





# 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 					A__       (				a__      ):




   """simple docstring"""



   __A  :							Dict			    =    4_2
   __A  :							Optional[Any]			    =    4_2





class 					A__       (				a__					,    a__      ):




   """simple docstring"""



   __A  :							Dict			    =    1

   @register_to_config
   def __init__(   self   ,    lowercase = 2000   ,    lowercase = 0.15   ,    lowercase = 0.01   ,    lowercase = 1348.0   ,    lowercase = 1e-5   ,    lowercase = 1   ,    )     ->		List[str]:







          '''simple docstring'''

          a__		:      Any					=							sigma_max

          # setable values
          a__		:      Any					=							None

          self.set_sigmas(__a   ,    __a   ,    __a   ,    __a)

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase = None)     ->		torch.FloatTensor:







          '''simple docstring'''

          return sample

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase = None   ,    lowercase = None)     ->		List[str]:







          '''simple docstring'''

          a__		:      int					=							sampling_eps if sampling_eps is not None else self.config.sampling_eps

          a__		:      Any					=							torch.linspace(1   ,    __a   ,    __a   ,    device=__a)

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase = None   ,    lowercase = None   ,    lowercase = None)     ->		Any:







          '''simple docstring'''

          a__		:      Union[str, Any]					=							sigma_min if sigma_min is not None else self.config.sigma_min
          a__		:      Optional[Any]					=							sigma_max if sigma_max is not None else self.config.sigma_max
          a__		:      Dict					=							sampling_eps if sampling_eps is not None else self.config.sampling_eps
          if self.timesteps is None:
                 self.set_timesteps(__a   ,    __a)

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

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase)     ->		Any:







          '''simple docstring'''

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

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase   ,    lowercase   ,    lowercase = None   ,    lowercase = 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')

          a__		:      Dict					=							timestep * torch.ones(
              sample.shape[0]   ,    device=sample.device)  # torch.repeat_interleave(timestep, sample.shape[0])
          a__		:      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
          a__		:      Optional[Any]					=							timesteps.to(self.discrete_sigmas.device)

          a__		:      Tuple					=							self.discrete_sigmas[timesteps].to(sample.device)
          a__		:      int					=							self.get_adjacent_sigma(__a   ,    __a).to(sample.device)
          a__		:      int					=							torch.zeros_like(__a)
          a__		:      Any					=							(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
          a__		:      List[str]					=							diffusion.flatten()
          while len(diffusion.shape) < len(sample.shape):
                 a__		:      Dict					=							diffusion.unsqueeze(-1)
          a__		:      Dict					=							drift - diffusion**2 * model_output

          #  equation 6: sample noise for the diffusion term of
          a__		:      Dict					=							randn_tensor(
              sample.shape   ,    layout=sample.layout   ,    generator=__a   ,    device=sample.device   ,    dtype=sample.dtype)
          a__		:      Any					=							sample - drift  # subtract because `dt` is a small negative timestep
          # TODO is the variable diffusion the correct scaling term for the noise?
          a__		:      List[str]					=							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=__a   ,    prev_sample_mean=__a)

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase   ,    lowercase = None   ,    lowercase = 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
          a__		:      Union[str, Any]					=							randn_tensor(sample.shape   ,    layout=sample.layout   ,    generator=__a).to(sample.device)

          # compute step size from the model_output, the noise, and the snr
          a__		:      Optional[int]					=							torch.norm(model_output.reshape(model_output.shape[0]   ,    -1)   ,    dim=-1).mean()
          a__		:      List[str]					=							torch.norm(noise.reshape(noise.shape[0]   ,    -1)   ,    dim=-1).mean()
          a__		:      Any					=							(self.config.snr * noise_norm / grad_norm) ** 2 * 2
          a__		:      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
          a__		:      Union[str, Any]					=							step_size.flatten()
          while len(step_size.shape) < len(sample.shape):
                 a__		:      List[Any]					=							step_size.unsqueeze(-1)
          a__		:      Optional[int]					=							sample + step_size * model_output
          a__		:      Optional[int]					=							prev_sample_mean + ((step_size * 2) ** 0.5) * noise

          if not return_dict:
                 return (prev_sample,)

          return SchedulerOutput(prev_sample=__a)

