
"""simple docstring"""




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=0.999,     SCREAMING_SNAKE_CASE_="cosine",     ):
					if alpha_transform_type == "cosine":

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

					elif alpha_transform_type == "exp":

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

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

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




class      UpperCamelCase	(       __magic_name__  ,      __magic_name__      ):

				"""simple docstring"""



				UpperCAmelCase_				:      Tuple						  =				[e.name for e in KarrasDiffusionSchedulers]
				UpperCAmelCase_				:      List[str]						  =				2


				@register_to_config
				def __init__(				self		,	lowercase__ = 1000		,	lowercase__ = 0.00_085		,	lowercase__ = 0.012		,	lowercase__ = "linear"		,	lowercase__ = None		,	lowercase__ = "epsilon"		,	lowercase__ = "linspace"		,	lowercase__ = 0		,	)    ->    str:
									"""simple docstring"""







									if trained_betas is not None:
														SCREAMING_SNAKE_CASE				=	torch.tensor(lowercase__		,	dtype=torch.floataa			)
									elif beta_schedule == "linear":
														SCREAMING_SNAKE_CASE				=	torch.linspace(lowercase__		,	lowercase__		,	lowercase__		,	dtype=torch.floataa			)
									elif beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														SCREAMING_SNAKE_CASE				=	(
														    torch.linspace(beta_start**0.5		,	beta_end**0.5		,	lowercase__		,	dtype=torch.floataa			) ** 2
														)
									elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														SCREAMING_SNAKE_CASE				=	betas_for_alpha_bar(lowercase__			)
									else:
														raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)

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

									#  set all values
									self.set_timesteps(lowercase__		,	lowercase__		,	lowercase__			)


				def 							A					(				self		,	lowercase__		,	lowercase__=None			)    ->    Union[str, Any]:
									"""simple docstring"""







									if schedule_timesteps is None:
														SCREAMING_SNAKE_CASE				=	self.timesteps

									SCREAMING_SNAKE_CASE				=	(schedule_timesteps == timestep).nonzero()

									# The sigma index that is taken for the **very** first `step`
									# is always the second index (or the last index if there is only 1)
									# This way we can ensure we don't accidentally skip a sigma in
									# case we start in the middle of the denoising schedule (e.g. for image-to-image)
									if len(self._index_counter			) == 0:
														SCREAMING_SNAKE_CASE				=	1 if len(lowercase__			) > 1 else 0
									else:
														SCREAMING_SNAKE_CASE				=	timestep.cpu().item() if torch.is_tensor(lowercase__			) else timestep
														SCREAMING_SNAKE_CASE				=	self._index_counter[timestep_int]

									return indices[pos].item()


				@property
				def 							A					(				self			)    ->    Union[str, Any]:
									"""simple docstring"""







									if self.config.timestep_spacing in ["linspace", "trailing"]:
														return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5


				def 							A					(				self		,	lowercase__		,	lowercase__		,	)    ->    torch.FloatTensor:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.index_for_timestep(lowercase__			)

									if self.state_in_first_order:
														SCREAMING_SNAKE_CASE				=	self.sigmas[step_index]
									else:
														SCREAMING_SNAKE_CASE				=	self.sigmas_interpol[step_index - 1]

									SCREAMING_SNAKE_CASE				=	sample / ((sigma**2 + 1) ** 0.5)
									return sample


				def 							A					(				self		,	lowercase__		,	lowercase__ = None		,	lowercase__ = None		,	)    ->    List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	num_inference_steps

