






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







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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Union[str, Any]:
     UpperCamelCase__      :       Dict	    =  [len(__lowerCAmelCase      ) for line in example["content"].splitlines()]
     return {"line_mean": np.mean(__lowerCAmelCase      ), "line_max": max(__lowerCAmelCase      )}







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Optional[int]:
     UpperCamelCase__      :       str	    =  np.mean([c.isalnum() for c in example["content"]]      )
     return {"alpha_frac": alpha_frac}







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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase=5      )			->     Union[str, Any]:
     UpperCamelCase__      :       int	    =  ["auto-generated", "autogenerated", "automatically generated"]
     UpperCamelCase__      :       Optional[Any]	    =  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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase=5 ,   __lowerCAmelCase=0.0_5      )			->     Tuple:
     UpperCamelCase__      :       Optional[Any]	    =  ["unit tests", "test file", "configuration file"]
     UpperCamelCase__      :       str	    =  example["content"].splitlines()
     UpperCamelCase__      :       Optional[int]	    =  0
     UpperCamelCase__      :       Any	    =  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
     UpperCamelCase__      :       Any	    =  example["content"].count("\n"      )
     UpperCamelCase__      :       List[Any]	    =  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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     str:
     UpperCamelCase__      :       Union[str, Any]	    =  ["def ", "class ", "for ", "while "]
     UpperCamelCase__      :       Union[str, Any]	    =  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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase=4      )			->     int:
     UpperCamelCase__      :       Dict	    =  example["content"].splitlines()
     UpperCamelCase__      :       Optional[int]	    =  0
     for line in lines:
          counter += line.lower().count("="      )
          if counter > minimum:
               return {"has_few_assignments": False}
     return {"has_few_assignments": True}







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Dict:
     UpperCamelCase__      :       Tuple	    =  tokenizer(example["content"] ,   truncation=__lowerCAmelCase      )["input_ids"]
     UpperCamelCase__      :       List[str]	    =  len(example["content"]      ) / len(__lowerCAmelCase      )
     return {"ratio": ratio}







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Optional[int]:
     UpperCamelCase__      :       List[str]	    =  {}
     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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     Optional[Any]:
     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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Tuple:
     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
lowerCamelCase     :							List[Any]    =HfArgumentParser(PreprocessingArguments)
lowerCamelCase     :							Any    =parser.parse_args()
if args.num_workers is None:
 lowerCamelCase     :							Optional[Any]    =multiprocessing.cpu_count()
lowerCamelCase     :							str    =AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

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

lowerCamelCase     :							Tuple    =time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
 lowerCamelCase     :							List[str]    =str(data_dir / F"""file-{file_number+1:012}.json""")
 lowerCamelCase     :							str    =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}""")







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)





@flax.struct.dataclass
class       __a  :
 _lowerCAmelCase	:	CommonSchedulerState

 # setable values
 _lowerCAmelCase	:	jnp.ndarray
 _lowerCAmelCase	:	jnp.ndarray
 _lowerCAmelCase	:	Optional[int]				=			None







 @classmethod
 def  __lowercase (		cls :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       CommonSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray						):


      '''simple docstring'''






      return cls(common=SCREAMING_SNAKE_CASE							,       init_noise_sigma=SCREAMING_SNAKE_CASE							,       timesteps=SCREAMING_SNAKE_CASE						)







@dataclass
class       __a  (   A__	):
 _lowerCAmelCase	:	DDPMSchedulerState







class       __a  (   A__	,       A__	):
 _lowerCAmelCase	:	Tuple				=			[e.name for e in FlaxKarrasDiffusionSchedulers]

 _lowerCAmelCase	:	jnp.dtype







 @property
 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      return True







 @register_to_config
 def __init__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       int = 10_00							,       SCREAMING_SNAKE_CASE :       float = 0.0_0_0_1							,       SCREAMING_SNAKE_CASE :       float = 0.0_2							,       SCREAMING_SNAKE_CASE :       str = "linear"							,       SCREAMING_SNAKE_CASE :       Optional[jnp.ndarray] = None							,       SCREAMING_SNAKE_CASE :       str = "fixed_small"							,       SCREAMING_SNAKE_CASE :       bool = True							,       SCREAMING_SNAKE_CASE :       str = "epsilon"							,       SCREAMING_SNAKE_CASE :       jnp.dtype = jnp.floataa							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  dtype







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       Optional[CommonSchedulerState] = None						):


      '''simple docstring'''






      if common is None:
           UpperCamelCase__      :       Tuple	    =  CommonSchedulerState.create(self						)

      # standard deviation of the initial noise distribution
      UpperCamelCase__      :       str	    =  jnp.array(1.0							,       dtype=self.dtype						)

      UpperCamelCase__      :       Any	    =  jnp.arange(0							,       self.config.num_train_timesteps						).round()[::-1]

      return DDPMSchedulerState.create(
          common=SCREAMING_SNAKE_CASE							,       init_noise_sigma=SCREAMING_SNAKE_CASE							,       timesteps=SCREAMING_SNAKE_CASE							,       )







 def  __lowercase (		self :       Tuple							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       Optional[int] = None						):


