
from typing import List, Optional, Tuple, Union

import torch

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


class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):
		__lowercase			:			UNetaDModel
		__lowercase			:			PNDMScheduler



		def __init__(					self							, A_							, A_			)    ->   Optional[int]:
				"""simple docstring"""
				super().__init__()

				UpperCamelCase            =	PNDMScheduler.from_config(scheduler.config			)

				self.register_modules(unet=A_							, scheduler=A_			)



		@torch.no_grad()
		def __call__(					self							, A_ = 1							, A_ = 50							, A_ = None							, A_ = "pil"							, A_ = True							, **A_							, )    ->   Union[ImagePipelineOutput, Tuple]:
				"""simple docstring"""
				UpperCamelCase            =	randn_tensor(
				    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							, generator=A_							, device=self.device							, )

				self.scheduler.set_timesteps(A_			)
				for t in self.progress_bar(self.scheduler.timesteps			):
						UpperCamelCase            =	self.unet(A_							, A_			).sample

						UpperCamelCase            =	self.scheduler.step(A_							, A_							, A_			).prev_sample

				UpperCamelCase            =	(image / 2 + 0.5).clamp(0							, 1			)
				UpperCamelCase            =	image.cpu().permute(0							, 2							, 3							, 1			).numpy()
				if output_type == "pil":
						UpperCamelCase            =	self.numpy_to_pil(A_			)

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=A_			)


import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_UpperCAmelCase   :					str    =			100_000_000_000
_UpperCAmelCase   :					Optional[int]    =			10_000

_UpperCAmelCase  ,_UpperCAmelCase   :					Optional[int]    =			os.path.split(__file__)
_UpperCAmelCase   :					Optional[int]    =			os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


def  A			(   lowercase       ,			lowercase					)  ->    datasets.Dataset:
		'''simple docstring'''

		UpperCamelCase            =	pa.Table.from_pydict({'col': [0] * chunk_size}					)
		UpperCamelCase            =	pa.concat_tables([table] * (num_examples // chunk_size)					)
		return datasets.Dataset(lowercase       ,			fingerprint='table_100B'					)


@dataclass
class 							lowercase   :
		__lowercase			:			int
		__lowercase			:			int
		__lowercase			:			int
		__lowercase			:			int



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	np.random.default_rng(self.seed			)
				UpperCamelCase            =	rng.integers(low=self.low							, high=self.high							, size=self.size			).tolist()



		def __iter__(					self			)    ->   List[str]:
				"""simple docstring"""
				return iter(self._sampled_values			)



		def __len__(					self			)    ->   int:
				"""simple docstring"""
				return self.size






@get_duration
def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	dataset[0]


@get_duration
def  A			(   lowercase					)  ->    Any:
		'''simple docstring'''

		UpperCamelCase            =	dataset[-1]


@get_duration
def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	dataset[range(len(lowercase					) // 2       ,			len(lowercase					) // 2 + 1_024					)]


@get_duration
def  A			(   lowercase					)  ->    Any:
		'''simple docstring'''

		UpperCamelCase            =	dataset[RandIter(0       ,			len(lowercase					)       ,			1_024       ,			seed=42					)]


def  A			(   )  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	{'num examples': SPEED_TEST_N_EXAMPLES}
		UpperCamelCase            =	(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
		print('generating dataset'					)
		UpperCamelCase            =	generate_100B_dataset(num_examples=lowercase       ,			chunk_size=lowercase					)
		print('Functions'					)
		for func in functions:
				print(func.__name__					)
				UpperCamelCase            =	func(lowercase					)

		with open(lowercase       ,			'wb'					) as f:
				f.write(json.dumps(lowercase					).encode('utf-8'					)					)


if __name__ == "__main__":  # useful to run the profiler
					benchmark_table_100B()
