


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






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


snake_case__			 =						{
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'albert'




						def __init__(	self						:   Any   ,     __A						:   Optional[int]=30000   ,     __A						:   List[str]=128   ,     __A						:   int=4096   ,     __A						:   Any=12   ,     __A						:   Union[str, Any]=1   ,     __A						:   Optional[int]=64   ,     __A						:   Dict=16384   ,     __A						:   List[str]=1   ,     __A						:   Any="gelu_new"   ,     __A						:   List[Any]=0   ,     __A						:   str=0   ,     __A						:   List[str]=512   ,     __A						:   Optional[Any]=2   ,     __A						:   Tuple=0.02   ,     __A						:   int=1E-12   ,     __A						:   str=0.1   ,     __A						:   Optional[Any]="absolute"   ,     __A						:   Tuple=0   ,     __A						:   str=2   ,     __A						:   Union[str, Any]=3   ,     **__A						:   Union[str, Any]   ,     )      ->str:






									"""simple docstring"""






									super().__init__(pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A   ,     **__A						)

									a__ :Tuple						  =    vocab_size
									a__ :Dict						  =    embedding_size
									a__ :Union[str, Any]						  =    hidden_size
									a__ :int						  =    num_hidden_layers
									a__ :List[Any]						  =    num_hidden_groups
									a__ :str						  =    num_attention_heads
									a__ :Optional[Any]						  =    inner_group_num
									a__ :Any						  =    hidden_act
									a__ :Optional[int]						  =    intermediate_size
									a__ :Optional[Any]						  =    hidden_dropout_prob
									a__ :Optional[int]						  =    attention_probs_dropout_prob
									a__ :int						  =    max_position_embeddings
									a__ :List[Any]						  =    type_vocab_size
									a__ :Dict						  =    initializer_range
									a__ :Optional[int]						  =    layer_norm_eps
									a__ :Optional[Any]						  =    classifier_dropout_prob
									a__ :Optional[Any]						  =    position_embedding_type






class 					lowerCAmelCase_ (       _a):




						@property
						def  _snake_case					(	self						:   Optional[int]						)      ->Mapping[str, Mapping[int, str]]:






									"""simple docstring"""






									if self.task == "multiple-choice":
												a__ :List[Any]						  =    {0: "batch", 1: "choice", 2: "sequence"}
									else:
												a__ :Dict						  =    {0: "batch", 1: "sequence"}
									return OrderedDict(
									    [
									        ("input_ids", dynamic_axis),
									        ("attention_mask", dynamic_axis),
									        ("token_type_ids", dynamic_axis),
									    ]						)


