






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_					(   lowerCamelCase			):
				a__       							=	['''image_processor''', '''tokenizer''']
				a__       							=	'''LayoutLMv3ImageProcessor'''
				a__       							=	('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')







				def __init__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		: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							,       )
												__magic_name__		:Union[str, Any]   =   kwargs.pop('''feature_extractor'''  )

								__magic_name__		:List[str]   =   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							,       ):
								"""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.'''  )

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

								__magic_name__		:Dict   =   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
								__magic_name__		:List[str]   =   features.pop('''pixel_values'''  )
								if return_overflowing_tokens is True:
												__magic_name__		:Any   =   self.get_overflowing_images(__lowerCAmelCase							,       encoded_inputs['''overflow_to_sample_mapping''']  )
								__magic_name__		:List[str]   =   images

								return encoded_inputs







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								__magic_name__		:Optional[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       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.batch_decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







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







				@property
				def       A						(    self  ):
								"""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       A						(    self  ):
								"""simple docstring"""
								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,       __lowerCAmelCase							,       )
								return self.image_processor














import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger
class     lowerCamelCase_					(   lowerCamelCase			):







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   params

								__magic_name__		:Any   =   np.array(__lowerCAmelCase  )
								__magic_name__		:Optional[Any]   =   np.array([len(__lowerCAmelCase  ) for t in data]  )

								self.check()
								self.remove_long_sequences()
								self.remove_empty_sequences()
								self.remove_unknown_sequences()
								self.check()
								self.print_statistics()







				def __getitem__(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								return (self.token_ids[index], self.lengths[index])







				def __len__(    self  ):
								"""simple docstring"""
								return len(self.lengths  )







				def       A						(    self  ):
								"""simple docstring"""
								assert len(self.token_ids  ) == len(self.lengths  )
								assert all(self.lengths[i] == len(self.token_ids[i]  ) for i in range(len(self.lengths  )  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   self.params.max_model_input_size
								__magic_name__		:int   =   self.lengths > max_len
								logger.info(F'''Splitting {sum(__lowerCAmelCase  )} too long sequences.'''  )

								def divide_chunks(__lowerCAmelCase							,       __lowerCAmelCase  ):
												return [l[i : i + n] for i in range(0							,       len(__lowerCAmelCase  )							,       __lowerCAmelCase  )]

								__magic_name__		:Optional[int]   =   []
								__magic_name__		:List[Any]   =   []
								if self.params.mlm:
												__magic_name__  ,					__magic_name__		:Optional[Any]   =   self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
								else:
												__magic_name__  ,					__magic_name__		:Tuple   =   self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

								for seq_, len_ in zip(self.token_ids							,       self.lengths  ):
												assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
												if len_ <= max_len:
																new_tok_ids.append(seq_  )
																new_lengths.append(len_  )
												else:
																__magic_name__		:int   =   []
																for sub_s in divide_chunks(seq_							,       max_len - 2  ):
																				if sub_s[0] != cls_id:
																								__magic_name__		:List[Any]   =   np.insert(__lowerCAmelCase							,       0							,       __lowerCAmelCase  )
																				if sub_s[-1] != sep_id:
																								__magic_name__		:Union[str, Any]   =   np.insert(__lowerCAmelCase							,       len(__lowerCAmelCase  )							,       __lowerCAmelCase  )
																				assert len(__lowerCAmelCase  ) <= max_len
																				assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																				sub_seqs.append(__lowerCAmelCase  )

																new_tok_ids.extend(__lowerCAmelCase  )
																new_lengths.extend([len(__lowerCAmelCase  ) for l in sub_seqs]  )

								__magic_name__		:Tuple   =   np.array(__lowerCAmelCase  )
								__magic_name__		:Optional[int]   =   np.array(__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   len(self  )
								__magic_name__		:int   =   self.lengths > 1_1
								__magic_name__		:List[str]   =   self.token_ids[indices]
								__magic_name__		:Union[str, Any]   =   self.lengths[indices]
								__magic_name__		:List[str]   =   len(self  )
								logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''  )







				def       A						(    self  ):
								"""simple docstring"""
								if "unk_token" not in self.params.special_tok_ids:
												return
								else:
												__magic_name__		:Tuple   =   self.params.special_tok_ids['''unk_token''']
								__magic_name__		:Dict   =   len(self  )
								__magic_name__		:Tuple   =   np.array([np.count_nonzero(a == unk_token_id  ) for a in self.token_ids]  )
								__magic_name__		:int   =   (unk_occs / self.lengths) < 0.5
								__magic_name__		:str   =   self.token_ids[indices]
								__magic_name__		:str   =   self.lengths[indices]
								__magic_name__		:Any   =   len(self  )
								logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''  )







				def       A						(    self  ):
								"""simple docstring"""
								if not self.params.is_master:
												return
								logger.info(F'''{len(self  )} sequences'''  )
								# data_len = sum(self.lengths)
								# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
								# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

								# unk_idx = self.params.special_tok_ids['unk_token']
								# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
								# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   [t[0] for t in batch]
								__magic_name__		:List[Any]   =   [t[1] for t in batch]
								assert len(__lowerCAmelCase  ) == len(__lowerCAmelCase  )

								# Max for paddings
								__magic_name__		:Tuple   =   max(__lowerCAmelCase  )

								# Pad token ids
								if self.params.mlm:
												__magic_name__		:Any   =   self.params.special_tok_ids['''pad_token''']
								else:
												__magic_name__		:str   =   self.params.special_tok_ids['''unk_token''']
								__magic_name__		:Any   =   [list(t.astype(__lowerCAmelCase  )  ) + [pad_idx] * (max_seq_len_ - len(__lowerCAmelCase  )) for t in token_ids]
								assert len(tk_  ) == len(__lowerCAmelCase  )
								assert all(len(__lowerCAmelCase  ) == max_seq_len_ for t in tk_  )

								__magic_name__		:Optional[int]   =   torch.tensor(tk_  )  # (bs, max_seq_len_)
								__magic_name__		:Optional[int]   =   torch.tensor(__lowerCAmelCase  )  # (bs)
								return tk_t, lg_t






