


'''simple docstring'''





import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      List[str]						=   image.size
   UpperCAmelCase_							:      List[Any]						=   (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
   UpperCAmelCase_							:      Union[str, Any]						=   image.resize((w, h)			,       resample=PIL_INTERPOLATION['''lanczos''']       )
   UpperCAmelCase_							:      List[Any]						=   np.array(lowerCamelCase_       ).astype(np.floataa       ) / 2_5_5.0
   UpperCAmelCase_							:      Any						=   image[None].transpose(0			,       3			,       1			,       2       )
   UpperCAmelCase_							:      Dict						=   torch.from_numpy(lowerCamelCase_       )
   return 2.0 * image - 1.0





class  lowerCAmelCase__(    __lowerCamelCase						):






    '''simple docstring'''





    def __init__(	self				:   List[Any]       ,   __snake_case				:   VQModel       ,   __snake_case				:   UNetaDModel       ,   __snake_case				:   Union[
            DDIMScheduler,
            PNDMScheduler,
            LMSDiscreteScheduler,
            EulerDiscreteScheduler,
            EulerAncestralDiscreteScheduler,
            DPMSolverMultistepScheduler,
        ]       ,   ):





       '''simple docstring'''
       super().__init__()
       self.register_modules(vqvae=UpperCAmelCase_       ,   unet=UpperCAmelCase_       ,   scheduler=UpperCAmelCase_	)





    @torch.no_grad()
    def __call__(	self				:   Optional[Any]       ,   __snake_case				:   Union[torch.Tensor, PIL.Image.Image] = None       ,   __snake_case				:   Optional[int] = 1       ,   __snake_case				:   Optional[int] = 100       ,   __snake_case				:   Optional[float] = 0.0       ,   __snake_case				:   Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,   __snake_case				:   Optional[str] = "pil"       ,   __snake_case				:   bool = True       ,   ):





       '''simple docstring'''
       if isinstance(UpperCAmelCase_       ,   PIL.Image.Image	):
          UpperCAmelCase_							:      List[str]						=   1
       elif isinstance(UpperCAmelCase_       ,   torch.Tensor	):
          UpperCAmelCase_							:      Any						=   image.shape[0]
       else:
          raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCAmelCase_	)}'''	)

       if isinstance(UpperCAmelCase_       ,   PIL.Image.Image	):
          UpperCAmelCase_							:      str						=   preprocess(UpperCAmelCase_	)

       UpperCAmelCase_							:      Optional[int]						=   image.shape[-2:]

       # in_channels should be 6: 3 for latents, 3 for low resolution image
       UpperCAmelCase_							:      Optional[int]						=   (batch_size, self.unet.config.in_channels // 2, height, width)
       UpperCAmelCase_							:      int						=   next(self.unet.parameters()	).dtype

       UpperCAmelCase_							:      Union[str, Any]						=   randn_tensor(UpperCAmelCase_       ,   generator=UpperCAmelCase_       ,   device=self.device       ,   dtype=UpperCAmelCase_	)

       UpperCAmelCase_							:      Union[str, Any]						=   image.to(device=self.device       ,   dtype=UpperCAmelCase_	)

       # set timesteps and move to the correct device
       self.scheduler.set_timesteps(UpperCAmelCase_       ,   device=self.device	)
       UpperCAmelCase_							:      List[Any]						=   self.scheduler.timesteps

       # scale the initial noise by the standard deviation required by the scheduler
       UpperCAmelCase_							:      str						=   latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       UpperCAmelCase_							:      Tuple						=   'eta' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
       UpperCAmelCase_							:      List[Any]						=   {}
       if accepts_eta:
          UpperCAmelCase_							:      Tuple						=   eta

       for t in self.progress_bar(UpperCAmelCase_	):
          # concat latents and low resolution image in the channel dimension.
          UpperCAmelCase_							:      Union[str, Any]						=   torch.cat([latents, image]       ,   dim=1	)
          UpperCAmelCase_							:      Optional[Any]						=   self.scheduler.scale_model_input(UpperCAmelCase_       ,   UpperCAmelCase_	)
          # predict the noise residual
          UpperCAmelCase_							:      Union[str, Any]						=   self.unet(UpperCAmelCase_       ,   UpperCAmelCase_	).sample
          # compute the previous noisy sample x_t -> x_t-1
          UpperCAmelCase_							:      List[str]						=   self.scheduler.step(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   **UpperCAmelCase_	).prev_sample

