


from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


snake_case       :					int        =  logging.get_logger(__name__)

# General docstring
snake_case       :					Optional[int]        =  '''ResNetConfig'''

# Base docstring
snake_case       :					Dict        =  '''microsoft/resnet-50'''
snake_case       :					List[str]        =  [1, 20_48, 7, 7]

# Image classification docstring
snake_case       :					int        =  '''microsoft/resnet-50'''
snake_case       :					Tuple        =  '''tiger cat'''

snake_case       :					Union[str, Any]        =  [
    '''microsoft/resnet-50''',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]







class 	_snake_case						(   tf.keras.layers.Layer							):




	def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase = 3      ,						_lowerCamelCase = 1      ,						_lowerCamelCase = "relu"      ,						**_lowerCamelCase					):
							super().__init__(**_lowerCamelCase					)
							a		:List[Any]											= kernel_size // 2
							a		:Optional[Any]											= tf.keras.layers.ConvaD(
							    _lowerCamelCase      ,						kernel_size=_lowerCamelCase      ,						strides=_lowerCamelCase      ,						padding='''valid'''      ,						use_bias=_lowerCamelCase      ,						name='''convolution'''					)
							# Use same default momentum and epsilon as PyTorch equivalent
							a		:Tuple											= tf.keras.layers.BatchNormalization(epsilon=1e-5      ,						momentum=0.9      ,						name='''normalization'''					)
							a		:int											= ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('''linear'''					)




	def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
							# Pad to match that done in the PyTorch Conv2D model
							a		:Optional[Any]											= (self.pad_value, self.pad_value)
							a		:List[str]											= tf.pad(_lowerCamelCase      ,						[(0, 0), height_pad, width_pad, (0, 0)]					)
							a		:Optional[Any]											= self.conv(_lowerCamelCase					)
							return hidden_state




	def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
							a		:Tuple											= self.convolution(_lowerCamelCase					)
							a		:str											= self.normalization(_lowerCamelCase      ,						training=_lowerCamelCase					)
							a		:int											= self.activation(_lowerCamelCase					)
							return hidden_state







class 	_snake_case						(   tf.keras.layers.Layer							):




	def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
							super().__init__(**_lowerCamelCase					)
							a		:Optional[int]											= TFResNetConvLayer(
							    config.embedding_size      ,						kernel_size=7      ,						stride=2      ,						activation=config.hidden_act      ,						name='''embedder'''      ,						)
							a		:List[Any]											= tf.keras.layers.MaxPoolaD(pool_size=3      ,						strides=2      ,						padding='''valid'''      ,						name='''pooler'''					)
							a		:Any											= config.num_channels




	def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
							a		:int											= shape_list(_lowerCamelCase					)
							if tf.executing_eagerly() and num_channels != self.num_channels:
													raise ValueError(
													    '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''					)
							a		:Dict											= pixel_values
							a		:int											= self.embedder(_lowerCamelCase					)
							a		:str											= tf.pad(_lowerCamelCase      ,						[[0, 0], [1, 1], [1, 1], [0, 0]]					)
							a		:int											= self.pooler(_lowerCamelCase					)
							return hidden_state







class 	_snake_case						(   tf.keras.layers.Layer							):




	def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase = 2      ,						**_lowerCamelCase					):
							super().__init__(**_lowerCamelCase					)
							a		:int											= tf.keras.layers.ConvaD(
							    _lowerCamelCase      ,						kernel_size=1      ,						strides=_lowerCamelCase      ,						use_bias=_lowerCamelCase      ,						name='''convolution'''					)
							# Use same default momentum and epsilon as PyTorch equivalent
							a		:Optional[Any]											= tf.keras.layers.BatchNormalization(epsilon=1e-5      ,						momentum=0.9      ,						name='''normalization'''					)




	def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
							a		:Optional[Any]											= x
							a		:Union[str, Any]											= self.convolution(_lowerCamelCase					)
							a		:str											= self.normalization(_lowerCamelCase      ,						training=_lowerCamelCase					)
							return hidden_state