									SCREAMING_SNAKE_CASE				=	num_train_timesteps or self.config.num_train_timesteps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
														SCREAMING_SNAKE_CASE				=	np.linspace(0		,	num_train_timesteps - 1		,	lowercase__		,	dtype=lowercase__			)[::-1].copy()
									elif self.config.timestep_spacing == "leading":
														SCREAMING_SNAKE_CASE				=	num_train_timesteps // self.num_inference_steps
														# creates integer timesteps by multiplying by ratio
														# casting to int to avoid issues when num_inference_step is power of 3
														SCREAMING_SNAKE_CASE				=	(np.arange(0		,	lowercase__			) * step_ratio).round()[::-1].copy().astype(lowercase__			)
														timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
														SCREAMING_SNAKE_CASE				=	num_train_timesteps / self.num_inference_steps
														# creates integer timesteps by multiplying by ratio
														# casting to int to avoid issues when num_inference_step is power of 3
														SCREAMING_SNAKE_CASE				=	(np.arange(lowercase__		,	0		,	-step_ratio			)).round().copy().astype(lowercase__			)
														timesteps -= 1
									else:
														raise ValueError(
														    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''			)

									SCREAMING_SNAKE_CASE				=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
									SCREAMING_SNAKE_CASE				=	torch.from_numpy(np.log(lowercase__			)			).to(lowercase__			)

									SCREAMING_SNAKE_CASE				=	np.interp(lowercase__		,	np.arange(0		,	len(lowercase__			)			)		,	lowercase__			)
									SCREAMING_SNAKE_CASE				=	np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
									SCREAMING_SNAKE_CASE				=	torch.from_numpy(lowercase__			).to(device=lowercase__			)

									# compute up and down sigmas
									SCREAMING_SNAKE_CASE				=	sigmas.roll(-1			)
									SCREAMING_SNAKE_CASE				=	0.0
									SCREAMING_SNAKE_CASE				=	(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
									SCREAMING_SNAKE_CASE				=	(sigmas_next**2 - sigmas_up**2) ** 0.5
									SCREAMING_SNAKE_CASE				=	0.0

									# compute interpolated sigmas
									SCREAMING_SNAKE_CASE				=	sigmas.log().lerp(sigmas_down.log()		,	0.5			).exp()
									SCREAMING_SNAKE_CASE				=	0.0

									# set sigmas
									SCREAMING_SNAKE_CASE				=	torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2			), sigmas[-1:]]			)
									SCREAMING_SNAKE_CASE				=	torch.cat(
									    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2			), sigmas_interpol[-1:]]			)
									SCREAMING_SNAKE_CASE				=	torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2			), sigmas_up[-1:]]			)
									SCREAMING_SNAKE_CASE				=	torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2			), sigmas_down[-1:]]			)

									if str(lowercase__			).startswith('mps'			):
														# mps does not support float64
														SCREAMING_SNAKE_CASE				=	torch.from_numpy(lowercase__			).to(lowercase__		,	dtype=torch.floataa			)
									else:
														SCREAMING_SNAKE_CASE				=	torch.from_numpy(lowercase__			).to(lowercase__			)

									SCREAMING_SNAKE_CASE				=	self.sigma_to_t(lowercase__			).to(lowercase__		,	dtype=timesteps.dtype			)
									SCREAMING_SNAKE_CASE				=	torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])		,	dim=-1			).flatten()

									SCREAMING_SNAKE_CASE				=	torch.cat([timesteps[:1], interleaved_timesteps]			)

									SCREAMING_SNAKE_CASE				=	None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									SCREAMING_SNAKE_CASE				=	defaultdict(lowercase__			)


				def 							A					(				self		,	lowercase__			)    ->    Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	sigma.log()

									# get distribution
									SCREAMING_SNAKE_CASE				=	log_sigma - self.log_sigmas[:, None]

									# get sigmas range
									SCREAMING_SNAKE_CASE				=	dists.ge(0			).cumsum(dim=0			).argmax(dim=0			).clamp(max=self.log_sigmas.shape[0] - 2			)
									SCREAMING_SNAKE_CASE				=	low_idx + 1

									SCREAMING_SNAKE_CASE				=	self.log_sigmas[low_idx]
									SCREAMING_SNAKE_CASE				=	self.log_sigmas[high_idx]

