from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers.models.swin.modeling_tf_swin import (
        TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFSwinForImageClassification,
        TFSwinForMaskedImageModeling,
        TFSwinModel,
    )


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor
class 							a_					:






 '''simple docstring'''






 def __init__(     self   :    Union[str, Any]     ,       lowercase__   :    Optional[Any]     ,       lowercase__   :    Optional[Any]=13     ,       lowercase__   :    Any=32     ,       lowercase__   :    Optional[Any]=2     ,       lowercase__   :    Any=3     ,       lowercase__   :    List[Any]=16     ,       lowercase__   :    str=[1, 2, 1]     ,       lowercase__   :    int=[2, 2, 4]     ,       lowercase__   :    str=2     ,       lowercase__   :    Any=2.0     ,       lowercase__   :    List[Any]=True     ,       lowercase__   :    Dict=0.0     ,       lowercase__   :    Optional[Any]=0.0     ,       lowercase__   :    Union[str, Any]=0.1     ,       lowercase__   :    Union[str, Any]="gelu"     ,       lowercase__   :    List[str]=False     ,       lowercase__   :    Union[str, Any]=True     ,       lowercase__   :    str=0.02     ,       lowercase__   :    Any=1e-5     ,       lowercase__   :    Optional[Any]=True     ,       lowercase__   :    Dict=None     ,       lowercase__   :    Union[str, Any]=True     ,       lowercase__   :    List[str]=10     ,       lowercase__   :    Optional[Any]=8     ,       ):







        '''simple docstring'''



        lowerCAmelCase__	      =   parent
        lowerCAmelCase__	      =   batch_size
        lowerCAmelCase__	      =   image_size
        lowerCAmelCase__	      =   patch_size
        lowerCAmelCase__	      =   num_channels
        lowerCAmelCase__	      =   embed_dim
        lowerCAmelCase__	      =   depths
        lowerCAmelCase__	      =   num_heads
        lowerCAmelCase__	      =   window_size
        lowerCAmelCase__	      =   mlp_ratio
        lowerCAmelCase__	      =   qkv_bias
        lowerCAmelCase__	      =   hidden_dropout_prob
        lowerCAmelCase__	      =   attention_probs_dropout_prob
        lowerCAmelCase__	      =   drop_path_rate
        lowerCAmelCase__	      =   hidden_act
        lowerCAmelCase__	      =   use_absolute_embeddings
        lowerCAmelCase__	      =   patch_norm
        lowerCAmelCase__	      =   layer_norm_eps
        lowerCAmelCase__	      =   initializer_range
        lowerCAmelCase__	      =   is_training
        lowerCAmelCase__	      =   scope
        lowerCAmelCase__	      =   use_labels
        lowerCAmelCase__	      =   type_sequence_label_size
        lowerCAmelCase__	      =   encoder_stride




 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        lowerCAmelCase__	      =   None
        if self.use_labels:
               lowerCAmelCase__	      =   ids_tensor([self.batch_size]     ,       self.type_sequence_label_size)

        lowerCAmelCase__	      =   self.get_config()

        return config, pixel_values, labels




 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        return SwinConfig(
            image_size=self.image_size     ,       patch_size=self.patch_size     ,       num_channels=self.num_channels     ,       embed_dim=self.embed_dim     ,       depths=self.depths     ,       num_heads=self.num_heads     ,       window_size=self.window_size     ,       mlp_ratio=self.mlp_ratio     ,       qkv_bias=self.qkv_bias     ,       hidden_dropout_prob=self.hidden_dropout_prob     ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,       drop_path_rate=self.drop_path_rate     ,       hidden_act=self.hidden_act     ,       use_absolute_embeddings=self.use_absolute_embeddings     ,       path_norm=self.patch_norm     ,       layer_norm_eps=self.layer_norm_eps     ,       initializer_range=self.initializer_range     ,       encoder_stride=self.encoder_stride     ,       )




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    Optional[Any]     ,       lowercase__   :    Dict     ,       lowercase__   :    Any):







        '''simple docstring'''



        lowerCAmelCase__	      =   TFSwinModel(config=lowerCAmelCase__)
        lowerCAmelCase__	      =   model(lowerCAmelCase__)

        lowerCAmelCase__	      =   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
        lowerCAmelCase__	      =   int(config.embed_dim * 2 ** (len(config.depths) - 1))

        self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, expected_seq_len, expected_dim))




 def 						__snake_case		(     self   :    Tuple     ,       lowercase__   :    List[Any]     ,       lowercase__   :    List[str]     ,       lowercase__   :    Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__	      =   TFSwinForMaskedImageModeling(config=lowerCAmelCase__)
        lowerCAmelCase__	      =   model(lowerCAmelCase__)
        self.parent.assertEqual(
            result.logits.shape     ,       (self.batch_size, self.num_channels, self.image_size, self.image_size))

