

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


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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str] )   ->       List[Any]:
				return {"hash": hashlib.mda(re.sub(lowercase_  ,      ""  ,      example["content"] ).encode("utf-8" ) ).hexdigest()}

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any] )   ->       int:
				_UpperCAmelCase       :		Any 		=    [len(lowercase_ ) for line in example["""content"""].splitlines()]
				return {"line_mean": np.mean(lowercase_ ), "line_max": max(lowercase_ )}

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict )   ->       Union[str, Any]:
				_UpperCAmelCase       :		List[Any] 		=    np.mean([c.isalnum() for c in example["content"]] )
				return {"alpha_frac": alpha_frac}

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[Any]  ,      lowerCAmelCase:  str )   ->       int:
				if example["hash"] in uniques:
								uniques.remove(example["hash"] )
								return True
				else:
								return False

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  Any=5 )   ->       List[str]:
				_UpperCAmelCase       :		Optional[int] 		=    ["""auto-generated""", """autogenerated""", """automatically generated"""]
				_UpperCAmelCase       :		Any 		=    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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int]  ,      lowerCAmelCase:  Tuple=5  ,      lowerCAmelCase:  int=0.05 )   ->       Union[str, Any]:
				_UpperCAmelCase       :		List[Any] 		=    ["""unit tests""", """test file""", """configuration file"""]
				_UpperCAmelCase       :		str 		=    example["""content"""].splitlines()
				_UpperCAmelCase       :		Optional[int] 		=    0
				_UpperCAmelCase       :		Any 		=    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
				_UpperCAmelCase       :		List[Any] 		=    example["""content"""].count("\n" )
				_UpperCAmelCase       :		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							(				lowerCAmelCase:  Optional[Any] )   ->       List[str]:
				_UpperCAmelCase       :		Union[str, Any] 		=    ["""def """, """class """, """for """, """while """]
				_UpperCAmelCase       :		Tuple 		=    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							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  str=4 )   ->       List[str]:
				_UpperCAmelCase       :		List[str] 		=    example["""content"""].splitlines()
				_UpperCAmelCase       :		Union[str, Any] 		=    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							(				lowerCAmelCase:  int )   ->       int:
				_UpperCAmelCase       :		str 		=    tokenizer(example["content"]  ,      truncation=lowercase_ )["""input_ids"""]
				_UpperCAmelCase       :		int 		=    len(example["content"] ) / len(lowercase_ )
				return {"ratio": ratio}

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Tuple )   ->       Optional[Any]:
				_UpperCAmelCase       :		Dict 		=    {}
				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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  Optional[int] )   ->       Tuple:
				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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int] )   ->       Optional[int]:
				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
SCREAMING_SNAKE_CASE_        =      HfArgumentParser(PreprocessingArguments)
SCREAMING_SNAKE_CASE_        =      parser.parse_args()
if args.num_workers is None:
					SCREAMING_SNAKE_CASE_        =      multiprocessing.cpu_count()
SCREAMING_SNAKE_CASE_        =      AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

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

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

SCREAMING_SNAKE_CASE_        =      time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
					SCREAMING_SNAKE_CASE_        =      str(data_dir / F'''file-{file_number+1:012}.json''')
					SCREAMING_SNAKE_CASE_        =      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 collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
					from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"bloom"
						_lowercase    						=			["past_key_values"]
						_lowercase    						=			{
						    "num_hidden_layers": "n_layer",
						    "num_attention_heads": "n_head",
						}


