






'''simple docstring'''




import flax.linen as nn
import jax
import jax.numpy as jnp







class    A_	(nn.Module							):
							"""simple docstring"""




							a__   		=	42
							a__   		=	jnp.floataa

							def 							_A	(		self			:Optional[int]				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Tuple   =       nn.Conv(
										    self.out_channels       ,							kernel_size=(3, 3)       ,							strides=(1, 1)       ,							padding=((1, 1), (1, 1))       ,							dtype=self.dtype       ,							)






							def __call__(		self			:Any       ,							lowerCAmelCase__			:int				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       hidden_states.shape
										snake_case_				:					str   =       jax.image.resize(
										    __lowerCamelCase       ,							shape=(batch, height * 2, width * 2, channels)       ,							method="nearest"       ,							)
										snake_case_				:					Tuple   =       self.conv(__lowerCamelCase				)
										return hidden_states







class    A_	(nn.Module							):
							"""simple docstring"""




							a__   		=	42
							a__   		=	jnp.floataa

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


										'''simple docstring'''




										snake_case_				:					Tuple   =       nn.Conv(
										    self.out_channels       ,							kernel_size=(3, 3)       ,							strides=(2, 2)       ,							padding=((1, 1), (1, 1))       ,							dtype=self.dtype       ,							)






							def __call__(		self			:Optional[Any]       ,							lowerCAmelCase__			:Union[str, Any]				)				->  int:


										'''simple docstring'''




										snake_case_				:					int   =       self.conv(__lowerCamelCase				)
										return hidden_states







class    A_	(nn.Module							):
							"""simple docstring"""




							a__   		=	42
							a__   		=	None
							a__   		=	0.0
							a__   		=	None
							a__   		=	jnp.floataa

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


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       self.in_channels if self.out_channels is None else self.out_channels

										snake_case_				:					List[Any]   =       nn.GroupNorm(num_groups=32       ,							epsilon=1E-5				)
										snake_case_				:					int   =       nn.Conv(
										    __lowerCamelCase       ,							kernel_size=(3, 3)       ,							strides=(1, 1)       ,							padding=((1, 1), (1, 1))       ,							dtype=self.dtype       ,							)

										snake_case_				:					List[str]   =       nn.Dense(__lowerCamelCase       ,							dtype=self.dtype				)

										snake_case_				:					Dict   =       nn.GroupNorm(num_groups=32       ,							epsilon=1E-5				)
										snake_case_				:					Dict   =       nn.Dropout(self.dropout_prob				)
										snake_case_				:					Any   =       nn.Conv(
										    __lowerCamelCase       ,							kernel_size=(3, 3)       ,							strides=(1, 1)       ,							padding=((1, 1), (1, 1))       ,							dtype=self.dtype       ,							)

										snake_case_				:					Dict   =       self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

										snake_case_				:					Union[str, Any]   =       None
										if use_nin_shortcut:
													snake_case_				:					Dict   =       nn.Conv(
													    __lowerCamelCase       ,							kernel_size=(1, 1)       ,							strides=(1, 1)       ,							padding="VALID"       ,							dtype=self.dtype       ,							)






							def __call__(		self			:Tuple       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:Any       ,							lowerCAmelCase__			:Tuple=True				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       hidden_states
										snake_case_				:					Any   =       self.norma(__lowerCamelCase				)
										snake_case_				:					Dict   =       nn.swish(__lowerCamelCase				)
										snake_case_				:					Tuple   =       self.conva(__lowerCamelCase				)

										snake_case_				:					List[str]   =       self.time_emb_proj(nn.swish(__lowerCamelCase				)				)
										snake_case_				:					Optional[int]   =       jnp.expand_dims(jnp.expand_dims(__lowerCamelCase       ,							1				)       ,							1				)
										snake_case_				:					Dict   =       hidden_states + temb

										snake_case_				:					Optional[Any]   =       self.norma(__lowerCamelCase				)
										snake_case_				:					Optional[int]   =       nn.swish(__lowerCamelCase				)
										snake_case_				:					Dict   =       self.dropout(__lowerCamelCase       ,							__lowerCamelCase				)
										snake_case_				:					Dict   =       self.conva(__lowerCamelCase				)

										if self.conv_shortcut is not None:
													snake_case_				:					List[str]   =       self.conv_shortcut(__lowerCamelCase				)

										return hidden_states + residual












'''simple docstring'''




