



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


_lowercase		:				List[str]      		=8

def      lowerCAmelCase_					(  _lowercase    :       Optional[Any]						,       _lowercase    :       Union[str, Any]=BITS)							->	Tuple:







			"""simple docstring"""
			a__       :						Any							=  x.device

			a__       :						Optional[int]							=  (x * 255).int().clamp(0						,       255)

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

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

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       Tuple=BITS)							->	List[Any]:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  x.device

			a__       :						int							=  (x > 0).int()
			a__       :						List[str]							=  2 ** torch.arange(bits - 1						,       -1						,       -1						,       device=_lowercase						,       dtype=torch.intaa)

			a__       :						str							=  rearrange(_lowercase						,       """d -> d 1 1""")
			a__       :						Optional[Any]							=  rearrange(_lowercase						,       """b (c d) h w -> b c d h w"""						,       d=8)
			a__       :						Any							=  reduce(x * mask						,       """b c d h w -> b c h w"""						,       """sum""")
			return (dec / 255).clamp(0.0						,       1.0)

def      lowerCAmelCase_					(  self    :       Optional[Any]						,       _lowercase    :       torch.FloatTensor						,       _lowercase    :       int						,       _lowercase    :       torch.FloatTensor						,       _lowercase    :       float = 0.0						,       _lowercase    :       bool = True						,       _lowercase    :       List[Any]=None						,       _lowercase    :       bool = True						,       )							->	Union[DDIMSchedulerOutput, Tuple]:







			"""simple docstring"""
			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__       :						Dict							=  timestep - self.config.num_train_timesteps // self.num_inference_steps

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

			a__       :						Union[str, Any]							=  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__       :						List[str]							=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

			# 4. Clip "predicted x_0"
			a__       :						int							=  self.bit_scale
			if self.config.clip_sample:
						a__       :						Dict							=  torch.clamp(_lowercase						,       -scale						,       _lowercase)

			# 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__       :						List[Any]							=  self._get_variance(_lowercase						,       _lowercase)
			a__       :						Tuple							=  eta * variance ** 0.5

			if use_clipped_model_output:
						# the model_output is always re-derived from the clipped x_0 in Glide
						a__       :						int							=  (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__       :						int							=  (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__       :						int							=  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__       :						Any							=  model_output.device if torch.is_tensor(_lowercase) else """cpu"""
						a__       :						Dict							=  torch.randn(model_output.shape						,       dtype=model_output.dtype						,       generator=_lowercase).to(_lowercase)
						a__       :						Union[str, Any]							=  self._get_variance(_lowercase						,       _lowercase) ** 0.5 * eta * noise

						a__       :						int							=  prev_sample + variance

			if not return_dict:
						return (prev_sample,)

			return DDIMSchedulerOutput(prev_sample=_lowercase						,       pred_original_sample=_lowercase)

def      lowerCAmelCase_					(  self    :       List[str]						,       _lowercase    :       torch.FloatTensor						,       _lowercase    :       int						,       _lowercase    :       torch.FloatTensor						,       _lowercase    :       int="epsilon"						,       _lowercase    :       Optional[Any]=None						,       _lowercase    :       bool = True						,       )							->	Union[DDPMSchedulerOutput, Tuple]:







			"""simple docstring"""
			a__       :						List[str]							=  timestep

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

			# 1. compute alphas, betas
			a__       :						Dict							=  self.alphas_cumprod[t]
			a__       :						Any							=  self.alphas_cumprod[t - 1] if t > 0 else self.one
			a__       :						int							=  1 - alpha_prod_t
			a__       :						Dict							=  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__       :						List[str]							=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
			elif prediction_type == "sample":
						a__       :						Optional[int]							=  model_output
			else:
						raise ValueError(F'''Unsupported prediction_type {prediction_type}.''')

			# 3. Clip "predicted x_0"
			a__       :						Dict							=  self.bit_scale
			if self.config.clip_sample:
						a__       :						Tuple							=  torch.clamp(_lowercase						,       -scale						,       _lowercase)

			# 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__       :						Optional[int]							=  (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
			a__       :						int							=  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__       :						List[str]							=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

			a__       :						Optional[int]							=  pred_prev_sample + variance

			if not return_dict:
						return (pred_prev_sample,)

			return DDPMSchedulerOutput(prev_sample=_lowercase						,       pred_original_sample=_lowercase)





class        snake_case__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase ,       __lowercase = 1.0 ,       ) ->				Tuple:
									"""simple docstring"""




									super().__init__()
									a__       :						int							=  bit_scale
									a__       :						Union[str, Any]							=  (
									    ddim_bit_scheduler_step if isinstance(__lowercase ,       __lowercase  ) else ddpm_bit_scheduler_step
									)

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

						@torch.no_grad()
						def __call__(       self ,       __lowercase = 2_5_6 ,       __lowercase = 2_5_6 ,       __lowercase = 5_0 ,       __lowercase = None ,       __lowercase = 1 ,       __lowercase = "pil" ,       __lowercase = True ,       **__lowercase ,       ) ->				Union[Tuple, ImagePipelineOutput]:
									"""simple docstring"""




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

									self.scheduler.set_timesteps(__lowercase  )

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

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

									a__       :						List[Any]							=  bits_to_decimal(__lowercase  )

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

									if not return_dict:
												return (image,)

									return ImagePipelineOutput(images=__lowercase  )









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase		:				str      		=logging.getLogger(__name__)

@dataclass
class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Optional[float]      =					field(
						    default=0.0 ,     metadata={"help": "The label smoothing epsilon to apply (if not zero)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "Whether to SortishSamler or not."}		)
						__lowerCAmelCase					:bool      =					field(
						    default=A__ ,     metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "whether to use adafactor"}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Encoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Decoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(default=A__ ,     metadata={"help": "Dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Attention dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[str]      =					field(
						    default="linear" ,     metadata={"help": f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}"} ,     )