class 	_snake_case						(   tf.keras.layers.Layer							):




	def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = 1      ,						_lowerCamelCase = "relu"      ,						**_lowerCamelCase					):
							super().__init__(**_lowerCamelCase					)
							a		:Union[str, Any]											= in_channels != out_channels or stride != 1
							a		:str											= TFResNetConvLayer(_lowerCamelCase      ,						stride=_lowerCamelCase      ,						name='''layer.0'''					)
							a		:Any											= TFResNetConvLayer(_lowerCamelCase      ,						activation=_lowerCamelCase      ,						name='''layer.1'''					)
							a		:Any											= (
							    TFResNetShortCut(_lowerCamelCase      ,						stride=_lowerCamelCase      ,						name='''shortcut'''					)
							    if should_apply_shortcut
							    else tf.keras.layers.Activation('''linear'''      ,						name='''shortcut'''					)
							)
							a		:Union[str, Any]											= ACTaFN[activation]




	def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
							a		:List[str]											= hidden_state
							a		:Optional[int]											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
							a		:Any											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
							a		:Tuple											= self.shortcut(_lowerCamelCase      ,						training=_lowerCamelCase					)
							hidden_state += residual
							a		:Dict											= self.activation(_lowerCamelCase					)
							return hidden_state







class 	_snake_case						(   tf.keras.layers.Layer							):




	def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = 1      ,						_lowerCamelCase = "relu"      ,						_lowerCamelCase = 4      ,						**_lowerCamelCase      ,						):
							super().__init__(**_lowerCamelCase					)
							a		:str											= in_channels != out_channels or stride != 1
							a		:Optional[int]											= out_channels // reduction
							a		:List[Any]											= TFResNetConvLayer(_lowerCamelCase      ,						kernel_size=1      ,						name='''layer.0'''					)
							a		:Tuple											= TFResNetConvLayer(_lowerCamelCase      ,						stride=_lowerCamelCase      ,						name='''layer.1'''					)
							a		:Union[str, Any]											= TFResNetConvLayer(_lowerCamelCase      ,						kernel_size=1      ,						activation=_lowerCamelCase      ,						name='''layer.2'''					)
							a		:Optional[int]											= (
							    TFResNetShortCut(_lowerCamelCase      ,						stride=_lowerCamelCase      ,						name='''shortcut'''					)
							    if should_apply_shortcut
							    else tf.keras.layers.Activation('''linear'''      ,						name='''shortcut'''					)
							)
							a		:str											= ACTaFN[activation]




	def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
							a		:Union[str, Any]											= hidden_state
							a		:Tuple											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
							a		:int											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
							a		:Any											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
							a		:int											= self.shortcut(_lowerCamelCase      ,						training=_lowerCamelCase					)
							hidden_state += residual
							a		:List[Any]											= self.activation(_lowerCamelCase					)
							return hidden_state







class 	_snake_case						(   tf.keras.layers.Layer							):




	def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = 2      ,						_lowerCamelCase = 2      ,						**_lowerCamelCase					):
							super().__init__(**_lowerCamelCase					)

							a		:Dict											= TFResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else TFResNetBasicLayer

							a		:Dict											= [layer(_lowerCamelCase      ,						_lowerCamelCase      ,						stride=_lowerCamelCase      ,						activation=config.hidden_act      ,						name='''layers.0'''					)]
							layers += [
							    layer(_lowerCamelCase      ,						_lowerCamelCase      ,						activation=config.hidden_act      ,						name=F'''layers.{i + 1}'''					)
							    for i in range(depth - 1					)
							]
							a		:str											= layers




	def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
							for layer in self.stage_layers:
													a		:List[Any]											= layer(_lowerCamelCase      ,						training=_lowerCamelCase					)
							return hidden_state







class 	_snake_case						(   tf.keras.layers.Layer							):




