






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

import numpy as np
import torch

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







@dataclass
class  lowerCamelCase_   (		__lowercase   ):
							SCREAMING_SNAKE_CASE_     =	42
							SCREAMING_SNAKE_CASE_     =	42





def      SCREAMING_SNAKE_CASE__  (      snake_case_,     snake_case_=0.999,     snake_case_="cosine",     )      ->     Dict:


	"""simple docstring"""



	if alpha_transform_type == "cosine":

		def alpha_bar_fn(snake_case_     ):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(snake_case_     ):
			return math.exp(t * -12.0     )

	else:
		raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""     )

	a				=	[]
	for i in range(__lowerCamelCase     ):
		a				=	i / num_diffusion_timesteps
		a				=	(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(__lowerCamelCase     ) / alpha_bar_fn(__lowerCamelCase     ),     __lowerCamelCase     )     )
	return torch.tensor(__lowerCamelCase,     dtype=torch.floataa     )







class  lowerCamelCase_   (		__lowercase       ,  __lowercase   ):
							SCREAMING_SNAKE_CASE_     =	1
							@register_to_config
							def __init__(  self						:    List[Any]							,__lowerCamelCase						:    Any = 10_00							,__lowerCamelCase						:    Any = 0.0_001							,__lowerCamelCase						:    List[str] = 0.02							,__lowerCamelCase						:    str = "linear"							,__lowerCamelCase						:    Dict = 0.0							,__lowerCamelCase						:    Optional[int] = None							,__lowerCamelCase						:    Any = True							,):





								'''simple docstring'''



								if trained_betas is not None:
									a				=	torch.from_numpy(__lowerCamelCase    )
								elif beta_schedule == "linear":
									a				=	torch.linspace(__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,dtype=torch.floataa    )
								elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									a				=	(
									    torch.linspace(beta_start**0.5							,beta_end**0.5							,__lowerCamelCase							,dtype=torch.floataa    ) ** 2
									)
								elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									a				=	betas_for_alpha_bar(__lowerCamelCase    )
								elif beta_schedule == "sigmoid":
									# GeoDiff sigmoid schedule
									a				=	torch.linspace(-6							,6							,__lowerCamelCase    )
									a				=	torch.sigmoid(__lowerCamelCase    ) * (beta_end - beta_start) + beta_start
								else:
									raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""    )

								a				=	1.0 - self.betas
								a				=	torch.cumprod(self.alphas							,dim=0    )
								a				=	torch.tensor(1.0    )

								a				=	torch.tensor(1.0    )

								# standard deviation of the initial noise distribution
								a				=	1.0

								# setable values
								a				=	None
								a				=	torch.from_numpy(np.arange(0							,__lowerCamelCase    )[::-1].copy()    )

								a				=	eta
							def        SCREAMING_SNAKE_CASE_       (  self						:    str							,__lowerCamelCase						:    Any							,__lowerCamelCase						:    List[Any] = None    ):





								'''simple docstring'''



								return sample
							def        SCREAMING_SNAKE_CASE_       (  self						:    int							,__lowerCamelCase						:    int							,__lowerCamelCase						:    Tuple = 10							,__lowerCamelCase						:    Optional[Any] = 10							,__lowerCamelCase						:    int = None							,):





								'''simple docstring'''



								a				=	min(self.config.num_train_timesteps							,__lowerCamelCase    )
								a				=	num_inference_steps

								a				=	[]

								a				=	{}
								for j in range(0							,num_inference_steps - jump_length							,__lowerCamelCase    ):
									a				=	jump_n_sample - 1

								a				=	num_inference_steps
								while t >= 1:
									a				=	t - 1
									timesteps.append(__lowerCamelCase    )

									if jumps.get(__lowerCamelCase							,0    ) > 0:
										a				=	jumps[t] - 1
										for _ in range(__lowerCamelCase    ):
											a				=	t + 1
											timesteps.append(__lowerCamelCase    )

								a				=	np.array(__lowerCamelCase    ) * (self.config.num_train_timesteps // self.num_inference_steps)
								a				=	torch.from_numpy(__lowerCamelCase    ).to(__lowerCamelCase    )
							def        SCREAMING_SNAKE_CASE_       (  self						:    Optional[Any]							,__lowerCamelCase						:    Dict    ):





