





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__:      int									=		re.compile(r'\s+')


def 	UpperCamelCase__(       UpperCamelCase__			:       Tuple  )->Optional[int]:
       return {"hash": hashlib.mda(re.sub(UpperCamelCase__ ,     '''''' ,     example['''content''']  ).encode('''utf-8'''  )  ).hexdigest()}


def 	UpperCamelCase__(       UpperCamelCase__			:       List[Any]  )->List[Any]:
       A__											=						[len(UpperCamelCase__  ) for line in example['''content'''].splitlines()]
       return {"line_mean": np.mean(UpperCamelCase__  ), "line_max": max(UpperCamelCase__  )}


def 	UpperCamelCase__(       UpperCamelCase__			:       List[Any]  )->Optional[int]:
       A__											=						np.mean([c.isalnum() for c in example['''content''']]  )
       return {"alpha_frac": alpha_frac}


def 	UpperCamelCase__(       UpperCamelCase__			:       int ,     UpperCamelCase__			:       Optional[Any]  )->int:
       if example["hash"] in uniques:
              uniques.remove(example['''hash''']  )
              return True
       else:
              return False


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


def 	UpperCamelCase__(       UpperCamelCase__			:       Union[str, Any] ,     UpperCamelCase__			:       Tuple=5 ,     UpperCamelCase__			:       List[str]=0.05  )->Any:
       A__											=						['''unit tests''', '''test file''', '''configuration file''']
       A__											=						example['''content'''].splitlines()
       A__											=						0
       A__											=						0
       # first test
       for _, line in zip(range(UpperCamelCase__  ) ,     UpperCamelCase__  ):
              for keyword in keywords:
                     if keyword in line.lower():
                            return {"config_or_test": True}
    # second test
       A__											=						example['''content'''].count('''\n'''  )
       A__											=						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__(       UpperCamelCase__			:       int  )->Optional[int]:
       A__											=						['''def ''', '''class ''', '''for ''', '''while ''']
       A__											=						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__(       UpperCamelCase__			:       Optional[Any] ,     UpperCamelCase__			:       Tuple=4  )->List[str]:
       A__											=						example['''content'''].splitlines()
       A__											=						0
       for line in lines:
              counter += line.lower().count('''='''  )
              if counter > minimum:
                     return {"has_few_assignments": False}
       return {"has_few_assignments": True}


def 	UpperCamelCase__(       UpperCamelCase__			:       Tuple  )->int:
       A__											=						tokenizer(example['''content'''] ,     truncation=UpperCamelCase__  )['''input_ids''']
       A__											=						len(example['''content''']  ) / len(UpperCamelCase__  )
       return {"ratio": ratio}


def 	UpperCamelCase__(       UpperCamelCase__			:       Dict  )->List[str]:
       A__											=						{}
       results.update(get_hash(UpperCamelCase__  )  )
       results.update(line_stats(UpperCamelCase__  )  )
       results.update(alpha_stats(UpperCamelCase__  )  )
       results.update(char_token_ratio(UpperCamelCase__  )  )
       results.update(is_autogenerated(UpperCamelCase__  )  )
       results.update(is_config_or_test(UpperCamelCase__  )  )
       results.update(has_no_keywords(UpperCamelCase__  )  )
       results.update(has_few_assignments(UpperCamelCase__  )  )
       return results


def 	UpperCamelCase__(       UpperCamelCase__			:       List[Any] ,     UpperCamelCase__			:       str ,     UpperCamelCase__			:       int  )->int:
       if not check_uniques(UpperCamelCase__ ,     UpperCamelCase__  ):
              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__(       UpperCamelCase__			:       int  )->Any:
       with open(UpperCamelCase__ ,     '''rb'''  ) as f_in:
              with gzip.open(str(UpperCamelCase__  ) + '''.gz''' ,     '''wb''' ,     compresslevel=6  ) as f_out:
                     shutil.copyfileobj(UpperCamelCase__ ,     UpperCamelCase__  )
       os.unlink(UpperCamelCase__  )


# Settings
a__:      Dict									=		HfArgumentParser(PreprocessingArguments)
a__:      Any									=		parser.parse_args()
if args.num_workers is None:
       a__:      List[str]									=		multiprocessing.cpu_count()
a__:      Union[str, Any]									=		AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
a__:      Union[str, Any]									=		time.time()
a__:      str									=		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__:      Any									=		time.time()
       a__					,			a__:      Any									=		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__:      Dict									=		output_dir / 'data'
data_dir.mkdir(exist_ok=True)

a__:      int									=		time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
       a__:      Union[str, Any]									=		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}")












import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(DDIMParallelScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(('''eta''', 0.0), ('''num_inference_steps''', 50))



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**__lowerCamelCase )
        return config



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(**__lowerCamelCase )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0

        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter

        scheduler.set_timesteps(__lowerCamelCase )

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

        return sample



 def 				UpperCamelCase			(							self ):
        for timesteps in [100, 500, 1000]:
               self.check_over_configs(num_train_timesteps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__lowerCamelCase )

        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(steps_offset=1 )
        A__											=						scheduler_class(**__lowerCamelCase )
        scheduler.set_timesteps(5 )
        assert torch.equal(scheduler.timesteps,torch.LongTensor([801, 601, 401, 201, 1] ) )



 def 				UpperCamelCase			(							self ):
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1],[0.002, 0.02, 0.2, 2] ):
               self.check_over_configs(beta_start=__lowerCamelCase,beta_end=__lowerCamelCase )



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



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



 def 				UpperCamelCase			(							self ):
        for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__lowerCamelCase )



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



 def 				UpperCamelCase			(							self ):
        for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase )



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



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



 def 				UpperCamelCase			(							self ):
        for t, num_inference_steps in zip([1, 10, 50],[10, 50, 500] ):
               self.check_over_forward(time_step=__lowerCamelCase,num_inference_steps=__lowerCamelCase )



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



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        assert torch.sum(torch.abs(scheduler._get_variance(0,0 ) - 0.0 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(420,400 ) - 0.14771 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(980,960 ) - 0.32460 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(0,0 ) - 0.0 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487,486 ) - 0.00979 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999,998 ) - 0.02 ) ) < 1E-5



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0
        scheduler.set_timesteps(__lowerCamelCase )

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

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

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

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 1147.7904 ) < 1E-2
        assert abs(result_mean.item() - 0.4982 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 172.0067 ) < 1E-2
        assert abs(result_mean.item() - 0.223967 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 52.5302 ) < 1E-2
        assert abs(result_mean.item() - 0.0684 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 149.8295 ) < 1E-2
        assert abs(result_mean.item() - 0.1951 ) < 1E-3




 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 149.0784 ) < 1E-2
        assert abs(result_mean.item() - 0.1941 ) < 1E-3





