



'''simple docstring'''





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


lowercase_	 =						re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase_	 =						10
lowercase_	 =						256




def lowerCAmelCase						(__A):



     """simple docstring"""



     if len(SCREAMING_SNAKE_CASE__) < MIN_NUM_TOKENS:
          return None
     _a											=						MinHash(num_perm=SCREAMING_SNAKE_CASE__)
     for token in set(SCREAMING_SNAKE_CASE__):
          min_hash.update(token.encode())
     return min_hash




def lowerCAmelCase						(__A):



     """simple docstring"""



     return {t for t in NON_ALPHA.split(SCREAMING_SNAKE_CASE__) if len(t.strip()) > 0}






class __A    :



 '''simple docstring'''







 def __init__(self				,     *,
     A = 0.85				,     )					->  str:


      """simple docstring"""



      _a											=						duplication_jaccard_threshold
      _a											=						NUM_PERM
      _a											=						MinHashLSH(threshold=self._duplication_jaccard_threshold				,     num_perm=self._num_perm )

      _a											=						defaultdict(lowercase__ )



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


      """simple docstring"""



      _a											=						self._index.query(lowercase__ )
      if code_key in self._index.keys:
           print(f'''Duplicate key {code_key}''' )
           return

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



 def       a__						(self )					->  Any:


      """simple docstring"""



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



 def       a__						(self				,     A )					->  Tuple:


      """simple docstring"""



      _a											=						self.get_duplicate_clusters()
      with open(lowercase__				,     '''w''' ) as f:
           json.dump(lowercase__				,     lowercase__ )





def lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						element
     _a											=						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						(__A):



     """simple docstring"""



     with mp.Pool() as pool:
          for data in pool.imap_unordered(
              _compute_min_hash						,			ThreadedIterator(SCREAMING_SNAKE_CASE__						,			max_queue_size=10_000)						,			chunksize=100						,			):
               if data is not None:
                    yield data




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     _a											=						DuplicationIndex(duplication_jaccard_threshold=SCREAMING_SNAKE_CASE__)

     for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(SCREAMING_SNAKE_CASE__))						,			max_queue_size=100)):
          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 lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     _a											=						get_tokens(SCREAMING_SNAKE_CASE__)
     _a											=						get_tokens(SCREAMING_SNAKE_CASE__)
     return len(tokensa & tokensa) / len(tokensa | tokensa)


lowercase_	 =						None




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     _a											=						[]
     for elementa in cluster:
          _a											=						_shared_dataset[elementa["""base_index"""]]["""content"""]
          for elementa in extremes:
               _a											=						_shared_dataset[elementa["""base_index"""]]["""content"""]
               if jaccard_similarity(SCREAMING_SNAKE_CASE__						,			SCREAMING_SNAKE_CASE__) >= jaccard_threshold:
                    elementa["copies"] += 1
                    break
          else:
               _a											=						1
               extremes.append(SCREAMING_SNAKE_CASE__)
     return extremes




def lowerCAmelCase						(__A						,			__A						,			__A):



     """simple docstring"""



     global _shared_dataset
     _a											=						dataset
     _a											=						[]
     _a											=						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 lowerCAmelCase						(__A						,			__A = 0.85):



     """simple docstring"""



     _a											=						make_duplicate_clusters(SCREAMING_SNAKE_CASE__						,			SCREAMING_SNAKE_CASE__)
     _a											=						{x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
     _a											=						{}
     _a											=						find_extremes(SCREAMING_SNAKE_CASE__						,			SCREAMING_SNAKE_CASE__						,			SCREAMING_SNAKE_CASE__)
     for extremes in extremes_clusters:
          for element in extremes:
               _a											=						element
     _a											=						duplicate_indices - set(extreme_dict.keys())
     _a											=						dataset.filter(lambda __A						,			__A: idx not in remove_indices						,			with_indices=SCREAMING_SNAKE_CASE__)

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








'''simple docstring'''





import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
  from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


lowercase_	 =						logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase_	 =						256






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Optional[int]    = ['melgan']



 def __init__(self				,     A				,     A				,     A				,     A				,     A				,     )					->  None:


      """simple docstring"""



      super().__init__()

      # From MELGAN
      _a											=						math.log(1E-5 )  # Matches MelGAN training.
      _a											=						4.0  # Largest value for most examples
      _a											=						128

      self.register_modules(
          notes_encoder=A				,     continuous_encoder=A				,     decoder=A				,     scheduler=A				,     melgan=A				,     )



 def       a__						(self				,     A				,     A=(-1.0, 1.0)				,     A=False )					->  List[str]:


      """simple docstring"""



