



			@staticmethod
			def 				__lowercase					(			)							->					Optional[Any]:
									snake_case   :			Tuple						    = [
									    {
									        "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(_lowercase		)}		)		)

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



			def 				__lowercase					(			self							:				Optional[int]		)							->					str:
									print("Sending the following payload"		)
									print(json.dumps({"blocks": json.loads(self.payload		)}		)		)

									snake_case   :			Dict						    = F'''{self.n_failures} failures out of {self.n_tests} tests,''' if self.n_failures else "All tests passed."

									snake_case   :			Dict						    = client.chat_postMessage(
									    channel=os.environ["CI_SLACK_CHANNEL_ID_DAILY"]   ,	blocks=self.payload   ,	text=_lowercase   ,	)



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				List[str]   ,	_lowercase							:				Dict   ,	_lowercase							:				Tuple   ,	_lowercase							:				Dict		)							->					Tuple:
									snake_case   :			List[str]						    = ""
									for key, value in failures.items():
															snake_case   :			Optional[int]						    = value[:200] + " [Truncated]" if len(_lowercase		) > 250 else value
															failures_text += F'''*{key}*\n_{value}_\n\n'''

									snake_case   :			Union[str, Any]						    = job_name
									snake_case   :			List[str]						    = {"type": "section", "text": {"type": "mrkdwn", "text": text}}

									if job_link is not None:
															snake_case   :			int						    = {
															    "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 				__lowercase					(			self							:				Optional[Any]		)							->					Optional[int]:
									if self.thread_ts is None:
															raise ValueError("Can only post reply if a post has been made."		)

									snake_case   :			Tuple						    = 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"		)

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

																					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=_lowercase   ,	thread_ts=self.thread_ts["ts"]   ,	)

																					time.sleep(1		)



def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = os.environ["GITHUB_RUN_ID"]
						snake_case   :			Union[str, Any]						    = f'''https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'''
						snake_case   :			List[Any]						    = requests.get(lowerCamelCase_ ).json()
						snake_case   :			Optional[Any]						    = {}

						try:
												jobs.update({job["name"]: job["html_url"] for job in result["jobs"]} )
												snake_case   :			Tuple						    = math.ceil((result["total_count"] - 1_0_0) / 1_0_0 )

												for i in range(lowerCamelCase_ ):
																		snake_case   :			int						    = 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."     ,		lowerCamelCase_ )

						return {}

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			Tuple						    = {}

						if os.path.exists(lowerCamelCase_ ):
												snake_case   :			Tuple						    = os.listdir(lowerCamelCase_ )
												for file in files:
																		try:
																								with open(os.path.join(lowerCamelCase_     ,		lowerCamelCase_ )     ,		encoding="utf-8" ) as f:
																														snake_case   :			Optional[Any]						    = f.read()
																		except UnicodeDecodeError as e:
																								raise ValueError(f'''Could not open {os.path.join(lowerCamelCase_     ,		lowerCamelCase_ )}.''' ) from e

						return _artifact

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						class       _a  :



									def __init__(			self							:				int   ,	_lowercase							:				str		)							->					Tuple:
															snake_case   :			str						    = name
															snake_case   :			Optional[Any]						    = []



									def __str__(			self							:				List[Any]		)							->					List[Any]:
															return self.name



									def 				__lowercase					(			self							:				int   ,	_lowercase							:				str		)							->					str:
															self.paths.append({"name": self.name, "path": path}		)



						snake_case   :			Dict[str, Artifact]						    = {}

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

																		_available_artifacts[artifact_name].add_path(lowerCamelCase_ )

						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()








"""simple docstring"""







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


A		=						False

class       _a  ( unittest.TestCase):



			def 				__lowercase					(			self							:				str		)							->					Optional[int]:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			@property
			def 				__lowercase					(			self							:				List[str]		)							->					Any:
									return 12



			@property
			def 				__lowercase					(			self							:				List[str]		)							->					Dict:
									return 12



			@property
			def 				__lowercase					(			self							:				Optional[int]		)							->					Union[str, Any]:
									return 32



			@property
			def 				__lowercase					(			self							:				int		)							->					Dict:
									torch.manual_seed(0		)
									snake_case   :			List[str]						    = VQModel(
									    block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]   ,	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]   ,	latent_channels=3   ,	num_vq_embeddings=self.num_embed   ,	vq_embed_dim=3   ,	)
									return model



			@property
			def 				__lowercase					(			self							:				str		)							->					List[Any]:
									snake_case   :			Tuple						    = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
									return tokenizer



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Any:
									torch.manual_seed(0		)
									snake_case   :			Optional[int]						    = CLIPTextConfig(
									    bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=self.text_embedder_hidden_size   ,	intermediate_size=37   ,	layer_norm_eps=1E-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1000   ,	)
									return CLIPTextModel(_lowercase		)



