





"""simple docstring"""
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  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							42
					UpperCamelCase								=							42







def  A__    (      UpperCamelCase ,  UpperCamelCase=0.9_99 ,  UpperCamelCase="cosine" ,  ):
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(UpperCamelCase	):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2	) ** 2

							elif alpha_transform_type == "exp":

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

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

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






class  _UpperCAmelCase       (     lowercase_					,							lowercase_			):
					UpperCamelCase								=							1


					@register_to_config
					def __init__( self      :str					,	__UpperCamelCase      :int = 10_00					,	__UpperCamelCase      :float = 0.0_001					,	__UpperCamelCase      :float = 0.02					,	__UpperCamelCase      :str = "linear"					,	__UpperCamelCase      :float = 0.0					,	__UpperCamelCase      :Optional[np.ndarray] = None					,	__UpperCamelCase      :bool = True					,	):
												if trained_betas is not None:
																			A		=  torch.from_numpy(__UpperCamelCase		)
												elif beta_schedule == "linear":
																			A		=  torch.linspace(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase					,	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					,	__UpperCamelCase					,	dtype=torch.floataa		) ** 2
																			)
												elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			A		=  betas_for_alpha_bar(__UpperCamelCase		)
												elif beta_schedule == "sigmoid":
																			# GeoDiff sigmoid schedule
																			A		=  torch.linspace(-6					,	6					,	__UpperCamelCase		)
																			A		=  torch.sigmoid(__UpperCamelCase		) * (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					,	__UpperCamelCase		)[::-1].copy()		)

												A		=  eta


					def      lowerCamelCase	( self      :Tuple					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Optional[int] = None		):
												return sample


					def      lowerCamelCase	( self      :Any					,	__UpperCamelCase      :int					,	__UpperCamelCase      :int = 10					,	__UpperCamelCase      :int = 10					,	__UpperCamelCase      :Union[str, torch.device] = None					,	):
												A		=  min(self.config.num_train_timesteps					,	__UpperCamelCase		)
												A		=  num_inference_steps

												A		=  []

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

												A		=  num_inference_steps
												while t >= 1:
																			A		=  t - 1
																			timesteps.append(__UpperCamelCase		)

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

												A		=  np.array(__UpperCamelCase		) * (self.config.num_train_timesteps // self.num_inference_steps)
												A		=  torch.from_numpy(__UpperCamelCase		).to(__UpperCamelCase		)


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :Optional[Any]		):
												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      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :int					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Optional[torch.Generator] = None					,	__UpperCamelCase      :bool = True					,	):
												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(__UpperCamelCase					,	-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=__UpperCamelCase					,	device=__UpperCamelCase					,	dtype=model_output.dtype		)
												A		=  self.eta * self._get_variance(__UpperCamelCase		) ** 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=__UpperCamelCase					,	pred_original_sample=__UpperCamelCase		)


					def      lowerCamelCase	( self      :str					,	__UpperCamelCase      :Optional[Any]					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :List[Any]=None		):
												A		=  self.config.num_train_timesteps // self.num_inference_steps

												for i in range(__UpperCamelCase		):
																			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=__UpperCamelCase		)
																										A		=  noise.to(sample.device		)
																			else:
																										A		=  randn_tensor(sample.shape					,	generator=__UpperCamelCase					,	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      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :torch.IntTensor					,	):
												raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."		)


					def __len__( self      :List[str]		):
												return self.config.num_train_timesteps









"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							['''image_processor''', '''tokenizer''']
					UpperCamelCase								=							'''LayoutLMv2ImageProcessor'''
					UpperCamelCase								=							('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')


					def __init__( self      :List[Any]					,	__UpperCamelCase      :List[Any]=None					,	__UpperCamelCase      :Tuple=None					,	**__UpperCamelCase      :int		):
												A		=  None
												if "feature_extractor" in kwargs:
																			warnings.warn(
																			    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																			    " instead."					,	__UpperCamelCase					,	)
																			A		=  kwargs.pop("feature_extractor"		)

