





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase__						:			Any									=logging.get_logger(__name__)


if is_vision_available():
							import PIL

class       __A     (						a    ):
				__A      =				["""pixel_values"""]


				def __init__(		self   , UpperCAmelCase_ = True   , UpperCAmelCase_ = None   , UpperCAmelCase_ = PILImageResampling.BICUBIC   , UpperCAmelCase_ = True   , UpperCAmelCase_ = None   , UpperCAmelCase_ = True   , UpperCAmelCase_ = 1 / 255   , UpperCAmelCase_ = True   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = True   , **UpperCAmelCase_   , ):
								super().__init__(**UpperCAmelCase_							)
								lowerCamelCase	 =size if size is not None else {"""shortest_edge""": 224}
								lowerCamelCase	 =get_size_dict(UpperCAmelCase_   , default_to_square=UpperCAmelCase_							)
								lowerCamelCase	 =crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								lowerCamelCase	 =get_size_dict(UpperCAmelCase_   , default_to_square=UpperCAmelCase_   , param_name="""crop_size"""							)

								lowerCamelCase	 =do_resize
								lowerCamelCase	 =size
								lowerCamelCase	 =resample
								lowerCamelCase	 =do_center_crop
								lowerCamelCase	 =crop_size
								lowerCamelCase	 =do_rescale
								lowerCamelCase	 =rescale_factor
								lowerCamelCase	 =do_normalize
								lowerCamelCase	 =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								lowerCamelCase	 =image_std if image_std is not None else OPENAI_CLIP_STD
								lowerCamelCase	 =do_convert_rgb


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_ = PILImageResampling.BICUBIC   , UpperCAmelCase_ = None   , **UpperCAmelCase_   , ):
								lowerCamelCase	 =get_size_dict(UpperCAmelCase_   , default_to_square=UpperCAmelCase_							)
								if "shortest_edge" not in size:
												raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""							)
								lowerCamelCase	 =get_resize_output_image_size(UpperCAmelCase_   , size=size["""shortest_edge"""]   , default_to_square=UpperCAmelCase_							)
								return resize(UpperCAmelCase_   , size=UpperCAmelCase_   , resample=UpperCAmelCase_   , data_format=UpperCAmelCase_   , **UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_ = None   , **UpperCAmelCase_   , ):
								lowerCamelCase	 =get_size_dict(UpperCAmelCase_							)
								if "height" not in size or "width" not in size:
												raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""							)
								return center_crop(UpperCAmelCase_   , size=(size["""height"""], size["""width"""])   , data_format=UpperCAmelCase_   , **UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_ = None   , **UpperCAmelCase_   , ):
								return rescale(UpperCAmelCase_   , scale=UpperCAmelCase_   , data_format=UpperCAmelCase_   , **UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_ = None   , **UpperCAmelCase_   , ):
								return normalize(UpperCAmelCase_   , mean=UpperCAmelCase_   , std=UpperCAmelCase_   , data_format=UpperCAmelCase_   , **UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = ChannelDimension.FIRST   , **UpperCAmelCase_   , ):
								lowerCamelCase	 =do_resize if do_resize is not None else self.do_resize
								lowerCamelCase	 =size if size is not None else self.size
								lowerCamelCase	 =get_size_dict(UpperCAmelCase_   , param_name="""size"""   , default_to_square=UpperCAmelCase_							)
								lowerCamelCase	 =resample if resample is not None else self.resample
								lowerCamelCase	 =do_center_crop if do_center_crop is not None else self.do_center_crop
								lowerCamelCase	 =crop_size if crop_size is not None else self.crop_size
								lowerCamelCase	 =get_size_dict(UpperCAmelCase_   , param_name="""crop_size"""   , default_to_square=UpperCAmelCase_							)
								lowerCamelCase	 =do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase	 =rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase	 =do_normalize if do_normalize is not None else self.do_normalize
								lowerCamelCase	 =image_mean if image_mean is not None else self.image_mean
								lowerCamelCase	 =image_std if image_std is not None else self.image_std
								lowerCamelCase	 =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								lowerCamelCase	 =make_list_of_images(UpperCAmelCase_							)

								if not valid_images(UpperCAmelCase_							):
												raise ValueError(
												    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
												    """torch.Tensor, tf.Tensor or jax.ndarray."""							)

								if do_resize and size is None:
												raise ValueError("""Size must be specified if do_resize is True."""							)

								if do_center_crop and crop_size is None:
												raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

								if do_rescale and rescale_factor is None:
												raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

								if do_normalize and (image_mean is None or image_std is None):
												raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
												lowerCamelCase	 =[convert_to_rgb(UpperCAmelCase_							) for image in images]

								# All transformations expect numpy arrays.
								lowerCamelCase	 =[to_numpy_array(UpperCAmelCase_							) for image in images]

								if do_resize:
												lowerCamelCase	 =[self.resize(image=UpperCAmelCase_   , size=UpperCAmelCase_   , resample=UpperCAmelCase_							) for image in images]

								if do_center_crop:
												lowerCamelCase	 =[self.center_crop(image=UpperCAmelCase_   , size=UpperCAmelCase_							) for image in images]

								if do_rescale:
												lowerCamelCase	 =[self.rescale(image=UpperCAmelCase_   , scale=UpperCAmelCase_							) for image in images]

								if do_normalize:
												lowerCamelCase	 =[self.normalize(image=UpperCAmelCase_   , mean=UpperCAmelCase_   , std=UpperCAmelCase_							) for image in images]

								lowerCamelCase	 =[to_channel_dimension_format(UpperCAmelCase_   , UpperCAmelCase_							) for image in images]

								lowerCamelCase	 ={"""pixel_values""": images}
								return BatchFeature(data=UpperCAmelCase_   , tensor_type=UpperCAmelCase_							)












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)





