





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {'vocab_file': 'sentencepiece.bpe.model'}

UpperCAmelCase_     							= {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

UpperCAmelCase_     							= {
    'camembert-base': 512,
}

UpperCAmelCase_     							= '▁'






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							int   		=		VOCAB_FILES_NAMES
   a						:							Union[str, Any]   		=		PRETRAINED_VOCAB_FILES_MAP
   a						:							Optional[int]   		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a						:							List[str]   		=		["input_ids", "attention_mask"]




   def __init__(    self,		__magic_name__,		__magic_name__="<s>",		__magic_name__="</s>",		__magic_name__="</s>",		__magic_name__="<s>",		__magic_name__="<unk>",		__magic_name__="<pad>",		__magic_name__="<mask>",		__magic_name__=["<s>NOTUSED", "</s>NOTUSED"],		__magic_name__ = None,		**__magic_name__,		) ->      None:






       """simple docstring"""
       # Mask token behave like a normal word, i.e. include the space before it
       UpperCamelCase__							:				Tuple          =      AddedToken(__magic_name__,		lstrip=__magic_name__,		rstrip=__magic_name__							) if isinstance(__magic_name__,		__magic_name__							) else mask_token

       UpperCamelCase__							:				Optional[Any]          =      {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=__magic_name__,		eos_token=__magic_name__,		unk_token=__magic_name__,		sep_token=__magic_name__,		cls_token=__magic_name__,		pad_token=__magic_name__,		mask_token=__magic_name__,		additional_special_tokens=__magic_name__,		sp_model_kwargs=self.sp_model_kwargs,		**__magic_name__,		)
       UpperCamelCase__							:				Optional[int]          =      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
       self.sp_model.Load(str(__magic_name__							)							)
       UpperCamelCase__							:				Optional[Any]          =      vocab_file
       # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
       # sentencepiece vocabulary (this is the case for <s> and </s>
       UpperCamelCase__							:				Union[str, Any]          =      {'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
       UpperCamelCase__							:				Tuple          =      len(self.fairseq_tokens_to_ids							)
       UpperCamelCase__							:				Any          =      len(self.sp_model							) + len(self.fairseq_tokens_to_ids							)
       UpperCamelCase__							:				Optional[Any]          =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}




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






       """simple docstring"""

       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCamelCase__							:				Tuple          =      [self.cls_token_id]
       UpperCamelCase__							:				str          =      [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = False							) ->      List[int]:






       """simple docstring"""
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__magic_name__,		token_ids_a=__magic_name__,		already_has_special_tokens=__magic_name__							)

       if token_ids_a is None:
           return [1] + ([0] * len(__magic_name__							)) + [1]
       return [1] + ([0] * len(__magic_name__							)) + [1, 1] + ([0] * len(__magic_name__							)) + [1]




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






       """simple docstring"""
       UpperCamelCase__							:				str          =      [self.sep_token_id]
       UpperCamelCase__							:				Dict          =      [self.cls_token_id]

       if token_ids_a is None:
           return len(cls + token_ids_a + sep							) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]




   @property
   def       UpperCamelCase__  (    self							) ->      Any:






       """simple docstring"""
       return len(self.fairseq_tokens_to_ids							) + len(self.sp_model							)




   def       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      {self.convert_ids_to_tokens(__magic_name__							): i for i in range(self.vocab_size							)}
       vocab.update(self.added_tokens_encoder							)
       return vocab




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       return self.sp_model.encode(__magic_name__,		out_type=__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Dict:






       """simple docstring"""
       if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
       elif self.sp_model.PieceToId(__magic_name__							) == 0:
           # Convert sentence piece unk token to fairseq unk token index
           return self.unk_token_id
       return self.fairseq_offset + self.sp_model.PieceToId(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      int:






       """simple docstring"""
       if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      []
       UpperCamelCase__							:				str          =      ''''''
       UpperCamelCase__							:				Dict          =      False
       for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
               if not prev_is_special:
                   out_string += " "
               out_string += self.sp_model.decode(__magic_name__							) + token
               UpperCamelCase__							:				str          =      True
               UpperCamelCase__							:				List[Any]          =      []
           else:
               current_sub_tokens.append(__magic_name__							)
               UpperCamelCase__							:				List[Any]          =      False
       out_string += self.sp_model.decode(__magic_name__							)
       return out_string.strip()




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






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      self.__dict__.copy()
       UpperCamelCase__							:				Optional[int]          =      None
       return state




   def __setstate__(    self,		__magic_name__							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      d

       # for backward compatibility
       if not hasattr(self,		'''sp_model_kwargs'''							):
           UpperCamelCase__							:				Tuple          =      {}

       UpperCamelCase__							:				Tuple          =      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
       self.sp_model.Load(self.vocab_file							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None							) ->      Tuple[str]:






       """simple docstring"""
       if not os.path.isdir(__magic_name__							):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory"							)
           return
       UpperCamelCase__							:				Tuple          =      os.path.join(
           __magic_name__,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

       if os.path.abspath(self.vocab_file							) != os.path.abspath(__magic_name__							) and os.path.isfile(self.vocab_file							):
           copyfile(self.vocab_file,		__magic_name__							)
       elif not os.path.isfile(self.vocab_file							):
           with open(__magic_name__,		'''wb'''							) as fi:
               UpperCamelCase__							:				List[str]          =      self.sp_model.serialized_model_proto()
               fi.write(__magic_name__							)

       return (out_vocab_file,)








