

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 DeformableDetrImageProcessor

class     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase=7      , _UpperCamelCase=3      , _UpperCamelCase=30      , _UpperCamelCase=4_00      , _UpperCamelCase=True      , _UpperCamelCase=None      , _UpperCamelCase=True      , _UpperCamelCase=[0.5, 0.5, 0.5]      , _UpperCamelCase=[0.5, 0.5, 0.5]      , _UpperCamelCase=True      , _UpperCamelCase=1 / 2_55      , _UpperCamelCase=True      , )      ->							Optional[Any]:






              """simple docstring"""


              __snake_case       	=							size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
              __snake_case       	=							parent
              __snake_case       	=							batch_size
              __snake_case       	=							num_channels
              __snake_case       	=							min_resolution
              __snake_case       	=							max_resolution
              __snake_case       	=							do_resize
              __snake_case       	=							size
              __snake_case       	=							do_normalize
              __snake_case       	=							image_mean
              __snake_case       	=							image_std
              __snake_case       	=							do_rescale
              __snake_case       	=							rescale_factor
              __snake_case       	=							do_pad


       def 			a		(		self   )      ->							str:






              """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 			a		(		self      , _UpperCamelCase      , _UpperCamelCase=False   )      ->							List[Any]:






              """simple docstring"""


              if not batched:
                     __snake_case       	=							image_inputs[0]
                     if isinstance(_UpperCamelCase      , Image.Image   ):
                            __snake_case	,    __snake_case       	=							image.size
                     else:
                            __snake_case	,    __snake_case       	=							image.shape[1], image.shape[2]
                     if w < h:
                            __snake_case       	=							int(self.size["""shortest_edge"""] * h / w   )
                            __snake_case       	=							self.size["""shortest_edge"""]
                     elif w > h:
                            __snake_case       	=							self.size["""shortest_edge"""]
                            __snake_case       	=							int(self.size["""shortest_edge"""] * w / h   )
                     else:
                            __snake_case       	=							self.size["""shortest_edge"""]
                            __snake_case       	=							self.size["""shortest_edge"""]

              else:
                     __snake_case       	=							[]
                     for image in image_inputs:
                            __snake_case	,    __snake_case       	=							self.get_expected_values([image]   )
                            expected_values.append((expected_height, expected_width)   )
                     __snake_case       	=							max(_UpperCamelCase      , key=lambda _UpperCamelCase   : item[0]   )[0]
                     __snake_case       	=							max(_UpperCamelCase      , key=lambda _UpperCamelCase   : item[1]   )[1]

              return expected_height, expected_width






@require_torch
@require_vision
class     __snake_case    (				snake_case__			,	unittest.TestCase  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							DeformableDetrImageProcessor if is_vision_available() else None


       def 			a		(		self   )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							DeformableDetrImageProcessingTester(self   )


       @property
       def 			a		(		self   )      ->							int:






              """simple docstring"""


              return self.image_processor_tester.prepare_image_processor_dict()


       def 			a		(		self   )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							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_rescale"""   )   )
              self.assertTrue(hasattr(_UpperCamelCase      , """do_pad"""   )   )
              self.assertTrue(hasattr(_UpperCamelCase      , """size"""   )   )


       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


              __snake_case       	=							self.image_processing_class.from_dict(self.image_processor_dict   )
              self.assertEqual(image_processor.size      , {"""shortest_edge""": 18, """longest_edge""": 13_33}   )
              self.assertEqual(image_processor.do_pad      , _UpperCamelCase   )

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


       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


              pass


       def 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							self.image_processing_class(**self.image_processor_dict   )
              # create random PIL images
              __snake_case       	=							prepare_image_inputs(self.image_processor_tester      , equal_resolution=_UpperCamelCase   )
              for image in image_inputs:
                     self.assertIsInstance(_UpperCamelCase      , Image.Image   )

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

              __snake_case	,    __snake_case       	=							self.image_processor_tester.get_expected_values(_UpperCamelCase   )

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

              # Test batched
              __snake_case	,    __snake_case       	=							self.image_processor_tester.get_expected_values(_UpperCamelCase      , batched=_UpperCamelCase   )

