
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


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






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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    [len(__SCREAMING_SNAKE_CASE							) for line in example['content'].splitlines()]
	return {"line_mean": np.mean(__SCREAMING_SNAKE_CASE							), "line_max": max(__SCREAMING_SNAKE_CASE							)}






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






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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=5							):
	lowercase       =    ['auto-generated', 'autogenerated', 'automatically generated']
	lowercase       =    example['content'].splitlines()
	for _, line in zip(range(__SCREAMING_SNAKE_CASE							) ,							__SCREAMING_SNAKE_CASE							):
		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							):
	lowercase       =    ['unit tests', 'test file', 'configuration file']
	lowercase       =    example['content'].splitlines()
	lowercase       =    0
	lowercase       =    0
	# first test
	for _, line in zip(range(__SCREAMING_SNAKE_CASE							) ,							__SCREAMING_SNAKE_CASE							):
		for keyword in keywords:
			if keyword in line.lower():
				return {"config_or_test": True}
    # second test
	lowercase       =    example['content'].count('\n'							)
	lowercase       =    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							):
	lowercase       =    ['def ', 'class ', 'for ', 'while ']
	lowercase       =    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							):
	lowercase       =    example['content'].splitlines()
	lowercase       =    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							):
	lowercase       =    tokenizer(example['content'] ,							truncation=__SCREAMING_SNAKE_CASE							)['input_ids']
	lowercase       =    len(example['content']							) / len(__SCREAMING_SNAKE_CASE							)
	return {"ratio": ratio}






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    {}
	results.update(get_hash(__SCREAMING_SNAKE_CASE							)							)
	results.update(line_stats(__SCREAMING_SNAKE_CASE							)							)
	results.update(alpha_stats(__SCREAMING_SNAKE_CASE							)							)
	results.update(char_token_ratio(__SCREAMING_SNAKE_CASE							)							)
	results.update(is_autogenerated(__SCREAMING_SNAKE_CASE							)							)
	results.update(is_config_or_test(__SCREAMING_SNAKE_CASE							)							)
	results.update(has_no_keywords(__SCREAMING_SNAKE_CASE							)							)
	results.update(has_few_assignments(__SCREAMING_SNAKE_CASE							)							)
	return results






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	if not check_uniques(__SCREAMING_SNAKE_CASE ,							__SCREAMING_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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	with open(__SCREAMING_SNAKE_CASE ,							'rb'							) as f_in:
		with gzip.open(str(__SCREAMING_SNAKE_CASE							) + '.gz' ,							'wb' ,							compresslevel=6							) as f_out:
			shutil.copyfileobj(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	os.unlink(__SCREAMING_SNAKE_CASE							)


# Settings
UpperCAmelCase	      = HfArgumentParser(PreprocessingArguments)
UpperCAmelCase	      = parser.parse_args()
if args.num_workers is None:
	UpperCAmelCase	      = multiprocessing.cpu_count()
UpperCAmelCase	      = AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

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

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

UpperCAmelCase	      = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
	UpperCAmelCase	      = str(data_dir / F"""file-{file_number+1:012}.json""")
	UpperCAmelCase	      = 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}""")



import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


UpperCAmelCase	      = logging.get_logger(__name__)  # pylint: disable=invalid-name







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case=768       ):
		super().__init__(snake_case       )
		lowercase       =    proj_size

		lowercase       =    CLIPVisionModel(snake_case       )
		lowercase       =    PaintByExampleMapper(snake_case       )
		lowercase       =    nn.LayerNorm(config.hidden_size       )
		lowercase       =    nn.Linear(config.hidden_size		,    self.proj_size       )

		# uncondition for scaling
		lowercase       =    nn.Parameter(torch.randn((1, 1, self.proj_size)       )       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case=False       ):
		lowercase       =    self.model(pixel_values=snake_case       )
		lowercase       =    clip_output.pooler_output
		lowercase       =    self.mapper(latent_states[:, None]       )
		lowercase       =    self.final_layer_norm(snake_case       )
		lowercase       =    self.proj_out(snake_case       )
		if return_uncond_vector:
			return latent_states, self.uncond_vector

		return latent_states







class        A_   (			nn.Module   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case       ):
		super().__init__()
		lowercase       =    (config.num_hidden_layers + 1) // 5
		lowercase       =    config.hidden_size
		lowercase       =    1
		lowercase       =    nn.ModuleList(
		    [
		        BasicTransformerBlock(snake_case		,    snake_case		,    snake_case		,    activation_fn='gelu'		,    attention_bias=snake_case       )
		        for _ in range(snake_case       )
		    ]       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		for block in self.blocks:
			lowercase       =    block(snake_case       )

		return hidden_states