	def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
							super().__init__(**_lowerCamelCase					)
							# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
							a		:List[Any]											= [
							    TFResNetStage(
							        _lowerCamelCase      ,						config.embedding_size      ,						config.hidden_sizes[0]      ,						stride=2 if config.downsample_in_first_stage else 1      ,						depth=config.depths[0]      ,						name='''stages.0'''      ,						)
							]
							for i, (in_channels, out_channels, depth) in enumerate(
							    zip(config.hidden_sizes      ,						config.hidden_sizes[1:]      ,						config.depths[1:]					)					):
													self.stages.append(TFResNetStage(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						depth=_lowerCamelCase      ,						name=F'''stages.{i + 1}'''					)					)




	def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False      ,						_lowerCamelCase = True      ,						_lowerCamelCase = False      ,						):
							a		:str											= () if output_hidden_states else None

							for stage_module in self.stages:
													if output_hidden_states:
																			a		:str											= hidden_states + (hidden_state,)

													a		:Optional[int]											= stage_module(_lowerCamelCase      ,						training=_lowerCamelCase					)

							if output_hidden_states:
													a		:Union[str, Any]											= hidden_states + (hidden_state,)

							if not return_dict:
													return tuple(v for v in [hidden_state, hidden_states] if v is not None					)

							return TFBaseModelOutputWithNoAttention(last_hidden_state=_lowerCamelCase      ,						hidden_states=_lowerCamelCase					)







class 	_snake_case						(   _snake_case							):
	SCREAMING_SNAKE_CASE__   = ResNetConfig
	SCREAMING_SNAKE_CASE__   = 'resnet'
	SCREAMING_SNAKE_CASE__   = 'pixel_values'




	@property
	def  SCREAMING_SNAKE_CASE__       (	self					):
							return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)      ,						dtype=tf.floataa					)}







snake_case       :					List[str]        =  R'''
    This model is a TensorFlow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
'''


snake_case       :					Optional[Any]        =  R'''
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''







@keras_serializable
class 	_snake_case						(   tf.keras.layers.Layer							):
	SCREAMING_SNAKE_CASE__   = ResNetConfig




	def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
							super().__init__(**_lowerCamelCase					)
							a		:str											= config
							a		:Union[str, Any]											= TFResNetEmbeddings(_lowerCamelCase      ,						name='''embedder'''					)
							a		:Tuple											= TFResNetEncoder(_lowerCamelCase      ,						name='''encoder'''					)
							a		:List[str]											= tf.keras.layers.GlobalAveragePoolingaD(keepdims=_lowerCamelCase					)




	@unpack_inputs
	def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						):
							a		:Union[str, Any]											= (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							a		:List[Any]											= return_dict if return_dict is not None else self.config.use_return_dict

							# TF 2.0 image layers can't use NCHW format when running on CPU.
							# We transpose to NHWC format and then transpose back after the full forward pass.
							# (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
							a		:Dict											= tf.transpose(_lowerCamelCase      ,						perm=[0, 2, 3, 1]					)
							a		:List[Any]											= self.embedder(_lowerCamelCase      ,						training=_lowerCamelCase					)

							a		:Union[str, Any]											= self.encoder(
							    _lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						return_dict=_lowerCamelCase      ,						training=_lowerCamelCase					)

							a		:List[str]											= encoder_outputs[0]

							a		:int											= self.pooler(_lowerCamelCase					)

							# Transpose all the outputs to the NCHW format
							# (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
							a		:List[Any]											= tf.transpose(_lowerCamelCase      ,						(0, 3, 1, 2)					)
							a		:Optional[int]											= tf.transpose(_lowerCamelCase      ,						(0, 3, 1, 2)					)
							a		:Dict											= ()
							for hidden_state in encoder_outputs[1:]:
													a		:Any											= hidden_states + tuple(tf.transpose(_lowerCamelCase      ,						(0, 3, 1, 2)					) for h in hidden_state					)

							if not return_dict:
													return (last_hidden_state, pooled_output) + hidden_states

							a		:str											= hidden_states if output_hidden_states else None

							return TFBaseModelOutputWithPoolingAndNoAttention(
							    last_hidden_state=_lowerCamelCase      ,						pooler_output=_lowerCamelCase      ,						hidden_states=_lowerCamelCase      ,						)