								'''simple docstring'''



								a				=	t - self.config.num_train_timesteps // self.num_inference_steps

								a				=	self.alphas_cumprod[t]
								a				=	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
								a				=	1 - alpha_prod_t
								a				=	1 - alpha_prod_t_prev

								# For t > 0, compute predicted variance βt (see formula (6) and (7) from
								# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
								# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
								# variance to pred_sample
								# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
								# without eta.
								# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
								a				=	(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

								return variance
							def        SCREAMING_SNAKE_CASE_       (  self						:    Optional[int]							,__lowerCamelCase						:    List[str]							,__lowerCamelCase						:    Tuple							,__lowerCamelCase						:    List[str]							,__lowerCamelCase						:    Optional[Any]							,__lowerCamelCase						:    int							,__lowerCamelCase						:    Tuple = None							,__lowerCamelCase						:    Optional[int] = True							,):





								'''simple docstring'''



								a				=	timestep
								a				=	timestep - self.config.num_train_timesteps // self.num_inference_steps

								# 1. compute alphas, betas
								a				=	self.alphas_cumprod[t]
								a				=	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
								a				=	1 - alpha_prod_t

								# 2. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
								a				=	(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
									a				=	torch.clamp(__lowerCamelCase							,-1							,1    )

								# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
								# substitute formula (7) in the algorithm coming from DDPM paper
								# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
								# DDIM schedule gives the same results as DDPM with eta = 1.0
								# Noise is being reused in 7. and 8., but no impact on quality has
								# been observed.

								# 5. Add noise
								a				=	model_output.device
								a				=	randn_tensor(model_output.shape							,generator=__lowerCamelCase							,device=__lowerCamelCase							,dtype=model_output.dtype    )
								a				=	self.eta * self._get_variance(__lowerCamelCase    ) ** 0.5

								a				=	0
								if t > 0 and self.eta > 0:
									a				=	std_dev_t * noise

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

								# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								a				=	alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

								# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
								a				=	(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

								# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
								a				=	mask * prev_known_part + (1.0 - mask) * prev_unknown_part

								if not return_dict:
									return (
									    pred_prev_sample,
									    pred_original_sample,
									)

								return RePaintSchedulerOutput(prev_sample=__lowerCamelCase							,pred_original_sample=__lowerCamelCase    )
							def        SCREAMING_SNAKE_CASE_       (  self						:    Optional[Any]							,__lowerCamelCase						:    Tuple							,__lowerCamelCase						:    List[str]							,__lowerCamelCase						:    int=None    ):





								'''simple docstring'''



								a				=	self.config.num_train_timesteps // self.num_inference_steps

								for i in range(__lowerCamelCase    ):
									a				=	self.betas[timestep + i]
									if sample.device.type == "mps":
										# randn does not work reproducibly on mps
										a				=	randn_tensor(sample.shape							,dtype=sample.dtype							,generator=__lowerCamelCase    )
										a				=	noise.to(sample.device    )
									else:
										a				=	randn_tensor(sample.shape							,generator=__lowerCamelCase							,device=sample.device							,dtype=sample.dtype    )

									# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
									a				=	(1 - beta) ** 0.5 * sample + beta**0.5 * noise

								return sample
							def        SCREAMING_SNAKE_CASE_       (  self						:    Optional[Any]							,__lowerCamelCase						:    Union[str, Any]							,__lowerCamelCase						:    int							,__lowerCamelCase						:    Tuple							,):





								'''simple docstring'''



								raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''    )




							def __len__(  self						:    Union[str, Any]    ):