              __snake_case       	=							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,
                      expected_height,
                      expected_width,
                  )      , )


       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							self.image_processing_class(**self.image_processor_dict   )
              # create random numpy tensors
              __snake_case       	=							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
              __snake_case       	=							image_processing(image_inputs[0]      , return_tensors="""pt"""   ).pixel_values

              __snake_case	,    __snake_case       	=							self.image_processor_tester.get_expected_values(_UpperCamelCase   )

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

              # Test batched
              __snake_case       	=							image_processing(_UpperCamelCase      , return_tensors="""pt"""   ).pixel_values

              __snake_case	,    __snake_case       	=							self.image_processor_tester.get_expected_values(_UpperCamelCase      , batched=_UpperCamelCase   )

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


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							self.image_processing_class(**self.image_processor_dict   )
              # create random PyTorch tensors
              __snake_case       	=							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
              __snake_case       	=							image_processing(image_inputs[0]      , return_tensors="""pt"""   ).pixel_values

              __snake_case	,    __snake_case       	=							self.image_processor_tester.get_expected_values(_UpperCamelCase   )

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

              # Test batched
              __snake_case       	=							image_processing(_UpperCamelCase      , return_tensors="""pt"""   ).pixel_values

              __snake_case	,    __snake_case       	=							self.image_processor_tester.get_expected_values(_UpperCamelCase      , batched=_UpperCamelCase   )

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


       @slow
       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


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

              __snake_case       	=							{"""image_id""": 3_97_69, """annotations""": target}

              # encode them
              __snake_case       	=							DeformableDetrImageProcessor()
              __snake_case       	=							image_processing(images=_UpperCamelCase      , annotations=_UpperCamelCase      , return_tensors="""pt"""   )

              # verify pixel values
              __snake_case       	=							torch.Size([1, 3, 8_00, 10_66]   )
              self.assertEqual(encoding["""pixel_values"""].shape      , _UpperCamelCase   )

              __snake_case       	=							torch.tensor([0.2796, 0.3138, 0.3481]   )
              self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      , _UpperCamelCase      , atol=1E-4   )   )

              # verify area
              __snake_case       	=							torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]   )
              self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      , _UpperCamelCase   )   )
              # verify boxes
              __snake_case       	=							torch.Size([6, 4]   )
              self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      , _UpperCamelCase   )
              __snake_case       	=							torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
              self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      , _UpperCamelCase      , atol=1E-3   )   )
              # verify image_id
              __snake_case       	=							torch.tensor([3_97_69]   )
              self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      , _UpperCamelCase   )   )
              # verify is_crowd
              __snake_case       	=							torch.tensor([0, 0, 0, 0, 0, 0]   )
              self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      , _UpperCamelCase   )   )
              # verify class_labels
              __snake_case       	=							torch.tensor([75, 75, 63, 65, 17, 17]   )
              self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      , _UpperCamelCase   )   )
              # verify orig_size
              __snake_case       	=							torch.tensor([4_80, 6_40]   )
              self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      , _UpperCamelCase   )   )
              # verify size
              __snake_case       	=							torch.tensor([8_00, 10_66]   )
              self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      , _UpperCamelCase   )   )






       @slow
       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


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

              __snake_case       	=							{"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

              __snake_case       	=							pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""   )

              # encode them
              __snake_case       	=							DeformableDetrImageProcessor(format="""coco_panoptic"""   )
              __snake_case       	=							image_processing(images=_UpperCamelCase      , annotations=_UpperCamelCase      , masks_path=_UpperCamelCase      , return_tensors="""pt"""   )

              # verify pixel values
              __snake_case       	=							torch.Size([1, 3, 8_00, 10_66]   )
              self.assertEqual(encoding["""pixel_values"""].shape      , _UpperCamelCase   )

              __snake_case       	=							torch.tensor([0.2796, 0.3138, 0.3481]   )
              self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      , _UpperCamelCase      , atol=1E-4   )   )