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


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

UpperCAmelCase_     							= 256






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							List[str]   		=		["melgan"]




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






       """simple docstring"""
       super().__init__()

       # From MELGAN
       UpperCamelCase__							:				Optional[int]          =      math.log(1E-5							)  # Matches MelGAN training.
       UpperCamelCase__							:				int          =      4.0  # Largest value for most examples
       UpperCamelCase__							:				Optional[int]          =      128

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




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=(-1.0, 1.0),		__magic_name__=False							) ->      Any:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				str          =      output_range
       if clip:
           UpperCamelCase__							:				Union[str, Any]          =      torch.clip(__magic_name__,		self.min_value,		self.max_value							)
       # Scale to [0, 1].
       UpperCamelCase__							:				List[str]          =      (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       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=(-1.0, 1.0),		__magic_name__=False							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				List[str]          =      input_range
       UpperCamelCase__							:				Any          =      torch.clip(__magic_name__,		__magic_name__,		__magic_name__							) if clip else outputs
       # Scale to [0, 1].
       UpperCamelCase__							:				Any          =      (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       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      input_tokens > 0
       UpperCamelCase__   ,UpperCamelCase__							:				Any          =      self.notes_encoder(
           encoder_input_tokens=__magic_name__,		encoder_inputs_mask=__magic_name__							)

       UpperCamelCase__   ,UpperCamelCase__							:				Optional[Any]          =      self.continuous_encoder(
           encoder_inputs=__magic_name__,		encoder_inputs_mask=__magic_name__							)

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




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






       """simple docstring"""
       UpperCamelCase__							:				Any          =      noise_time
       if not torch.is_tensor(__magic_name__							):
           UpperCamelCase__							:				Tuple          =      torch.tensor([timesteps],		dtype=torch.long,		device=input_tokens.device							)
       elif torch.is_tensor(__magic_name__							) and len(timesteps.shape							) == 0:
           UpperCamelCase__							:				Union[str, Any]          =      timesteps[None].to(input_tokens.device							)

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

       UpperCamelCase__							:				List[str]          =      self.decoder(
           encodings_and_masks=__magic_name__,		decoder_input_tokens=__magic_name__,		decoder_noise_time=__magic_name__							)
       return logits




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






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

       UpperCamelCase__							:				Dict          =      np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims],		dtype=np.floataa							)
       UpperCamelCase__							:				Tuple          =      np.zeros([1, 0, self.n_dims],		np.floataa							)
       UpperCamelCase__							:				List[Any]          =      torch.ones((1, TARGET_FEATURE_LENGTH),		dtype=__magic_name__,		device=self.device							)

       for i, encoder_input_tokens in enumerate(__magic_name__							):
           if i == 0:
               UpperCamelCase__							:				str          =      torch.from_numpy(pred_mel[:1].copy()							).to(
                   device=self.device,		dtype=self.decoder.dtype							)
               # The first chunk has no previous context.
               UpperCamelCase__							:				Any          =      torch.zeros((1, TARGET_FEATURE_LENGTH),		dtype=__magic_name__,		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.
               UpperCamelCase__							:				List[str]          =      ones

           UpperCamelCase__							:				int          =      self.scale_features(
               __magic_name__,		output_range=[-1.0, 1.0],		clip=__magic_name__							)

           UpperCamelCase__							:				Union[str, Any]          =      self.encode(
               input_tokens=torch.IntTensor([encoder_input_tokens]							).to(device=self.device							),		continuous_inputs=__magic_name__,		continuous_mask=__magic_name__,		)

           # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
           UpperCamelCase__							:				Optional[int]          =      randn_tensor(
               shape=encoder_continuous_inputs.shape,		generator=__magic_name__,		device=self.device,		dtype=self.decoder.dtype,		)

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

           # Denoising diffusion loop
           for j, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
               UpperCamelCase__							:				Union[str, Any]          =      self.decode(
                   encodings_and_masks=__magic_name__,		input_tokens=__magic_name__,		noise_time=t / self.scheduler.config.num_train_timesteps,		)

               # Compute previous output: x_t -> x_t-1
               UpperCamelCase__							:				List[Any]          =      self.scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		generator=__magic_name__							).prev_sample

           UpperCamelCase__							:				List[Any]          =      self.scale_to_features(__magic_name__,		input_range=[-1.0, 1.0]							)
           UpperCamelCase__							:				List[Any]          =      mel[:1]
           UpperCamelCase__							:				int          =      mel.cpu().float().numpy()

           UpperCamelCase__							:				Union[str, Any]          =      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(__magic_name__,		__magic_name__							)

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

       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":
           UpperCamelCase__							:				Optional[int]          =      self.melgan(input_features=full_pred_mel.astype(np.floataa							)							)
       else:
           UpperCamelCase__							:				Any          =      full_pred_mel

       if not return_dict:
           return (output,)

       return AudioPipelineOutput(audios=__magic_name__							)

