

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


lowerCamelCase      				=     re.compile(R'''\s+''')


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=5      ):

					"""simple docstring"""






					__lowerCAmelCase					=							['auto-generated', 'autogenerated', 'automatically generated']
					__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=5		,					UpperCAmelCase__=0.05      ):

					"""simple docstring"""






					__lowerCAmelCase					=							['unit tests', 'test file', 'configuration file']
					__lowerCAmelCase					=							example['content'].splitlines()
					__lowerCAmelCase					=							0
					__lowerCAmelCase					=							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
					__lowerCAmelCase					=							example['content'].count('\n'      )
					__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							['def ', 'class ', 'for ', 'while ']
					__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=4      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							{}
					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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""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        __lowercase    (  UpperCAmelCase__      ):

					"""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
lowerCamelCase      				=     HfArgumentParser(PreprocessingArguments)
lowerCamelCase      				=     parser.parse_args()
if args.num_workers is None:
					lowerCamelCase      				=     multiprocessing.cpu_count()
lowerCamelCase      				=     AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
lowerCamelCase      				=     time.time()
lowerCamelCase      				=     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:
					lowerCamelCase      				=     time.time()
					lowerCamelCase				,				lowerCamelCase      				=     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
lowerCamelCase      				=     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)

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

lowerCamelCase      				=     time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
					lowerCamelCase      				=     str(data_dir / F'''file-{file_number+1:012}.json''')
					lowerCamelCase      				=     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}''')









import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image
else:





		class     snake_case_			:


									"""simple docstring"""





									@staticmethod
									def        A__  ( *_A					,						**_A							):
														pass


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							hashlib.mda(image.tobytes()      )
					return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =MODEL_FOR_DEPTH_ESTIMATION_MAPPING




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							DepthEstimationPipeline(model=_A					,						image_processor=_A							)
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
												self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}					,						_A							)
												import datasets

												__lowerCAmelCase					=							datasets.load_dataset('hf-internal-testing/fixtures_image_utils'					,						'image'					,						split='test'							)
												__lowerCAmelCase					=							depth_estimator(
												    [
												        Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
												        'http://images.cocodataset.org/val2017/000000039769.jpg',
												        # RGBA
												        dataset[0]['file'],
												        # LA
												        dataset[1]['file'],
												        # L
												        dataset[2]['file'],
												    ]							)
												self.assertEqual(
												    [
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												    ]					,						_A					,						)




							@require_tf
							@unittest.skip('Depth estimation is not implemented in TF'							)
							def        A__  ( self							):
												pass




							@slow
							@require_torch
							def        A__  ( self							):
												__lowerCAmelCase					=							'Intel/dpt-large'
												__lowerCAmelCase					=							pipeline('depth-estimation'					,						model=_A							)
												__lowerCAmelCase					=							depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'							)
												__lowerCAmelCase					=							hashimage(outputs['depth']							)

												# This seems flaky.
												# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
												self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()							)					,						29.304							)
												self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()							)					,						2.662							)




							@require_torch
							def        A__  ( self							):
												# This is highly irregular to have no small tests.
												self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'							)






