
"""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






"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCamelCase_					:						Optional[int]				= logging.get_logger(__name__)

UpperCamelCase_					:						Dict				= {
    '''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}



class __lowerCAmelCase				(					_lowercase     ,				_lowercase      ):




			"""simple docstring"""







			snake_case	      =				"focalnet"




			def __init__(				self						:					Any       ,   _snake_case						:					Optional[int]=224       ,   _snake_case						:					List[str]=4       ,   _snake_case						:					Any=3       ,   _snake_case						:					List[Any]=96       ,   _snake_case						:					Union[str, Any]=False       ,   _snake_case						:					Optional[Any]=[192, 384, 768, 768]       ,   _snake_case						:					List[str]=[2, 2, 6, 2]       ,   _snake_case						:					Any=[2, 2, 2, 2]       ,   _snake_case						:					List[Any]=[3, 3, 3, 3]       ,   _snake_case						:					Optional[Any]="gelu"       ,   _snake_case						:					Dict=4.0       ,   _snake_case						:					List[Any]=0.0       ,   _snake_case						:					List[str]=0.1       ,   _snake_case						:					Optional[Any]=False       ,   _snake_case						:					Any=1e-4       ,   _snake_case						:					str=False       ,   _snake_case						:					Optional[int]=False       ,   _snake_case						:					Tuple=False       ,   _snake_case						:					Any=0.0_2       ,   _snake_case						:					List[Any]=1e-5       ,   _snake_case						:					Union[str, Any]=32       ,   _snake_case						:					Tuple=None       ,   _snake_case						:					Any=None       ,   **_snake_case						:					Tuple       ,   )      -> str:







								"""simple docstring"""
								super().__init__(**_snake_case	)

								A_				 =						image_size
								A_				 =						patch_size
								A_				 =						num_channels
								A_				 =						embed_dim
								A_				 =						use_conv_embed
								A_				 =						hidden_sizes
								A_				 =						depths
								A_				 =						focal_levels
								A_				 =						focal_windows
								A_				 =						hidden_act
								A_				 =						mlp_ratio
								A_				 =						hidden_dropout_prob
								A_				 =						drop_path_rate
								A_				 =						use_layerscale
								A_				 =						layerscale_value
								A_				 =						use_post_layernorm
								A_				 =						use_post_layernorm_in_modulation
								A_				 =						normalize_modulator
								A_				 =						initializer_range
								A_				 =						layer_norm_eps
								A_				 =						encoder_stride
								A_				 =						["stem"] + [F'stage{idx}' for idx in range(1       ,   len(self.depths	) + 1	)]
								A_ ,    A_				 =						get_aligned_output_features_output_indices(
								    out_features=_snake_case       ,   out_indices=_snake_case       ,   stage_names=self.stage_names	)