								'''simple docstring'''



								return self.config.num_train_timesteps












import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


UpperCamelCase__          =logging.get_logger(__name__)

UpperCamelCase__          ={'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__          ={
    'vocab_file': {
        'microsoft/layoutlm-base-uncased': (
            'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'
        ),
        'microsoft/layoutlm-large-uncased': (
            'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'microsoft/layoutlm-base-uncased': (
            'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'
        ),
        'microsoft/layoutlm-large-uncased': (
            'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'
        ),
    },
}

UpperCamelCase__          ={
    'microsoft/layoutlm-base-uncased': 512,
    'microsoft/layoutlm-large-uncased': 512,
}

UpperCamelCase__          ={
    'microsoft/layoutlm-base-uncased': {'do_lower_case': True},
    'microsoft/layoutlm-large-uncased': {'do_lower_case': True},
}





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       __snake_case       	=			VOCAB_FILES_NAMES
       __snake_case       	=			PRETRAINED_VOCAB_FILES_MAP
       __snake_case       	=			PRETRAINED_INIT_CONFIGURATION
       __snake_case       	=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __snake_case       	=			LayoutLMTokenizer




       def __init__(					self			,  __lowerCamelCase=None			,  __lowerCamelCase=None			,  __lowerCamelCase=True			,  __lowerCamelCase="[UNK]"			,  __lowerCamelCase="[SEP]"			,  __lowerCamelCase="[PAD]"			,  __lowerCamelCase="[CLS]"			,  __lowerCamelCase="[MASK]"			,  __lowerCamelCase=True			,  __lowerCamelCase=None			,  **__lowerCamelCase			,  )	->   Dict:
            super().__init__(
                __lowerCamelCase			,  tokenizer_file=__lowerCamelCase			,  do_lower_case=__lowerCamelCase			,  unk_token=__lowerCamelCase			,  sep_token=__lowerCamelCase			,  pad_token=__lowerCamelCase			,  cls_token=__lowerCamelCase			,  mask_token=__lowerCamelCase			,  tokenize_chinese_chars=__lowerCamelCase			,  strip_accents=__lowerCamelCase			,  **__lowerCamelCase			,  )

            _SCREAMING_SNAKE_CASE				:		int  			=							json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
            if (
                normalizer_state.get("lowercase"			,  __lowerCamelCase							) != do_lower_case
                or normalizer_state.get("strip_accents"			,  __lowerCamelCase							) != strip_accents
                or normalizer_state.get("handle_chinese_chars"			,  __lowerCamelCase							) != tokenize_chinese_chars
            ):
                 _SCREAMING_SNAKE_CASE				:		List[Any]  			=							getattr(__lowerCamelCase			,  normalizer_state.pop("type"							)							)
                 _SCREAMING_SNAKE_CASE				:		str  			=							do_lower_case
                 _SCREAMING_SNAKE_CASE				:		int  			=							strip_accents
                 _SCREAMING_SNAKE_CASE				:		List[Any]  			=							tokenize_chinese_chars
                 _SCREAMING_SNAKE_CASE				:		List[str]  			=							normalizer_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							do_lower_case




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase=None							)	->   Tuple:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							[self.cls_token_id] + token_ids_a + [self.sep_token_id]

            if token_ids_a:
                 output += token_ids_a + [self.sep_token_id]

            return output




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase = None							)	->   List[int]:
            _SCREAMING_SNAKE_CASE				:		Any  			=							[self.sep_token_id]
            _SCREAMING_SNAKE_CASE				:		str  			=							[self.cls_token_id]
            if token_ids_a is None:
                 return len(cls + token_ids_a + sep							) * [0]
            return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]







       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase = None							)	->   Tuple[str]:
            _SCREAMING_SNAKE_CASE				:		int  			=							self._tokenizer.model.save(__lowerCamelCase			,  name=__lowerCamelCase							)
            return tuple(__lowerCamelCase							)