import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def    lowerCAmelCase__    (			_UpperCamelCase       :				str ,			_UpperCamelCase       :				int ,			_UpperCamelCase       :				List[str] ,			_UpperCamelCase       :				str=1_0_2_4					)					->				Optional[Any]:





						"""simple docstring"""






						snake_case					,snake_case								=			[], []

						snake_case								=			list(zip(_UpperCamelCase ,			_UpperCamelCase					)					)
						snake_case					,snake_case								=			sorted_examples[0]

						def is_too_big(_UpperCamelCase       :				Tuple					):
												return tok(_UpperCamelCase ,			return_tensors='pt'					).input_ids.shape[1] > max_tokens

						for src, tgt in tqdm(sorted_examples[1:]					):
												snake_case								=			new_src + ' ' + src
												snake_case								=			new_tgt + ' ' + tgt
												if is_too_big(_UpperCamelCase					) or is_too_big(_UpperCamelCase					):  # cant fit, finalize example
																		finished_src.append(_UpperCamelCase					)
																		finished_tgt.append(_UpperCamelCase					)
																		snake_case					,snake_case								=			src, tgt
												else:  # can fit, keep adding
																		snake_case					,snake_case								=			cand_src, cand_tgt

    # cleanup
						if new_src:
												assert new_tgt
												finished_src.append(_UpperCamelCase					)
												finished_tgt.append(_UpperCamelCase					)
						return finished_src, finished_tgt





def    lowerCAmelCase__    (			_UpperCamelCase       :				List[str] ,			_UpperCamelCase       :				Path ,			_UpperCamelCase       :				Optional[int] ,			_UpperCamelCase       :				Any					)					->				List[Any]:





						"""simple docstring"""






						snake_case								=			Path(_UpperCamelCase					)
						save_path.mkdir(exist_ok=_UpperCamelCase					)
						for split in ["train"]:
												snake_case					,snake_case								=			data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
												snake_case								=			[x.rstrip() for x in Path(_UpperCamelCase					).open().readlines()]
												snake_case								=			[x.rstrip() for x in Path(_UpperCamelCase					).open().readlines()]
												snake_case					,snake_case								=			pack_examples(_UpperCamelCase ,			_UpperCamelCase ,			_UpperCamelCase ,			_UpperCamelCase					)
												print(f"""packed {split} split from {len(_UpperCamelCase					)} examples -> {len(_UpperCamelCase					)}."""					)
												Path(save_path / f"""{split}.source"""					).open('w'					).write('\n'.join(_UpperCamelCase					)					)
												Path(save_path / f"""{split}.target"""					).open('w'					).write('\n'.join(_UpperCamelCase					)					)
						for split in ["val", "test"]:
												snake_case					,snake_case								=			data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
												shutil.copyfile(_UpperCamelCase ,			save_path / f"""{split}.source"""					)
												shutil.copyfile(_UpperCamelCase ,			save_path / f"""{split}.target"""					)





def    lowerCAmelCase__    (			)					->				List[Any]:





						"""simple docstring"""






						snake_case								=			argparse.ArgumentParser()
						parser.add_argument('--tok_name' ,			type=_UpperCamelCase ,			help='like facebook/bart-large-cnn,t5-base, etc.'					)
						parser.add_argument('--max_seq_len' ,			type=_UpperCamelCase ,			default=1_2_8					)
						parser.add_argument('--data_dir' ,			type=_UpperCamelCase					)
						parser.add_argument('--save_path' ,			type=_UpperCamelCase					)
						snake_case								=			parser.parse_args()
						snake_case								=			AutoTokenizer.from_pretrained(args.tok_name					)
						return pack_data_dir(_UpperCamelCase ,			Path(args.data_dir					) ,			args.max_seq_len ,			args.save_path					)


if __name__ == "__main__":
					packer_cli()



"""simple docstring"""
import numpy as np
from PIL import Image





def    lowerCAmelCase__    (			_UpperCamelCase       :				np.ndarray ,			_UpperCamelCase       :				int ,			_UpperCamelCase       :				int					)					->				np.ndarray:





						"""simple docstring"""






						snake_case								=			np.array(_UpperCamelCase					)
						if arr.shape[0] != arr.shape[1]:
												raise ValueError('The input array is not a square matrix'					)
						snake_case								=			0
						snake_case								=			0
						snake_case								=			0
						snake_case								=			0

						# compute the shape of the output matrix
						snake_case								=			(arr.shape[0] - size) // stride + 1
						# initialize the output matrix with zeros of shape maxpool_shape
						snake_case								=			np.zeros((maxpool_shape, maxpool_shape)					)

						while i < arr.shape[0]:
												if i + size > arr.shape[0]:
																		# if the end of the matrix is reached, break
																		break
												while j < arr.shape[1]:
																		# if the end of the matrix is reached, break
																		if j + size > arr.shape[1]:
																								break
																		# compute the maximum of the pooling matrix
																		snake_case								=			np.max(arr[i : i + size, j : j + size]					)
																		# shift the pooling matrix by stride of column pixels
																		j += stride
																		mat_j += 1

												# shift the pooling matrix by stride of row pixels
												i += stride
												mat_i += 1

												# reset the column index to 0
												snake_case								=			0
												snake_case								=			0

						return updated_arr





def    lowerCAmelCase__    (			_UpperCamelCase       :				np.ndarray ,			_UpperCamelCase       :				int ,			_UpperCamelCase       :				int					)					->				np.ndarray:





						"""simple docstring"""






						snake_case								=			np.array(_UpperCamelCase					)
						if arr.shape[0] != arr.shape[1]:
												raise ValueError('The input array is not a square matrix'					)
						snake_case								=			0
						snake_case								=			0
						snake_case								=			0
						snake_case								=			0

						# compute the shape of the output matrix
						snake_case								=			(arr.shape[0] - size) // stride + 1
						# initialize the output matrix with zeros of shape avgpool_shape
						snake_case								=			np.zeros((avgpool_shape, avgpool_shape)					)

						while i < arr.shape[0]:
												# if the end of the matrix is reached, break
												if i + size > arr.shape[0]:
																		break
												while j < arr.shape[1]:
																		# if the end of the matrix is reached, break
																		if j + size > arr.shape[1]:
																								break
																		# compute the average of the pooling matrix
																		snake_case								=			int(np.average(arr[i : i + size, j : j + size]					)					)
																		# shift the pooling matrix by stride of column pixels
																		j += stride
																		mat_j += 1

												# shift the pooling matrix by stride of row pixels
												i += stride
												mat_i += 1
												# reset the column index to 0
												snake_case								=			0
												snake_case								=			0

						return updated_arr


# Main Function
if __name__ == "__main__":
					from doctest import testmod

					testmod(name="avgpooling", verbose=True)

					# Loading the image
					SCREAMING_SNAKE_CASE__      						=  Image.open("path_to_image")

					# Converting the image to numpy array and maxpooling, displaying the result
					# Ensure that the image is a square matrix

					Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

					# Converting the image to numpy array and averagepooling, displaying the result
					# Ensure that the image is a square matrix

					Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()


