




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      :						Any	,					__UpperCamelCase      :						Dict=BITS				)  ->							Tuple:
				UpperCAmelCase_										=					x.device

				UpperCAmelCase_										=					(x * 255).int().clamp(0	,					255				)

				UpperCAmelCase_										=					2 ** torch.arange(bits - 1	,					-1	,					-1	,					device=__UpperCamelCase				)
				UpperCAmelCase_										=					rearrange(__UpperCamelCase	,					'''d -> d 1 1'''				)
				UpperCAmelCase_										=					rearrange(__UpperCamelCase	,					'''b c h w -> b c 1 h w'''				)

				UpperCAmelCase_										=					((x & mask) != 0).float()
				UpperCAmelCase_										=					rearrange(__UpperCamelCase	,					'''b c d h w -> b (c d) h w'''				)
				UpperCAmelCase_										=					bits * 2 - 1
				return bits




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						Union[str, Any]=BITS				)  ->							Optional[Any]:
				UpperCAmelCase_										=					x.device

				UpperCAmelCase_										=					(x > 0).int()
				UpperCAmelCase_										=					2 ** torch.arange(bits - 1	,					-1	,					-1	,					device=__UpperCamelCase	,					dtype=torch.intaa				)

				UpperCAmelCase_										=					rearrange(__UpperCamelCase	,					'''d -> d 1 1'''				)
				UpperCAmelCase_										=					rearrange(__UpperCamelCase	,					'''b (c d) h w -> b c d h w'''	,					d=8				)
				UpperCAmelCase_										=					reduce(x * mask	,					'''b c d h w -> b c h w'''	,					'''sum'''				)
				return (dec / 255).clamp(0.0	,					1.0				)




def  SCREAMING_SNAKE_CASE     (	self      :						str	,					__UpperCamelCase      :						torch.FloatTensor	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						torch.FloatTensor	,					__UpperCamelCase      :						float = 0.0	,					__UpperCamelCase      :						bool = True	,					__UpperCamelCase      :						List[str]=None	,					__UpperCamelCase      :						bool = 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)
				UpperCAmelCase_										=					timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 2. compute alphas, betas
				UpperCAmelCase_										=					self.alphas_cumprod[timestep]
				UpperCAmelCase_										=					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

				UpperCAmelCase_										=					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
				UpperCAmelCase_										=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

				# 4. Clip "predicted x_0"
				UpperCAmelCase_										=					self.bit_scale
				if self.config.clip_sample:
								UpperCAmelCase_										=					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)
				UpperCAmelCase_										=					self._get_variance(__UpperCamelCase	,					__UpperCamelCase				)
				UpperCAmelCase_										=					eta * variance ** 0.5

				if use_clipped_model_output:
								# the model_output is always re-derived from the clipped x_0 in Glide
								UpperCAmelCase_										=					(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
				UpperCAmelCase_										=					(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
				UpperCAmelCase_										=					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
								UpperCAmelCase_										=					model_output.device if torch.is_tensor(__UpperCamelCase				) else '''cpu'''
								UpperCAmelCase_										=					torch.randn(model_output.shape	,					dtype=model_output.dtype	,					generator=__UpperCamelCase				).to(__UpperCamelCase				)
								UpperCAmelCase_										=					self._get_variance(__UpperCamelCase	,					__UpperCamelCase				) ** 0.5 * eta * noise

								UpperCAmelCase_										=					prev_sample + variance

				if not return_dict:
								return (prev_sample,)

				return DDIMSchedulerOutput(prev_sample=__UpperCamelCase	,					pred_original_sample=__UpperCamelCase				)




def  SCREAMING_SNAKE_CASE     (	self      :						int	,					__UpperCamelCase      :						torch.FloatTensor	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						torch.FloatTensor	,					__UpperCamelCase      :						Dict="epsilon"	,					__UpperCamelCase      :						Tuple=None	,					__UpperCamelCase      :						bool = True	,					)  ->							Union[DDPMSchedulerOutput, Tuple]:
				UpperCAmelCase_										=					timestep

