
'''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__	):





  """simple docstring"""

  __UpperCAmelCase				:				Dict   =					['''image_processor''', '''tokenizer''']
  __UpperCAmelCase				:				Optional[int]   =					'''LayoutLMv2ImageProcessor'''
  __UpperCAmelCase				:				Union[str, Any]   =					('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')


  def __init__(		self      :     List[str]							,_a      :     str=None							,_a      :     List[str]=None							,**_a      :     Union[str, 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.'							,_a							,)
        _a				:	Union[str, Any]			=   kwargs.pop('feature_extractor'       )

     _a				:	Dict			=   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      :     str							,_a      :     Union[str, 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      :     Tuple							,):


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

     _a				:	Any			=   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
     _a				:	List[Any]			=   features.pop('pixel_values'       )
     if return_overflowing_tokens is True:
        _a				:	Optional[Any]			=   self.get_overflowing_images(_a							,encoded_inputs['overflow_to_sample_mapping']       )
     _a				:	Tuple			=   images

     return encoded_inputs


  def   __lowercase		(		self      :     Optional[Any]							,_a      :     Tuple							,_a      :     List[Any]       ):


     '''simple docstring'''






     _a				:	List[str]			=   []
     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   __lowercase		(		self      :     Tuple							,*_a      :     Optional[Any]							,**_a      :     Dict       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     str							,*_a      :     List[str]							,**_a      :     Optional[int]       ):


     '''simple docstring'''






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


  @property
  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






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


  @property
  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     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   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






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





'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

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


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	int			=   UNetaDModel(
         block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=3							,out_channels=3							,down_block_types=('DownBlock2D', 'AttnDownBlock2D')							,up_block_types=('AttnUpBlock2D', 'UpBlock2D')							,)
     return model


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	str			=   self.dummy_uncond_unet
     _a				:	int			=   PNDMScheduler()

     _a				:	str			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )

     _a				:	Optional[int]			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'       ).images

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	Any			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'							,return_dict=_a       )[0]

     _a				:	List[Any]			=   image[0, -3:, -3:, -1]
     _a				:	Any			=   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	List[Any]			=   np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2







@slow
@require_torch
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[str]			=   'google/ddpm-cifar10-32'

     _a				:	str			=   UNetaDModel.from_pretrained(_a       )
     _a				:	Union[str, Any]			=   PNDMScheduler()

     _a				:	Tuple			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )
     _a				:	str			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,output_type='numpy'       ).images

     _a				:	Dict			=   image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	Tuple			=   np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2



