





'''simple docstring'''







import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


_lowercase       						=			logging.get_logger(__name__)


_lowercase       						=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all MVP models at https://huggingface.co/models?filter=mvp
_lowercase       						=			{
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json',
    },
}

_lowercase       						=			{
    'RUCAIBox/mvp': 1_024,
}
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					VOCAB_FILES_NAMES
     _UpperCAmelCase										=					PRETRAINED_VOCAB_FILES_MAP
     _UpperCAmelCase										=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCAmelCase										=					['''input_ids''', '''attention_mask''']
     _UpperCAmelCase										=					MvpTokenizer







     def __init__( self    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__="replace"    ,   A__="<s>"    ,   A__="</s>"    ,   A__="</s>"    ,   A__="<s>"    ,   A__="<unk>"    ,   A__="<pad>"    ,   A__="<mask>"    ,   A__=False    ,   A__=True    ,   **A__    ,   ) ->	Optional[int]:
      super().__init__(
          A__    ,   A__    ,   tokenizer_file=A__    ,   errors=A__    ,   bos_token=A__    ,   eos_token=A__    ,   sep_token=A__    ,   cls_token=A__    ,   unk_token=A__    ,   pad_token=A__    ,   mask_token=A__    ,   add_prefix_space=A__    ,   trim_offsets=A__    ,   **A__    ,   )

      snake_case				=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
      if pre_tok_state.get('''add_prefix_space'''    ,   A__    ) != add_prefix_space:
       snake_case				=			getattr(A__    ,   pre_tok_state.pop('''type'''    )    )
       snake_case				=			add_prefix_space
       snake_case				=			pre_tok_class(**A__    )

      snake_case				=			add_prefix_space

      # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
      snake_case				=			'''post_processor'''
      snake_case				=			getattr(self.backend_tokenizer    ,   A__    ,   A__    )
      if tokenizer_component_instance:
       snake_case				=			json.loads(tokenizer_component_instance.__getstate__()    )

       # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
       if "sep" in state:
        snake_case				=			tuple(state['''sep''']    )
       if "cls" in state:
        snake_case				=			tuple(state['''cls''']    )

       snake_case				=			False

       if state.get('''add_prefix_space'''    ,   A__    ) != add_prefix_space:
        snake_case				=			add_prefix_space
        snake_case				=			True

       if state.get('''trim_offsets'''    ,   A__    ) != trim_offsets:
        snake_case				=			trim_offsets
        snake_case				=			True

       if changes_to_apply:
        snake_case				=			getattr(A__    ,   state.pop('''type'''    )    )
        snake_case				=			component_class(**A__    )
        setattr(self.backend_tokenizer    ,   A__    ,   A__    )







     @property
     def     UpperCamelCase			( self    ) ->	str:
      if self._mask_token is None:
       if self.verbose:
        logger.error('''Using mask_token, but it is not set yet.'''    )
       return None
      return str(self._mask_token    )







     @mask_token.setter
     def     UpperCamelCase			( self    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			AddedToken(A__    ,   lstrip=A__    ,   rstrip=A__    ) if isinstance(A__    ,   A__    ) else value
      snake_case				=			value







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	BatchEncoding:
      snake_case				=			kwargs.get('''is_split_into_words'''    ,   A__    )

      if is_split_into_words and not self.add_prefix_space:
       raise ValueError(
           F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
           '''to use it with pretokenized inputs.'''    )

      return super()._batch_encode_plus(*A__    ,   **A__    )







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	BatchEncoding:
      snake_case				=			kwargs.get('''is_split_into_words'''    ,   A__    )

      if is_split_into_words and not self.add_prefix_space:
       raise ValueError(
           F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
           '''to use it with pretokenized inputs.'''    )

      return super()._encode_plus(*A__    ,   **A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ) ->	Tuple[str]:
      snake_case				=			self._tokenizer.model.save(A__    ,   name=A__    )
      return tuple(A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__=None    ) ->	List[str]:
      snake_case				=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
       return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ) ->	List[int]:
      snake_case				=			[self.sep_token_id]
      snake_case				=			[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]








'''simple docstring'''







from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .notes_encoder import SpectrogramNotesEncoder
   from .continous_encoder import SpectrogramContEncoder
   from .pipeline_spectrogram_diffusion import (
       SpectrogramContEncoder,
       SpectrogramDiffusionPipeline,
       TaFilmDecoder,
   )

try:
   if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
   from .midi_utils import MidiProcessor