      _a						,					_a											=						output_range
      if clip:
           _a											=						torch.clip(A				,     self.min_value				,     self.max_value )
      # Scale to [0, 1].
      _a											=						(features - self.min_value) / (self.max_value - self.min_value)
      # Scale to [min_out, max_out].
      return zero_one * (max_out - min_out) + min_out



 def       a__						(self				,     A				,     A=(-1.0, 1.0)				,     A=False )					->  Optional[Any]:


      """simple docstring"""



      _a						,					_a											=						input_range
      _a											=						torch.clip(A				,     A				,     A ) if clip else outputs
      # Scale to [0, 1].
      _a											=						(outputs - min_out) / (max_out - min_out)
      # Scale to [self.min_value, self.max_value].
      return zero_one * (self.max_value - self.min_value) + self.min_value



 def       a__						(self				,     A				,     A				,     A )					->  Optional[int]:


      """simple docstring"""



      _a											=						input_tokens > 0
      _a						,					_a											=						self.notes_encoder(
          encoder_input_tokens=A				,     encoder_inputs_mask=A )

      _a						,					_a											=						self.continuous_encoder(
          encoder_inputs=A				,     encoder_inputs_mask=A )

      return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]



 def       a__						(self				,     A				,     A				,     A )					->  Any:


      """simple docstring"""



      _a											=						noise_time
      if not torch.is_tensor(A ):
           _a											=						torch.tensor([timesteps]				,     dtype=torch.long				,     device=input_tokens.device )
      elif torch.is_tensor(A ) and len(timesteps.shape ) == 0:
           _a											=						timesteps[None].to(input_tokens.device )

      # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
      _a											=						timesteps * torch.ones(input_tokens.shape[0]				,     dtype=timesteps.dtype				,     device=timesteps.device )

      _a											=						self.decoder(
          encodings_and_masks=A				,     decoder_input_tokens=A				,     decoder_noise_time=A )
      return logits



 @torch.no_grad()
 def __call__(self				,     A				,     A = None				,     A = 100				,     A = True				,     A = "numpy"				,     A = None				,     A = 1				,     )					->  Union[AudioPipelineOutput, Tuple]:


      """simple docstring"""



      if (callback_steps is None) or (
          callback_steps is not None and (not isinstance(A				,     A ) or callback_steps <= 0)
      ):
           raise ValueError(
               f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
               f''' {type(A )}.''' )

      _a											=						np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]				,     dtype=np.floataa )
      _a											=						np.zeros([1, 0, self.n_dims]				,     np.floataa )
      _a											=						torch.ones((1, TARGET_FEATURE_LENGTH)				,     dtype=A				,     device=self.device )

      for i, encoder_input_tokens in enumerate(A ):
           if i == 0:
                _a											=						torch.from_numpy(pred_mel[:1].copy() ).to(
                    device=self.device				,     dtype=self.decoder.dtype )
                # The first chunk has no previous context.
                _a											=						torch.zeros((1, TARGET_FEATURE_LENGTH)				,     dtype=A				,     device=self.device )
           else:
                # The full song pipeline does not feed in a context feature, so the mask
                # will be all 0s after the feature converter. Because we know we're
                # feeding in a full context chunk from the previous prediction, set it
                # to all 1s.
                _a											=						ones

           _a											=						self.scale_features(
               A				,     output_range=[-1.0, 1.0]				,     clip=A )

           _a											=						self.encode(
               input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device )				,     continuous_inputs=A				,     continuous_mask=A				,     )

           # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
           _a											=						randn_tensor(
               shape=encoder_continuous_inputs.shape				,     generator=A				,     device=self.device				,     dtype=self.decoder.dtype				,     )

           # set step values
           self.scheduler.set_timesteps(A )

           # Denoising diffusion loop
           for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
                _a											=						self.decode(
                    encodings_and_masks=A				,     input_tokens=A				,     noise_time=t / self.scheduler.config.num_train_timesteps				,     )

                # Compute previous output: x_t -> x_t-1
                _a											=						self.scheduler.step(A				,     A				,     A				,     generator=A ).prev_sample

           _a											=						self.scale_to_features(A				,     input_range=[-1.0, 1.0] )
           _a											=						mel[:1]
           _a											=						mel.cpu().float().numpy()

           _a											=						np.concatenate([full_pred_mel, pred_mel[:1]]				,     axis=1 )

           # call the callback, if provided
           if callback is not None and i % callback_steps == 0:
                callback(A				,     A )

           logger.info('''Generated segment'''				,     A )

      if output_type == "numpy" and not is_onnx_available():
           raise ValueError(
               '''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.''' )
      elif output_type == "numpy" and self.melgan is None:
           raise ValueError(
               '''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.''' )

      if output_type == "numpy":
           _a											=						self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
      else:
           _a											=						full_pred_mel

      if not return_dict:
           return (output,)

      return AudioPipelineOutput(audios=A )



