"""simple docstring"""





import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer


def    lowercase_   (   _lowerCamelCase:	str						,   _lowerCamelCase:	List[str]						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	int=1024      ) ->      Dict:



							'''simple docstring'''

							__lowerCamelCase					:		Union[str, Any]		   =     [], []

							__lowerCamelCase					:		Dict		   =     list(zip(snake_case_						,   snake_case_      )      )
							__lowerCamelCase					:		str		   =     sorted_examples[0]

							def is_too_big(_lowerCamelCase:	List[str]      ):
														return tok(snake_case_						,   return_tensors="pt"      ).input_ids.shape[1] > max_tokens

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

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


def    lowercase_   (   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	Path						,   _lowerCamelCase:	str						,   _lowerCamelCase:	Union[str, Any]      ) ->      Any:



							'''simple docstring'''

							__lowerCamelCase					:		int		   =     Path(snake_case_      )
							save_path.mkdir(exist_ok=snake_case_      )
							for split in ["train"]:
														__lowerCamelCase					:		Optional[Any]		   =     data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
														__lowerCamelCase					:		str		   =     [x.rstrip() for x in Path(snake_case_      ).open().readlines()]
														__lowerCamelCase					:		str		   =     [x.rstrip() for x in Path(snake_case_      ).open().readlines()]
														__lowerCamelCase					:		Optional[int]		   =     pack_examples(snake_case_						,   snake_case_						,   snake_case_						,   snake_case_      )
														print(F"""packed {split} split from {len(snake_case_      )} examples -> {len(snake_case_      )}."""      )
														Path(save_path / F"""{split}.source"""      ).open("w"      ).write("\n".join(snake_case_      )      )
														Path(save_path / F"""{split}.target"""      ).open("w"      ).write("\n".join(snake_case_      )      )
							for split in ["val", "test"]:
														__lowerCamelCase					:		Tuple		   =     data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
														shutil.copyfile(snake_case_						,   save_path / F"""{split}.source"""      )
														shutil.copyfile(snake_case_						,   save_path / F"""{split}.target"""      )


def    lowercase_   (   ) ->      Optional[int]:



							'''simple docstring'''

							__lowerCamelCase					:		Union[str, Any]		   =     argparse.ArgumentParser()
							parser.add_argument("--tok_name"						,   type=snake_case_						,   help="like facebook/bart-large-cnn,t5-base, etc."      )
							parser.add_argument("--max_seq_len"						,   type=snake_case_						,   default=128      )
							parser.add_argument("--data_dir"						,   type=snake_case_      )
							parser.add_argument("--save_path"						,   type=snake_case_      )
							__lowerCamelCase					:		str		   =     parser.parse_args()
							__lowerCamelCase					:		int		   =     AutoTokenizer.from_pretrained(args.tok_name      )
							return pack_data_dir(snake_case_						,   Path(args.data_dir      )						,   args.max_seq_len						,   args.save_path      )


if __name__ == "__main__":
				packer_cli()
"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import LevitImageProcessor
class  _snake_case     (					unittest.TestCase		):






						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Optional[int]=7							,					UpperCAmelCase      :			Any=3							,					UpperCAmelCase      :			str=18							,					UpperCAmelCase      :			str=30							,					UpperCAmelCase      :			Any=400							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			Optional[Any]=True							,					UpperCAmelCase      :			Dict=None							,					UpperCAmelCase      :			List[str]=True							,					UpperCAmelCase      :			int=[0.5, 0.5, 0.5]							,					UpperCAmelCase      :			Tuple=[0.5, 0.5, 0.5]							,					):
													__lowerCamelCase					:		Any		   =     size if size is not None else {"shortest_edge": 18}
													__lowerCamelCase					:		Dict		   =     crop_size if crop_size is not None else {"height": 18, "width": 18}
													__lowerCamelCase					:		List[str]		   =     parent
													__lowerCamelCase					:		int		   =     batch_size
													__lowerCamelCase					:		Any		   =     num_channels
													__lowerCamelCase					:		Tuple		   =     image_size
													__lowerCamelCase					:		int		   =     min_resolution
													__lowerCamelCase					:		List[Any]		   =     max_resolution
													__lowerCamelCase					:		List[str]		   =     do_resize
													__lowerCamelCase					:		str		   =     size
													__lowerCamelCase					:		Tuple		   =     do_center_crop
													__lowerCamelCase					:		Optional[int]		   =     crop_size
													__lowerCamelCase					:		Optional[Any]		   =     do_normalize
													__lowerCamelCase					:		Optional[Any]		   =     image_mean
													__lowerCamelCase					:		List[Any]		   =     image_std







						def       lowerCamelCase__     (       self      :			int    ):
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "do_center_crop": self.do_center_crop,
													    "size": self.size,
													    "crop_size": self.crop_size,
													}

@require_torch
@require_vision
class  _snake_case     (					a__				, unittest.TestCase		):
						snake_case__			  =			LevitImageProcessor if is_vision_available() else None






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Optional[int]		   =     LevitImageProcessingTester(self    )






						@property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Dict		   =     self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_mean"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_std"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_normalize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_resize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_center_crop"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"size"    )    )






						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		str		   =     self.image_processing_class.from_dict(self.image_processor_dict    )
													self.assertEqual(image_processor.size							,					{"shortest_edge": 18}    )
													self.assertEqual(image_processor.crop_size							,					{"height": 18, "width": 18}    )

													__lowerCamelCase					:		Tuple		   =     self.image_processing_class.from_dict(self.image_processor_dict							,					size=42							,					crop_size=84    )
													self.assertEqual(image_processor.size							,					{"shortest_edge": 42}    )
													self.assertEqual(image_processor.crop_size							,					{"height": 84, "width": 84}    )






						def       lowerCamelCase__     (       self      :			Dict    ):
													pass






						def       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													__lowerCamelCase					:		List[str]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					Image.Image    )

													# Test not batched input
													__lowerCamelCase					:		Dict		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		int		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)






						def       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													__lowerCamelCase					:		Tuple		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					numpify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					np.ndarray    )

													# Test not batched input
													__lowerCamelCase					:		Optional[Any]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		Dict		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)







						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													# Initialize image_processing
													__lowerCamelCase					:		int		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													__lowerCamelCase					:		List[Any]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					torchify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					torch.Tensor    )

													# Test not batched input
													__lowerCamelCase					:		List[Any]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		List[str]		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)