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





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





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





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





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





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





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





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     {}
				results.update(get_hash(lowerCamelCase__       )       )
				results.update(line_stats(lowerCamelCase__       )       )
				results.update(alpha_stats(lowerCamelCase__       )       )
				results.update(char_token_ratio(lowerCamelCase__       )       )
				results.update(is_autogenerated(lowerCamelCase__       )       )
				results.update(is_config_or_test(lowerCamelCase__       )       )
				results.update(has_no_keywords(lowerCamelCase__       )       )
				results.update(has_few_assignments(lowerCamelCase__       )       )
				return results





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


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

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

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

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

# Deduplicate data and apply heuristics
__A							=time.time()
__A							=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							=time.time()
			__A,		__A							=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							=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							=output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

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

import numpy as np
import torch




@dataclass
class        _SCREAMING_SNAKE_CASE	:
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
					assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
					assert len(self.x.shape					) == len(self.y.shape					) == len(self.z.shape					) == len(self.origin.shape					) == 2



	def        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					return torch.from_numpy(np.array([self.width, self.height]		,    dtype=np.floataa					)					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					return torch.from_numpy(np.array([self.x_fov, self.y_fov]		,    dtype=np.floataa					)					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			torch.Tensor:
					lowerCamelCase_         =     torch.arange(self.height * self.width					)
					lowerCamelCase_         =     torch.stack(
					    [
					        pixel_indices % self.width,
					        torch.div(lowercase		,    self.width		,    rounding_mode="trunc"					),
					    ]		,    axis=1		,    )
					return coords



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					lowerCamelCase_      ,       *lowerCamelCase_         =     self.shape
					lowerCamelCase_         =     int(np.prod(lowercase					)					)

					lowerCamelCase_         =     self.get_image_coords()
					lowerCamelCase_         =     torch.broadcast_to(coords.unsqueeze(0					)		,    [batch_size * inner_batch_size, *coords.shape]					)
					lowerCamelCase_         =     self.get_camera_rays(lowercase					)

					lowerCamelCase_         =     rays.view(lowercase		,    inner_batch_size * self.height * self.width		,    2		,    3					)

					return rays



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			torch.Tensor:
					lowerCamelCase_      ,       *lowerCamelCase_      ,       lowerCamelCase_         =     coords.shape
					assert n_coords == 2
					assert batch_size == self.origin.shape[0]

					lowerCamelCase_         =     coords.view(lowercase		,    -1		,    2					)

					lowerCamelCase_         =     self.resolution()
					lowerCamelCase_         =     self.fov()

					lowerCamelCase_         =     (flat.float() / (res - 1)) * 2 - 1
					lowerCamelCase_         =     fracs * torch.tan(fov / 2					)

					lowerCamelCase_         =     fracs.view(lowercase		,    -1		,    2					)
					lowerCamelCase_         =     (
					    self.z.view(lowercase		,    1		,    3					)
					    + self.x.view(lowercase		,    1		,    3					) * fracs[:, :, :1]
					    + self.y.view(lowercase		,    1		,    3					) * fracs[:, :, 1:]
					)
					lowerCamelCase_         =     directions / directions.norm(dim=-1		,    keepdim=lowercase					)
					lowerCamelCase_         =     torch.stack(
					    [
					        torch.broadcast_to(self.origin.view(lowercase		,    1		,    3					)		,    [batch_size, directions.shape[1], 3]					),
					        directions,
					    ]		,    dim=2		,    )
					return rays.view(lowercase		,    *lowercase		,    2		,    3					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase					)							->			"DifferentiableProjectiveCamera":
					assert width * self.height == height * self.width, "The aspect ratio should not change."
					return DifferentiableProjectiveCamera(
					    origin=self.origin		,    x=self.x		,    y=self.y		,    z=self.z		,    width=lowercase		,    height=lowercase		,    x_fov=self.x_fov		,    y_fov=self.y_fov		,    )





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     []
				lowerCamelCase_         =     []
				lowerCamelCase_         =     []
				lowerCamelCase_         =     []
				for theta in np.linspace(0      ,					2 * np.pi      ,					num=2_0       ):
								lowerCamelCase_         =     np.array([np.sin(lowerCamelCase__       ), np.cos(lowerCamelCase__       ), -0.5]       )
								z /= np.sqrt(np.sum(z**2       )       )
								lowerCamelCase_         =     -z * 4
								lowerCamelCase_         =     np.array([np.cos(lowerCamelCase__       ), -np.sin(lowerCamelCase__       ), 0.0]       )
								lowerCamelCase_         =     np.cross(lowerCamelCase__      ,					lowerCamelCase__       )
								origins.append(lowerCamelCase__       )
								xs.append(lowerCamelCase__       )
								ys.append(lowerCamelCase__       )
								zs.append(lowerCamelCase__       )
				return DifferentiableProjectiveCamera(
				    origin=torch.from_numpy(np.stack(lowerCamelCase__      ,					axis=0       )       ).float()      ,					x=torch.from_numpy(np.stack(lowerCamelCase__      ,					axis=0       )       ).float()      ,					y=torch.from_numpy(np.stack(lowerCamelCase__      ,					axis=0       )       ).float()      ,					z=torch.from_numpy(np.stack(lowerCamelCase__      ,					axis=0       )       ).float()      ,					width=lowerCamelCase__      ,					height=lowerCamelCase__      ,					x_fov=0.7      ,					y_fov=0.7      ,					shape=(1, len(lowerCamelCase__       ))      ,					)


