



'''simple docstring'''






import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


UpperCamelCase__:   Dict								=			TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow("", "|", "|"),
    datarow=DataRow("", "|", "|"),
    padding=1,
    with_header_hide=None,
)


UpperCamelCase__:   List[str]								=			[]
UpperCamelCase__:   int								=			[]

UpperCamelCase__:   Optional[int]								=			{"type": "section", "text": {"type": "plain_text", "text": "No failed tests! 🤗", "emoji": True}}

UpperCamelCase__:   List[str]								=			[
    {
        "type": "header",
        "text": {
            "type": "plain_text",
            "text": F"🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results",
            "emoji": True,
        },
    }
]

UpperCamelCase__:   List[str]								=			0
for log in Path().glob("*.log"):
						UpperCamelCase__:   Tuple								=			0
						with open(log, "r") as f:
												for line in f:
																		UpperCamelCase__:   Optional[int]								=			json.loads(line)
																		if line.get("nodeid", "") != "":
																								UpperCamelCase__:   Tuple								=			line["nodeid"]
																								if line.get("duration", None) is not None:
																														UpperCamelCase__:   Union[str, Any]								=			F"{line['duration']:.4f}"
																														if line.get("outcome", "") == "failed":
																																				section_num_failed += 1
																																				failed.append([test, duration, log.name.split("_")[0]])
																																				total_num_failed += 1
						group_info.append([str(log), section_num_failed, failed])
						UpperCamelCase__:   Dict								=			[]
						log.unlink()

UpperCamelCase__:   Dict								=			""
UpperCamelCase__:   Union[str, Any]								=			[]
if total_num_failed > 0:
						for name, num_failed, failed_tests in group_info:
												if num_failed > 0:
																		if num_failed == 1:
																								message += F"*{name[1:]}: {num_failed} failed test*\n"
																		else:
																								message += F"*{name[1:]}: {num_failed} failed tests*\n"
																		UpperCamelCase__:   str								=			[]
																		UpperCamelCase__:   Any								=			{}
																		for test in failed_tests:
																								UpperCamelCase__:   Optional[int]								=			test[0].split("::")
																								UpperCamelCase__:   Any								=			data[0].split("/")[-1]
																								if data[0] not in filesafailed:
																														UpperCamelCase__:   List[Any]								=			[data[1:]]
																								else:
																														filesafailed[data[0]] += [data[1:]]
																								failed_table.append(data)

																		UpperCamelCase__:   Tuple								=			[test[0] for test in failed_table]
																		UpperCamelCase__:   Optional[int]								=			list(set(files))
																		# Count number of instances in failed_tests
																		UpperCamelCase__:   Optional[Any]								=			[]
																		for file in individual_files:
																								table.append([file, len(filesafailed[file])])

																		UpperCamelCase__:   Optional[int]								=			tabulate(
																		    table,
																		    headers=["Test Location", "Num Failed"],
																		    tablefmt=hf_table_format,
																		    stralign="right",
																		)
																		message += F"\n```\n{failed_table}\n```"
																		all_filesafailed.append(filesafailed)
						if len(message) > 3000:
												UpperCamelCase__:   Optional[Any]								=			"Too many failed tests, please see the full report in the Action results."
												UpperCamelCase__:   List[Any]								=			len(err) + 10
												UpperCamelCase__:   Any								=			message[: 3000 - offset] + F"\n...\n```\n{err}"
						print(F"### {message}")
else:
						UpperCamelCase__:   int								=			"No failed tests! 🤗"
						print(F"## {message}")
						payload.append(no_error_payload)