              # verify area
              __snake_case       	=							torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]   )
              self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      , _UpperCamelCase   )   )
              # verify boxes
              __snake_case       	=							torch.Size([6, 4]   )
              self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      , _UpperCamelCase   )
              __snake_case       	=							torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
              self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      , _UpperCamelCase      , atol=1E-3   )   )
              # verify image_id
              __snake_case       	=							torch.tensor([3_97_69]   )
              self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      , _UpperCamelCase   )   )
              # verify is_crowd
              __snake_case       	=							torch.tensor([0, 0, 0, 0, 0, 0]   )
              self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      , _UpperCamelCase   )   )
              # verify class_labels
              __snake_case       	=							torch.tensor([17, 17, 63, 75, 75, 93]   )
              self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      , _UpperCamelCase   )   )
              # verify masks
              __snake_case       	=							82_28_73
              self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      , _UpperCamelCase   )
              # verify orig_size
              __snake_case       	=							torch.tensor([4_80, 6_40]   )
              self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      , _UpperCamelCase   )   )
              # verify size
              __snake_case       	=							torch.tensor([8_00, 10_66]   )
              self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      , _UpperCamelCase   )   )




import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


UpperCamelCase__		 =     re.compile(r'''\s+''')







def 						lowerCamelCase__      ( __A    :int		):


       """simple docstring"""




       return {"hash": hashlib.mda(re.sub(__A	,""""""	,example["""content"""]		).encode("""utf-8"""		)		).hexdigest()}







def 						lowerCamelCase__      ( __A    :Any		):


       """simple docstring"""




       __snake_case       	=							[len(__A		) for line in example["""content"""].splitlines()]
       return {"line_mean": np.mean(__A		), "line_max": max(__A		)}







def 						lowerCamelCase__      ( __A    :Any		):


       """simple docstring"""




       __snake_case       	=							np.mean([c.isalnum() for c in example["""content"""]]		)
       return {"alpha_frac": alpha_frac}







def 						lowerCamelCase__      ( __A    :str	,__A    :int		):


       """simple docstring"""




       if example["hash"] in uniques:
              uniques.remove(example["""hash"""]		)
              return True
       else:
              return False







def 						lowerCamelCase__      ( __A    :Optional[int]	,__A    :int=5		):


       """simple docstring"""




       __snake_case       	=							["""auto-generated""", """autogenerated""", """automatically generated"""]
       __snake_case       	=							example["""content"""].splitlines()
       for _, line in zip(range(__A		)	,__A		):
              for keyword in keywords:
                     if keyword in line.lower():
                            return {"autogenerated": True}
       else:
              return {"autogenerated": False}







def 						lowerCamelCase__      ( __A    :int	,__A    :int=5	,__A    :str=0.05		):


       """simple docstring"""




       __snake_case       	=							["""unit tests""", """test file""", """configuration file"""]
       __snake_case       	=							example["""content"""].splitlines()
       __snake_case       	=							0
       __snake_case       	=							0
       # first test
       for _, line in zip(range(__A		)	,__A		):
              for keyword in keywords:
                     if keyword in line.lower():
                            return {"config_or_test": True}
    # second test
       __snake_case       	=							example["""content"""].count("""\n"""		)
       __snake_case       	=							int(coeff * nlines		)
       for line in lines:
              count_config += line.lower().count("""config"""		)
              count_test += line.lower().count("""test"""		)
              if count_config > threshold or count_test > threshold:
                     return {"config_or_test": True}
       return {"config_or_test": False}







def 						lowerCamelCase__      ( __A    :Dict		):


       """simple docstring"""




       __snake_case       	=							["""def """, """class """, """for """, """while """]
       __snake_case       	=							example["""content"""].splitlines()
       for line in lines:
              for keyword in keywords:
                     if keyword in line.lower():
                            return {"has_no_keywords": False}
       return {"has_no_keywords": True}







def 						lowerCamelCase__      ( __A    :Dict	,__A    :List[str]=4		):


       """simple docstring"""




