





"""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


a		:				Union[str, Any]  			=    re.compile(R'''\s+''')






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Any      )				->List[str]:






 '''simple docstring'''






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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->List[Any]:






 '''simple docstring'''






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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int      )				->List[Any]:






 '''simple docstring'''






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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					Optional[Any]      )				->Tuple:






 '''simple docstring'''






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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					Dict=5      )				->Union[str, Any]:






 '''simple docstring'''






 a   :	List[str]			=       ["auto-generated", "autogenerated", "automatically generated"]
 a   :	Tuple			=       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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					Optional[int]=5					, _lowercase		:					Optional[int]=0.05      )				->List[str]:






 '''simple docstring'''






 a   :	Optional[int]			=       ["unit tests", "test file", "configuration file"]
 a   :	int			=       example["content"].splitlines()
 a   :	List[Any]			=       0
 a   :	Optional[int]			=       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
 a   :	Any			=       example["content"].count("\n"      )
 a   :	List[Any]			=       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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[Any]      )				->Dict:






 '''simple docstring'''






 a   :	List[Any]			=       ["def ", "class ", "for ", "while "]
 a   :	str			=       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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Tuple					, _lowercase		:					List[str]=4      )				->int:






 '''simple docstring'''






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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Union[str, Any]      )				->Union[str, Any]:






 '''simple docstring'''






 a   :	Tuple			=       tokenizer(example["content"]					, truncation=_A      )["input_ids"]
 a   :	List[str]			=       len(example["content"]      ) / len(_A      )
 return {"ratio": ratio}






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int      )				->List[Any]:






 '''simple docstring'''






 a   :	List[Any]			=       {}
 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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[Any]					, _lowercase		:					Dict					, _lowercase		:					int      )				->List[Any]:






 '''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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Dict      )				->int:






 '''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
a		:				str  			=    HfArgumentParser(PreprocessingArguments)
a		:				List[str]  			=    parser.parse_args()
if args.num_workers is None:
  a		:				Union[str, Any]  			=    multiprocessing.cpu_count()
a		:				int  			=    AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
a		:				List[str]  			=    time.time()
a		:				List[Any]  			=    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		:				str  			=    time.time()
  a		:				List[str]  			=    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		:				Optional[int]  			=    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		:				str  			=    output_dir / "data"
data_dir.mkdir(exist_ok=True)

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









"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      torch.FloatTensor
							lowerCamelCase     :      torch.FloatTensor
							lowerCamelCase     :      Optional[torch.FloatTensor]         =None


class  __UpperCamelCase       (		a__	, a__		):
							lowerCamelCase     :      Tuple         =2






							@register_to_config
							def __init__(     self   ,      lowerCAmelCase__ = 0.02   ,      lowerCAmelCase__ = 100   ,      lowerCAmelCase__ = 1.007   ,      lowerCAmelCase__ = 80   ,      lowerCAmelCase__ = 0.05   ,      lowerCAmelCase__ = 50   ,      )			->				Union[str, Any]:
								# standard deviation of the initial noise distribution
								a   :	Tuple			=       sigma_max

								# setable values
								a   :	int			=       None
								a   :	np.IntTensor			=       None
								a   :	torch.FloatTensor			=       None  # sigma(t_i)






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				torch.FloatTensor:
								return sample






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[str]:
								a   :	List[Any]			=       num_inference_steps
								a   :	List[str]			=       np.arange(0   ,      self.num_inference_steps   )[::-1].copy()
								a   :	int			=       torch.from_numpy(lowerCAmelCase__   ).to(lowerCAmelCase__   )
								a   :	List[str]			=       [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								a   :	Any			=       torch.tensor(lowerCAmelCase__   ,      dtype=torch.floataa   ,      device=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Tuple[torch.FloatTensor, float]:
								if self.config.s_min <= sigma <= self.config.s_max:
									a   :	str			=       min(self.config.s_churn / self.num_inference_steps   ,      2**0.5 - 1   )
								else:
									a   :	Dict			=       0

								# sample eps ~ N(0, S_noise^2 * I)
								a   :	Union[str, Any]			=       self.config.s_noise * randn_tensor(sample.shape   ,      generator=lowerCAmelCase__   ).to(sample.device   )
								a   :	Any			=       sigma + gamma * sigma
								a   :	Tuple			=       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      )			->				Union[KarrasVeOutput, Tuple]:
								a   :	Union[str, Any]			=       sample_hat + sigma_hat * model_output
								a   :	Tuple			=       (sample_hat - pred_original_sample) / sigma_hat
								a   :	List[Any]			=       sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
									return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=lowerCAmelCase__   ,      derivative=lowerCAmelCase__   ,      pred_original_sample=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      )			->				Union[KarrasVeOutput, Tuple]:
								a   :	Optional[int]			=       sample_prev + sigma_prev * model_output
								a   :	str			=       (sample_prev - pred_original_sample) / sigma_prev
								a   :	Dict			=       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
									return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=lowerCAmelCase__   ,      derivative=lowerCAmelCase__   ,      pred_original_sample=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				str:
								raise NotImplementedError()


