






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


__snake_case    :    Tuple         =     re.compile(R"""\s+""")







def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   Any:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[Any]		)  ->   Optional[int]:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[Any]		)  ->   int:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					str    ,       UpperCamelCase_       :					Optional[int]		)  ->   List[Any]:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					str    ,       UpperCamelCase_       :					Union[str, Any]=5		)  ->   Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						["auto-generated", "autogenerated", "automatically generated"]
						lowerCAmelCase__    						=						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 	_UpperCamelCase   (    UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					Optional[int]=5    ,       UpperCamelCase_       :					Any=0.05		)  ->   Tuple:

						"""simple docstring"""



						lowerCAmelCase__    						=						["unit tests", "test file", "configuration file"]
						lowerCAmelCase__    						=						example["content"].splitlines()
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						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
						lowerCAmelCase__    						=						example["content"].count('\n'		)
						lowerCAmelCase__    						=						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   (    UpperCamelCase_       :					Optional[int]		)  ->   Tuple:

						"""simple docstring"""



						lowerCAmelCase__    						=						["def ", "class ", "for ", "while "]
						lowerCAmelCase__    						=						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   (    UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					Any=4		)  ->   List[Any]:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[Any]		)  ->   Tuple:

						"""simple docstring"""



						lowerCAmelCase__    						=						tokenizer(example['content']    ,       truncation=lowercase_		)["input_ids"]
						lowerCAmelCase__    						=						len(example['content']		) / len(lowercase_		)
						return {"ratio": ratio}







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[Any]		)  ->   Tuple:

						"""simple docstring"""



						lowerCAmelCase__    						=						{}
						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 	_UpperCamelCase   (    UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					List[str]		)  ->   Union[str, Any]:

						"""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 	_UpperCamelCase   (    UpperCamelCase_       :					str		)  ->   str:

						"""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
__snake_case    :    Optional[int]         =     HfArgumentParser(PreprocessingArguments)
__snake_case    :    Tuple         =     parser.parse_args()
if args.num_workers is None:
							__snake_case    :    Any         =     multiprocessing.cpu_count()
__snake_case    :    List[Any]         =     AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
__snake_case    :    Dict         =     time.time()
__snake_case    :    List[str]         =     load_dataset(args.dataset_name, split="""train""")
print(f'Time to load dataset: {time.time()-t_start:.2f}')

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

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

# Deduplicate data and apply heuristics
__snake_case    :    Tuple         =     time.time()
__snake_case    :    str         =     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:
							__snake_case    :    Any         =     time.time()
							__snake_case     , __snake_case    :    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
__snake_case    :    Tuple         =     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)

__snake_case    :    int         =     output_dir / """data"""
data_dir.mkdir(exist_ok=True)

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













# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__snake_case    :    Optional[int]         =     """facebook/wmt19-en-de"""
__snake_case    :    Optional[Any]         =     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__snake_case    :    List[str]         =     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__snake_case    :    Optional[Any]         =     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
__snake_case    :    List[Any]         =     tokenizer(["""Making tiny model"""], return_tensors="""pt""")
__snake_case    :    Any         =     tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
__snake_case    :    Any         =     """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de





