


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 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			['image_processor', 'tokenizer']
 SCREAMING_SNAKE_CASE__ =			'LayoutLMv3ImageProcessor'
 SCREAMING_SNAKE_CASE__ =			('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')




 def __init__(						self   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None   ,    **_lowerCamelCase       ):
       a		:List[str]									=			None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''   ,    _lowerCamelCase   ,    )
             a		:Union[str, Any]									=			kwargs.pop('''feature_extractor'''       )

       a		:Tuple									=			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   ,    _lowerCamelCase   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = True   ,    _lowerCamelCase = False   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = 0   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = False   ,    _lowerCamelCase = False   ,    _lowerCamelCase = False   ,    _lowerCamelCase = False   ,    _lowerCamelCase = True   ,    _lowerCamelCase = None   ,    **_lowerCamelCase   ,    ):
       # 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.'''       )

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

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

       return encoded_inputs




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       a		:Optional[Any]									=			[]
       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 			SCREAMING_SNAKE_CASE__							(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       return self.tokenizer.batch_decode(*_lowerCamelCase   ,    **_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       return self.tokenizer.decode(*_lowerCamelCase   ,    **_lowerCamelCase       )




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return ["input_ids", "bbox", "attention_mask", "pixel_values"]




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       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 			SCREAMING_SNAKE_CASE__							(						self       ):
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,    _lowerCamelCase   ,    )
       return self.image_processor









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 						_snake_case    ( unittest.TestCase				):




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )
       a		:List[Any]									=			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 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			self.dummy_uncond_unet
       a		:Any									=			PNDMScheduler()

       a		:Any									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )

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

       a		:int									=			torch.manual_seed(0       )
       a		:str									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''   ,    return_dict=_lowerCamelCase       )[0]

       a		:int									=			image[0, -3:, -3:, -1]
       a		:List[str]									=			image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:str									=			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 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			'''google/ddpm-cifar10-32'''

       a		:str									=			UNetaDModel.from_pretrained(_lowerCamelCase       )
       a		:Optional[Any]									=			PNDMScheduler()

       a		:Union[str, Any]									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )
       a		:Dict									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    output_type='''numpy'''       ).images

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

       assert image.shape == (1, 32, 32, 3)
       a		:int									=			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





