



import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor







class     __lowerCAmelCase					(    unittest.TestCase		):



			def __init__(		self     :						Optional[int]				,	A     :						List[Any]				,	A     :						Optional[Any]=7				,	A     :						Union[str, Any]=3				,	A     :						Any=18				,	A     :						Union[str, Any]=30				,	A     :						Optional[int]=4_00				,	A     :						Tuple=True				,	A     :						Any=None				,	A     :						int=True				,	A     :						Any=False				,	A     :						Any=True				,	A     :						Any=True				,	A     :						int=[0.5, 0.5, 0.5]				,	A     :						List[str]=[0.5, 0.5, 0.5]				,	)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     parent
						_UpperCAmelCase	 =     batch_size
						_UpperCAmelCase	 =     num_channels
						_UpperCAmelCase	 =     image_size
						_UpperCAmelCase	 =     min_resolution
						_UpperCAmelCase	 =     max_resolution
						_UpperCAmelCase	 =     do_resize
						_UpperCAmelCase	 =     size if size is not None else {'height': 18, 'width': 20}
						_UpperCAmelCase	 =     do_thumbnail
						_UpperCAmelCase	 =     do_align_axis
						_UpperCAmelCase	 =     do_pad
						_UpperCAmelCase	 =     do_normalize
						_UpperCAmelCase	 =     image_mean
						_UpperCAmelCase	 =     image_std



			def 					_lowerCamelCase (		self     :						str)     ->					str:



						"""simple docstring"""






						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_thumbnail": self.do_thumbnail,
						    "do_align_long_axis": self.do_align_axis,
						    "do_pad": self.do_pad,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						}







@require_torch
@require_vision
class     __lowerCAmelCase					(    A       ,		unittest.TestCase		):
			UpperCamelCase           =  DonutImageProcessor if is_vision_available() else None



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



						"""simple docstring"""






						_UpperCAmelCase	 =     DonutImageProcessingTester(self)



			@property
			def 					_lowerCamelCase (		self     :						List[str])     ->					List[Any]:



						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



			def 					_lowerCamelCase (		self     :						Any)     ->					List[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(A				,	'do_resize'))
						self.assertTrue(hasattr(A				,	'size'))
						self.assertTrue(hasattr(A				,	'do_thumbnail'))
						self.assertTrue(hasattr(A				,	'do_align_long_axis'))
						self.assertTrue(hasattr(A				,	'do_pad'))
						self.assertTrue(hasattr(A				,	'do_normalize'))
						self.assertTrue(hasattr(A				,	'image_mean'))
						self.assertTrue(hasattr(A				,	'image_std'))



			def 					_lowerCamelCase (		self     :						List[str])     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size				,	{'height': 18, 'width': 20})

						_UpperCAmelCase	 =     self.image_processing_class.from_dict(self.image_processor_dict				,	size=42)
						self.assertEqual(image_processor.size				,	{'height': 42, 'width': 42})

						# Previous config had dimensions in (width, height) order
						_UpperCAmelCase	 =     self.image_processing_class.from_dict(self.image_processor_dict				,	size=(42, 84))
						self.assertEqual(image_processor.size				,	{'height': 84, 'width': 42})



			def 					_lowerCamelCase (		self     :						int)     ->					List[Any]:



						"""simple docstring"""






						pass



			@is_flaky()
			def 					_lowerCamelCase (		self     :						List[str])     ->					Optional[Any]:



						"""simple docstring"""






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

						# Test not batched input
						_UpperCAmelCase	 =     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.size['height'],
						        self.image_processor_tester.size['width'],
						    )				,	)

						# Test batched
						_UpperCAmelCase	 =     image_processing(A				,	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.size['height'],
						        self.image_processor_tester.size['width'],
						    )				,	)



			@is_flaky()
			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						_UpperCAmelCase	 =     prepare_image_inputs(self.image_processor_tester				,	equal_resolution=A				,	numpify=A)
						for image in image_inputs:
									self.assertIsInstance(A				,	np.ndarray)