						def __init__(       self						,					A_=250880						,					A_=64						,					A_=2						,					A_=8						,					A_=1e-5						,					A_=0.02						,					A_=True						,					A_=1						,					A_=2						,					A_=False						,					A_=0.0						,					A_=0.0						,					A_=1						,					A_=False						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    vocab_size
										# Backward compatibility with n_embed kwarg
										_UpperCAmelCase       :		str 		=    kwargs.pop("n_embed"						,					A_	)
										_UpperCAmelCase       :		List[Any] 		=    hidden_size if n_embed is None else n_embed
										_UpperCAmelCase       :		Dict 		=    n_layer
										_UpperCAmelCase       :		Optional[Any] 		=    n_head
										_UpperCAmelCase       :		Optional[Any] 		=    layer_norm_epsilon
										_UpperCAmelCase       :		List[Any] 		=    initializer_range
										_UpperCAmelCase       :		Union[str, Any] 		=    use_cache
										_UpperCAmelCase       :		List[str] 		=    pretraining_tp
										_UpperCAmelCase       :		List[Any] 		=    apply_residual_connection_post_layernorm
										_UpperCAmelCase       :		int 		=    hidden_dropout
										_UpperCAmelCase       :		List[str] 		=    attention_dropout

										_UpperCAmelCase       :		int 		=    bos_token_id
										_UpperCAmelCase       :		str 		=    eos_token_id
										_UpperCAmelCase       :		Optional[Any] 		=    slow_but_exact

										super().__init__(bos_token_id=A_						,					eos_token_id=A_						,					**A_	)







class 				a  (	UpperCAmelCase				):
						_lowercase    						=			version.parse("1.12"				)


						def __init__(       self						,					A_						,					A_ = "default"						,					A_ = None						,					A_ = False						,					):







										'''simple docstring'''


										super().__init__(A_						,					task=A_						,					patching_specs=A_						,					use_past=A_	)
										if not getattr(self._config						,					"pad_token_id"						,					A_	):
														# TODO: how to do that better?
														_UpperCAmelCase       :		str 		=    0


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}	)
										if self.use_past:
														# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
														self.fill_with_past_key_values_(A_						,					direction="inputs"						,					inverted_values_shape=A_	)
														_UpperCAmelCase       :		Optional[int] 		=    {0: "batch", 1: "past_sequence + sequence"}
										else:
														_UpperCAmelCase       :		Union[str, Any] 		=    {0: "batch", 1: "sequence"}

										return common_inputs


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._config.n_layer


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._config.n_head


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 1e-3


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = -1						,					A_ = -1						,					A_ = False						,					A_ = None						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    super(A_						,					self	).generate_dummy_inputs(
										    A_						,					batch_size=A_						,					seq_length=A_						,					is_pair=A_						,					framework=A_	)

										# We need to order the input in the way they appears in the forward()
										_UpperCAmelCase       :		Tuple 		=    OrderedDict({"input_ids": common_inputs["input_ids"]}	)

										# Need to add the past_keys
										if self.use_past:
														if not is_torch_available():
																		raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
														else:
																		import torch

																		_UpperCAmelCase		,							_UpperCAmelCase       :		Union[str, Any] 		=    common_inputs["input_ids"].shape
																		# Not using the same length for past_key_values
																		_UpperCAmelCase       :		List[Any] 		=    seqlen + 2
																		_UpperCAmelCase       :		Dict 		=    self._config.hidden_size // self.num_attention_heads
																		_UpperCAmelCase       :		List[Any] 		=    (
																		    batch * self.num_attention_heads,
																		    head_dim,
																		    past_key_values_length,
																		)
																		_UpperCAmelCase       :		Optional[Any] 		=    (
																		    batch * self.num_attention_heads,
																		    past_key_values_length,
																		    head_dim,
																		)
																		_UpperCAmelCase       :		Tuple 		=    [
																		    (torch.zeros(A_	), torch.zeros(A_	)) for _ in range(self.num_layers	)
																		]

										_UpperCAmelCase       :		int 		=    common_inputs["attention_mask"]
										if self.use_past:
														_UpperCAmelCase       :		str 		=    ordered_inputs["attention_mask"].dtype
														_UpperCAmelCase       :		Optional[int] 		=    torch.cat(
														    [ordered_inputs["attention_mask"], torch.ones(A_						,					A_						,					dtype=A_	)]						,					dim=1	)

										return ordered_inputs





						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 13


