


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


							def __init__(self      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				**lowerCAmelCase					):
									if "feature_extractor" in kwargs:
											warnings.warn(
											    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
											    ' instead.'      ,				lowerCAmelCase      ,				)
											__lowercase=			kwargs.pop('feature_extractor'					)

									__lowercase=			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.'					)

									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
									__lowercase=			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					):
													__lowercase=			[text]  # add batch dimension (as the image processor always adds a batch dimension)
											__lowercase=			features['words']

									__lowercase=			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
									__lowercase=			features.pop('pixel_values'					)
									if return_overflowing_tokens is True:
											__lowercase=			self.get_overflowing_images(lowerCAmelCase      ,				encoded_inputs['overflow_to_sample_mapping']					)
									__lowercase=			images

									return encoded_inputs


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


							def 			_A       (self      ,				*lowerCAmelCase      ,				**lowerCAmelCase					):
									return self.tokenizer.decode(*lowerCAmelCase      ,				**lowerCAmelCase					)


							@property
							def 			_A       (self					):
									return ["input_ids", "bbox", "attention_mask", "image"]


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










from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer





@dataclass
class       A    (   A_ ):
							UpperCamelCase_   :							torch.FloatTensor





class       A    (   A_					,				A_ ):


							@register_to_config
							def __init__(self      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = ("DownEncoderBlock2D",)      ,				lowerCAmelCase = ("UpDecoderBlock2D",)      ,				lowerCAmelCase = (6_4,)      ,				lowerCAmelCase = 1      ,				lowerCAmelCase = "silu"      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = 2_5_6      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = None      ,				lowerCAmelCase = 0.1_82_15      ,				lowerCAmelCase = "group"      ,				):
									super().__init__()

									# pass init params to Encoder
									__lowercase=			Encoder(
									    in_channels=lowerCAmelCase      ,				out_channels=lowerCAmelCase      ,				down_block_types=lowerCAmelCase      ,				block_out_channels=lowerCAmelCase      ,				layers_per_block=lowerCAmelCase      ,				act_fn=lowerCAmelCase      ,				norm_num_groups=lowerCAmelCase      ,				double_z=lowerCAmelCase      ,				)

									__lowercase=			vq_embed_dim if vq_embed_dim is not None else latent_channels

									__lowercase=			nn.Convad(lowerCAmelCase      ,				lowerCAmelCase      ,				1					)
									__lowercase=			VectorQuantizer(lowerCAmelCase      ,				lowerCAmelCase      ,				beta=0.25      ,				remap=lowerCAmelCase      ,				sane_index_shape=lowerCAmelCase					)
									__lowercase=			nn.Convad(lowerCAmelCase      ,				lowerCAmelCase      ,				1					)

									# pass init params to Decoder
									__lowercase=			Decoder(
									    in_channels=lowerCAmelCase      ,				out_channels=lowerCAmelCase      ,				up_block_types=lowerCAmelCase      ,				block_out_channels=lowerCAmelCase      ,				layers_per_block=lowerCAmelCase      ,				act_fn=lowerCAmelCase      ,				norm_num_groups=lowerCAmelCase      ,				norm_type=lowerCAmelCase      ,				)


							@apply_forward_hook
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			self.encoder(lowerCAmelCase					)
									__lowercase=			self.quant_conv(lowerCAmelCase					)

									if not return_dict:
											return (h,)

									return VQEncoderOutput(latents=lowerCAmelCase					)


							@apply_forward_hook
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = False      ,				lowerCAmelCase = True					):
									# also go through quantization layer
									if not force_not_quantize:
											__lowercase,							__lowercase,							__lowercase=			self.quantize(lowerCAmelCase					)
									else:
											__lowercase=			h
									__lowercase=			self.post_quant_conv(lowerCAmelCase					)
									__lowercase=			self.decoder(lowerCAmelCase      ,				quant if self.config.norm_type == 'spatial' else None					)

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			sample
									__lowercase=			self.encode(lowerCAmelCase					).latents
									__lowercase=			self.decode(lowerCAmelCase					).sample

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)






