

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


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





def a(lowercase__	):





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





def a(lowercase__	):





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





def a(lowercase__	):





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





def a(lowercase__			, lowercase__	):





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





def a(lowercase__			, lowercase__=5	):





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





def a(lowercase__			, lowercase__=5			, lowercase__=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(lowercase__	)			, lowercase__	):
		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 a(lowercase__	):





	'''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 a(lowercase__			, lowercase__=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 a(lowercase__	):





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





def a(lowercase__	):





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





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	if not check_uniques(lowercase__			, lowercase__	):
		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(lowercase__	):





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


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

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

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

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

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

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

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



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A											=     logging.get_logger(__name__)

A											=     {
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}


class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							):






			"""simple docstring"""




			__A						  =  """bit"""
			__A						  =  ["""preactivation""", """bottleneck"""]
			__A						  =  ["""SAME""", """VALID"""]


			def __init__(    self ,				__UpperCamelCase=3 ,				__UpperCamelCase=64 ,				__UpperCamelCase=[2_56, 5_12, 10_24, 20_48] ,				__UpperCamelCase=[3, 4, 6, 3] ,				__UpperCamelCase="preactivation" ,				__UpperCamelCase="relu" ,				__UpperCamelCase=None ,				__UpperCamelCase=32 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=False ,				__UpperCamelCase=32 ,				__UpperCamelCase=1 ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase							)
				if layer_type not in self.layer_types:
					raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types							)}"""							)
				if global_padding is not None:
					if global_padding.upper() in self.supported_padding:
						snake_case_      		= global_padding.upper()
					else:
						raise ValueError(f"""Padding strategy {global_padding} not supported"""							)
				snake_case_      		= num_channels
				snake_case_      		= embedding_size
				snake_case_      		= hidden_sizes
				snake_case_      		= depths
				snake_case_      		= layer_type
				snake_case_      		= hidden_act
				snake_case_      		= global_padding
				snake_case_      		= num_groups
				snake_case_      		= drop_path_rate
				snake_case_      		= embedding_dynamic_padding
				snake_case_      		= output_stride
				snake_case_      		= width_factor

				snake_case_      		= ['stem'] + [f"""stage{idx}""" for idx in range(1 ,				len(__UpperCamelCase							) + 1							)]
				snake_case_		,       snake_case_      		= get_aligned_output_features_output_indices(
				    out_features=__UpperCamelCase ,				out_indices=__UpperCamelCase ,				stage_names=self.stage_names							)
