"""simple docstring"""



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_=7   ,      snake_case_=3   ,      snake_case_=30   ,      snake_case_=400   ,      snake_case_=True   ,      snake_case_=None   ,      snake_case_=True   ,      snake_case_=[0.5, 0.5, 0.5]   ,      snake_case_=[0.5, 0.5, 0.5]   ,      snake_case_=True   ,      snake_case_=1 / 255   ,      snake_case_=True   ,      ):







										'''simple docstring'''


										A__       :     Any								=			size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
										A__       :     List[str]								=			parent
										A__       :     Dict								=			batch_size
										A__       :     str								=			num_channels
										A__       :     List[str]								=			min_resolution
										A__       :     Any								=			max_resolution
										A__       :     List[str]								=			do_resize
										A__       :     int								=			size
										A__       :     Tuple								=			do_normalize
										A__       :     str								=			image_mean
										A__       :     List[Any]								=			image_std
										A__       :     int								=			do_rescale
										A__       :     List[str]								=			rescale_factor
										A__       :     Dict								=			do_pad





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_pad": self.do_pad,
										}





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_=False							):







										'''simple docstring'''


										if not batched:
																	A__       :     str								=			image_inputs[0]
																	if isinstance(snake_case_   ,      Image.Image							):
																								A__				,							A__       :     List[str]								=			image.size
																	else:
																								A__				,							A__       :     Dict								=			image.shape[1], image.shape[2]
																	if w < h:
																								A__       :     List[str]								=			int(self.size["""shortest_edge"""] * h / w							)
																								A__       :     Optional[int]								=			self.size["""shortest_edge"""]
																	elif w > h:
																								A__       :     List[Any]								=			self.size["""shortest_edge"""]
																								A__       :     Tuple								=			int(self.size["""shortest_edge"""] * w / h							)
																	else:
																								A__       :     Dict								=			self.size["""shortest_edge"""]
																								A__       :     Optional[int]								=			self.size["""shortest_edge"""]

										else:
																	A__       :     List[Any]								=			[]
																	for image in image_inputs:
																								A__				,							A__       :     List[str]								=			self.get_expected_values([image]							)
																								expected_values.append((expected_height, expected_width)							)
																	A__       :     List[Any]								=			max(snake_case_   ,      key=lambda snake_case_							: item[0]							)[0]
																	A__       :     Dict								=			max(snake_case_   ,      key=lambda snake_case_							: item[1]							)[1]

										return expected_height, expected_width





@require_torch
@require_vision
class 						__UpperCAmelCase    (__A							,   unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						Optional[Any]					     =				ConditionalDetrImageProcessor if is_vision_available() else None





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			ConditionalDetrImageProcessingTester(self							)





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return self.image_processor_tester.prepare_image_processor_dict()





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(snake_case_   ,      """image_mean"""							)							)
										self.assertTrue(hasattr(snake_case_   ,      """image_std"""							)							)
										self.assertTrue(hasattr(snake_case_   ,      """do_normalize"""							)							)
										self.assertTrue(hasattr(snake_case_   ,      """do_resize"""							)							)
										self.assertTrue(hasattr(snake_case_   ,      """size"""							)							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[int]								=			self.image_processing_class.from_dict(self.image_processor_dict							)
										self.assertEqual(image_processor.size   ,      {"""shortest_edge""": 18, """longest_edge""": 1_333}							)
										self.assertEqual(image_processor.do_pad   ,      snake_case_							)

										A__       :     int								=			self.image_processing_class.from_dict(
										    self.image_processor_dict   ,      size=42   ,      max_size=84   ,      pad_and_return_pixel_mask=snake_case_							)
										self.assertEqual(image_processor.size   ,      {"""shortest_edge""": 42, """longest_edge""": 84}							)
										self.assertEqual(image_processor.do_pad   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										pass





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[int]								=			self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										A__       :     int								=			prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=snake_case_							)
										for image in image_inputs:
																	self.assertIsInstance(snake_case_   ,      Image.Image							)

										# Test not batched input
										A__       :     Dict								=			image_processing(image_inputs[0]   ,      return_tensors="""pt"""							).pixel_values

										A__				,							A__       :     Any								=			self.image_processor_tester.get_expected_values(snake_case_							)

										self.assertEqual(
										    encoded_images.shape   ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,      )

										# Test batched
										A__				,							A__       :     Optional[int]								=			self.image_processor_tester.get_expected_values(snake_case_   ,      batched=snake_case_							)

										A__       :     int								=			image_processing(snake_case_   ,      return_tensors="""pt"""							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,      (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )   ,      )





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										A__       :     List[Any]								=			prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=snake_case_   ,      numpify=snake_case_							)
										for image in image_inputs:
																	self.assertIsInstance(snake_case_   ,      np.ndarray							)

										# Test not batched input
										A__       :     List[Any]								=			image_processing(image_inputs[0]   ,      return_tensors="""pt"""							).pixel_values

										A__				,							A__       :     Tuple								=			self.image_processor_tester.get_expected_values(snake_case_							)

										self.assertEqual(
										    encoded_images.shape   ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,      )

										# Test batched
										A__       :     Dict								=			image_processing(snake_case_   ,      return_tensors="""pt"""							).pixel_values

										A__				,							A__       :     Any								=			self.image_processor_tester.get_expected_values(snake_case_   ,      batched=snake_case_							)

										self.assertEqual(
										    encoded_images.shape   ,      (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )   ,      )





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										A__       :     Tuple								=			prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=snake_case_   ,      torchify=snake_case_							)
										for image in image_inputs:
																	self.assertIsInstance(snake_case_   ,      torch.Tensor							)

