



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput





@dataclass
class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     42
   SCREAMING_SNAKE_CASE__							=     42






class 		UpperCAmelCase						(						snake_case_       ,snake_case_				):
   SCREAMING_SNAKE_CASE__							=     1





   @register_to_config
   def __init__(	self       ,     _lowerCAmelCase = 2_000       ,     _lowerCAmelCase = 0.15       ,     _lowerCAmelCase = 0.01       ,     _lowerCAmelCase = 1_348.0       ,     _lowerCAmelCase = 1E-5       ,     _lowerCAmelCase = 1       ,     ):
         # standard deviation of the initial noise distribution
         _lowerCAmelCase 			=  sigma_max

         # setable values
         _lowerCAmelCase 			=  None

         self.set_sigmas(_lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None			):
         return sample





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None			):
         _lowerCAmelCase 			=  sampling_eps if sampling_eps is not None else self.config.sampling_eps

         _lowerCAmelCase 			=  torch.linspace(1       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     device=_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None			):
         _lowerCAmelCase 			=  sigma_min if sigma_min is not None else self.config.sigma_min
         _lowerCAmelCase 			=  sigma_max if sigma_max is not None else self.config.sigma_max
         _lowerCAmelCase 			=  sampling_eps if sampling_eps is not None else self.config.sampling_eps
         if self.timesteps is None:
               self.set_timesteps(_lowerCAmelCase       ,     _lowerCAmelCase			)

         _lowerCAmelCase 			=  sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
         _lowerCAmelCase 			=  torch.exp(torch.linspace(math.log(_lowerCAmelCase			)       ,     math.log(_lowerCAmelCase			)       ,     _lowerCAmelCase			)			)
         _lowerCAmelCase 			=  torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         return torch.where(
             timesteps == 0       ,     torch.zeros_like(t.to(timesteps.device			)			)       ,     self.discrete_sigmas[timesteps - 1].to(timesteps.device			)       ,     )





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = True       ,     ):
         if self.timesteps is None:
               raise ValueError(
                   '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''			)

         _lowerCAmelCase 			=  timestep * torch.ones(
             sample.shape[0]       ,     device=sample.device			)  # torch.repeat_interleave(timestep, sample.shape[0])
         _lowerCAmelCase 			=  (timestep * (len(self.timesteps			) - 1)).long()

         # mps requires indices to be in the same device, so we use cpu as is the default with cuda
         _lowerCAmelCase 			=  timesteps.to(self.discrete_sigmas.device			)

         _lowerCAmelCase 			=  self.discrete_sigmas[timesteps].to(sample.device			)
         _lowerCAmelCase 			=  self.get_adjacent_sigma(_lowerCAmelCase       ,     _lowerCAmelCase			).to(sample.device			)
         _lowerCAmelCase 			=  torch.zeros_like(_lowerCAmelCase			)
         _lowerCAmelCase 			=  (sigma**2 - adjacent_sigma**2) ** 0.5

         # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
         # also equation 47 shows the analog from SDE models to ancestral sampling methods
         _lowerCAmelCase 			=  diffusion.flatten()
         while len(diffusion.shape			) < len(sample.shape			):
               _lowerCAmelCase 			=  diffusion.unsqueeze(-1			)
         _lowerCAmelCase 			=  drift - diffusion**2 * model_output

         #  equation 6: sample noise for the diffusion term of
         _lowerCAmelCase 			=  randn_tensor(
             sample.shape       ,     layout=sample.layout       ,     generator=_lowerCAmelCase       ,     device=sample.device       ,     dtype=sample.dtype			)
         _lowerCAmelCase 			=  sample - drift  # subtract because `dt` is a small negative timestep
         # TODO is the variable diffusion the correct scaling term for the noise?
         _lowerCAmelCase 			=  prev_sample_mean + diffusion * noise  # add impact of diffusion field g

         if not return_dict:
               return (prev_sample, prev_sample_mean)

         return SdeVeOutput(prev_sample=_lowerCAmelCase       ,     prev_sample_mean=_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = True       ,     ):
         if self.timesteps is None:
               raise ValueError(
                   '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''			)

         # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
         # sample noise for correction
         _lowerCAmelCase 			=  randn_tensor(sample.shape       ,     layout=sample.layout       ,     generator=_lowerCAmelCase			).to(sample.device			)

         # compute step size from the model_output, the noise, and the snr
         _lowerCAmelCase 			=  torch.norm(model_output.reshape(model_output.shape[0]       ,     -1			)       ,     dim=-1			).mean()
         _lowerCAmelCase 			=  torch.norm(noise.reshape(noise.shape[0]       ,     -1			)       ,     dim=-1			).mean()
         _lowerCAmelCase 			=  (self.config.snr * noise_norm / grad_norm) ** 2 * 2
         _lowerCAmelCase 			=  step_size * torch.ones(sample.shape[0]			).to(sample.device			)
         # self.repeat_scalar(step_size, sample.shape[0])

         # compute corrected sample: model_output term and noise term
         _lowerCAmelCase 			=  step_size.flatten()
         while len(step_size.shape			) < len(sample.shape			):
               _lowerCAmelCase 			=  step_size.unsqueeze(-1			)
         _lowerCAmelCase 			=  sample + step_size * model_output
         _lowerCAmelCase 			=  prev_sample_mean + ((step_size * 2) ** 0.5) * noise

         if not return_dict:
               return (prev_sample,)

         return SchedulerOutput(prev_sample=_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     ):
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         _lowerCAmelCase 			=  timesteps.to(original_samples.device			)
         _lowerCAmelCase 			=  self.discrete_sigmas.to(original_samples.device			)[timesteps]
         _lowerCAmelCase 			=  (
             noise * sigmas[:, None, None, None]
             if noise is not None
             else torch.randn_like(_lowerCAmelCase			) * sigmas[:, None, None, None]
         )
         _lowerCAmelCase 			=  noise + original_samples
         return noisy_samples





   def __len__(	self			):
         return self.config.num_train_timesteps




import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


UpperCAmelCase_									=							re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
UpperCAmelCase_									=							1_0
UpperCAmelCase_									=							2_5_6
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[str]				)->Optional[MinHash]:
      if len(_SCREAMING_SNAKE_CASE				) < MIN_NUM_TOKENS:
            return None
      _lowerCAmelCase 			=  MinHash(num_perm=_SCREAMING_SNAKE_CASE				)
      for token in set(_SCREAMING_SNAKE_CASE				):
            min_hash.update(token.encode()				)
      return min_hash
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str				)->Set[str]:
      return {t for t in NON_ALPHA.split(_SCREAMING_SNAKE_CASE				) if len(t.strip()				) > 0}





class 		UpperCAmelCase						:





   def __init__(	self       ,     *,
       _lowerCAmelCase = 0.85       ,     ):
         _lowerCAmelCase 			=  duplication_jaccard_threshold
         _lowerCAmelCase 			=  NUM_PERM
         _lowerCAmelCase 			=  MinHashLSH(threshold=self._duplication_jaccard_threshold       ,     num_perm=self._num_perm			)

         _lowerCAmelCase 			=  defaultdict(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  self._index.query(_lowerCAmelCase			)
         if code_key in self._index.keys:
               print(F'''Duplicate key {code_key}'''			)
               return

         self._index.insert(_lowerCAmelCase       ,     _lowerCAmelCase			)
         if len(_lowerCAmelCase			) > 0:
               for base_duplicate in close_duplicates:
                     if base_duplicate in self._duplicate_clusters:
                           self._duplicate_clusters[base_duplicate].add(_lowerCAmelCase			)
                           break
               else:
                     self._duplicate_clusters[close_duplicates[0]].add(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  []
         for base, duplicates in self._duplicate_clusters.items():
               _lowerCAmelCase 			=  [base] + list(_lowerCAmelCase			)
               # reformat the cluster to be a list of dict
               _lowerCAmelCase 			=  [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
               duplicate_clusters.append(_lowerCAmelCase			)
         return duplicate_clusters





