





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_  							=				10
UpperCAmelCase_  							=				256




def 	lowerCAmelCase_					(						__UpperCAmelCase:    List[str]   )   ->			Optional[MinHash]:
    if len(__UpperCAmelCase   ) < MIN_NUM_TOKENS:
        return None
    UpperCamelCase__							:       Optional[Any]      =   MinHash(num_perm=__UpperCAmelCase   )
    for token in set(__UpperCAmelCase   ):
        min_hash.update(token.encode()   )
    return min_hash




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str   )   ->			Set[str]:
    return {t for t in NON_ALPHA.split(__UpperCAmelCase   ) if len(t.strip()   ) > 0}






class 					lowercase__ :

   '''simple docstring'''


   def __init__(					self,	*,
       __magic_name__ = 0.85,	)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   duplication_jaccard_threshold
       UpperCamelCase__							:       int      =   NUM_PERM
       UpperCamelCase__							:       Dict      =   MinHashLSH(threshold=self._duplication_jaccard_threshold,	num_perm=self._num_perm							)

       UpperCamelCase__							:       str      =   defaultdict(__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   None:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   self._index.query(__magic_name__							)
       if code_key in self._index.keys:
           print(f"Duplicate key {code_key}"							)
           return

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




   def    UpperCamelCase__						(					self							)				->   List[List[Dict]]:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   []
       for base, duplicates in self._duplicate_clusters.items():
           UpperCamelCase__							:       Dict      =   [base] + list(__magic_name__							)
           # reformat the cluster to be a list of dict
           UpperCamelCase__							:       int      =   [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
           duplicate_clusters.append(__magic_name__							)
       return duplicate_clusters




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   None:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   self.get_duplicate_clusters()
       with open(__magic_name__,	'''w'''							) as f:
           json.dump(__magic_name__,	__magic_name__							)




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[Any]   )   ->			Tuple:
    UpperCamelCase__							:       Any      =   element
    UpperCamelCase__							:       List[Any]      =   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 	lowerCAmelCase_					(						__UpperCAmelCase:    Type[Dataset]   )   ->			List[Any]:
    with mp.Pool() as pool:
        for data in pool.imap_unordered(
            _compute_min_hash	,     ThreadedIterator(__UpperCAmelCase	,     max_queue_size=1_0000   )	,     chunksize=100	,     ):
            if data is not None:
                yield data




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Type[Dataset]	,     __UpperCAmelCase:    float   )   ->			Any:
    UpperCamelCase__							:       Any      =   DuplicationIndex(duplication_jaccard_threshold=__UpperCAmelCase   )

    for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__UpperCAmelCase   )   )	,     max_queue_size=100   )   ):
        di.add(__UpperCAmelCase	,     __UpperCAmelCase   )

    # Returns a List[Cluster] where Cluster is List[str] with the filenames.
    return di.get_duplicate_clusters()




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str	,     __UpperCAmelCase:    str   )   ->			float:
    UpperCamelCase__							:       Optional[Any]      =   get_tokens(__UpperCAmelCase   )
    UpperCamelCase__							:       str      =   get_tokens(__UpperCAmelCase   )
    return len(tokensa & tokensa   ) / len(tokensa | tokensa   )


UpperCAmelCase_  							=				None




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    Dict   )   ->			Any:
    UpperCamelCase__							:       str      =   []
    for elementa in cluster:
        UpperCamelCase__							:       List[Any]      =   _shared_dataset[elementa['''base_index''']]['''content''']
        for elementa in extremes:
            UpperCamelCase__							:       List[Any]      =   _shared_dataset[elementa['''base_index''']]['''content''']
            if jaccard_similarity(__UpperCAmelCase	,     __UpperCAmelCase   ) >= jaccard_threshold:
                elementa["copies"] += 1
                break
        else:
            UpperCamelCase__							:       Dict      =   1
            extremes.append(__UpperCAmelCase   )
    return extremes




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    Optional[Any]   )   ->			Dict:
    global _shared_dataset
    UpperCamelCase__							:       str      =   dataset
    UpperCamelCase__							:       Any      =   []
    UpperCamelCase__							:       Optional[int]      =   partial(_find_cluster_extremes_shared	,     jaccard_threshold=__UpperCAmelCase   )
    with mp.Pool() as pool:
        for extremes in tqdm(
            pool.imap_unordered(
                __UpperCAmelCase	,     __UpperCAmelCase	,     )	,     total=len(__UpperCAmelCase   )	,     ):
            extremes_list.append(__UpperCAmelCase   )
    return extremes_list





def 	lowerCAmelCase_					(						__UpperCAmelCase:    Type[Dataset]	,     __UpperCAmelCase:    float = 0.85   )   ->			Tuple[Type[Dataset], List[List[Dict]]]:
    UpperCamelCase__							:       List[str]      =   make_duplicate_clusters(__UpperCAmelCase	,     __UpperCAmelCase   )
    UpperCamelCase__							:       Any      =   {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
    UpperCamelCase__							:       Union[str, Any]      =   {}
    UpperCamelCase__							:       int      =   find_extremes(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )
    for extremes in extremes_clusters:
        for element in extremes:
            UpperCamelCase__							:       Union[str, Any]      =   element
    UpperCamelCase__							:       Optional[Any]      =   duplicate_indices - set(extreme_dict.keys()   )
    UpperCamelCase__							:       Optional[Any]      =   dataset.filter(lambda __UpperCAmelCase	,     __UpperCAmelCase   : idx not in remove_indices	,     with_indices=__UpperCAmelCase   )