						# Test not batched input
						_UpperCAmelCase	 =     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.size['height'],
						        self.image_processor_tester.size['width'],
						    )				,	)

						# Test batched
						_UpperCAmelCase	 =     image_processing(A				,	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.size['height'],
						        self.image_processor_tester.size['width'],
						    )				,	)



			@is_flaky()
			def 					_lowerCamelCase (		self     :						Tuple)     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						_UpperCAmelCase	 =     prepare_image_inputs(self.image_processor_tester				,	equal_resolution=A				,	torchify=A)
						for image in image_inputs:
									self.assertIsInstance(A				,	torch.Tensor)

						# Test not batched input
						_UpperCAmelCase	 =     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.size['height'],
						        self.image_processor_tester.size['width'],
						    )				,	)

						# Test batched
						_UpperCAmelCase	 =     image_processing(A				,	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.size['height'],
						        self.image_processor_tester.size['width'],
						    )				,	)





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 							A			(  _UpperCAmelCase       :     Tuple					)       ->				str:







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





def 							A			(  _UpperCAmelCase       :     Optional[Any]					)       ->				Optional[int]:







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





def 							A			(  _UpperCAmelCase       :     Any					)       ->				Optional[Any]:







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





def 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     List[Any]					)       ->				Dict:







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





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Dict=5					)       ->				Optional[Any]:







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





def 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Tuple=5							,     _UpperCAmelCase       :     Optional[int]=0.05					)       ->				Tuple:







			'''simple docstring'''
			_UpperCAmelCase	 =     ['unit tests', 'test file', 'configuration file']
			_UpperCAmelCase	 =     example['content'].splitlines()
			_UpperCAmelCase	 =     0
			_UpperCAmelCase	 =     0
			# first test
			for _, line in zip(range(_UpperCAmelCase					)							,     _UpperCAmelCase					):
						for keyword in keywords:
									if keyword in line.lower():
												return {"config_or_test": True}
    # second test
			_UpperCAmelCase	 =     example['content'].count('\n'					)
			_UpperCAmelCase	 =     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 							A			(  _UpperCAmelCase       :     Any					)       ->				Tuple:







			'''simple docstring'''
			_UpperCAmelCase	 =     ['def ', 'class ', 'for ', 'while ']
			_UpperCAmelCase	 =     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 							A			(  _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Optional[Any]=4					)       ->				Dict:







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





def 							A			(  _UpperCAmelCase       :     Optional[Any]					)       ->				str:







			'''simple docstring'''
			_UpperCAmelCase	 =     tokenizer(example['content']							,     truncation=_UpperCAmelCase					)['input_ids']
			_UpperCAmelCase	 =     len(example['content']					) / len(_UpperCAmelCase					)
			return {"ratio": ratio}





def 							A			(  _UpperCAmelCase       :     Dict					)       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     {}
			results.update(get_hash(_UpperCAmelCase					)					)
			results.update(line_stats(_UpperCAmelCase					)					)
			results.update(alpha_stats(_UpperCAmelCase					)					)
			results.update(char_token_ratio(_UpperCAmelCase					)					)
			results.update(is_autogenerated(_UpperCAmelCase					)					)
			results.update(is_config_or_test(_UpperCAmelCase					)					)
			results.update(has_no_keywords(_UpperCAmelCase					)					)
			results.update(has_few_assignments(_UpperCAmelCase					)					)
			return results





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     List[Any]					)       ->				Any:







			'''simple docstring'''
			if not check_uniques(_UpperCAmelCase							,     _UpperCAmelCase					):
						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 							A			(  _UpperCAmelCase       :     Optional[Any]					)       ->				Any:







			'''simple docstring'''
			with open(_UpperCAmelCase							,     'rb'					) as f_in:
						with gzip.open(str(_UpperCAmelCase					) + '.gz'							,     'wb'							,     compresslevel=6					) as f_out:
									shutil.copyfileobj(_UpperCAmelCase							,     _UpperCAmelCase					)
			os.unlink(_UpperCAmelCase					)


# 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}""")