			@property
			def 				__lowercase					(			self							:				Optional[int]		)							->					Optional[int]:
									torch.manual_seed(0		)

									snake_case   :			List[Any]						    = 12
									snake_case   :			Dict						    = 12

									snake_case   :			Tuple						    = {
									    "attention_bias": True,
									    "cross_attention_dim": 32,
									    "attention_head_dim": height * width,
									    "num_attention_heads": 1,
									    "num_vector_embeds": self.num_embed,
									    "num_embeds_ada_norm": self.num_embeds_ada_norm,
									    "norm_num_groups": 32,
									    "sample_size": width,
									    "activation_fn": "geglu-approximate",
									}

									snake_case   :			Dict						    = TransformeraDModel(**_lowercase		)
									return model



			def 				__lowercase					(			self							:				Optional[int]		)							->					Tuple:
									snake_case   :			Optional[Any]						    = "cpu"

									snake_case   :			Optional[int]						    = self.dummy_vqvae
									snake_case   :			Dict						    = self.dummy_text_encoder
									snake_case   :			Tuple						    = self.dummy_tokenizer
									snake_case   :			List[Any]						    = self.dummy_transformer
									snake_case   :			List[Any]						    = VQDiffusionScheduler(self.num_embed		)
									snake_case   :			List[Any]						    = LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase		)

									snake_case   :			Dict						    = VQDiffusionPipeline(
									    vqvae=_lowercase   ,	text_encoder=_lowercase   ,	tokenizer=_lowercase   ,	transformer=_lowercase   ,	scheduler=_lowercase   ,	learned_classifier_free_sampling_embeddings=_lowercase   ,	)
									snake_case   :			Any						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Optional[Any]						    = "teddy bear playing in the pool"

									snake_case   :			Tuple						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Tuple						    = pipe([prompt]   ,	generator=_lowercase   ,	num_inference_steps=2   ,	output_type="np"		)
									snake_case   :			Optional[int]						    = output.images

									snake_case   :			int						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			List[str]						    = pipe(
									    [prompt]   ,	generator=_lowercase   ,	output_type="np"   ,	return_dict=_lowercase   ,	num_inference_steps=2		)[0]

									snake_case   :			List[Any]						    = image[0, -3:, -3:, -1]
									snake_case   :			Tuple						    = image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 24, 24, 3)

									snake_case   :			str						    = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Optional[int]:
									snake_case   :			List[str]						    = "cpu"

									snake_case   :			Dict						    = self.dummy_vqvae
									snake_case   :			List[Any]						    = self.dummy_text_encoder
									snake_case   :			Optional[Any]						    = self.dummy_tokenizer
									snake_case   :			int						    = self.dummy_transformer
									snake_case   :			str						    = VQDiffusionScheduler(self.num_embed		)
									snake_case   :			Optional[int]						    = LearnedClassifierFreeSamplingEmbeddings(
									    learnable=_lowercase   ,	hidden_size=self.text_embedder_hidden_size   ,	length=tokenizer.model_max_length		)

									snake_case   :			List[str]						    = VQDiffusionPipeline(
									    vqvae=_lowercase   ,	text_encoder=_lowercase   ,	tokenizer=_lowercase   ,	transformer=_lowercase   ,	scheduler=_lowercase   ,	learned_classifier_free_sampling_embeddings=_lowercase   ,	)
									snake_case   :			Optional[Any]						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Dict						    = "teddy bear playing in the pool"

									snake_case   :			Union[str, Any]						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Union[str, Any]						    = pipe([prompt]   ,	generator=_lowercase   ,	num_inference_steps=2   ,	output_type="np"		)
									snake_case   :			Optional[Any]						    = output.images

									snake_case   :			Dict						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Union[str, Any]						    = pipe(
									    [prompt]   ,	generator=_lowercase   ,	output_type="np"   ,	return_dict=_lowercase   ,	num_inference_steps=2		)[0]

									snake_case   :			Any						    = image[0, -3:, -3:, -1]
									snake_case   :			Any						    = image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 24, 24, 3)

									snake_case   :			Optional[Any]						    = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 2.0
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2



@slow
@require_torch_gpu
class       _a  ( unittest.TestCase):



			def 				__lowercase					(			self							:				Optional[int]		)							->					Tuple:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def 				__lowercase					(			self							:				Dict		)							->					Tuple:
									snake_case   :			Union[str, Any]						    = load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"		)

									snake_case   :			Tuple						    = VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"		)
									snake_case   :			Union[str, Any]						    = pipeline.to(_lowercase		)
									pipeline.set_progress_bar_config(disable=_lowercase		)

									# requires GPU generator for gumbel softmax
									# don't use GPU generator in tests though
									snake_case   :			Tuple						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Optional[int]						    = pipeline(
									    "teddy bear playing in the pool"   ,	num_images_per_prompt=1   ,	generator=_lowercase   ,	output_type="np"   ,	)

									snake_case   :			List[str]						    = output.images[0]

									assert image.shape == (256, 256, 3)
									assert np.abs(expected_image - image		).max() < 2.0