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

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

    return ds_filter, duplicate_clusters








# 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 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							torch.FloatTensor
   a		:							torch.FloatTensor






class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase       ):

   '''simple docstring'''


   a		:							List[Any]     	=	1




   @register_to_config
   def __init__(					self,	__magic_name__ = 2000,	__magic_name__ = 0.15,	__magic_name__ = 0.01,	__magic_name__ = 1348.0,	__magic_name__ = 1E-5,	__magic_name__ = 1,	)				->   int:






       """simple docstring"""
       # standard deviation of the initial noise distribution
       UpperCamelCase__							:       int      =   sigma_max

       # setable values
       UpperCamelCase__							:       Optional[int]      =   None

       self.set_sigmas(__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   torch.FloatTensor:






       """simple docstring"""
       return sample




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       str      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

       UpperCamelCase__							:       List[Any]      =   torch.linspace(1,	__magic_name__,	__magic_name__,	device=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   sigma_min if sigma_min is not None else self.config.sigma_min
       UpperCamelCase__							:       str      =   sigma_max if sigma_max is not None else self.config.sigma_max
       UpperCamelCase__							:       Optional[int]      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if self.timesteps is None:
           self.set_timesteps(__magic_name__,	__magic_name__							)

       UpperCamelCase__							:       str      =   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       UpperCamelCase__							:       Optional[Any]      =   torch.exp(torch.linspace(math.log(__magic_name__							),	math.log(__magic_name__							),	__magic_name__							)							)
       UpperCamelCase__							:       Any      =   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   str:






       """simple docstring"""
       return torch.where(
           timesteps == 0,	torch.zeros_like(t.to(timesteps.device							)							),	self.discrete_sigmas[timesteps - 1].to(timesteps.device							),	)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[SdeVeOutput, Tuple]:






       """simple docstring"""
       if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

       UpperCamelCase__							:       Any      =   timestep * torch.ones(
           sample.shape[0],	device=sample.device							)  # torch.repeat_interleave(timestep, sample.shape[0])
       UpperCamelCase__							:       Any      =   (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
       UpperCamelCase__							:       List[Any]      =   timesteps.to(self.discrete_sigmas.device							)

       UpperCamelCase__							:       str      =   self.discrete_sigmas[timesteps].to(sample.device							)
       UpperCamelCase__							:       List[Any]      =   self.get_adjacent_sigma(__magic_name__,	__magic_name__							).to(sample.device							)
       UpperCamelCase__							:       Optional[Any]      =   torch.zeros_like(__magic_name__							)
       UpperCamelCase__							:       str      =   (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
       UpperCamelCase__							:       Union[str, Any]      =   diffusion.flatten()
       while len(diffusion.shape							) < len(sample.shape							):
           UpperCamelCase__							:       Any      =   diffusion.unsqueeze(-1							)
       UpperCamelCase__							:       str      =   drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       UpperCamelCase__							:       Union[str, Any]      =   randn_tensor(
           sample.shape,	layout=sample.layout,	generator=__magic_name__,	device=sample.device,	dtype=sample.dtype							)
       UpperCamelCase__							:       Optional[Any]      =   sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       UpperCamelCase__							:       str      =   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=__magic_name__,	prev_sample_mean=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[SchedulerOutput, Tuple]:






       """simple docstring"""
       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
       UpperCamelCase__							:       List[str]      =   randn_tensor(sample.shape,	layout=sample.layout,	generator=__magic_name__							).to(sample.device							)

       # compute step size from the model_output, the noise, and the snr
       UpperCamelCase__							:       str      =   torch.norm(model_output.reshape(model_output.shape[0],	-1							),	dim=-1							).mean()
       UpperCamelCase__							:       Tuple      =   torch.norm(noise.reshape(noise.shape[0],	-1							),	dim=-1							).mean()
       UpperCamelCase__							:       Union[str, Any]      =   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
       UpperCamelCase__							:       Optional[Any]      =   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
       UpperCamelCase__							:       Tuple      =   step_size.flatten()
       while len(step_size.shape							) < len(sample.shape							):
           UpperCamelCase__							:       int      =   step_size.unsqueeze(-1							)
       UpperCamelCase__							:       int      =   sample + step_size * model_output
       UpperCamelCase__							:       List[Any]      =   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	)				->   torch.FloatTensor:






       """simple docstring"""
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCamelCase__							:       Any      =   timesteps.to(original_samples.device							)
       UpperCamelCase__							:       List[str]      =   self.discrete_sigmas.to(original_samples.device							)[timesteps]
       UpperCamelCase__							:       Any      =   (
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(__magic_name__							) * sigmas[:, None, None, None]
       )
       UpperCamelCase__							:       int      =   noise + original_samples
       return noisy_samples




   def __len__(					self							)				->   Union[str, Any]:






       """simple docstring"""
       return self.config.num_train_timesteps

