
"""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


lowerCAmelCase__							=				re.compile(r'\s+')



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



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



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = 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    ):
		SCREAMING_SNAKE_CASE_						 = ['''auto-generated''', '''autogenerated''', '''automatically generated''']
		SCREAMING_SNAKE_CASE_						 = 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=0.0_5    ):
		SCREAMING_SNAKE_CASE_						 = ['''unit tests''', '''test file''', '''configuration file''']
		SCREAMING_SNAKE_CASE_						 = example['''content'''].splitlines()
		SCREAMING_SNAKE_CASE_						 = 0
		SCREAMING_SNAKE_CASE_						 = 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
		SCREAMING_SNAKE_CASE_						 = example['''content'''].count('''\n'''    )
		SCREAMING_SNAKE_CASE_						 = 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    ):
		SCREAMING_SNAKE_CASE_						 = ['''def ''', '''class ''', '''for ''', '''while ''']
		SCREAMING_SNAKE_CASE_						 = 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    ):
		SCREAMING_SNAKE_CASE_						 = example['''content'''].splitlines()
		SCREAMING_SNAKE_CASE_						 = 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    ):
		SCREAMING_SNAKE_CASE_						 = tokenizer(example['''content'''],							truncation=__a    )['''input_ids''']
		SCREAMING_SNAKE_CASE_						 = len(example['''content''']    ) / len(__a    )
		return {"ratio": ratio}



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = {}
		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
lowerCAmelCase__							=				HfArgumentParser(PreprocessingArguments)
lowerCAmelCase__							=				parser.parse_args()
if args.num_workers is None:
						lowerCAmelCase__							=				multiprocessing.cpu_count()
lowerCAmelCase__							=				AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

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

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

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

"""simple docstring"""




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      (DDIMParallelScheduler,)
							UpperCAmelCase__						     =      (('''eta''', 0.0), ('''num_inference_steps''', 50))

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {
									    '''num_train_timesteps''': 10_00,
									    '''beta_start''': 0.00_01,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									    '''clip_sample''': True,
									}

									config.update(**SCREAMING_SNAKE_CASE_    )
									return config

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config(**SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 10, 0.0

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter

									scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

									for t in scheduler.timesteps:
											SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = scheduler.step(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ).prev_sample

									return sample

							def _lowercase					(self    ):

									"""simple docstring"""







									for timesteps in [1_00, 5_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config(steps_offset=1    )
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )
									scheduler.set_timesteps(5    )
									assert torch.equal(scheduler.timesteps       ,       torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]       ,       [0.0_02, 0.02, 0.2, 2]    ):
											self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,       beta_end=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for timestep_spacing in ["trailing", "leading"]:
											self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_    )
									for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(
													    thresholding=SCREAMING_SNAKE_CASE_       ,       prediction_type=SCREAMING_SNAKE_CASE_       ,       sample_max_value=SCREAMING_SNAKE_CASE_       ,       )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t in [1, 10, 49]:
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t, num_inference_steps in zip([1, 10, 50]       ,       [10, 50, 5_00]    ):
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_       ,       num_inference_steps=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t, eta in zip([1, 10, 49]       ,       [0.0, 0.5, 1.0]    ):
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_       ,       eta=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									assert torch.sum(torch.abs(scheduler._get_variance(0       ,       0    ) - 0.0    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_20       ,       4_00    ) - 0.1_47_71    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_80       ,       9_60    ) - 0.3_24_60    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(0       ,       0    ) - 0.0    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87       ,       4_86    ) - 0.0_09_79    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99       ,       9_98    ) - 0.02    )    ) < 1e-5

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 10, 0.0
									scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter + 0.1
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter - 0.1

									SCREAMING_SNAKE_CASE_						 = samplea.shape[0]
									SCREAMING_SNAKE_CASE_						 = torch.stack([samplea, samplea, samplea]       ,       dim=0    )
									SCREAMING_SNAKE_CASE_						 = torch.arange(SCREAMING_SNAKE_CASE_    )[0:3, None].repeat(1       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = model(samples.flatten(0       ,       1    )       ,       timesteps.flatten(0       ,       1    )    )
									SCREAMING_SNAKE_CASE_						 = scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE_       ,       timesteps.flatten(0       ,       1    )       ,       samples.flatten(0       ,       1    )       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 11_47.79_04    ) < 1e-2
									assert abs(result_mean.item() - 0.49_82    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop()

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_72.00_67    ) < 1e-2
									assert abs(result_mean.item() - 0.22_39_67    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(prediction_type='''v_prediction'''    )

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 52.53_02    ) < 1e-2
									assert abs(result_mean.item() - 0.06_84    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       beta_start=0.01    )
									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_49.82_95    ) < 1e-2
									assert abs(result_mean.item() - 0.19_51    ) < 1e-3







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       beta_start=0.01    )
									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_49.07_84    ) < 1e-2
									assert abs(result_mean.item() - 0.19_41    ) < 1e-3