
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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Tuple:







							"""simple docstring"""


							return {"hash": hashlib.mda(re.sub(UpperCAmelCase__							,    ""							,    example["content"] ).encode("utf-8" ) ).hexdigest()}
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							__snake_case     			=       [len(UpperCAmelCase__ ) for line in example["content"].splitlines()]
							return {"line_mean": np.mean(UpperCAmelCase__ ), "line_max": max(UpperCAmelCase__ )}
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Tuple:







							"""simple docstring"""


							__snake_case     			=       np.mean([c.isalnum() for c in example["content"]] )
							return {"alpha_frac": alpha_frac}
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Any:







							"""simple docstring"""


							if example["hash"] in uniques:
														uniques.remove(example["hash"] )
														return True
							else:
														return False
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=5 )  ->     int:







							"""simple docstring"""


							__snake_case     			=       ["auto-generated", "autogenerated", "automatically generated"]
							__snake_case     			=       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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=5							,    SCREAMING_SNAKE_CASE=0.05 )  ->     str:







							"""simple docstring"""


							__snake_case     			=       ["unit tests", "test file", "configuration file"]
							__snake_case     			=       example["content"].splitlines()
							__snake_case     			=       0
							__snake_case     			=       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
							__snake_case     			=       example["content"].count("\n" )
							__snake_case     			=       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   (					SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""simple docstring"""


							__snake_case     			=       ["def ", "class ", "for ", "while "]
							__snake_case     			=       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   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=4 )  ->     Union[str, Any]:







							"""simple docstring"""


							__snake_case     			=       example["content"].splitlines()
							__snake_case     			=       0
							for line in lines:
														counter += line.lower().count("=" )
														if counter > minimum:
																					return {"has_few_assignments": False}
							return {"has_few_assignments": True}
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


							__snake_case     			=       tokenizer(example["content"]							,    truncation=UpperCAmelCase__ )["input_ids"]
							__snake_case     			=       len(example["content"] ) / len(UpperCAmelCase__ )
							return {"ratio": ratio}
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[int]:







							"""simple docstring"""


							__snake_case     			=       {}
							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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     List[str]:







							"""simple docstring"""


							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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Dict:







							"""simple docstring"""


							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
_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       ,							_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}""")


"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE	       =      {
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """GroupViTModel""",
		    """GroupViTPreTrainedModel""",
		    """GroupViTTextModel""",
		    """GroupViTVisionModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFGroupViTModel""",
		    """TFGroupViTPreTrainedModel""",
		    """TFGroupViTTextModel""",
		    """TFGroupViTVisionModel""",
		]

if TYPE_CHECKING:
		from .configuration_groupvit import (
		    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    GroupViTConfig,
		    GroupViTOnnxConfig,
		    GroupViTTextConfig,
		    GroupViTVisionConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_groupvit import (
				    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GroupViTModel,
				    GroupViTPreTrainedModel,
				    GroupViTTextModel,
				    GroupViTVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_groupvit import (
				    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFGroupViTModel,
				    TFGroupViTPreTrainedModel,
				    TFGroupViTTextModel,
				    TFGroupViTVisionModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE	       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
