






"""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							=  re.compile(r'\s+')



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



def   a    (     __UpperCAmelCase      :    Optional[int]  )    ->							int:
						__magic_name__:	Tuple							=       [len(__UpperCAmelCase  ) for line in example["""content"""].splitlines()]
						return {"line_mean": np.mean(__UpperCAmelCase  ), "line_max": max(__UpperCAmelCase  )}



def   a    (     __UpperCAmelCase      :    List[str]  )    ->							Union[str, Any]:
						__magic_name__:	Optional[int]							=       np.mean([c.isalnum() for c in example["""content"""]]  )
						return {"alpha_frac": alpha_frac}



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    str  )    ->							str:
						if example["hash"] in uniques:
												uniques.remove(example["""hash"""]  )
												return True
						else:
												return False



def   a    (     __UpperCAmelCase      :    List[str]      ,   __UpperCAmelCase      :    Union[str, Any]=5  )    ->							Optional[Any]:
						__magic_name__:	List[Any]							=       ["""auto-generated""", """autogenerated""", """automatically generated"""]
						__magic_name__:	str							=       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   a    (     __UpperCAmelCase      :    Any      ,   __UpperCAmelCase      :    List[str]=5      ,   __UpperCAmelCase      :    Optional[Any]=0.05  )    ->							List[str]:
						__magic_name__:	str							=       ["""unit tests""", """test file""", """configuration file"""]
						__magic_name__:	Tuple							=       example["""content"""].splitlines()
						__magic_name__:	int							=       0
						__magic_name__:	Any							=       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
						__magic_name__:	Dict							=       example["""content"""].count("""\n"""  )
						__magic_name__:	Tuple							=       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    (     __UpperCAmelCase      :    List[str]  )    ->							Dict:
						__magic_name__:	Dict							=       ["""def """, """class """, """for """, """while """]
						__magic_name__:	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   a    (     __UpperCAmelCase      :    List[Any]      ,   __UpperCAmelCase      :    Optional[int]=4  )    ->							Optional[int]:
						__magic_name__:	Optional[Any]							=       example["""content"""].splitlines()
						__magic_name__:	Optional[int]							=       0
						for line in lines:
												counter += line.lower().count("""="""  )
												if counter > minimum:
																		return {"has_few_assignments": False}
						return {"has_few_assignments": True}



def   a    (     __UpperCAmelCase      :    Tuple  )    ->							List[Any]:
						__magic_name__:	Optional[int]							=       tokenizer(example["""content"""]      ,   truncation=__UpperCAmelCase  )["""input_ids"""]
						__magic_name__:	List[str]							=       len(example["""content"""]  ) / len(__UpperCAmelCase  )
						return {"ratio": ratio}



def   a    (     __UpperCAmelCase      :    Union[str, Any]  )    ->							List[str]:
						__magic_name__:	Any							=       {}
						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   a    (     __UpperCAmelCase      :    Optional[Any]      ,   __UpperCAmelCase      :    Dict      ,   __UpperCAmelCase      :    Any  )    ->							Dict:
						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   a    (     __UpperCAmelCase      :    str  )    ->							str:
						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}''')











"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"openai/whisper-base"
					UpperCAmelCase__								=				(
					    "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
					    "transcribed text."
					)
					UpperCAmelCase__								=				"transcriber"
					UpperCAmelCase__								=				WhisperProcessor
					UpperCAmelCase__								=				WhisperForConditionalGeneration

					UpperCAmelCase__								=				["audio"]
					UpperCAmelCase__								=				["text"]

					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      List[str]					)			->							Dict:
											return self.pre_processor(__snake_case     ,		return_tensors="""pt"""					).input_features

					def   lowerCamelCase__   (			self  :      int     ,		__snake_case  :      Union[str, Any]					)			->							List[str]:
											return self.model.generate(inputs=__snake_case					)






					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      List[Any]					)			->							Any:
											return self.pre_processor.batch_decode(__snake_case     ,		skip_special_tokens=__snake_case					)[0]



