

"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a							=							logging.get_logger(__name__)

_a							=							{
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : Dict       		=     "transfo-xl"
 __UpperCAmelCase     : Union[str, Any]       		=     ["mems"]
 __UpperCAmelCase     : Tuple       		=     {
     "n_token": "vocab_size",
     "hidden_size": "d_model",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }


 def __init__(      self							:      int, UpperCAmelCase__							:      Optional[Any]=2_6_7_7_3_5, UpperCAmelCase__							:      int=[2_0_0_0_0, 4_0_0_0_0, 2_0_0_0_0_0], UpperCAmelCase__							:      Optional[Any]=1_0_2_4, UpperCAmelCase__							:      str=1_0_2_4, UpperCAmelCase__							:      Union[str, Any]=1_6, UpperCAmelCase__							:      Optional[Any]=6_4, UpperCAmelCase__							:      Optional[Any]=4_0_9_6, UpperCAmelCase__							:      Tuple=4, UpperCAmelCase__							:      List[str]=False, UpperCAmelCase__							:      Optional[int]=1_8, UpperCAmelCase__							:      int=1_6_0_0, UpperCAmelCase__							:      List[str]=1_0_0_0, UpperCAmelCase__							:      Union[str, Any]=True, UpperCAmelCase__							:      Union[str, Any]=True, UpperCAmelCase__							:      List[str]=0, UpperCAmelCase__							:      List[Any]=-1, UpperCAmelCase__							:      int=True, UpperCAmelCase__							:      Dict=0.1, UpperCAmelCase__							:      int=0.0, UpperCAmelCase__							:      str=True, UpperCAmelCase__							:      List[str]="normal", UpperCAmelCase__							:      List[str]=0.01, UpperCAmelCase__							:      Optional[int]=0.01, UpperCAmelCase__							:      List[str]=0.02, UpperCAmelCase__							:      Union[str, Any]=1E-5, UpperCAmelCase__							:      List[str]=0, **UpperCAmelCase__							:      Tuple, ):
    __lowercase								=						vocab_size
    __lowercase								=						[]
    self.cutoffs.extend(UpperCAmelCase__     )
    if proj_share_all_but_first:
       __lowercase								=						[False] + [True] * len(self.cutoffs     )
    else:
       __lowercase								=						[False] + [False] * len(self.cutoffs     )
    __lowercase								=						d_model
    __lowercase								=						d_embed
    __lowercase								=						d_head
    __lowercase								=						d_inner
    __lowercase								=						div_val
    __lowercase								=						pre_lnorm
    __lowercase								=						n_layer
    __lowercase								=						n_head
    __lowercase								=						mem_len
    __lowercase								=						same_length
    __lowercase								=						attn_type
    __lowercase								=						clamp_len
    __lowercase								=						sample_softmax
    __lowercase								=						adaptive
    __lowercase								=						dropout
    __lowercase								=						dropatt
    __lowercase								=						untie_r
    __lowercase								=						init
    __lowercase								=						init_range
    __lowercase								=						proj_init_std
    __lowercase								=						init_std
    __lowercase								=						layer_norm_epsilon
    super().__init__(eos_token_id=UpperCAmelCase__, **UpperCAmelCase__     )


 @property
 def  _lowercase			(      self							:      List[Any]     ):
    # Message copied from Transformer-XL documentation
    logger.info(F"""The model {self.model_type} is one of the few models that has no sequence length limit."""     )
    return -1



 @max_position_embeddings.setter
 def  _lowercase			(      self							:      List[str], UpperCAmelCase__							:      Optional[int]     ):
    # Message copied from Transformer-XL documentation
    raise NotImplementedError(
        F"""The model {self.model_type} is one of the few models that has no sequence length limit."""     )







"""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