if os.environ.get("TEST_TYPE", "") != "":
						from slack_sdk import WebClient

						UpperCamelCase__:   str								=			WebClient(token=os.environ["SLACK_API_TOKEN"])
						if message != "No failed tests! 🤗":
												UpperCamelCase__:   int								=			{
												    "type": "section",
												    "text": {
												        "type": "mrkdwn",
												        "text": message,
												    },
												}
												payload.append(md_report)
												UpperCamelCase__:   int								=			{
												    "type": "section",
												    "text": {
												        "type": "mrkdwn",
												        "text": "*For more details:*",
												    },
												    "accessory": {
												        "type": "button",
												        "text": {
												            "type": "plain_text",
												            "text": "Check Action results",
												            "emoji": True,
												        },
												        "url": F"https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}",
												    },
												}
												payload.append(action_button)
												UpperCamelCase__:   Optional[Any]								=			{
												    "type": "context",
												    "elements": [
												        {
												            "type": "plain_text",
												            "text": F"Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}",
												        }
												    ],
												}
												payload.append(date_report)
						UpperCamelCase__:   Dict								=			client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
						UpperCamelCase__:   int								=			response.data["ts"]
						for failed_file in all_filesafailed:
												for test_location, test_failures in failed_file.items():
																		# Keep only the first instance of the test name
																		UpperCamelCase__:   Dict								=			""
																		for i, row in enumerate(test_failures):
																								if row[0] != test_class:
																														UpperCamelCase__:   List[Any]								=			row[0]
																								else:
																														UpperCamelCase__:   Tuple								=			""

																		UpperCamelCase__:   Dict								=			{
																		    "type": "section",
																		    "text": {
																		        "type": "mrkdwn",
																		        "text": F"Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```",
																		    },
																		}

																		client.chat_postMessage(
																		    channel="#accelerate-ci-daily",
																		    thread_ts=ts,
																		    blocks=[payload],
																		)










'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       42
	lowerCamelCase__			 =       42







	def __init__(						self			:     Union[str, Any]		,			__snake_case			:     UNetaDModel		,			__snake_case			:     ScoreSdeVeScheduler		)			->		int:
								super().__init__()
								self.register_modules(unet=__snake_case		,			scheduler=__snake_case		)







	@torch.no_grad()
	def __call__(						self			:     Optional[int]		,			__snake_case			:     int = 1		,			__snake_case			:     int = 2000		,			__snake_case			:     Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			__snake_case			:     Optional[str] = "pil"		,			__snake_case			:     bool = True		,			**__snake_case			:     Optional[int]		,			)			->		Union[ImagePipelineOutput, Tuple]:
								UpperCAmelCase :       str 							=		self.unet.config.sample_size
								UpperCAmelCase :       Union[str, Any] 							=		(batch_size, 3, img_size, img_size)

								UpperCAmelCase :       int 							=		self.unet

								UpperCAmelCase :       Any 							=		randn_tensor(__snake_case		,			generator=__snake_case		) * self.scheduler.init_noise_sigma
								UpperCAmelCase :       List[Any] 							=		sample.to(self.device		)

								self.scheduler.set_timesteps(__snake_case		)
								self.scheduler.set_sigmas(__snake_case		)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
															UpperCAmelCase :       Any 							=		self.scheduler.sigmas[i] * torch.ones(shape[0]		,			device=self.device		)

															# correction step
															for _ in range(self.scheduler.config.correct_steps		):
																						UpperCAmelCase :       Union[str, Any] 							=		self.unet(__snake_case		,			__snake_case		).sample
																						UpperCAmelCase :       Optional[Any] 							=		self.scheduler.step_correct(__snake_case		,			__snake_case		,			generator=__snake_case		).prev_sample

															# prediction step
															UpperCAmelCase :       Optional[Any] 							=		model(__snake_case		,			__snake_case		).sample
															UpperCAmelCase :       List[str] 							=		self.scheduler.step_pred(__snake_case		,			__snake_case		,			__snake_case		,			generator=__snake_case		)

															UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		output.prev_sample, output.prev_sample_mean

								UpperCAmelCase :       int 							=		sample_mean.clamp(0		,			1		)
								UpperCAmelCase :       Union[str, Any] 							=		sample.cpu().permute(0		,			2		,			3		,			1		).numpy()
								if output_type == "pil":
															UpperCAmelCase :       Optional[Any] 							=		self.numpy_to_pil(__snake_case		)

								if not return_dict:
															return (sample,)

								return ImagePipelineOutput(images=__snake_case		)