									# interpolate sigmas
									SCREAMING_SNAKE_CASE				=	(low - log_sigma) / (low - high)
									SCREAMING_SNAKE_CASE				=	w.clamp(0		,	1			)

									# transform interpolation to time range
									SCREAMING_SNAKE_CASE				=	(1 - w) * low_idx + w * high_idx
									SCREAMING_SNAKE_CASE				=	t.view(sigma.shape			)
									return t


				@property
				def 							A					(				self			)    ->    Tuple:
									"""simple docstring"""







									return self.sample is None


				def 							A					(				self		,	lowercase__		,	lowercase__		,	lowercase__		,	lowercase__ = None		,	lowercase__ = True		,	)    ->    Union[SchedulerOutput, Tuple]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.index_for_timestep(lowercase__			)

									# advance index counter by 1
									SCREAMING_SNAKE_CASE				=	timestep.cpu().item() if torch.is_tensor(lowercase__			) else timestep
									self._index_counter[timestep_int] += 1

									if self.state_in_first_order:
														SCREAMING_SNAKE_CASE				=	self.sigmas[step_index]
														SCREAMING_SNAKE_CASE				=	self.sigmas_interpol[step_index]
														SCREAMING_SNAKE_CASE				=	self.sigmas_up[step_index]
														SCREAMING_SNAKE_CASE				=	self.sigmas_down[step_index - 1]
									else:
														# 2nd order / KPDM2's method
														SCREAMING_SNAKE_CASE				=	self.sigmas[step_index - 1]
														SCREAMING_SNAKE_CASE				=	self.sigmas_interpol[step_index - 1]
														SCREAMING_SNAKE_CASE				=	self.sigmas_up[step_index - 1]
														SCREAMING_SNAKE_CASE				=	self.sigmas_down[step_index - 1]

									# currently only gamma=0 is supported. This usually works best anyways.
									# We can support gamma in the future but then need to scale the timestep before
									# passing it to the model which requires a change in API
									SCREAMING_SNAKE_CASE				=	0
									SCREAMING_SNAKE_CASE				=	sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									SCREAMING_SNAKE_CASE				=	model_output.device
									SCREAMING_SNAKE_CASE				=	randn_tensor(model_output.shape		,	dtype=model_output.dtype		,	device=lowercase__		,	generator=lowercase__			)

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
														SCREAMING_SNAKE_CASE				=	sigma_hat if self.state_in_first_order else sigma_interpol
														SCREAMING_SNAKE_CASE				=	sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
														SCREAMING_SNAKE_CASE				=	sigma_hat if self.state_in_first_order else sigma_interpol
														SCREAMING_SNAKE_CASE				=	model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
														    sample / (sigma_input**2 + 1)
														)
									elif self.config.prediction_type == "sample":
														raise NotImplementedError('prediction_type not implemented yet: sample'			)
									else:
														raise ValueError(
														    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''			)

									if self.state_in_first_order:
														# 2. Convert to an ODE derivative for 1st order
														SCREAMING_SNAKE_CASE				=	(sample - pred_original_sample) / sigma_hat
														# 3. delta timestep
														SCREAMING_SNAKE_CASE				=	sigma_interpol - sigma_hat

														# store for 2nd order step
														SCREAMING_SNAKE_CASE				=	sample
														SCREAMING_SNAKE_CASE				=	dt
														SCREAMING_SNAKE_CASE				=	sample + derivative * dt
									else:
														# DPM-Solver-2
														# 2. Convert to an ODE derivative for 2nd order
														SCREAMING_SNAKE_CASE				=	(sample - pred_original_sample) / sigma_interpol
														# 3. delta timestep
														SCREAMING_SNAKE_CASE				=	sigma_down - sigma_hat

														SCREAMING_SNAKE_CASE				=	self.sample
														SCREAMING_SNAKE_CASE				=	None