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  self.get_duplicate_clusters()
         with open(_lowerCAmelCase       ,     '''w'''			) as f:
               json.dump(_lowerCAmelCase       ,     _lowerCAmelCase			)






def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str				)->Optional[Any]:
      _lowerCAmelCase			,   _lowerCAmelCase 			=  element
      _lowerCAmelCase 			=  get_min_hash([t for t in NON_ALPHA.split(data['''content''']				) if len(t.strip()				) > 0]				)
      if min_hash is not None:
            return (index, data["repo_name"], data["path"]), min_hash
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Type[Dataset]				)->Any:
      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash     ,				ThreadedIterator(_SCREAMING_SNAKE_CASE     ,				max_queue_size=1_0_0_0_0				)     ,				chunksize=1_0_0     ,				):
                  if data is not None:
                        yield data
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Type[Dataset]     ,				_SCREAMING_SNAKE_CASE   :	float				)->str:
      _lowerCAmelCase 			=  DuplicationIndex(duplication_jaccard_threshold=_SCREAMING_SNAKE_CASE				)

      for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_SCREAMING_SNAKE_CASE				)				)     ,				max_queue_size=1_0_0				)				):
            di.add(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

      # Returns a List[Cluster] where Cluster is List[str] with the filenames.
      return di.get_duplicate_clusters()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	str				)->float:
      _lowerCAmelCase 			=  get_tokens(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  get_tokens(_SCREAMING_SNAKE_CASE				)
      return len(tokensa & tokensa				) / len(tokensa | tokensa				)


UpperCAmelCase_									=							None
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[Any]     ,				_SCREAMING_SNAKE_CASE   :	Any				)->List[Any]:
      _lowerCAmelCase 			=  []
      for elementa in cluster:
            _lowerCAmelCase 			=  _shared_dataset[elementa['''base_index''']]['''content''']
            for elementa in extremes:
                  _lowerCAmelCase 			=  _shared_dataset[elementa['''base_index''']]['''content''']
                  if jaccard_similarity(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				) >= jaccard_threshold:
                        elementa["copies"] += 1
                        break
            else:
                  _lowerCAmelCase 			=  1
                  extremes.append(_SCREAMING_SNAKE_CASE				)
      return extremes
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	str				)->Tuple:
      global _shared_dataset
      _lowerCAmelCase 			=  dataset
      _lowerCAmelCase 			=  []
      _lowerCAmelCase 			=  partial(_find_cluster_extremes_shared     ,				jaccard_threshold=_SCREAMING_SNAKE_CASE				)
      with mp.Pool() as pool:
            for extremes in tqdm(
                pool.imap_unordered(
                    _SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				)     ,				total=len(_SCREAMING_SNAKE_CASE				)     ,				):
                  extremes_list.append(_SCREAMING_SNAKE_CASE				)
      return extremes_list


def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Type[Dataset]     ,				_SCREAMING_SNAKE_CASE   :	float = 0.85				)->Tuple[Type[Dataset], List[List[Dict]]]:
      _lowerCAmelCase 			=  make_duplicate_clusters(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
      _lowerCAmelCase 			=  {}
      _lowerCAmelCase 			=  find_extremes(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
      for extremes in extremes_clusters:
            for element in extremes:
                  _lowerCAmelCase 			=  element
      _lowerCAmelCase 			=  duplicate_indices - set(extreme_dict.keys()				)
      _lowerCAmelCase 			=  dataset.filter(lambda _SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				: idx not in remove_indices     ,				with_indices=_SCREAMING_SNAKE_CASE				)

      # update duplicate_clusters
      for cluster in duplicate_clusters:
            for element in cluster:
                  _lowerCAmelCase 			=  element['''base_index'''] in extreme_dict
                  if element["is_extreme"]:
                        _lowerCAmelCase 			=  extreme_dict[element['''base_index''']]['''copies''']

      print(f'''Original dataset size: {len(_SCREAMING_SNAKE_CASE				)}'''				)
      print(f'''Number of duplicate clusters: {len(_SCREAMING_SNAKE_CASE				)}'''				)
      print(f'''Files in duplicate cluster: {len(_SCREAMING_SNAKE_CASE				)}'''				)
      print(f'''Unique files in duplicate cluster: {len(_SCREAMING_SNAKE_CASE				)}'''				)
      print(f'''Filtered dataset size: {len(_SCREAMING_SNAKE_CASE				)}'''				)

      return ds_filter, duplicate_clusters