


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_roberta import RobertaTokenizer


lowerCamelCase						=		logging.get_logger(__name__)

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

lowerCamelCase						=		{
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCamelCase						=		{
    '''roberta-base''': 512,
    '''roberta-large''': 512,
    '''roberta-large-mnli''': 512,
    '''distilroberta-base''': 512,
    '''roberta-base-openai-detector''': 512,
    '''roberta-large-openai-detector''': 512,
}





class  _a							(    _lowercase):
	_a  :  Union[str, Any]				   =							VOCAB_FILES_NAMES
	_a  :  Tuple				   =							PRETRAINED_VOCAB_FILES_MAP
	_a  :  Union[str, Any]				   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_a  :  Dict				   =							['''input_ids''', '''attention_mask''']
	_a  :  str				   =							RobertaTokenizer




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

			lowerCAmelCase__       :      str   					= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
			if pre_tok_state.get('''add_prefix_space'''		, _SCREAMING_SNAKE_CASE		) != add_prefix_space:
					lowerCAmelCase__       :      int   					= getattr(_SCREAMING_SNAKE_CASE		, pre_tok_state.pop('''type'''		)		)
					lowerCAmelCase__       :      List[str]   					= add_prefix_space
					lowerCAmelCase__       :      Union[str, Any]   					= pre_tok_class(**_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      str   					= add_prefix_space

			lowerCAmelCase__       :      Optional[int]   					= '''post_processor'''
			lowerCAmelCase__       :      Dict   					= getattr(self.backend_tokenizer		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
			if tokenizer_component_instance:
					lowerCAmelCase__       :      List[Any]   					= 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:
							lowerCAmelCase__       :      Optional[int]   					= tuple(state['''sep''']		)
					if "cls" in state:
							lowerCAmelCase__       :      Any   					= tuple(state['''cls''']		)

					lowerCAmelCase__       :      List[str]   					= False

					if state.get('''add_prefix_space'''		, _SCREAMING_SNAKE_CASE		) != add_prefix_space:
							lowerCAmelCase__       :      Dict   					= add_prefix_space
							lowerCAmelCase__       :      int   					= True

					if state.get('''trim_offsets'''		, _SCREAMING_SNAKE_CASE		) != trim_offsets:
							lowerCAmelCase__       :      Any   					= trim_offsets
							lowerCAmelCase__       :      int   					= True

					if changes_to_apply:
							lowerCAmelCase__       :      Any   					= getattr(_SCREAMING_SNAKE_CASE		, state.pop('''type'''		)		)
							lowerCAmelCase__       :      Union[str, Any]   					= component_class(**_SCREAMING_SNAKE_CASE		)
							setattr(self.backend_tokenizer		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




	@property
	def 							UpperCAmelCase__( self						:		List[Any]		)->	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						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Dict		)->	Tuple:
			lowerCAmelCase__       :      Tuple   					= AddedToken(_SCREAMING_SNAKE_CASE		, lstrip=_SCREAMING_SNAKE_CASE		, rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		) else value
			lowerCAmelCase__       :      Any   					= value




	def 							UpperCAmelCase__( self						:		Any		, *_SCREAMING_SNAKE_CASE						:		Any		, **_SCREAMING_SNAKE_CASE						:		Optional[int]		)->	BatchEncoding:
			lowerCAmelCase__       :      Dict   					= kwargs.get('''is_split_into_words'''		, _SCREAMING_SNAKE_CASE		)
			assert self.add_prefix_space or not is_split_into_words, (
			    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
			    "to use it with pretokenized inputs."
			)

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




	def 							UpperCAmelCase__( self						:		List[str]		, *_SCREAMING_SNAKE_CASE						:		List[Any]		, **_SCREAMING_SNAKE_CASE						:		Union[str, Any]		)->	BatchEncoding:
			lowerCAmelCase__       :      Optional[Any]   					= kwargs.get('''is_split_into_words'''		, _SCREAMING_SNAKE_CASE		)

			assert self.add_prefix_space or not is_split_into_words, (
			    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
			    "to use it with pretokenized inputs."
			)

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




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		Optional[str] = None		)->	Tuple[str]:
			lowerCAmelCase__       :      Optional[Any]   					= self._tokenizer.model.save(_SCREAMING_SNAKE_CASE		, name=_SCREAMING_SNAKE_CASE		)
			return tuple(_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Optional[int]=None		)->	List[Any]:
			lowerCAmelCase__       :      str   					= [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						:		int		, _SCREAMING_SNAKE_CASE						:		List[int]		, _SCREAMING_SNAKE_CASE						:		Optional[List[int]] = None		)->	List[int]:
			lowerCAmelCase__       :      Tuple   					= [self.sep_token_id]
			lowerCAmelCase__       :      Optional[Any]   					= [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]








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