@add_start_docstrings(
    'The bare ResNet model outputting raw features without any specific head on top.'		,					_snake_case		,					)
class 	_snake_case						(   _snake_case							):




	def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
							super().__init__(_lowerCamelCase      ,						**_lowerCamelCase					)
							a		:str											= TFResNetMainLayer(config=_lowerCamelCase      ,						name='''resnet'''					)




	@add_start_docstrings_to_model_forward(_lowerCamelCase					)
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC      ,						output_type=_lowerCamelCase      ,						config_class=_CONFIG_FOR_DOC      ,						modality='''vision'''      ,						expected_output=_EXPECTED_OUTPUT_SHAPE      ,						)
	@unpack_inputs
	def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						):
							a		:Tuple											= (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							a		:List[str]											= return_dict if return_dict is not None else self.config.use_return_dict

							a		:Optional[Any]											= self.resnet(
							    pixel_values=_lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						return_dict=_lowerCamelCase      ,						training=_lowerCamelCase      ,						)
							return resnet_outputs







@add_start_docstrings(
    '\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '		,					_snake_case		,					)
class 	_snake_case						(   _snake_case		,					_snake_case							):




	def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
							super().__init__(_lowerCamelCase      ,						**_lowerCamelCase					)
							a		:Optional[int]											= config.num_labels
							a		:List[str]											= TFResNetMainLayer(_lowerCamelCase      ,						name='''resnet'''					)
							# classification head
							a		:Optional[Any]											= (
							    tf.keras.layers.Dense(config.num_labels      ,						name='''classifier.1'''					)
							    if config.num_labels > 0
							    else tf.keras.layers.Activation('''linear'''      ,						name='''classifier.1'''					)
							)




	def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
							a		:Optional[int]											= tf.keras.layers.Flatten()(_lowerCamelCase					)
							a		:Tuple											= self.classifier_layer(_lowerCamelCase					)
							return logits




	@add_start_docstrings_to_model_forward(_lowerCamelCase					)
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT      ,						output_type=_lowerCamelCase      ,						config_class=_CONFIG_FOR_DOC      ,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,						)
	@unpack_inputs
	def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						):
							a		:Optional[Any]											= return_dict if return_dict is not None else self.config.use_return_dict

							a		:Any											= self.resnet(
							    _lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						return_dict=_lowerCamelCase      ,						training=_lowerCamelCase					)

							a		:Tuple											= outputs.pooler_output if return_dict else outputs[1]

							a		:Optional[int]											= self.classifier(_lowerCamelCase					)

							a		:List[Any]											= None if labels is None else self.hf_compute_loss(_lowerCamelCase      ,						_lowerCamelCase					)

							if not return_dict:
													a		:Any											= (logits,) + outputs[2:]
													return (loss,) + output if loss is not None else output

							return TFImageClassifierOutputWithNoAttention(loss=_lowerCamelCase      ,						logits=_lowerCamelCase      ,						hidden_states=outputs.hidden_states					)









import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


snake_case       :					Dict        =  logging.get_logger(__name__)







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'semantic'




 def __init__(	self      ,						_lowerCamelCase=1_0000      ,						_lowerCamelCase=True      ,						_lowerCamelCase=768      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=0.7      ,						_lowerCamelCase=True      ,						_lowerCamelCase=1_0048      ,						_lowerCamelCase=12_9595      ,						_lowerCamelCase=12_9599      ,						_lowerCamelCase=1_0000      ,						_lowerCamelCase=256      ,						_lowerCamelCase=49.9      ,						**_lowerCamelCase      ,						):
       super().__init__(
           temperature=_lowerCamelCase      ,						do_sample=_lowerCamelCase      ,						eos_token_id=_lowerCamelCase      ,						renormalize_logits=_lowerCamelCase      ,						max_new_tokens=_lowerCamelCase      ,						output_scores=_lowerCamelCase      ,						return_dict_in_generate=_lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						output_attentions=_lowerCamelCase      ,						**_lowerCamelCase      ,						)

