
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


lowercase__							:							str									=			re.compile(R"\s+")




def 	A_ (							snake_case							:   int )      ->    List[str]:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   List[str] )      ->    Dict:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   Dict )      ->    str:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   Optional[int] ,      snake_case							:   List[Any] )      ->    Optional[Any]:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   Dict ,      snake_case							:   Dict=5 )      ->    Tuple:



						'''simple docstring'''

						__UpperCamelCase       =			["""auto-generated""", """autogenerated""", """automatically generated"""]
						__UpperCamelCase       =			example["""content"""].splitlines()
						for _, line in zip(range(snake_case ) ,      snake_case ):
												for keyword in keywords:
																		if keyword in line.lower():
																								return {"autogenerated": True}
						else:
												return {"autogenerated": False}




def 	A_ (							snake_case							:   Tuple ,      snake_case							:   str=5 ,      snake_case							:   Union[str, Any]=0.05 )      ->    List[str]:



						'''simple docstring'''

						__UpperCamelCase       =			["""unit tests""", """test file""", """configuration file"""]
						__UpperCamelCase       =			example["""content"""].splitlines()
						__UpperCamelCase       =			0
						__UpperCamelCase       =			0
						# first test
						for _, line in zip(range(snake_case ) ,      snake_case ):
												for keyword in keywords:
																		if keyword in line.lower():
																								return {"config_or_test": True}
    # second test
						__UpperCamelCase       =			example["""content"""].count('''\n''' )
						__UpperCamelCase       =			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_ (							snake_case							:   Any )      ->    Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase       =			["""def """, """class """, """for """, """while """]
						__UpperCamelCase       =			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_ (							snake_case							:   Tuple ,      snake_case							:   List[Any]=4 )      ->    Tuple:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   Optional[Any] )      ->    List[Any]:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   Union[str, Any] )      ->    List[str]:



						'''simple docstring'''

						__UpperCamelCase       =			{}
						results.update(get_hash(snake_case ) )
						results.update(line_stats(snake_case ) )
						results.update(alpha_stats(snake_case ) )
						results.update(char_token_ratio(snake_case ) )
						results.update(is_autogenerated(snake_case ) )
						results.update(is_config_or_test(snake_case ) )
						results.update(has_no_keywords(snake_case ) )
						results.update(has_few_assignments(snake_case ) )
						return results




def 	A_ (							snake_case							:   Union[str, Any] ,      snake_case							:   Tuple ,      snake_case							:   List[Any] )      ->    int:



						'''simple docstring'''

						if not check_uniques(snake_case ,      snake_case ):
												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_ (							snake_case							:   Any )      ->    Union[str, Any]:



						'''simple docstring'''

						with open(snake_case ,      '''rb''' ) as f_in:
												with gzip.open(str(snake_case ) + '''.gz''' ,      '''wb''' ,      compresslevel=6 ) as f_out:
																		shutil.copyfileobj(snake_case ,      snake_case )
						os.unlink(snake_case )


# Settings
lowercase__							:							Tuple									=			HfArgumentParser(PreprocessingArguments)
lowercase__							:							Union[str, Any]									=			parser.parse_args()
if args.num_workers is None:
				lowercase__							:							Optional[Any]									=			multiprocessing.cpu_count()
lowercase__							:							Optional[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

# Run preprocessing
lowercase__							:							Dict									=			time.time()
lowercase__							:							Optional[int]									=			ds.map(preprocess, num_proc=args.num_workers)
print(F"Time to preprocess dataset: {time.time()-t_start:.2f}")

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

# Deduplicate data and apply heuristics
lowercase__							:							str									=			time.time()
lowercase__							:							Tuple									=			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:
				lowercase__							:							Any									=			time.time()
				lowercase__		,  lowercase__							:							Optional[int]									=			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
lowercase__							:							List[Any]									=			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)

lowercase__							:							Any									=			output_dir / "data"
data_dir.mkdir(exist_ok=True)

lowercase__							:							str									=			time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
				lowercase__							:							Tuple									=			str(data_dir / F"file-{file_number+1:012}.json")
				lowercase__							:							Dict									=			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 UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_torch_available():
				from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
				from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
				from ..tf_utils import stable_softmax

lowercase__							:							int									=			logging.get_logger(__name__)






@add_end_docstrings(SCREAMING_SNAKE_CASE_    )
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							def __init__(  self     ,	**SCREAMING_SNAKE_CASE_			)->   Tuple:





													'''simple docstring'''






													super().__init__(**SCREAMING_SNAKE_CASE_			)

													requires_backends(self     ,	'''vision'''			)
													self.check_model_type(
													    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
													    if self.framework == '''tf'''
													    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING			)



							def __call__(  self     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													return super().__call__(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	**SCREAMING_SNAKE_CASE_			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			{}
													if "candidate_labels" in kwargs:
																			__UpperCamelCase       =			kwargs['''candidate_labels''']
													if "hypothesis_template" in kwargs:
																			__UpperCamelCase       =			kwargs['''hypothesis_template''']

													return preprocess_params, {}, {}



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_="This is a photo of {}."			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			load_image(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			self.image_processor(images=[image]     ,	return_tensors=self.framework			)
													__UpperCamelCase       =			candidate_labels
													__UpperCamelCase       =			[hypothesis_template.format(SCREAMING_SNAKE_CASE_			) for x in candidate_labels]
													__UpperCamelCase       =			self.tokenizer(SCREAMING_SNAKE_CASE_     ,	return_tensors=self.framework     ,	padding=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			[text_inputs]
													return inputs



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			model_inputs.pop('''candidate_labels'''			)
													__UpperCamelCase       =			model_inputs.pop('''text_inputs'''			)
													if isinstance(text_inputs[0]     ,	SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			text_inputs[0]
													else:
																			# Batching case.
																			__UpperCamelCase       =			text_inputs[0][0]

													__UpperCamelCase       =			self.model(**SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			{
													    '''candidate_labels''': candidate_labels,
													    '''logits''': outputs.logits_per_image,
													}
													return model_outputs



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			model_outputs.pop('''candidate_labels'''			)
													__UpperCamelCase       =			model_outputs['''logits'''][0]
													if self.framework == "pt":
																			__UpperCamelCase       =			logits.softmax(dim=-1			).squeeze(-1			)
																			__UpperCamelCase       =			probs.tolist()
																			if not isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			):
																									__UpperCamelCase       =			[scores]
													elif self.framework == "tf":
																			__UpperCamelCase       =			stable_softmax(SCREAMING_SNAKE_CASE_     ,	axis=-1			)
																			__UpperCamelCase       =			probs.numpy().tolist()
													else:
																			raise ValueError(F"Unsupported framework: {self.framework}"			)

													__UpperCamelCase       =			[
													    {'''score''': score, '''label''': candidate_label}
													    for score, candidate_label in sorted(zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)     ,	key=lambda SCREAMING_SNAKE_CASE_			: -x[0]			)
													]
													return result




