from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Optional[Any]:
								super().__init__()
								self.register_modules(unet=__SCREAMING_SNAKE_CASE		,      scheduler=__SCREAMING_SNAKE_CASE    )




	@torch.no_grad()
	def __call__(							self		,      __SCREAMING_SNAKE_CASE = 1		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE = 1000		,      __SCREAMING_SNAKE_CASE = "pil"		,      __SCREAMING_SNAKE_CASE = True		,      )						->Union[ImagePipelineOutput, Tuple]:
								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size		,      __SCREAMING_SNAKE_CASE    ):
															lowerCAmelCase     		=		(
															    batch_size,
															    self.unet.config.in_channels,
															    self.unet.config.sample_size,
															    self.unet.config.sample_size,
															)
								else:
															lowerCAmelCase     		=		(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if self.device.type == "mps":
															# randn does not work reproducibly on mps
															lowerCAmelCase     		=		randn_tensor(__SCREAMING_SNAKE_CASE		,      generator=__SCREAMING_SNAKE_CASE    )
															lowerCAmelCase     		=		image.to(self.device    )
								else:
															lowerCAmelCase     		=		randn_tensor(__SCREAMING_SNAKE_CASE		,      generator=__SCREAMING_SNAKE_CASE		,      device=self.device    )

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

								for t in self.progress_bar(self.scheduler.timesteps    ):
															# 1. predict noise model_output
															lowerCAmelCase     		=		self.unet(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    ).sample

															# 2. compute previous image: x_t -> x_t-1
															lowerCAmelCase     		=		self.scheduler.step(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      generator=__SCREAMING_SNAKE_CASE    ).prev_sample

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

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE    )




lowercase__      :    dict[str, float]          =     {
    "kilogram": 1,
    "gram": pow(1_0, 3),
    "milligram": pow(1_0, 6),
    "metric-ton": pow(1_0, -3),
    "long-ton": 0.0_0_0_9_8_4_2_0_7_3,
    "short-ton": 0.0_0_1_1_0_2_3_1_2_2,
    "pound": 2.2_0_4_6_2_4_4_2_0_2,
    "stone": 0.1_5_7_4_7_3_1_7_2_8,
    "ounce": 3_5.2_7_3_9_9_0_7_2_3,
    "carrat": 5_0_0_0,
    "atomic-mass-unit": 6.0_22_13_66_52e26,
}

lowercase__      :    dict[str, float]          =     {
    "kilogram": 1,
    "gram": pow(1_0, -3),
    "milligram": pow(1_0, -6),
    "metric-ton": pow(1_0, 3),
    "long-ton": 1_0_1_6.0_4_6_0_8,
    "short-ton": 9_0_7.1_8_4,
    "pound": 0.4_5_3_5_9_2,
    "stone": 6.3_5_0_2_9,
    "ounce": 0.0_2_8_3_4_9_5,
    "carrat": 0.0_0_0_2,
    "atomic-mass-unit": 1.6_60_54_01_99e-27,
}

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     float:
							if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
														lowerCAmelCase     		=		(
														    f"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
														    f"Supported values are: {', '.join(snake_case__							)}"
														)
														raise ValueError(snake_case__							)
							return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()



