

'''simple docstring'''

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

from tabulate import DataRow, TableFormat, tabulate


_UpperCAmelCase				: Optional[int]							    =				TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow("""""", """|""", """|"""),
    datarow=DataRow("""""", """|""", """|"""),
    padding=1,
    with_header_hide=None,
)


_UpperCAmelCase				: Dict							    =				[]
_UpperCAmelCase				: Tuple							    =				[]

_UpperCAmelCase				: Dict							    =				{"""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				: Any							    =				0
for log in Path().glob("""*.log"""):
	_UpperCAmelCase				: str							    =				0
	with open(log, """r""") as f:
		for line in f:
			_UpperCAmelCase				: List[str]							    =				json.loads(line)
			if line.get("""nodeid""", """""") != "":
				_UpperCAmelCase				: List[Any]							    =				line["""nodeid"""]
				if line.get("""duration""", None) is not None:
					_UpperCAmelCase				: Optional[int]							    =				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				: int							    =				[]
	log.unlink()

_UpperCAmelCase				: Optional[Any]							    =				""""""
_UpperCAmelCase				: List[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				: Optional[int]							    =				[]
			_UpperCAmelCase				: Optional[int]							    =				{}
			for test in failed_tests:
				_UpperCAmelCase				: List[str]							    =				test[0].split("""::""")
				_UpperCAmelCase				: Union[str, Any]							    =				data[0].split("""/""")[-1]
				if data[0] not in filesafailed:
					_UpperCAmelCase				: Dict							    =				[data[1:]]
				else:
					filesafailed[data[0]] += [data[1:]]
				failed_table.append(data)

			_UpperCAmelCase				: Optional[Any]							    =				[test[0] for test in failed_table]
			_UpperCAmelCase				: Tuple							    =				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				: Dict							    =				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) > 3_0_0_0:
		_UpperCAmelCase				: List[Any]							    =				"""Too many failed tests, please see the full report in the Action results."""
		_UpperCAmelCase				: Optional[Any]							    =				len(err) + 1_0
		_UpperCAmelCase				: Optional[int]							    =				message[: 3_0_0_0 - offset] + f"""\n...\n```\n{err}"""
	print(f"""### {message}""")
else:
	_UpperCAmelCase				: Optional[Any]							    =				"""No failed tests! 🤗"""
	print(f"""## {message}""")
	payload.append(no_error_payload)

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

	_UpperCAmelCase				: Optional[int]							    =				WebClient(token=os.environ["""SLACK_API_TOKEN"""])
	if message != "No failed tests! 🤗":
		_UpperCAmelCase				: List[str]							    =				{
		    """type""": """section""",
		    """text""": {
		        """type""": """mrkdwn""",
		        """text""": message,
		    },
		}
		payload.append(md_report)
		_UpperCAmelCase				: Optional[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[int]							    =				{
		    """type""": """context""",
		    """elements""": [
		        {
		            """type""": """plain_text""",
		            """text""": f"""Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}""",
		        }
		    ],
		}
		payload.append(date_report)
	_UpperCAmelCase				: int							    =				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				: List[Any]							    =				""""""
			for i, row in enumerate(test_failures):
				if row[0] != test_class:
					_UpperCAmelCase				: Union[str, Any]							    =				row[0]
				else:
					_UpperCAmelCase				: List[Any]							    =				""""""

			_UpperCAmelCase				: str							    =				{
			    """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 Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			):
										super().__init__()
										self.register_modules(transformer=__lowercase       ,			vae=__lowercase       ,			scheduler=__lowercase		)

										# create a imagenet -> id dictionary for easier use
										__lowerCAmelCase							       =				{}
										if idalabel is not None:
																for key, value in idalabel.items():
																						for label in value.split(''','''		):
																												__lowerCAmelCase							       =				int(__lowercase		)
																__lowerCAmelCase							       =				dict(sorted(self.labels.items()		)		)




				def 					_snake_case  (self       ,			__lowercase		):

										if not isinstance(__lowercase       ,			__lowercase		):
																__lowerCAmelCase							       =				list(__lowercase		)

										for l in label:
																if l not in self.labels:
																						raise ValueError(
																						    F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""		)

										return [self.labels[l] for l in label]