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   :					List[str]   							=					re.compile(R'''\s+''')

def 	__UpperCAmelCase       ( __magic_name__						)->    Union[str, Any]:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__						)->    str:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__						)->    int:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Tuple:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=5						)->    Tuple:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=5     ,__magic_name__=0.05						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					str   =       ["unit tests", "test file", "configuration file"]
			snake_case_				:					int   =       example["content"].splitlines()
			snake_case_				:					Optional[Any]   =       0
			snake_case_				:					Any   =       0
			# first test
			for _, line in zip(range(__magic_name__						)     ,__magic_name__						):
						for keyword in keywords:
									if keyword in line.lower():
												return {"config_or_test": True}
    # second test
			snake_case_				:					Tuple   =       example["content"].count("\n"						)
			snake_case_				:					int   =       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 	__UpperCAmelCase       ( __magic_name__						)->    str:



			"""simple docstring"""



			snake_case_				:					List[Any]   =       ["def ", "class ", "for ", "while "]
			snake_case_				:					Optional[Any]   =       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 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=4						)->    Optional[int]:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__						)->    List[Any]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       tokenizer(example["content"]     ,truncation=__magic_name__						)["input_ids"]
			snake_case_				:					int   =       len(example["content"]						) / len(__magic_name__						)
			return {"ratio": ratio}

def 	__UpperCAmelCase       ( __magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					Union[str, Any]   =       {}
			results.update(get_hash(__magic_name__						)						)
			results.update(line_stats(__magic_name__						)						)
			results.update(alpha_stats(__magic_name__						)						)
			results.update(char_token_ratio(__magic_name__						)						)
			results.update(is_autogenerated(__magic_name__						)						)
			results.update(is_config_or_test(__magic_name__						)						)
			results.update(has_no_keywords(__magic_name__						)						)
			results.update(has_few_assignments(__magic_name__						)						)
			return results

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Tuple:



			"""simple docstring"""



			if not check_uniques(__magic_name__     ,__magic_name__						):
						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 	__UpperCAmelCase       ( __magic_name__						)->    Dict:



			"""simple docstring"""



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


# Settings
__lowerCamelCase   :					List[Any]   							=					HfArgumentParser(PreprocessingArguments)
__lowerCamelCase   :					str   							=					parser.parse_args()
if args.num_workers is None:
		__lowerCamelCase   :					List[Any]   							=					multiprocessing.cpu_count()
__lowerCamelCase   :					Optional[int]   							=					AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

# Run preprocessing
__lowerCamelCase   :					List[str]   							=					time.time()
__lowerCamelCase   :					Any   							=					ds.map(preprocess, num_proc=args.num_workers)
print(f'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
__lowerCamelCase   :					Any   							=					set(ds.unique('''hash'''))
__lowerCamelCase   :					Optional[int]   							=					len(uniques) / len(ds)
print(f'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
__lowerCamelCase   :					List[str]   							=					time.time()
__lowerCamelCase   :					Tuple   							=					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   :					List[str]   							=					time.time()
		__lowerCamelCase							,      __lowerCamelCase   :					Tuple   							=					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   :					List[Any]   							=					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   :					List[str]   							=					output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

__lowerCamelCase   :					int   							=					time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
		__lowerCamelCase   :					Union[str, Any]   							=					str(data_dir / f'''file-{file_number+1:012}.json''')
		__lowerCamelCase   :					List[Any]   							=					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}''')