       __snake_case       	=							example["""content"""].splitlines()
       __snake_case       	=							0
       for line in lines:
              counter += line.lower().count("""="""		)
              if counter > minimum:
                     return {"has_few_assignments": False}
       return {"has_few_assignments": True}







def 						lowerCamelCase__      ( __A    :str		):


       """simple docstring"""




       __snake_case       	=							tokenizer(example["""content"""]	,truncation=__A		)["""input_ids"""]
       __snake_case       	=							len(example["""content"""]		) / len(__A		)
       return {"ratio": ratio}







def 						lowerCamelCase__      ( __A    :Optional[Any]		):


       """simple docstring"""




       __snake_case       	=							{}
       results.update(get_hash(__A		)		)
       results.update(line_stats(__A		)		)
       results.update(alpha_stats(__A		)		)
       results.update(char_token_ratio(__A		)		)
       results.update(is_autogenerated(__A		)		)
       results.update(is_config_or_test(__A		)		)
       results.update(has_no_keywords(__A		)		)
       results.update(has_few_assignments(__A		)		)
       return results







def 						lowerCamelCase__      ( __A    :int	,__A    :str	,__A    :Dict		):


       """simple docstring"""




       if not check_uniques(__A	,__A		):
              return False
       elif example["autogenerated"]:
              return False
       elif example["line_max"] > args.line_max:
              return False
       elif example["line_mean"] > args.line_mean:
              return False
       elif example["alpha_frac"] < args.alpha_frac:
              return False
       elif example["ratio"] < args.min_token_ratio:
              return False
       elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
              return False
       elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
              return False
       elif example["has_few_assignments"]:
              return False
       else:
              return True







def 						lowerCamelCase__      ( __A    :str		):


       """simple docstring"""




       with open(__A	,"""rb"""		) as f_in:
              with gzip.open(str(__A		) + """.gz"""	,"""wb"""	,compresslevel=6		) as f_out:
                     shutil.copyfileobj(__A	,__A		)
       os.unlink(__A		)


# Settings
UpperCamelCase__		 =     HfArgumentParser(PreprocessingArguments)
UpperCamelCase__		 =     parser.parse_args()
if args.num_workers is None:
    UpperCamelCase__		 =     multiprocessing.cpu_count()
UpperCamelCase__		 =     AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
UpperCamelCase__		 =     time.time()
UpperCamelCase__		 =     load_dataset(args.dataset_name, split='''train''')
print(F'Time to load dataset: {time.time()-t_start:.2f}')

# Run preprocessing
UpperCamelCase__		 =     time.time()
UpperCamelCase__		 =     ds.map(preprocess, num_proc=args.num_workers)
print(F'Time to preprocess dataset: {time.time()-t_start:.2f}')

# Deduplicate hashes
UpperCamelCase__		 =     set(ds.unique('''hash'''))
UpperCamelCase__		 =     len(uniques) / len(ds)
print(F'Fraction of duplicates: {1-frac:.2%}')

# Deduplicate data and apply heuristics
UpperCamelCase__		 =     time.time()
UpperCamelCase__		 =     ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(F'Time to filter dataset: {time.time()-t_start:.2f}')
print(F'Size of filtered dataset: {len(ds_filter)}')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
    UpperCamelCase__		 =     time.time()
    UpperCamelCase__ ,UpperCamelCase__		 =     deduplicate_dataset(ds_filter, args.jaccard_threshold)
    print(F'Time to deduplicate dataset: {time.time()-t_start:.2f}')
    print(F'Size of deduplicate dataset: {len(ds_filter)}')

# Save data in batches of samples_per_file
UpperCamelCase__		 =     Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
    with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
        json.dump(duplicate_clusters, f)

UpperCamelCase__		 =     output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

UpperCamelCase__		 =     time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
    UpperCamelCase__		 =     str(data_dir / F'file-{file_number+1:012}.json')
    UpperCamelCase__		 =     min(len(ds_filter), index + args.samples_per_file)
    ds_filter.select(list(range(index, end_index))).to_json(file_path)
    compress_file(file_path)




print(F'Time to save dataset: {time.time()-t_start:.2f}')