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

				# 1. compute alphas, betas
				UpperCAmelCase_										=					self.alphas_cumprod[t]
				UpperCAmelCase_										=					self.alphas_cumprod[t - 1] if t > 0 else self.one
				UpperCAmelCase_										=					1 - alpha_prod_t
				UpperCAmelCase_										=					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":
								UpperCAmelCase_										=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif prediction_type == "sample":
								UpperCAmelCase_										=					model_output
				else:
								raise ValueError(f'Unsupported prediction_type {prediction_type}.'				)

				# 3. Clip "predicted x_0"
				UpperCAmelCase_										=					self.bit_scale
				if self.config.clip_sample:
								UpperCAmelCase_										=					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
				UpperCAmelCase_										=					(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
				UpperCAmelCase_										=					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
				UpperCAmelCase_										=					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

				UpperCAmelCase_										=					pred_prev_sample + variance

				if not return_dict:
								return (pred_prev_sample,)

				return DDPMSchedulerOutput(prev_sample=__UpperCamelCase	,					pred_original_sample=__UpperCamelCase				)



class       a					(       _A      ):

					'''simple docstring'''





					def __init__(     self       :      Any ,   __snake_case       :      UNetaDConditionModel ,   __snake_case       :      Union[DDIMScheduler, DDPMScheduler] ,   __snake_case       :      Optional[float] = 1.0 ,   ):
									super().__init__()
									UpperCAmelCase_										=					bit_scale
									UpperCAmelCase_										=					(
									    ddim_bit_scheduler_step if isinstance(__snake_case ,   __snake_case    ) else ddpm_bit_scheduler_step
									)

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




					@torch.no_grad()
					def __call__(     self       :      str ,   __snake_case       :      Optional[int] = 2_56 ,   __snake_case       :      Optional[int] = 2_56 ,   __snake_case       :      Optional[int] = 50 ,   __snake_case       :      Optional[torch.Generator] = None ,   __snake_case       :      Optional[int] = 1 ,   __snake_case       :      Optional[str] = "pil" ,   __snake_case       :      bool = True ,   **__snake_case       :      Dict ,   ):
									UpperCAmelCase_										=					torch.randn(
									    (batch_size, self.unet.config.in_channels, height, width) ,   generator=__snake_case ,   )
									UpperCAmelCase_										=					decimal_to_bits(__snake_case    ) * self.bit_scale
									UpperCAmelCase_										=					latents.to(self.device    )

									self.scheduler.set_timesteps(__snake_case    )

									for t in self.progress_bar(self.scheduler.timesteps    ):
													# predict the noise residual
													UpperCAmelCase_										=					self.unet(__snake_case ,   __snake_case    ).sample

													# compute the previous noisy sample x_t -> x_t-1
													UpperCAmelCase_										=					self.scheduler.step(__snake_case ,   __snake_case ,   __snake_case    ).prev_sample

									UpperCAmelCase_										=					bits_to_decimal(__snake_case    )

									if output_type == "pil":
													UpperCAmelCase_										=					self.numpy_to_pil(__snake_case    )

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=__snake_case    )











import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase      			=						logging.getLogger(__name__)

if __name__ == "__main__":
 _lowerCamelCase      			=						argparse.ArgumentParser(
     description='Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'
 )
 parser.add_argument(
     '--data_file', type=str, default='data/dump.bert-base-uncased.pickle', help='The binarized dataset.'
 )
 parser.add_argument(
     '--token_counts_dump', type=str, default='data/token_counts.bert-base-uncased.pickle', help='The dump file.'
 )
 parser.add_argument('--vocab_size', default=3_05_22, type=int)
 _lowerCamelCase      			=						parser.parse_args()

 logger.info(F"Loading data from {args.data_file}")
 with open(args.data_file, 'rb') as fp:
  _lowerCamelCase      			=						pickle.load(fp)

 logger.info('Counting occurrences for MLM.')
 _lowerCamelCase      			=						Counter()
 for tk_ids in data:
  counter.update(tk_ids)
 _lowerCamelCase      			=						[0] * args.vocab_size
 for k, v in counter.items():
  _lowerCamelCase      			=						v

 logger.info(F"Dump to {args.token_counts_dump}")
 with open(args.token_counts_dump, 'wb') as handle:
  pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)