														SCREAMING_SNAKE_CASE				=	sample + derivative * dt
														SCREAMING_SNAKE_CASE				=	prev_sample + noise * sigma_up

									if not return_dict:
														return (prev_sample,)

									return SchedulerOutput(prev_sample=lowercase__			)


				def 							A					(				self		,	lowercase__		,	lowercase__		,	lowercase__		,	)    ->    torch.FloatTensor:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.sigmas.to(device=original_samples.device		,	dtype=original_samples.dtype			)
									if original_samples.device.type == "mps" and torch.is_floating_point(lowercase__			):
														# mps does not support float64
														SCREAMING_SNAKE_CASE				=	self.timesteps.to(original_samples.device		,	dtype=torch.floataa			)
														SCREAMING_SNAKE_CASE				=	timesteps.to(original_samples.device		,	dtype=torch.floataa			)
									else:
														SCREAMING_SNAKE_CASE				=	self.timesteps.to(original_samples.device			)
														SCREAMING_SNAKE_CASE				=	timesteps.to(original_samples.device			)

									SCREAMING_SNAKE_CASE				=	[self.index_for_timestep(lowercase__		,	lowercase__			) for t in timesteps]

									SCREAMING_SNAKE_CASE				=	sigmas[step_indices].flatten()
									while len(sigma.shape			) < len(original_samples.shape			):
														SCREAMING_SNAKE_CASE				=	sigma.unsqueeze(-1			)

									SCREAMING_SNAKE_CASE				=	original_samples + noise * sigma
									return noisy_samples




				def __len__(				self			)    ->    Any:
									"""simple docstring"""







									return self.config.num_train_timesteps







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 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				List[str]					    =["""image_processor""", """tokenizer"""]
  a_							:				Optional[Any]					    ="""LayoutLMv2ImageProcessor"""
  a_							:				Any					    =("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")







  def __init__(					self					:    Union[str, Any]					, UpperCamelCase					:    str=None					, UpperCamelCase					:    List[Any]=None					, **UpperCamelCase					:    List[str]							):


    '''simple docstring'''





    _snake_case      : Dict														=		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					, )
      _snake_case      : Dict														=		kwargs.pop('feature_extractor'							)

    _snake_case      : Optional[Any]														=		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					:    int					, UpperCamelCase					:    Tuple					, 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					:    Optional[Any]					, ):


    '''simple docstring'''





    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
    _snake_case      : Optional[Any]														=		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							):
        _snake_case      : List[Any]														=		[text]  # add batch dimension (as the image processor always adds a batch dimension)
      _snake_case      : Dict														=		features['words']

    _snake_case      : Optional[int]														=		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
    _snake_case      : Optional[int]														=		features.pop('pixel_values'							)
    if return_overflowing_tokens is True:
      _snake_case      : Any														=		self.get_overflowing_images(UpperCamelCase					, encoded_inputs['overflow_to_sample_mapping']							)
    _snake_case      : int														=		images

    return encoded_inputs







  def UpperCamelCase_       (					self					:    List[Any]					, UpperCamelCase					:    List[str]					, UpperCamelCase					:    List[str]							):


    '''simple docstring'''





    _snake_case      : Optional[Any]														=		[]
    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 UpperCamelCase_       (					self					:    List[Any]					, *UpperCamelCase					:    Any					, **UpperCamelCase					:    int							):


    '''simple docstring'''





    return self.tokenizer.batch_decode(*UpperCamelCase					, **UpperCamelCase							)







  def UpperCamelCase_       (					self					:    Any					, *UpperCamelCase					:    Optional[int]					, **UpperCamelCase					:    Any							):


    '''simple docstring'''





    return self.tokenizer.decode(*UpperCamelCase					, **UpperCamelCase							)







  @property
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







  @property
  def UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





    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 UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





    warnings.warn(
        '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					, UpperCamelCase					, )
    return self.image_processor




