'''simple docstring'''





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput




@dataclass
class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							42
				UpperCAmelCase__							=							42





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							1





				@register_to_config
				def __init__(						self					:						Optional[Any]       ,		lowercase__					:						int = 2_000       ,		lowercase__					:						float = 0.1_5       ,		lowercase__					:						float = 0.0_1       ,		lowercase__					:						float = 1_3_4_8.0       ,		lowercase__					:						float = 1e-5       ,		lowercase__					:						int = 1       ,		)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[Any]   			=						sigma_max

										# setable values
										_UpperCamelCase			:			Tuple   			=						None

										self.set_sigmas(lowercase__       ,		lowercase__       ,		lowercase__       ,		lowercase__					)





				def  snake_case__				(						self					:						Union[str, Any]       ,		lowercase__					:						torch.FloatTensor       ,		lowercase__					:						Optional[int] = None					)							->torch.FloatTensor:


										'''simple docstring'''




										return sample





				def  snake_case__				(						self					:						List[str]       ,		lowercase__					:						int       ,		lowercase__					:						float = None       ,		lowercase__					:						Union[str, torch.device] = None					)							->Any:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						sampling_eps if sampling_eps is not None else self.config.sampling_eps

										_UpperCamelCase			:			List[str]   			=						torch.linspace(1       ,		lowercase__       ,		lowercase__       ,		device=lowercase__					)





				def  snake_case__				(						self					:						Optional[Any]       ,		lowercase__					:						int       ,		lowercase__					:						float = None       ,		lowercase__					:						float = None       ,		lowercase__					:						float = None					)							->Optional[int]:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						sigma_min if sigma_min is not None else self.config.sigma_min
										_UpperCamelCase			:			List[Any]   			=						sigma_max if sigma_max is not None else self.config.sigma_max
										_UpperCamelCase			:			List[Any]   			=						sampling_eps if sampling_eps is not None else self.config.sampling_eps
										if self.timesteps is None:
																self.set_timesteps(lowercase__       ,		lowercase__					)

										_UpperCamelCase			:			str   			=						sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
										_UpperCamelCase			:			int   			=						torch.exp(torch.linspace(math.log(lowercase__					)       ,		math.log(lowercase__					)       ,		lowercase__					)					)
										_UpperCamelCase			:			Optional[Any]   			=						torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]					)





				def  snake_case__				(						self					:						List[str]       ,		lowercase__					:						Dict       ,		lowercase__					:						int					)							->int:


										'''simple docstring'''




										return torch.where(
										    timesteps == 0       ,		torch.zeros_like(t.to(timesteps.device					)					)       ,		self.discrete_sigmas[timesteps - 1].to(timesteps.device					)       ,		)





				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						torch.FloatTensor       ,		lowercase__					:						int       ,		lowercase__					:						torch.FloatTensor       ,		lowercase__					:						Optional[torch.Generator] = None       ,		lowercase__					:						bool = True       ,		)							->Union[SdeVeOutput, Tuple]:


										'''simple docstring'''




										if self.timesteps is None:
																raise ValueError(
																    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"					)

										_UpperCamelCase			:			Any   			=						timestep * torch.ones(
										    sample.shape[0]       ,		device=sample.device					)  # torch.repeat_interleave(timestep, sample.shape[0])
										_UpperCamelCase			:			List[str]   			=						(timestep * (len(self.timesteps					) - 1)).long()

										# mps requires indices to be in the same device, so we use cpu as is the default with cuda
										_UpperCamelCase			:			List[Any]   			=						timesteps.to(self.discrete_sigmas.device					)

										_UpperCamelCase			:			List[str]   			=						self.discrete_sigmas[timesteps].to(sample.device					)
										_UpperCamelCase			:			str   			=						self.get_adjacent_sigma(lowercase__       ,		lowercase__					).to(sample.device					)
										_UpperCamelCase			:			Dict   			=						torch.zeros_like(lowercase__					)
										_UpperCamelCase			:			List[str]   			=						(sigma**2 - adjacent_sigma**2) ** 0.5

										# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
										# also equation 47 shows the analog from SDE models to ancestral sampling methods
										_UpperCamelCase			:			Optional[int]   			=						diffusion.flatten()
										while len(diffusion.shape					) < len(sample.shape					):
																_UpperCamelCase			:			str   			=						diffusion.unsqueeze(-1					)
										_UpperCamelCase			:			Tuple   			=						drift - diffusion**2 * model_output