				@torch.no_grad()
				def __call__(self       ,			__lowercase       ,			__lowercase = 4.0       ,			__lowercase = None       ,			__lowercase = 50       ,			__lowercase = "pil"       ,			__lowercase = True       ,			):
										__lowerCAmelCase							       =				len(__lowercase		)
										__lowerCAmelCase							       =				self.transformer.config.sample_size
										__lowerCAmelCase							       =				self.transformer.config.in_channels

										__lowerCAmelCase							       =				randn_tensor(
										    shape=(batch_size, latent_channels, latent_size, latent_size)       ,			generator=__lowercase       ,			device=self.device       ,			dtype=self.transformer.dtype       ,			)
										__lowerCAmelCase							       =				torch.cat([latents] * 2		) if guidance_scale > 1 else latents

										__lowerCAmelCase							       =				torch.tensor(__lowercase       ,			device=self.device		).reshape(-1		)
										__lowerCAmelCase							       =				torch.tensor([10_00] * batch_size       ,			device=self.device		)
										__lowerCAmelCase							       =				torch.cat([class_labels, class_null]       ,			0		) if guidance_scale > 1 else class_labels

										# set step values
										self.scheduler.set_timesteps(__lowercase		)

										for t in self.progress_bar(self.scheduler.timesteps		):
																if guidance_scale > 1:
																						__lowerCAmelCase							       =				latent_model_input[: len(__lowercase		) // 2]
																						__lowerCAmelCase							       =				torch.cat([half, half]       ,			dim=0		)
																__lowerCAmelCase							       =				self.scheduler.scale_model_input(__lowercase       ,			__lowercase		)

																__lowerCAmelCase							       =				t
																if not torch.is_tensor(__lowercase		):
																						# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																						# This would be a good case for the `match` statement (Python 3.10+)
																						__lowerCAmelCase							       =				latent_model_input.device.type == '''mps'''
																						if isinstance(__lowercase       ,			__lowercase		):
																												__lowerCAmelCase							       =				torch.floataa if is_mps else torch.floataa
																						else:
																												__lowerCAmelCase							       =				torch.intaa if is_mps else torch.intaa
																						__lowerCAmelCase							       =				torch.tensor([timesteps]       ,			dtype=__lowercase       ,			device=latent_model_input.device		)
																elif len(timesteps.shape		) == 0:
																						__lowerCAmelCase							       =				timesteps[None].to(latent_model_input.device		)
																# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																__lowerCAmelCase							       =				timesteps.expand(latent_model_input.shape[0]		)
																# predict noise model_output
																__lowerCAmelCase							       =				self.transformer(
																    __lowercase       ,			timestep=__lowercase       ,			class_labels=__lowercase		).sample

																# perform guidance
																if guidance_scale > 1:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.split(__lowercase       ,			len(__lowercase		) // 2       ,			dim=0		)

																						__lowerCAmelCase							       =				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																						__lowerCAmelCase							       =				torch.cat([half_eps, half_eps]       ,			dim=0		)

																						__lowerCAmelCase							       =				torch.cat([eps, rest]       ,			dim=1		)

																# learned sigma
																if self.transformer.config.out_channels // 2 == latent_channels:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.split(__lowercase       ,			__lowercase       ,			dim=1		)
																else:
																						__lowerCAmelCase							       =				noise_pred

																# compute previous image: x_t -> x_t-1
																__lowerCAmelCase							       =				self.scheduler.step(__lowercase       ,			__lowercase       ,			__lowercase		).prev_sample

										if guidance_scale > 1:
																__lowerCAmelCase       ,					__lowerCAmelCase							       =				latent_model_input.chunk(2       ,			dim=0		)
										else:
																__lowerCAmelCase							       =				latent_model_input

										__lowerCAmelCase							       =				1 / self.vae.config.scaling_factor * latents
										__lowerCAmelCase							       =				self.vae.decode(__lowercase		).sample

										__lowerCAmelCase							       =				(samples / 2 + 0.5).clamp(0       ,			1		)

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										__lowerCAmelCase							       =				samples.cpu().permute(0       ,			2       ,			3       ,			1		).float().numpy()

										if output_type == "pil":
																__lowerCAmelCase							       =				self.numpy_to_pil(__lowercase		)

										if not return_dict:
																return (samples,)

										return ImagePipelineOutput(images=__lowercase		)

