

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer

@dataclass
class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     42


class 						__UpperCamelCase    (					lowercase     ,     lowercase						):




	@register_to_config
	def __init__(		self	:						Optional[int]		,	lowerCAmelCase	:						int = 3		,	lowerCAmelCase	:						int = 3		,	lowerCAmelCase	:						Tuple[str] = ("DownEncoderBlock2D",)		,	lowerCAmelCase	:						Tuple[str] = ("UpDecoderBlock2D",)		,	lowerCAmelCase	:						Tuple[int] = (64,)		,	lowerCAmelCase	:						int = 1		,	lowerCAmelCase	:						str = "silu"		,	lowerCAmelCase	:						int = 3		,	lowerCAmelCase	:						int = 32		,	lowerCAmelCase	:						int = 256		,	lowerCAmelCase	:						int = 32		,	lowerCAmelCase	:						Optional[int] = None		,	lowerCAmelCase	:						float = 0.18_215		,	lowerCAmelCase	:						str = "group"		,	):


		'''simple docstring'''







		super().__init__()

		# pass init params to Encoder
		UpperCAmelCase_       				=			Encoder(
		    in_channels=lowerCAmelCase		,	out_channels=lowerCAmelCase		,	down_block_types=lowerCAmelCase		,	block_out_channels=lowerCAmelCase		,	layers_per_block=lowerCAmelCase		,	act_fn=lowerCAmelCase		,	norm_num_groups=lowerCAmelCase		,	double_z=lowerCAmelCase		,	)

		UpperCAmelCase_       				=			vq_embed_dim if vq_embed_dim is not None else latent_channels

		UpperCAmelCase_       				=			nn.Convad(lowerCAmelCase		,	lowerCAmelCase		,	1							)
		UpperCAmelCase_       				=			VectorQuantizer(lowerCAmelCase		,	lowerCAmelCase		,	beta=0.25		,	remap=lowerCAmelCase		,	sane_index_shape=lowerCAmelCase							)
		UpperCAmelCase_       				=			nn.Convad(lowerCAmelCase		,	lowerCAmelCase		,	1							)

		# pass init params to Decoder
		UpperCAmelCase_       				=			Decoder(
		    in_channels=lowerCAmelCase		,	out_channels=lowerCAmelCase		,	up_block_types=lowerCAmelCase		,	block_out_channels=lowerCAmelCase		,	layers_per_block=lowerCAmelCase		,	act_fn=lowerCAmelCase		,	norm_num_groups=lowerCAmelCase		,	norm_type=lowerCAmelCase		,	)




	@apply_forward_hook
	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						torch.FloatTensor		,	lowerCAmelCase	:						bool = True							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.encoder(lowerCAmelCase							)
		UpperCAmelCase_       				=			self.quant_conv(lowerCAmelCase							)

		if not return_dict:
			return (h,)

		return VQEncoderOutput(latents=lowerCAmelCase							)




	@apply_forward_hook
	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						torch.FloatTensor		,	lowerCAmelCase	:						bool = False		,	lowerCAmelCase	:						bool = True							):


		'''simple docstring'''







		if not force_not_quantize:
			UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			self.quantize(lowerCAmelCase							)
		else:
			UpperCAmelCase_       				=			h
		UpperCAmelCase_       				=			self.post_quant_conv(lowerCAmelCase							)
		UpperCAmelCase_       				=			self.decoder(lowerCAmelCase		,	quant if self.config.norm_type == "spatial" else None							)

		if not return_dict:
			return (dec,)

		return DecoderOutput(sample=lowerCAmelCase							)




	def       __A					(		self	:						int		,	lowerCAmelCase	:						torch.FloatTensor		,	lowerCAmelCase	:						bool = True							):


		'''simple docstring'''







		UpperCAmelCase_       				=			sample
		UpperCAmelCase_       				=			self.encode(lowerCAmelCase							).latents
		UpperCAmelCase_       				=			self.decode(lowerCAmelCase							).sample

		if not return_dict:
			return (dec,)

		return DecoderOutput(sample=lowerCAmelCase							)


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


_a:    Optional[int] 							=			re.compile(r"""\s+""")



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



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



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



def       __lowerCAmelCase      (						A       ,     A    ):
	if example["hash"] in uniques:
		uniques.remove(example["hash"]    )
		return True
	else:
		return False



def       __lowerCAmelCase      (						A       ,     A=5    ):
	UpperCAmelCase_       				=			["auto-generated", "autogenerated", "automatically generated"]
	UpperCAmelCase_       				=			example["content"].splitlines()
	for _, line in zip(range(A    )       ,     A    ):
		for keyword in keywords:
			if keyword in line.lower():
				return {"autogenerated": True}
	else:
		return {"autogenerated": False}



def       __lowerCAmelCase      (						A       ,     A=5       ,     A=0.05    ):
	UpperCAmelCase_       				=			["unit tests", "test file", "configuration file"]
	UpperCAmelCase_       				=			example["content"].splitlines()
	UpperCAmelCase_       				=			0
	UpperCAmelCase_       				=			0
	# first test
	for _, line in zip(range(A    )       ,     A    ):
		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       __lowerCAmelCase      (						A    ):
	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       __lowerCAmelCase      (						A       ,     A=4    ):
	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       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			tokenizer(example["content"]       ,     truncation=A    )["input_ids"]
	UpperCAmelCase_       				=			len(example["content"]    ) / len(A    )
	return {"ratio": ratio}



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



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	if not check_uniques(A       ,     A    ):
		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       __lowerCAmelCase      (						A    ):
	with open(A       ,     "rb"    ) as f_in:
		with gzip.open(str(A    ) + ".gz"       ,     "wb"       ,     compresslevel=6    ) as f_out:
			shutil.copyfileobj(A       ,     A    )
	os.unlink(A    )


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

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

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

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

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

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

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