



from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowerCamelCase		       =      8






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase=BITS )	->						int:
				a__	:							List[Any]      						= x.device

				a__	:							Union[str, Any]      						= (x * 2_55).int().clamp(0	,				2_55 )

				a__	:							str      						= 2 ** torch.arange(bits - 1	,				-1	,				-1	,				device=UpperCAmelCase_ )
				a__	:							List[str]      						= rearrange(UpperCAmelCase_	,				"d -> d 1 1" )
				a__	:							Optional[int]      						= rearrange(UpperCAmelCase_	,				"b c h w -> b c 1 h w" )

				a__	:							int      						= ((x & mask) != 0).float()
				a__	:							List[Any]      						= rearrange(UpperCAmelCase_	,				"b c d h w -> b (c d) h w" )
				a__	:							str      						= bits * 2 - 1
				return bits






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase=BITS )	->						Tuple:
				a__	:							int      						= x.device

				a__	:							Tuple      						= (x > 0).int()
				a__	:							Optional[int]      						= 2 ** torch.arange(bits - 1	,				-1	,				-1	,				device=UpperCAmelCase_	,				dtype=torch.intaa )

				a__	:							Tuple      						= rearrange(UpperCAmelCase_	,				"d -> d 1 1" )
				a__	:							List[Any]      						= rearrange(UpperCAmelCase_	,				"b (c d) h w -> b c d h w"	,				d=8 )
				a__	:							Dict      						= reduce(x * mask	,				"b c d h w -> b c h w"	,				"sum" )
				return (dec / 2_55).clamp(0.0	,				1.0 )






def     SCREAMING_SNAKE_CASE(      self	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = 0.0	,				__UpperCamelCase = True	,				__UpperCamelCase=None	,				__UpperCamelCase = True	,				)	->						Union[DDIMSchedulerOutput, Tuple]:
				if self.num_inference_steps is None:
								raise ValueError(
								    "Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler" )

				# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
				# Ideally, read DDIM paper in-detail understanding

				# Notation (<variable name> -> <name in paper>
				# - pred_noise_t -> e_theta(x_t, t)
				# - pred_original_sample -> f_theta(x_t, t) or x_0
				# - std_dev_t -> sigma_t
				# - eta -> η
				# - pred_sample_direction -> "direction pointing to x_t"
				# - pred_prev_sample -> "x_t-1"

				# 1. get previous step value (=t-1)
				a__	:							Tuple      						= timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 2. compute alphas, betas
				a__	:							Any      						= self.alphas_cumprod[timestep]
				a__	:							int      						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

				a__	:							Dict      						= 1 - alpha_prod_t

				# 3. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				a__	:							Union[str, Any]      						= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

				# 4. Clip "predicted x_0"
				a__	:							Any      						= self.bit_scale
				if self.config.clip_sample:
								a__	:							Tuple      						= torch.clamp(UpperCAmelCase_	,				-scale	,				UpperCAmelCase_ )

				# 5. compute variance: "sigma_t(η)" -> see formula (16)
				# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
				a__	:							int      						= self._get_variance(UpperCAmelCase_	,				UpperCAmelCase_ )
				a__	:							Any      						= eta * variance ** 0.5

				if use_clipped_model_output:
								# the model_output is always re-derived from the clipped x_0 in Glide
								a__	:							Any      						= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

				# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				a__	:							List[Any]      						= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

				# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				a__	:							List[Any]      						= alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

				if eta > 0:
								# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
								a__	:							int      						= model_output.device if torch.is_tensor(UpperCAmelCase_ ) else 'cpu'
								a__	:							List[Any]      						= torch.randn(model_output.shape	,				dtype=model_output.dtype	,				generator=UpperCAmelCase_ ).to(UpperCAmelCase_ )
								a__	:							int      						= self._get_variance(UpperCAmelCase_	,				UpperCAmelCase_ ) ** 0.5 * eta * noise

								a__	:							Tuple      						= prev_sample + variance

				if not return_dict:
								return (prev_sample,)

				return DDIMSchedulerOutput(prev_sample=UpperCAmelCase_	,				pred_original_sample=UpperCAmelCase_ )






def     SCREAMING_SNAKE_CASE(      self	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase="epsilon"	,				__UpperCamelCase=None	,				__UpperCamelCase = True	,				)	->						Union[DDPMSchedulerOutput, Tuple]:
				a__	:							Tuple      						= timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
								a__	:							int      						= torch.split(UpperCAmelCase_	,				sample.shape[1]	,				dim=1 )
				else:
								a__	:							Union[str, Any]      						= None