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase   ,    lowercase   ,    )     ->		torch.FloatTensor:







          '''simple docstring'''

          a__		:      List[Any]					=							timesteps.to(original_samples.device)
          a__		:      Optional[Any]					=							self.discrete_sigmas.to(original_samples.device)[timesteps]
          a__		:      Optional[int]					=							(
              noise * sigmas[:, None, None, None]
              if noise is not None
              else torch.randn_like(__a) * sigmas[:, None, None, None]
          )
          a__		:      Optional[Any]					=							noise + original_samples
          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   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							["image_processor", "tokenizer"]
       _a   							=							"LayoutLMv2ImageProcessor"
       _a   							=							("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")



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

          lowerCamelCase_						:   List[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__(__a		,				__a							)



       def __call__(						self				:				List[str]		,				__a				:				List[Any]		,				__a				:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,				__a				:				Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None		,				__a				:				Union[List[List[int]], List[List[List[int]]]] = None		,				__a				:				Optional[Union[List[int], List[List[int]]]] = None		,				__a				:				bool = True		,				__a				:				Union[bool, str, PaddingStrategy] = False		,				__a				:				Union[bool, str, TruncationStrategy] = None		,				__a				:				Optional[int] = None		,				__a				:				int = 0		,				__a				:				Optional[int] = None		,				__a				:				Optional[bool] = None		,				__a				:				Optional[bool] = None		,				__a				:				bool = False		,				__a				:				bool = False		,				__a				:				bool = False		,				__a				:				bool = False		,				__a				:				bool = True		,				__a				:				Optional[Union[str, TensorType]] = None		,				**__a				:				Any		,				)					->BatchEncoding:
          # 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
          lowerCamelCase_						:   Tuple		= self.image_processor(images=__a		,				return_tensors=__a							)

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

          lowerCamelCase_						:   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=__a		,				add_special_tokens=__a		,				padding=__a		,				truncation=__a		,				max_length=__a		,				stride=__a		,				pad_to_multiple_of=__a		,				return_token_type_ids=__a		,				return_attention_mask=__a		,				return_overflowing_tokens=__a		,				return_special_tokens_mask=__a		,				return_offsets_mapping=__a		,				return_length=__a		,				verbose=__a		,				return_tensors=__a		,				**__a		,				)

          # add pixel values
          lowerCamelCase_						:   Any		= features.pop("""pixel_values"""							)
          if return_overflowing_tokens is True:
             lowerCamelCase_						:   Dict		= self.get_overflowing_images(__a		,				encoded_inputs["""overflow_to_sample_mapping"""]							)
          lowerCamelCase_						:   Optional[int]		= images

          return encoded_inputs



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				str		,				__a				:				Union[str, Any]							)					->List[str]:
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          lowerCamelCase_						:   Dict		= []
          for sample_idx in overflow_to_sample_mapping:
             images_with_overflow.append(images[sample_idx]							)

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

          return images_with_overflow



       def 				_lowerCAmelCase							(						self				:				Dict		,				*__a				:				Dict		,				**__a				:				int							)					->int:
          return self.tokenizer.batch_decode(*__a		,				**__a							)



       def 				_lowerCAmelCase							(						self				:				List[Any]		,				*__a				:				List[str]		,				**__a				:				int							)					->List[Any]:
          return self.tokenizer.decode(*__a		,				**__a							)



       @property
       def 				_lowerCAmelCase							(						self				:				int							)					->List[str]:
          return ["input_ids", "bbox", "attention_mask", "image"]



       @property
       def 				_lowerCAmelCase							(						self				:				int							)					->str:
          warnings.warn(
              """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,				__a		,				)
          return self.image_processor_class




       @property
       def 				_lowerCAmelCase							(						self				:				int							)					->List[Any]:
          warnings.warn(
              """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,				__a		,				)
          return self.image_processor