      '''simple docstring'''






      return sample







 def  __lowercase (		self :       Tuple							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       Tuple = ()						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.config.num_train_timesteps // num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # rounding to avoid issues when num_inference_step is power of 3
      UpperCamelCase__      :       Optional[int]	    =  (jnp.arange(0							,       SCREAMING_SNAKE_CASE						) * step_ratio).round()[::-1]

      return state.replace(
          num_inference_steps=SCREAMING_SNAKE_CASE							,       timesteps=SCREAMING_SNAKE_CASE							,       )







 def  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       List[str]							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       Optional[Any]=None						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  state.common.alphas_cumprod[t]
      UpperCamelCase__      :       Tuple	    =  jnp.where(t > 0							,       state.common.alphas_cumprod[t - 1]							,       jnp.array(1.0							,       dtype=self.dtype						)						)

      # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
      # and sample from it to get previous sample
      # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
      UpperCamelCase__      :       int	    =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

      if variance_type is None:
           UpperCamelCase__      :       List[Any]	    =  self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small":
           UpperCamelCase__      :       Union[str, Any]	    =  jnp.clip(SCREAMING_SNAKE_CASE							,       a_min=1e-2_0						)
      # for rl-diffuser https://arxiv.org/abs/2205.09991
      elif variance_type == "fixed_small_log":
           UpperCamelCase__      :       Optional[Any]	    =  jnp.log(jnp.clip(SCREAMING_SNAKE_CASE							,       a_min=1e-2_0						)						)
      elif variance_type == "fixed_large":
           UpperCamelCase__      :       Dict	    =  state.common.betas[t]
      elif variance_type == "fixed_large_log":
           # Glide max_log
           UpperCamelCase__      :       List[str]	    =  jnp.log(state.common.betas[t]						)
      elif variance_type == "learned":
           return predicted_variance
      elif variance_type == "learned_range":
           UpperCamelCase__      :       Tuple	    =  variance
           UpperCamelCase__      :       int	    =  state.common.betas[t]
           UpperCamelCase__      :       Union[str, Any]	    =  (predicted_variance + 1) / 2
           UpperCamelCase__      :       List[str]	    =  frac * max_log + (1 - frac) * min_log

      return variance







 def  __lowercase (		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       Optional[jax.random.KeyArray] = None							,       SCREAMING_SNAKE_CASE :       bool = True							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  timestep

      if key is None:
           UpperCamelCase__      :       str	    =  jax.random.PRNGKey(0						)

      if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
           UpperCamelCase__    ,					UpperCamelCase__      :       Optional[int]	    =  jnp.split(SCREAMING_SNAKE_CASE							,       sample.shape[1]							,       axis=1						)
      else:
           UpperCamelCase__      :       Optional[int]	    =  None

      # 1. compute alphas, betas
      UpperCamelCase__      :       Optional[int]	    =  state.common.alphas_cumprod[t]
      UpperCamelCase__      :       Dict	    =  jnp.where(t > 0							,       state.common.alphas_cumprod[t - 1]							,       jnp.array(1.0							,       dtype=self.dtype						)						)
      UpperCamelCase__      :       Any	    =  1 - alpha_prod_t
      UpperCamelCase__      :       List[str]	    =  1 - alpha_prod_t_prev

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if self.config.prediction_type == "epsilon":
           UpperCamelCase__      :       Optional[Any]	    =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
           UpperCamelCase__      :       Dict	    =  model_output
      elif self.config.prediction_type == "v_prediction":
           UpperCamelCase__      :       str	    =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
      else:
           raise ValueError(
               F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
               " for the FlaxDDPMScheduler."						)

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
           UpperCamelCase__      :       List[str]	    =  jnp.clip(SCREAMING_SNAKE_CASE							,       -1							,       1						)

      # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      UpperCamelCase__      :       List[Any]	    =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
      UpperCamelCase__      :       str	    =  state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

      # 5. Compute predicted previous sample µ_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      UpperCamelCase__      :       Any	    =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      def random_variance():
           UpperCamelCase__      :       str	    =  jax.random.split(SCREAMING_SNAKE_CASE							,       num=1						)
           UpperCamelCase__      :       Tuple	    =  jax.random.normal(SCREAMING_SNAKE_CASE							,       shape=model_output.shape							,       dtype=self.dtype						)
           return (self._get_variance(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       predicted_variance=SCREAMING_SNAKE_CASE						) ** 0.5) * noise

      UpperCamelCase__      :       Any	    =  jnp.where(t > 0							,       random_variance()							,       jnp.zeros(model_output.shape							,       dtype=self.dtype						)						)

      UpperCamelCase__      :       List[str]	    =  pred_prev_sample + variance

      if not return_dict:
           return (pred_prev_sample, state)

      return FlaxDDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE							,       state=SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Any							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       ):


      '''simple docstring'''






      return add_noise_common(state.common							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       ):


      '''simple docstring'''






      return get_velocity_common(state.common							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)







 def __len__(		self :       int						):


      '''simple docstring'''






      return self.config.num_train_timesteps