

'''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     a			(				__lowerCAmelCase       ):






						"""simple docstring"""




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







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



								'''simple docstring'''



								if "feature_extractor" in kwargs:
										warnings.warn(
										    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
										    ''' instead.'''			,			lowerCamelCase__			,			)
										snake_case__						:       str       						=  kwargs.pop('''feature_extractor'''  )

								snake_case__						:       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__(lowerCamelCase__			,			lowerCamelCase__  )







						def __call__(							self					:      Optional[Any]			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,			snake_case_					:      Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,			snake_case_					:      Union[List[List[int]], List[List[List[int]]]] = None			,			snake_case_					:      Optional[Union[List[int], List[List[int]]]] = None			,			snake_case_					:      bool = True			,			snake_case_					:      Union[bool, str, PaddingStrategy] = False			,			snake_case_					:      Union[bool, str, TruncationStrategy] = None			,			snake_case_					:      Optional[int] = None			,			snake_case_					:      int = 0			,			snake_case_					:      Optional[int] = None			,			snake_case_					:      Optional[bool] = None			,			snake_case_					:      Optional[bool] = None			,			snake_case_					:      bool = False			,			snake_case_					:      bool = False			,			snake_case_					:      bool = False			,			snake_case_					:      bool = False			,			snake_case_					:      bool = True			,			snake_case_					:      Optional[Union[str, TensorType]] = None			,			**snake_case_					:      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=lowerCamelCase__			,			return_tensors=lowerCamelCase__  )

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
										if isinstance(lowerCamelCase__			,			lowerCamelCase__  ):
												snake_case__						:       Optional[int]       						=  [text]  # add batch dimension (as the image processor always adds a batch dimension)
										snake_case__						:       Optional[int]       						=  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=lowerCamelCase__			,			add_special_tokens=lowerCamelCase__			,			padding=lowerCamelCase__			,			truncation=lowerCamelCase__			,			max_length=lowerCamelCase__			,			stride=lowerCamelCase__			,			pad_to_multiple_of=lowerCamelCase__			,			return_token_type_ids=lowerCamelCase__			,			return_attention_mask=lowerCamelCase__			,			return_overflowing_tokens=lowerCamelCase__			,			return_special_tokens_mask=lowerCamelCase__			,			return_offsets_mapping=lowerCamelCase__			,			return_length=lowerCamelCase__			,			verbose=lowerCamelCase__			,			return_tensors=lowerCamelCase__			,			**lowerCamelCase__			,			)

								# add pixel values
								snake_case__						:       Optional[Any]       						=  features.pop('''pixel_values'''  )
								if return_overflowing_tokens is True:
										snake_case__						:       Union[str, Any]       						=  self.get_overflowing_images(lowerCamelCase__			,			encoded_inputs['''overflow_to_sample_mapping''']  )
								snake_case__						:       Tuple       						=  images

								return encoded_inputs







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      Any			,			snake_case_					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       int       						=  []
								for sample_idx in overflow_to_sample_mapping:
										images_with_overflow.append(images[sample_idx]  )

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

								return images_with_overflow







						def       __magic_name__							(							self					:      int			,			*snake_case_					:      Dict			,			**snake_case_					:      str  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      List[str]			,			*snake_case_					:      int			,			**snake_case_					:      str  ):



								'''simple docstring'''



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







						@property
						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



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







						@property
						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,			lowerCamelCase__			,			)
								return self.image_processor_class







						@property
						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



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






'''simple docstring'''


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     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    None





class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    2







						@register_to_config
						def __init__(							self					:      int			,			snake_case_					:      float = 0.0_2			,			snake_case_					:      float = 1_0_0			,			snake_case_					:      float = 1.0_0_7			,			snake_case_					:      float = 8_0			,			snake_case_					:      float = 0.0_5			,			snake_case_					:      float = 5_0			,			):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  sigma_max

								# setable values
								snake_case__						:       int       						=  None
								snake_case__						:       np.IntTensor       						=  None
								snake_case__						:       torch.FloatTensor       						=  None  # sigma(t_i)







						def       __magic_name__							(							self					:      Any			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      Optional[int] = None  ):



								'''simple docstring'''



								return sample







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      int			,			snake_case_					:      Union[str, torch.device] = None  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  num_inference_steps
								snake_case__						:       Tuple       						=  np.arange(0			,			self.num_inference_steps  )[::-1].copy()
								snake_case__						:       int       						=  torch.from_numpy(snake_case_  ).to(snake_case_  )
								snake_case__						:       List[str]       						=  [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								snake_case__						:       Union[str, Any]       						=  torch.tensor(snake_case_			,			dtype=torch.floataa			,			device=snake_case_  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      Optional[torch.Generator] = None  ):



								'''simple docstring'''



								if self.config.s_min <= sigma <= self.config.s_max:
										snake_case__						:       List[Any]       						=  min(self.config.s_churn / self.num_inference_steps			,			2**0.5 - 1  )
								else:
										snake_case__						:       List[Any]       						=  0

								# sample eps ~ N(0, S_noise^2 * I)
								snake_case__						:       Any       						=  self.config.s_noise * randn_tensor(sample.shape			,			generator=snake_case_  ).to(sample.device  )
								snake_case__						:       Tuple       						=  sigma + gamma * sigma
								snake_case__						:       Dict       						=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      float			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      bool = True			,			):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  sample_hat + sigma_hat * model_output
								snake_case__						:       List[Any]       						=  (sample_hat - pred_original_sample) / sigma_hat
								snake_case__						:       Any       						=  sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
										return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=snake_case_			,			derivative=snake_case_			,			pred_original_sample=snake_case_  )







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      float			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      bool = True			,			):



								'''simple docstring'''



								snake_case__						:       str       						=  sample_prev + sigma_prev * model_output
								snake_case__						:       List[Any]       						=  (sample_prev - pred_original_sample) / sigma_prev
								snake_case__						:       Any       						=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
										return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=snake_case_			,			derivative=snake_case_			,			pred_original_sample=snake_case_  )







						def       __magic_name__							(							self					:      str			,			snake_case_					:      List[Any]			,			snake_case_					:      Optional[int]			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								raise NotImplementedError()



