
"""simple docstring"""
import copy

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


lowercase		:    Dict			=       logging.get_logger(__name__)

lowercase		:    str			=       {
    '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  _snake_case	(    _lowercase							):
							SCREAMING_SNAKE_CASE		  =    '''musicgen_decoder'''
							SCREAMING_SNAKE_CASE		  =    ['''past_key_values''']






							def __init__(       self    :      Union[str, Any]    ,			snake_case__    :      int=2_0_4_8    ,			snake_case__    :      str=2_0_4_8    ,			snake_case__    :      int=2_4    ,			snake_case__    :      Optional[int]=4_0_9_6    ,			snake_case__    :      int=1_6    ,			snake_case__    :      Optional[Any]=0.0    ,			snake_case__    :      List[str]=True    ,			snake_case__    :      List[Any]="gelu"    ,			snake_case__    :      Tuple=1_0_2_4    ,			snake_case__    :      Optional[int]=0.1    ,			snake_case__    :      Union[str, Any]=0.0    ,			snake_case__    :      int=0.0    ,			snake_case__    :      str=0.02    ,			snake_case__    :      Dict=False    ,			snake_case__    :      Dict=4    ,			snake_case__    :      List[str]=2_0_4_8    ,			snake_case__    :      Optional[Any]=2_0_4_8    ,			snake_case__    :      Dict=None    ,			snake_case__    :      List[Any]=False    ,			**snake_case__    :      List[str]    ,			):






												"""simple docstring"""



												__a:    Optional[Any]		     =       vocab_size
												__a:    List[str]		     =       max_position_embeddings
												__a:    Tuple		     =       hidden_size
												__a:    Optional[Any]		     =       ffn_dim
												__a:    Optional[Any]		     =       num_hidden_layers
												__a:    List[str]		     =       num_attention_heads
												__a:    Union[str, Any]		     =       dropout
												__a:    Optional[int]		     =       attention_dropout
												__a:    Optional[Any]		     =       activation_dropout
												__a:    Dict		     =       activation_function
												__a:    str		     =       initializer_factor
												__a:    Dict		     =       layerdrop
												__a:    str		     =       use_cache
												__a:    List[str]		     =       scale_embedding  # scale factor will be sqrt(d_model) if True
												__a:    List[Any]		     =       num_codebooks
												super().__init__(
												    pad_token_id=snake_case__    ,			bos_token_id=snake_case__    ,			eos_token_id=snake_case__    ,			tie_word_embeddings=snake_case__    ,			**snake_case__    ,			)






class  _snake_case	(    _lowercase							):
							SCREAMING_SNAKE_CASE		  =    '''musicgen'''
							SCREAMING_SNAKE_CASE		  =    True






							def __init__(       self    :      Union[str, Any]    ,			**snake_case__    :      Dict     ):






												"""simple docstring"""



												super().__init__(**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"""     )

												__a:    Tuple		     =       kwargs.pop("""text_encoder"""     )
												__a:    Tuple		     =       text_encoder_config.pop("""model_type"""     )

												__a:    Optional[int]		     =       kwargs.pop("""audio_encoder"""     )
												__a:    Optional[int]		     =       audio_encoder_config.pop("""model_type"""     )

												__a:    Union[str, Any]		     =       kwargs.pop("""decoder"""     )

												__a:    List[str]		     =       AutoConfig.for_model(snake_case__    ,			**snake_case__     )
												__a:    List[Any]		     =       AutoConfig.for_model(snake_case__    ,			**snake_case__     )
												__a:    Optional[int]		     =       MusicgenDecoderConfig(**snake_case__     )
												__a:    List[Any]		     =       True






							@classmethod
							def 				__lowercase	(       cls    :      Union[str, Any]    ,			snake_case__    :      PretrainedConfig    ,			snake_case__    :      PretrainedConfig    ,			snake_case__    :      MusicgenDecoderConfig    ,			**snake_case__    :      Optional[int]    ,			):






												"""simple docstring"""



												return cls(
												    text_encoder=text_encoder_config.to_dict()    ,			audio_encoder=audio_encoder_config.to_dict()    ,			decoder=decoder_config.to_dict()    ,			**snake_case__    ,			)






							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    Tuple		     =       copy.deepcopy(self.__dict__     )
												__a:    Optional[Any]		     =       self.text_encoder.to_dict()
												__a:    Union[str, Any]		     =       self.audio_encoder.to_dict()
												__a:    Any		     =       self.decoder.to_dict()
												__a:    Dict		     =       self.__class__.model_type
												return output


"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase		:    Dict			=       {
    'configuration_mobilenet_v1': [
        'MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV1Config',
        'MobileNetV1OnnxConfig',
    ],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Optional[int]			=       ['MobileNetV1FeatureExtractor']
			lowercase		:    Union[str, Any]			=       ['MobileNetV1ImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Tuple			=       [
			    'MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'MobileNetV1ForImageClassification',
			    'MobileNetV1Model',
			    'MobileNetV1PreTrainedModel',
			    'load_tf_weights_in_mobilenet_v1',
			]


if TYPE_CHECKING:
			from .configuration_mobilenet_va import (
			    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileNetVaConfig,
			    MobileNetVaOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
						from .image_processing_mobilenet_va import MobileNetVaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilenet_va import (
						    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileNetVaForImageClassification,
						    MobileNetVaModel,
						    MobileNetVaPreTrainedModel,
						    load_tf_weights_in_mobilenet_va,
						)


else:
			import sys

			lowercase		:    Union[str, Any]			=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