        # test greyscale images
        lowerCAmelCase__	      =   1
        lowerCAmelCase__	      =   TFSwinForMaskedImageModeling(lowerCAmelCase__)

        lowerCAmelCase__	      =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
        lowerCAmelCase__	      =   model(lowerCAmelCase__)
        self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, 1, self.image_size, self.image_size))




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    Union[str, Any]     ,       lowercase__   :    List[str]     ,       lowercase__   :    Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.type_sequence_label_size
        lowerCAmelCase__	      =   TFSwinForImageClassification(lowerCAmelCase__)
        lowerCAmelCase__	      =   model(lowerCAmelCase__     ,       labels=lowerCAmelCase__)
        self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.type_sequence_label_size))

        # test greyscale images
        lowerCAmelCase__	      =   1
        lowerCAmelCase__	      =   TFSwinForImageClassification(lowerCAmelCase__)
        lowerCAmelCase__	      =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
        lowerCAmelCase__	      =   model(lowerCAmelCase__)
        self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.type_sequence_label_size))




 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.prepare_config_and_inputs()
        lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__	      =   config_and_inputs
        lowerCAmelCase__	      =   {'pixel_values': pixel_values}
        return config, inputs_dict






@require_tf
class 							a_					(		_a			,	_a			,	unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   (
     (
         TFSwinModel,
         TFSwinForImageClassification,
         TFSwinForMaskedImageModeling,
     )
     if is_tf_available()
     else ()
 )
 UpperCAmelCase_      					=   (
     {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
     if is_tf_available()
     else {}
 )

 UpperCAmelCase_      					=   False
 UpperCAmelCase_      					=   False
 UpperCAmelCase_      					=   False
 UpperCAmelCase_      					=   False




 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        lowerCAmelCase__	      =   TFSwinModelTester(self)
        lowerCAmelCase__	      =   ConfigTester(self     ,       config_class=lowerCAmelCase__     ,       embed_dim=37)




 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()




 def 						__snake_case		(     self   :    List[Any]):







        '''simple docstring'''



        return




 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__)




 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__)




 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__)




 @unittest.skip(reason='Swin does not use inputs_embeds')
 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        pass




 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowerCAmelCase__	      =   model_class(lowerCAmelCase__)
               self.assertIsInstance(model.get_input_embeddings()     ,       tf.keras.layers.Layer)
               lowerCAmelCase__	      =   model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCAmelCase__     ,       tf.keras.layers.Dense))




 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowerCAmelCase__	      =   model_class(lowerCAmelCase__)
               lowerCAmelCase__	      =   inspect.signature(model.call)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               lowerCAmelCase__	      =   [*signature.parameters.keys()]

               lowerCAmelCase__	      =   ['pixel_values']
               self.assertListEqual(arg_names[:1]     ,       lowerCAmelCase__)




 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase__	      =   True

        for model_class in self.all_model_classes:
               lowerCAmelCase__	      =   True
               lowerCAmelCase__	      =   False
               lowerCAmelCase__	      =   True
               lowerCAmelCase__	      =   model_class(lowerCAmelCase__)
               lowerCAmelCase__	      =   model(**self._prepare_for_class(lowerCAmelCase__     ,       lowerCAmelCase__))
               lowerCAmelCase__	      =   outputs.attentions
               lowerCAmelCase__	      =   len(self.model_tester.depths)
               self.assertEqual(len(lowerCAmelCase__)     ,       lowerCAmelCase__)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               lowerCAmelCase__	      =   True
               lowerCAmelCase__	      =   config.window_size**2
               lowerCAmelCase__	      =   model_class(lowerCAmelCase__)
               lowerCAmelCase__	      =   model(**self._prepare_for_class(lowerCAmelCase__     ,       lowerCAmelCase__))
               lowerCAmelCase__	      =   outputs.attentions
               self.assertEqual(len(lowerCAmelCase__)     ,       lowerCAmelCase__)

               self.assertListEqual(
                   list(attentions[0].shape[-3:])     ,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,       )
               lowerCAmelCase__	      =   len(lowerCAmelCase__)

               # Check attention is always last and order is fine
               lowerCAmelCase__	      =   True
               lowerCAmelCase__	      =   True
               lowerCAmelCase__	      =   model_class(lowerCAmelCase__)
               lowerCAmelCase__	      =   model(**self._prepare_for_class(lowerCAmelCase__     ,       lowerCAmelCase__))

               if hasattr(self.model_tester     ,       'num_hidden_states_types'):
                      lowerCAmelCase__	      =   self.model_tester.num_hidden_states_types
               else:
                      # also another +1 for reshaped_hidden_states
                      lowerCAmelCase__	      =   2
               self.assertEqual(out_len + added_hidden_states     ,       len(lowerCAmelCase__))

               lowerCAmelCase__	      =   outputs.attentions

               self.assertEqual(len(lowerCAmelCase__)     ,       lowerCAmelCase__)

               self.assertListEqual(
                   list(self_attentions[0].shape[-3:])     ,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,       )




 def 						__snake_case		(     self   :    Optional[int]     ,       lowercase__   :    int     ,       lowercase__   :    Optional[int]     ,       lowercase__   :    Optional[Any]     ,       lowercase__   :    int):