				# 1. compute alphas, betas
				a__	:							Tuple      						= self.alphas_cumprod[t]
				a__	:							Optional[Any]      						= self.alphas_cumprod[t - 1] if t > 0 else self.one
				a__	:							Optional[Any]      						= 1 - alpha_prod_t
				a__	:							int      						= 1 - alpha_prod_t_prev

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				if prediction_type == "epsilon":
								a__	:							Optional[int]      						= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif prediction_type == "sample":
								a__	:							Union[str, Any]      						= model_output
				else:
								raise ValueError(F'Unsupported prediction_type {prediction_type}.' )

				# 3. Clip "predicted x_0"
				a__	:							List[Any]      						= self.bit_scale
				if self.config.clip_sample:
								a__	:							List[Any]      						= torch.clamp(UpperCAmelCase_	,				-scale	,				UpperCAmelCase_ )

				# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				a__	:							List[str]      						= (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
				a__	:							Dict      						= self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

				# 5. Compute predicted previous sample µ_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				a__	:							str      						= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				a__	:							Any      						= 0
				if t > 0:
								a__	:							str      						= torch.randn(
								    model_output.size()	,				dtype=model_output.dtype	,				layout=model_output.layout	,				generator=UpperCAmelCase_ ).to(model_output.device )
								a__	:							str      						= (self._get_variance(UpperCAmelCase_	,				predicted_variance=UpperCAmelCase_ ) ** 0.5) * noise

				a__	:							List[Any]      						= pred_prev_sample + variance

				if not return_dict:
								return (pred_prev_sample,)

				return DDPMSchedulerOutput(prev_sample=UpperCAmelCase_	,				pred_original_sample=UpperCAmelCase_ )






class 				_a						(       _UpperCAmelCase			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = 1.0 ,					):







											"""simple docstring"""



											super().__init__()
											a__	:							Tuple      						= bit_scale
											a__	:							str      						= (
											    ddim_bit_scheduler_step if isinstance(SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_				) else ddpm_bit_scheduler_step
											)

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




							@torch.no_grad()
							def __call__(							self ,					__UpperCAmelCase = 256 ,					__UpperCAmelCase = 256 ,					__UpperCAmelCase = 50 ,					__UpperCAmelCase = None ,					__UpperCAmelCase = 1 ,					__UpperCAmelCase = "pil" ,					__UpperCAmelCase = True ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Optional[Any]      						= torch.randn(
											    (batch_size, self.unet.config.in_channels, height, width) ,					generator=SCREAMING_SNAKE_CASE_ ,					)
											a__	:							Optional[Any]      						= decimal_to_bits(SCREAMING_SNAKE_CASE_				) * self.bit_scale
											a__	:							Optional[int]      						= latents.to(self.device				)

											self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_				)

											for t in self.progress_bar(self.scheduler.timesteps				):
															# predict the noise residual
															a__	:							List[str]      						= self.unet(SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_				).sample

															# compute the previous noisy sample x_t -> x_t-1
															a__	:							Tuple      						= self.scheduler.step(SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_				).prev_sample

											a__	:							Dict      						= bits_to_decimal(SCREAMING_SNAKE_CASE_				)

											if output_type == "pil":
															a__	:							Tuple      						= self.numpy_to_pil(SCREAMING_SNAKE_CASE_				)

											if not return_dict:
															return (image,)

											return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_				)






'''simple docstring'''



# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


A__						:   List[str]				=    """3"""

print("""Python version:""", sys.version)

print("""OS platform:""", platform.platform())
print("""OS architecture:""", platform.machine())

try:
      import torch

      print("""Torch version:""", torch.__version__)
      print("""Cuda available:""", torch.cuda.is_available())
      print("""Cuda version:""", torch.version.cuda)
      print("""CuDNN version:""", torch.backends.cudnn.version())
      print("""Number of GPUs available:""", torch.cuda.device_count())
except ImportError:
      print("""Torch version:""", None)

try:
      import transformers

      print("""transformers version:""", transformers.__version__)
except ImportError:
      print("""transformers version:""", None)