       # decode the image latents with the VQVAE
       UpperCAmelCase_							:      Dict						=   self.vqvae.decode(UpperCAmelCase_	).sample
       UpperCAmelCase_							:      List[Any]						=   torch.clamp(UpperCAmelCase_       ,   -1.0       ,   1.0	)
       UpperCAmelCase_							:      Dict						=   image / 2 + 0.5
       UpperCAmelCase_							:      Union[str, Any]						=   image.cpu().permute(0       ,   2       ,   3       ,   1	).numpy()

       if output_type == "pil":
          UpperCAmelCase_							:      Optional[int]						=   self.numpy_to_pil(UpperCAmelCase_	)

       if not return_dict:
          return (image,)

       return ImagePipelineOutput(images=UpperCAmelCase_	)



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__(    snake_case__						):






    '''simple docstring'''





    A_  :     str			= ['image_processor', 'tokenizer']
    A_  :     int			= 'LayoutLMv2ImageProcessor'
    A_  :     str			= ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')





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





       '''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.'''       ,   __snake_case       ,   )
          UpperCAmelCase_							:      List[Any]						=   kwargs.pop('''feature_extractor'''	)

       UpperCAmelCase_							:      Optional[int]						=   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__(__snake_case       ,   __snake_case	)





    def __call__(	self				:   List[str]       ,   __snake_case				:   Dict       ,   __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				:   Optional[int]       ,   ):





       '''simple docstring'''
       # 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
       UpperCAmelCase_							:      Tuple						=   self.image_processor(images=__snake_case       ,   return_tensors=__snake_case	)

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

       UpperCAmelCase_							:      str						=   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=__snake_case       ,   add_special_tokens=__snake_case       ,   padding=__snake_case       ,   truncation=__snake_case       ,   max_length=__snake_case       ,   stride=__snake_case       ,   pad_to_multiple_of=__snake_case       ,   return_token_type_ids=__snake_case       ,   return_attention_mask=__snake_case       ,   return_overflowing_tokens=__snake_case       ,   return_special_tokens_mask=__snake_case       ,   return_offsets_mapping=__snake_case       ,   return_length=__snake_case       ,   verbose=__snake_case       ,   return_tensors=__snake_case       ,   **__snake_case       ,   )

       # add pixel values
       UpperCAmelCase_							:      List[str]						=   features.pop('''pixel_values'''	)
       if return_overflowing_tokens is True:
          UpperCAmelCase_							:      Optional[int]						=   self.get_overflowing_images(__snake_case       ,   encoded_inputs['''overflow_to_sample_mapping''']	)
       UpperCAmelCase_							:      List[Any]						=   images

       return encoded_inputs





    def 					_lowerCamelCase (	self				:   Union[str, Any]       ,   __snake_case				:   str       ,   __snake_case				:   List[Any]	):





       '''simple docstring'''
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       UpperCAmelCase_							:      List[str]						=   []
       for sample_idx in overflow_to_sample_mapping:
          images_with_overflow.append(images[sample_idx]	)

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

       return images_with_overflow





    def 					_lowerCamelCase (	self				:   List[Any]       ,   *__snake_case				:   Optional[int]       ,   **__snake_case				:   List[Any]	):





       '''simple docstring'''
       return self.tokenizer.batch_decode(*__snake_case       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   str       ,   *__snake_case				:   Optional[Any]       ,   **__snake_case				:   Union[str, Any]	):





       '''simple docstring'''
       return self.tokenizer.decode(*__snake_case       ,   **__snake_case	)





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





       '''simple docstring'''
       return ["input_ids", "bbox", "attention_mask", "image"]





    @property
    def 					_lowerCamelCase (	self				:   List[str]	):





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





    @property
    def 					_lowerCamelCase (	self				:   Dict	):





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