





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    ):
				__A      =				["""image_processor""", """tokenizer"""]
				__A      =				"""LayoutLMv2ImageProcessor"""
				__A      =				("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")


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

								lowerCamelCase	 =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__(UpperCAmelCase_   , UpperCAmelCase_							)


				def __call__(		self   , UpperCAmelCase_   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = True   , UpperCAmelCase_ = False   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = 0   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = False   , UpperCAmelCase_ = False   , UpperCAmelCase_ = False   , UpperCAmelCase_ = False   , UpperCAmelCase_ = True   , UpperCAmelCase_ = None   , **UpperCAmelCase_   , ):
								# 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
								lowerCamelCase	 =self.image_processor(images=UpperCAmelCase_   , return_tensors=UpperCAmelCase_							)

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

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

								# add pixel values
								lowerCamelCase	 =features.pop("""pixel_values"""							)
								if return_overflowing_tokens is True:
												lowerCamelCase	 =self.get_overflowing_images(UpperCAmelCase_   , encoded_inputs["""overflow_to_sample_mapping"""]							)
								lowerCamelCase	 =images

								return encoded_inputs


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

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

								return images_with_overflow


				def _snake_case       (		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								return self.tokenizer.batch_decode(*UpperCAmelCase_   , **UpperCAmelCase_							)


				def _snake_case       (		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								return self.tokenizer.decode(*UpperCAmelCase_   , **UpperCAmelCase_							)


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


				@property
				def _snake_case       (		self							):
								warnings.warn(
								    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   , UpperCAmelCase_   , )
								return self.image_processor_class


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












import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=10_24			,      _UpperCAmelCase=10_24			,      _UpperCAmelCase=False			,      **_UpperCAmelCase				)						->  Optional[Any]:
				lowerCamelCase	 =AutoTokenizer.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =SeqaSeqDataset(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      type_path="""train"""			,      **_UpperCAmelCase				)
				lowerCamelCase	 =tok.pad_token_id

				def get_lens(_UpperCAmelCase				):
								lowerCamelCase	 =tqdm(
								    DataLoader(_UpperCAmelCase			,      batch_size=5_12			,      num_workers=8			,      shuffle=_UpperCAmelCase			,      collate_fn=ds.collate_fn				)			,      desc=str(ds.len_file				)			,      )
								lowerCamelCase	 =[]
								for batch in dl:
												lowerCamelCase	 =batch["""input_ids"""].ne(_UpperCAmelCase				).sum(1				).tolist()
												lowerCamelCase	 =batch["""labels"""].ne(_UpperCAmelCase				).sum(1				).tolist()
												if consider_target:
																for src, tgt in zip(_UpperCAmelCase			,      _UpperCAmelCase				):
																				max_lens.append(max(_UpperCAmelCase			,      _UpperCAmelCase				)				)
												else:
																max_lens.extend(_UpperCAmelCase				)
								return max_lens

				lowerCamelCase	 =get_lens(_UpperCAmelCase				)
				lowerCamelCase	 =SeqaSeqDataset(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      type_path="""val"""			,      **_UpperCAmelCase				)
				lowerCamelCase	 =get_lens(_UpperCAmelCase				)
				pickle_save(_UpperCAmelCase			,      train_ds.len_file				)
				pickle_save(_UpperCAmelCase			,      val_ds.len_file				)


if __name__ == "__main__":
							fire.Fire(save_len_file)





