



"""simple docstring"""


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


snake_case_				     =       re.compile(R"""\s+""")

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

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

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

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

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

def  _lowerCAmelCase      (   lowercase_		,	lowercase_=5		,	lowercase_=0.0_5						):
						UpperCAmelCase             = ['unit tests', 'test file', 'configuration file']
						UpperCAmelCase             = example['content'].splitlines()
						UpperCAmelCase             = 0
						UpperCAmelCase             = 0
						# first test
						for _, line in zip(range(lowercase_						)		,	lowercase_						):
												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      (   lowercase_						):
						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      (   lowercase_		,	lowercase_=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      (   lowercase_						):
						UpperCAmelCase             = tokenizer(example['content']		,	truncation=lowercase_						)['input_ids']
						UpperCAmelCase             = len(example['content']						) / len(lowercase_						)
						return {"ratio": ratio}

def  _lowerCAmelCase      (   lowercase_						):
						UpperCAmelCase             = {}
						results.update(get_hash(lowercase_						)						)
						results.update(line_stats(lowercase_						)						)
						results.update(alpha_stats(lowercase_						)						)
						results.update(char_token_ratio(lowercase_						)						)
						results.update(is_autogenerated(lowercase_						)						)
						results.update(is_config_or_test(lowercase_						)						)
						results.update(has_no_keywords(lowercase_						)						)
						results.update(has_few_assignments(lowercase_						)						)
						return results

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


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

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

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

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

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

snake_case_				     =       output_dir / """data"""
data_dir.mkdir(exist_ok=True)

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







import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       int	     =				IFInpaintingPipeline
		__lowercase       :       str	     =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
		__lowercase       :       Optional[int]	     =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		__lowercase       :       Optional[int]	     =				PipelineTesterMixin.required_optional_params - {"latents"}



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				return self._get_dummy_components()



		def __UpperCamelCase ( self				,      A_				,      A_=0       )  ->					List[Any]:
				"""simple docstring"""
				if str(A_       ).startswith('mps'       ):
						UpperCamelCase		=     torch.manual_seed(A_       )
				else:
						UpperCamelCase		=     torch.Generator(device=A_       ).manual_seed(A_       )

				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )
				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )

				UpperCamelCase		=     {
				    'prompt': 'A painting of a squirrel eating a burger',
				    'image': image,
				    'mask_image': mask_image,
				    'generator': generator,
				    'num_inference_steps': 2,
				    'output_type': 'numpy',
				}

				return inputs



		@unittest.skipIf(
		    torch_device != 'cuda' or not is_xformers_available()				,      reason='XFormers attention is only available with CUDA and `xformers` installed'				,      )
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != 'cuda'				,      reason='float16 requires CUDA'       )
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
				super().test_save_load_floataa(expected_max_diff=1e-1       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_save_load_local()



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_inference_batch_single_identical(
				    expected_max_diff=1e-2				,      )