										# Test not batched input
										A__       :     Optional[Any]								=			image_processing(image_inputs[0]   ,      return_tensors="""pt"""							).pixel_values

										A__				,							A__       :     Dict								=			self.image_processor_tester.get_expected_values(snake_case_							)

										self.assertEqual(
										    encoded_images.shape   ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,      )

										# Test batched
										A__       :     str								=			image_processing(snake_case_   ,      return_tensors="""pt"""							).pixel_values

										A__				,							A__       :     Union[str, Any]								=			self.image_processor_tester.get_expected_values(snake_case_   ,      batched=snake_case_							)

										self.assertEqual(
										    encoded_images.shape   ,      (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )   ,      )





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
										with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""   ,      """r"""							) as f:
																	A__       :     Tuple								=			json.loads(f.read()							)

										A__       :     Optional[Any]								=			{"""image_id""": 39_769, """annotations""": target}

										# encode them
										A__       :     List[Any]								=			ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""							)
										A__       :     Tuple								=			image_processing(images=snake_case_   ,      annotations=snake_case_   ,      return_tensors="""pt"""							)

										# verify pixel values
										A__       :     Optional[int]								=			torch.Size([1, 3, 800, 1_066]							)
										self.assertEqual(encoding["""pixel_values"""].shape   ,      snake_case_							)

										A__       :     Optional[int]								=			torch.tensor([0.27_96, 0.31_38, 0.34_81]							)
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]   ,      snake_case_   ,      atol=1E-4							)							)

										# verify area
										A__       :     Union[str, Any]								=			torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]							)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]   ,      snake_case_							)							)
										# verify boxes
										A__       :     Optional[int]								=			torch.Size([6, 4]							)
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape   ,      snake_case_							)
										A__       :     Tuple								=			torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]							)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]   ,      snake_case_   ,      atol=1E-3							)							)
										# verify image_id
										A__       :     int								=			torch.tensor([39_769]							)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]   ,      snake_case_							)							)
										# verify is_crowd
										A__       :     Union[str, Any]								=			torch.tensor([0, 0, 0, 0, 0, 0]							)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]   ,      snake_case_							)							)
										# verify class_labels
										A__       :     List[Any]								=			torch.tensor([75, 75, 63, 65, 17, 17]							)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]   ,      snake_case_							)							)
										# verify orig_size
										A__       :     Tuple								=			torch.tensor([480, 640]							)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]   ,      snake_case_							)							)
										# verify size
										A__       :     List[Any]								=			torch.tensor([800, 1_066]							)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]   ,      snake_case_							)							)





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     str								=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
										with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""   ,      """r"""							) as f:
																	A__       :     Tuple								=			json.loads(f.read()							)

										A__       :     Union[str, Any]								=			{"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}

										A__       :     Dict								=			pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

										# encode them
										A__       :     List[Any]								=			ConditionalDetrImageProcessor(format="""coco_panoptic"""							)
										A__       :     Tuple								=			image_processing(images=snake_case_   ,      annotations=snake_case_   ,      masks_path=snake_case_   ,      return_tensors="""pt"""							)

										# verify pixel values
										A__       :     List[Any]								=			torch.Size([1, 3, 800, 1_066]							)
										self.assertEqual(encoding["""pixel_values"""].shape   ,      snake_case_							)

										A__       :     List[Any]								=			torch.tensor([0.27_96, 0.31_38, 0.34_81]							)
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]   ,      snake_case_   ,      atol=1E-4							)							)

										# verify area
										A__       :     List[str]								=			torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]							)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]   ,      snake_case_							)							)
										# verify boxes
										A__       :     Optional[Any]								=			torch.Size([6, 4]							)
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape   ,      snake_case_							)
										A__       :     Union[str, Any]								=			torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]							)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]   ,      snake_case_   ,      atol=1E-3							)							)
										# verify image_id
										A__       :     Any								=			torch.tensor([39_769]							)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]   ,      snake_case_							)							)
										# verify is_crowd
										A__       :     List[Any]								=			torch.tensor([0, 0, 0, 0, 0, 0]							)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]   ,      snake_case_							)							)
										# verify class_labels
										A__       :     Dict								=			torch.tensor([17, 17, 63, 75, 75, 93]							)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]   ,      snake_case_							)							)
										# verify masks
										A__       :     List[str]								=			822_873
										self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()   ,      snake_case_							)
										# verify orig_size
										A__       :     Any								=			torch.tensor([480, 640]							)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]   ,      snake_case_							)							)
										# verify size
										A__       :     Union[str, Any]								=			torch.tensor([800, 1_066]							)
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]   ,      snake_case_							)							)






"""simple docstring"""



import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase=1024							):
							A__				,							A__       :     Any								=			[], []

							A__       :     List[Any]								=			list(zip(lowerCAmelCase			,  lowerCAmelCase							)							)
							A__				,							A__       :     Optional[int]								=			sorted_examples[0]

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

							for src, tgt in tqdm(sorted_examples[1:]							):
														A__       :     List[Any]								=			new_src + """ """ + src
														A__       :     List[str]								=			new_tgt + """ """ + tgt
														if is_too_big(lowerCAmelCase							) or is_too_big(lowerCAmelCase							):  # cant fit, finalize example
																					finished_src.append(lowerCAmelCase							)
																					finished_tgt.append(lowerCAmelCase							)
																					A__				,							A__       :     str								=			src, tgt
														else:  # can fit, keep adding
																					A__				,							A__       :     Union[str, Any]								=			cand_src, cand_tgt

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

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

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


if __name__ == "__main__":
				packer_cli()