												A		=  image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																			raise ValueError("You need to specify an `image_processor`."		)
												if tokenizer is None:
																			raise ValueError("You need to specify a `tokenizer`."		)

												super().__init__(__UpperCamelCase					,	__UpperCamelCase		)


					def __call__( self      :Optional[Any]					,	__UpperCamelCase      :int					,	__UpperCamelCase      :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,	__UpperCamelCase      :Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None					,	__UpperCamelCase      :Union[List[List[int]], List[List[List[int]]]] = None					,	__UpperCamelCase      :Optional[Union[List[int], List[List[int]]]] = None					,	__UpperCamelCase      :bool = True					,	__UpperCamelCase      :Union[bool, str, PaddingStrategy] = False					,	__UpperCamelCase      :Union[bool, str, TruncationStrategy] = False					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :int = 0					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Optional[bool] = None					,	__UpperCamelCase      :Optional[bool] = None					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = True					,	__UpperCamelCase      :Optional[Union[str, TensorType]] = None					,	**__UpperCamelCase      :int					,	):
												# verify input
												if self.image_processor.apply_ocr and (boxes is not None):
																			raise ValueError(
																			    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."		)

												if self.image_processor.apply_ocr and (word_labels is not None):
																			raise ValueError(
																			    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."		)

												if return_overflowing_tokens is True and return_offsets_mapping is False:
																			raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."		)

												# first, apply the image processor
												A		=  self.image_processor(images=__UpperCamelCase					,	return_tensors=__UpperCamelCase		)

												# second, apply the tokenizer
												if text is not None and self.image_processor.apply_ocr and text_pair is None:
																			if isinstance(__UpperCamelCase					,	__UpperCamelCase		):
																										A		=  [text]  # add batch dimension (as the image processor always adds a batch dimension)
																			A		=  features["words"]

												A		=  self.tokenizer(
												    text=text if text is not None else features["words"]					,	text_pair=text_pair if text_pair is not None else None					,	boxes=boxes if boxes is not None else features["boxes"]					,	word_labels=__UpperCamelCase					,	add_special_tokens=__UpperCamelCase					,	padding=__UpperCamelCase					,	truncation=__UpperCamelCase					,	max_length=__UpperCamelCase					,	stride=__UpperCamelCase					,	pad_to_multiple_of=__UpperCamelCase					,	return_token_type_ids=__UpperCamelCase					,	return_attention_mask=__UpperCamelCase					,	return_overflowing_tokens=__UpperCamelCase					,	return_special_tokens_mask=__UpperCamelCase					,	return_offsets_mapping=__UpperCamelCase					,	return_length=__UpperCamelCase					,	verbose=__UpperCamelCase					,	return_tensors=__UpperCamelCase					,	**__UpperCamelCase					,	)

												# add pixel values
												A		=  features.pop("pixel_values"		)
												if return_overflowing_tokens is True:
																			A		=  self.get_overflowing_images(__UpperCamelCase					,	encoded_inputs["overflow_to_sample_mapping"]		)
												A		=  images

												return encoded_inputs


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :List[str]		):
												# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
												A		=  []
												for sample_idx in overflow_to_sample_mapping:
																			images_with_overflow.append(images[sample_idx]		)

												if len(__UpperCamelCase		) != len(__UpperCamelCase		):
																			raise ValueError(
																			    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
																			    f" {len(__UpperCamelCase		)} and {len(__UpperCamelCase		)}"		)

												return images_with_overflow


					def      lowerCamelCase	( self      :Tuple					,	*__UpperCamelCase      :Union[str, Any]					,	**__UpperCamelCase      :Tuple		):
												return self.tokenizer.batch_decode(*__UpperCamelCase					,	**__UpperCamelCase		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	*__UpperCamelCase      :str					,	**__UpperCamelCase      :List[str]		):
												return self.tokenizer.decode(*__UpperCamelCase					,	**__UpperCamelCase		)


					@property
					def      lowerCamelCase	( self      :str		):
												return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


					@property
					def      lowerCamelCase	( self      :str		):
												warnings.warn(
												    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,	__UpperCamelCase					,	)
												return self.image_processor_class


					@property
					def      lowerCamelCase	( self      :int		):
												warnings.warn(
												    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,	__UpperCamelCase					,	)
												return self.image_processor