										#  equation 6: sample noise for the diffusion term of
										_UpperCamelCase			:			Optional[int]   			=						randn_tensor(
										    sample.shape       ,		layout=sample.layout       ,		generator=lowercase__       ,		device=sample.device       ,		dtype=sample.dtype					)
										_UpperCamelCase			:			Dict   			=						sample - drift  # subtract because `dt` is a small negative timestep
										# TODO is the variable diffusion the correct scaling term for the noise?
										_UpperCamelCase			:			Optional[int]   			=						prev_sample_mean + diffusion * noise  # add impact of diffusion field g

										if not return_dict:
																return (prev_sample, prev_sample_mean)

										return SdeVeOutput(prev_sample=lowercase__       ,		prev_sample_mean=lowercase__					)





				def  snake_case__				(						self					:						Union[str, Any]       ,		lowercase__					:						torch.FloatTensor       ,		lowercase__					:						torch.FloatTensor       ,		lowercase__					:						Optional[torch.Generator] = None       ,		lowercase__					:						bool = True       ,		)							->Union[SchedulerOutput, Tuple]:


										'''simple docstring'''




										if self.timesteps is None:
																raise ValueError(
																    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"					)

										# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
										# sample noise for correction
										_UpperCamelCase			:			str   			=						randn_tensor(sample.shape       ,		layout=sample.layout       ,		generator=lowercase__					).to(sample.device					)

										# compute step size from the model_output, the noise, and the snr
										_UpperCamelCase			:			Dict   			=						torch.norm(model_output.reshape(model_output.shape[0]       ,		-1					)       ,		dim=-1					).mean()
										_UpperCamelCase			:			Optional[int]   			=						torch.norm(noise.reshape(noise.shape[0]       ,		-1					)       ,		dim=-1					).mean()
										_UpperCamelCase			:			Union[str, Any]   			=						(self.config.snr * noise_norm / grad_norm) ** 2 * 2
										_UpperCamelCase			:			Optional[int]   			=						step_size * torch.ones(sample.shape[0]					).to(sample.device					)
										# self.repeat_scalar(step_size, sample.shape[0])

										# compute corrected sample: model_output term and noise term
										_UpperCamelCase			:			Dict   			=						step_size.flatten()
										while len(step_size.shape					) < len(sample.shape					):
																_UpperCamelCase			:			Dict   			=						step_size.unsqueeze(-1					)
										_UpperCamelCase			:			Tuple   			=						sample + step_size * model_output
										_UpperCamelCase			:			Tuple   			=						prev_sample_mean + ((step_size * 2) ** 0.5) * noise

										if not return_dict:
																return (prev_sample,)

										return SchedulerOutput(prev_sample=lowercase__					)





				def  snake_case__				(						self					:						List[str]       ,		lowercase__					:						torch.FloatTensor       ,		lowercase__					:						torch.FloatTensor       ,		lowercase__					:						torch.FloatTensor       ,		)							->torch.FloatTensor:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						timesteps.to(original_samples.device					)
										_UpperCamelCase			:			str   			=						self.discrete_sigmas.to(original_samples.device					)[timesteps]
										_UpperCamelCase			:			int   			=						(
										    noise * sigmas[:, None, None, None]
										    if noise is not None
										    else torch.randn_like(lowercase__					) * sigmas[:, None, None, None]
										)
										_UpperCamelCase			:			Optional[int]   			=						noise + original_samples
										return noisy_samples







				def __len__(						self					:						Dict					)							->int:


										'''simple docstring'''




										return self.config.num_train_timesteps



'''simple docstring'''





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_	:		Optional[int]			=     logging.getLogger(__name__)

if __name__ == "__main__":
			lowerCAmelCase_	:		Optional[Any]			=     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_0522, type=int)
			lowerCAmelCase_	:		str			=     parser.parse_args()

			logger.info(f"""Loading data from {args.data_file}""")
			with open(args.data_file, """rb""") as fp:
						lowerCAmelCase_	:		Tuple			=     pickle.load(fp)

			logger.info("""Counting occurrences for MLM.""")
			lowerCAmelCase_	:		str			=     Counter()
			for tk_ids in data:
						counter.update(tk_ids)
			lowerCAmelCase_	:		Any			=     [0] * args.vocab_size
			for k, v in counter.items():
						lowerCAmelCase_	:		Optional[Any]			=     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)


