import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_reformer import ReformerTokenizer
else:
						lowerCAmelCase__  	=      None


lowerCAmelCase__  	=      logging.get_logger(__name__)


lowerCAmelCase__  	=      "▁"

lowerCAmelCase__  	=      {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase__  	=      {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

lowerCAmelCase__  	=      {
    "google/reformer-crime-and-punishment": 5_2_4_2_8_8,
}
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					VOCAB_FILES_NAMES
	__SCREAMING_SNAKE_CASE              =					PRETRAINED_VOCAB_FILES_MAP
	__SCREAMING_SNAKE_CASE              =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__SCREAMING_SNAKE_CASE              =					['input_ids', 'attention_mask']
	__SCREAMING_SNAKE_CASE              =					ReformerTokenizer


	def __init__(   self       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_="</s>"       ,  lowerCAmelCase_="<unk>"       ,  lowerCAmelCase_=[]       ,  **lowerCAmelCase_       ,  ):
		super().__init__(
		    lowerCAmelCase_       ,  tokenizer_file=lowerCAmelCase_       ,  eos_token=lowerCAmelCase_       ,  unk_token=lowerCAmelCase_       ,  additional_special_tokens=lowerCAmelCase_       ,  **lowerCAmelCase_       ,  )

		_lowercase   =vocab_file
		_lowercase   =False if not self.vocab_file else True


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ):
		if not self.can_save_slow_tokenizer:
			raise ValueError(
			    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
			    "tokenizer."       )

		if not os.path.isdir(lowerCAmelCase_       ):
			logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
			return
		_lowercase   =os.path.join(
		    lowerCAmelCase_       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

		if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowerCAmelCase_       ):
			copyfile(self.vocab_file       ,  lowerCAmelCase_       )

		return (out_vocab_file,)










"""simple docstring"""





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


_lowercase						   =     logging.get_logger(__name__)

_lowercase						   =     {
    """facebook/musicgen-small""": """https://huggingface.co/facebook/musicgen-small/resolve/main/config.json""",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}

class    __lowercase							(      snake_case_						):


				'''simple docstring'''


				_lowerCAmelCase						      ="musicgen_decoder"
				_lowerCAmelCase						      =["past_key_values"]

				def __init__( self :  List[str]   ,SCREAMING_SNAKE_CASE_ :  Dict=2_0_4_8   ,SCREAMING_SNAKE_CASE_ :  str=2_0_4_8   ,SCREAMING_SNAKE_CASE_ :  List[str]=2_4   ,SCREAMING_SNAKE_CASE_ :  int=4_0_9_6   ,SCREAMING_SNAKE_CASE_ :  List[Any]=1_6   ,SCREAMING_SNAKE_CASE_ :  Dict=0.0   ,SCREAMING_SNAKE_CASE_ :  Any=True   ,SCREAMING_SNAKE_CASE_ :  Any="gelu"   ,SCREAMING_SNAKE_CASE_ :  Tuple=1_0_2_4   ,SCREAMING_SNAKE_CASE_ :  Any=0.1   ,SCREAMING_SNAKE_CASE_ :  List[str]=0.0   ,SCREAMING_SNAKE_CASE_ :  Optional[Any]=0.0   ,SCREAMING_SNAKE_CASE_ :  Tuple=0.02   ,SCREAMING_SNAKE_CASE_ :  Optional[Any]=False   ,SCREAMING_SNAKE_CASE_ :  List[str]=4   ,SCREAMING_SNAKE_CASE_ :  str=2_0_4_8   ,SCREAMING_SNAKE_CASE_ :  str=2_0_4_8   ,SCREAMING_SNAKE_CASE_ :  Optional[int]=None   ,SCREAMING_SNAKE_CASE_ :  Tuple=False   ,**SCREAMING_SNAKE_CASE_ :  Union[str, Any]   ,):
										snake_case__							:       List[str]				      =       vocab_size
										snake_case__							:       List[str]				      =       max_position_embeddings
										snake_case__							:       List[Any]				      =       hidden_size
										snake_case__							:       Dict				      =       ffn_dim
										snake_case__							:       Union[str, Any]				      =       num_hidden_layers
										snake_case__							:       List[str]				      =       num_attention_heads
										snake_case__							:       Optional[Any]				      =       dropout
										snake_case__							:       Dict				      =       attention_dropout
										snake_case__							:       Union[str, Any]				      =       activation_dropout
										snake_case__							:       Dict				      =       activation_function
										snake_case__							:       Union[str, Any]				      =       initializer_factor
										snake_case__							:       List[str]				      =       layerdrop
										snake_case__							:       Any				      =       use_cache
										snake_case__							:       Tuple				      =       scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case__							:       Any				      =       num_codebooks
										super().__init__(
										    pad_token_id=SCREAMING_SNAKE_CASE_   ,bos_token_id=SCREAMING_SNAKE_CASE_   ,eos_token_id=SCREAMING_SNAKE_CASE_   ,tie_word_embeddings=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_   ,)







class    __lowercase							(      snake_case_						):


				'''simple docstring'''


				_lowerCAmelCase						      ="musicgen"
				_lowerCAmelCase						      =True

				def __init__( self :  Optional[Any]   ,**SCREAMING_SNAKE_CASE_ :  Dict   ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )
										if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
																raise ValueError('''Config has to be initialized with text_encoder, audio_encoder and decoder config'''   )

										snake_case__							:       Optional[int]				      =       kwargs.pop('''text_encoder'''   )
										snake_case__							:       Optional[Any]				      =       text_encoder_config.pop('''model_type'''   )

										snake_case__							:       Union[str, Any]				      =       kwargs.pop('''audio_encoder'''   )
										snake_case__							:       Dict				      =       audio_encoder_config.pop('''model_type'''   )

										snake_case__							:       Optional[Any]				      =       kwargs.pop('''decoder'''   )

										snake_case__							:       Union[str, Any]				      =       AutoConfig.for_model(SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_   )
										snake_case__							:       List[Any]				      =       AutoConfig.for_model(SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_   )
										snake_case__							:       List[str]				      =       MusicgenDecoderConfig(**SCREAMING_SNAKE_CASE_   )
										snake_case__							:       Union[str, Any]				      =       True

				@classmethod
				def    _lowerCamelCase       ( cls :  Dict   ,SCREAMING_SNAKE_CASE_ :  PretrainedConfig   ,SCREAMING_SNAKE_CASE_ :  PretrainedConfig   ,SCREAMING_SNAKE_CASE_ :  MusicgenDecoderConfig   ,**SCREAMING_SNAKE_CASE_ :  str   ,):
										return cls(
										    text_encoder=text_encoder_config.to_dict()   ,audio_encoder=audio_encoder_config.to_dict()   ,decoder=decoder_config.to_dict()   ,**SCREAMING_SNAKE_CASE_   ,)





				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										snake_case__							:       Optional[Any]				      =       copy.deepcopy(self.__dict__   )
										snake_case__							:       Any				      =       self.text_encoder.to_dict()
										snake_case__							:       Dict				      =       self.audio_encoder.to_dict()
										snake_case__							:       Dict				      =       self.decoder.to_dict()
										snake_case__							:       Optional[int]				      =       self.__class__.model_type
										return output