       a		:Dict											= text_encoding_offset
       a		:Any											= text_pad_token
       a		:List[Any]											= eos_token_id
       a		:str											= semantic_infer_token
       a		:Any											= semantic_vocab_size
       a		:Any											= max_input_semantic_length
       a		:Dict											= semantic_rate_hz







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'coarse_acoustics'




 def __init__(	self      ,						_lowerCamelCase=True      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=0.7      ,						_lowerCamelCase=True      ,						_lowerCamelCase=1_2048      ,						_lowerCamelCase=75      ,						_lowerCamelCase=2      ,						_lowerCamelCase=1_2050      ,						_lowerCamelCase=256      ,						_lowerCamelCase = 630      ,						_lowerCamelCase = 60      ,						**_lowerCamelCase      ,						):
       super().__init__(
           temperature=_lowerCamelCase      ,						do_sample=_lowerCamelCase      ,						renormalize_logits=_lowerCamelCase      ,						output_scores=_lowerCamelCase      ,						return_dict_in_generate=_lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						output_attentions=_lowerCamelCase      ,						**_lowerCamelCase      ,						)

       a		:Optional[Any]											= coarse_semantic_pad_token
       a		:Dict											= coarse_rate_hz
       a		:List[str]											= n_coarse_codebooks
       a		:List[Any]											= coarse_infer_token
       a		:Optional[Any]											= max_coarse_input_length
       a		:Any											= max_coarse_history
       a		:Optional[int]											= sliding_window_len







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'fine_acoustics'




 def __init__(	self      ,						_lowerCamelCase=0.5      ,						_lowerCamelCase=512      ,						_lowerCamelCase=1024      ,						_lowerCamelCase=8      ,						**_lowerCamelCase      ,						):
       super().__init__(temperature=_lowerCamelCase					)

       a		:Union[str, Any]											= max_fine_history_length
       a		:int											= max_fine_input_length
       a		:str											= n_fine_codebooks







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'bark'
 SCREAMING_SNAKE_CASE__   = True




 def __init__(	self      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase=2_4000      ,						_lowerCamelCase=1024      ,						**_lowerCamelCase      ,						):
       if semantic_config is None:
             a		:str											= {}
             logger.info('''semantic_config is None. initializing the semantic model with default values.'''					)

       if coarse_acoustics_config is None:
             a		:List[str]											= {}
             logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''					)

       if fine_acoustics_config is None:
             a		:Any											= {}
             logger.info('''fine_acoustics_config is None. initializing the fine model with default values.'''					)

       a		:Any											= BarkSemanticGenerationConfig(**_lowerCamelCase					)
       a		:int											= BarkCoarseGenerationConfig(**_lowerCamelCase					)
       a		:Optional[int]											= BarkFineGenerationConfig(**_lowerCamelCase					)

       a		:Tuple											= sample_rate
       a		:Dict											= codebook_size




 @classmethod
 def  SCREAMING_SNAKE_CASE__       (	cls      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						**_lowerCamelCase      ,						):
       return cls(
           semantic_config=semantic_config.to_dict()      ,						coarse_acoustics_config=coarse_acoustics_config.to_dict()      ,						fine_acoustics_config=fine_acoustics_config.to_dict()      ,						**_lowerCamelCase      ,						)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= copy.deepcopy(self.__dict__					)

       a		:List[Any]											= self.semantic_config.to_dict()
       a		:Union[str, Any]											= self.coarse_acoustics_config.to_dict()
       a		:int											= self.fine_acoustics_config.to_dict()

       a		:Tuple											= self.__class__.model_type
       return output