        '''simple docstring'''



        lowerCAmelCase__	      =   model_class(lowerCAmelCase__)
        lowerCAmelCase__	      =   model(**self._prepare_for_class(lowerCAmelCase__     ,       lowerCAmelCase__))
        lowerCAmelCase__	      =   outputs.hidden_states

        lowerCAmelCase__	      =   getattr(
            self.model_tester     ,       'expected_num_hidden_layers'     ,       len(self.model_tester.depths) + 1)
        self.assertEqual(len(lowerCAmelCase__)     ,       lowerCAmelCase__)

        # Swin has a different seq_length
        lowerCAmelCase__	      =   to_atuple(config.patch_size)

        lowerCAmelCase__	      =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:])     ,       [num_patches, self.model_tester.embed_dim]     ,       )

        lowerCAmelCase__	      =   outputs.reshaped_hidden_states
        self.assertEqual(len(lowerCAmelCase__)     ,       lowerCAmelCase__)

        lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__	      =   reshaped_hidden_states[0].shape

        lowerCAmelCase__	      =   tf.reshape(reshaped_hidden_states[0]     ,       (batch_size, num_channels, height * width))
        lowerCAmelCase__	      =   tf.transpose(lowerCAmelCase__     ,       (0, 2, 1))

        self.assertListEqual(
            list(reshaped_hidden_states.shape[-2:])     ,       [num_patches, self.model_tester.embed_dim]     ,       )




 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase__	      =   to_atuple(self.model_tester.image_size)

        for model_class in self.all_model_classes:
               lowerCAmelCase__	      =   True
               self.check_hidden_states_output(lowerCAmelCase__     ,       lowerCAmelCase__     ,       lowerCAmelCase__     ,       lowerCAmelCase__)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               lowerCAmelCase__	      =   True

               self.check_hidden_states_output(lowerCAmelCase__     ,       lowerCAmelCase__     ,       lowerCAmelCase__     ,       lowerCAmelCase__)




 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase__	      =   3

        lowerCAmelCase__	      =   to_atuple(self.model_tester.image_size)
        lowerCAmelCase__	      =   to_atuple(config.patch_size)

        lowerCAmelCase__	      =   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        lowerCAmelCase__	      =   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes:
               lowerCAmelCase__	      =   True
               self.check_hidden_states_output(lowerCAmelCase__     ,       lowerCAmelCase__     ,       lowerCAmelCase__     ,       (padded_height, padded_width))

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               lowerCAmelCase__	      =   True
               self.check_hidden_states_output(lowerCAmelCase__     ,       lowerCAmelCase__     ,       lowerCAmelCase__     ,       (padded_height, padded_width))




 @slow
 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowerCAmelCase__	      =   TFSwinModel.from_pretrained(lowerCAmelCase__)
               self.assertIsNotNone(lowerCAmelCase__)






@require_vision
@require_tf
class 							a_					(		unittest.TestCase		):






 '''simple docstring'''






 @cached_property
 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        return (
            AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224')
            if is_vision_available()
            else None
        )




 @slow
 def 						__snake_case		(     self   :    Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224')
        lowerCAmelCase__	      =   self.default_image_processor

        lowerCAmelCase__	      =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
        lowerCAmelCase__	      =   image_processor(images=lowerCAmelCase__     ,       return_tensors='tf')

        # forward pass
        lowerCAmelCase__	      =   model(lowerCAmelCase__)

        # verify the logits
        lowerCAmelCase__	      =   tf.TensorShape((1, 1_000))
        self.assertEqual(outputs.logits.shape     ,       lowerCAmelCase__)
        lowerCAmelCase__	      =   tf.constant([-0.0_948, -0.6_454, -0.0_921])
        self.assertTrue(np.allclose(outputs.logits[0, :3]     ,       lowerCAmelCase__     ,       atol=1e-4))

import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


UpperCAmelCase_    		=				logging.get_logger(__name__)


UpperCAmelCase_    		=				{
    '''suno/bark-small''': '''https://huggingface.co/suno/bark-small/resolve/main/config.json''',
    '''suno/bark''': '''https://huggingface.co/suno/bark/resolve/main/config.json''',
}

UpperCAmelCase_    		=				'''
    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        block_size (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        input_vocab_size (`int`, *optional*, defaults to 10_048):
            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
            regards to the chosen sub-model.
        output_vocab_size (`int`, *optional*, defaults to 10_048):
            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
            with regards to the chosen sub-model.
        num_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the given sub-model.
        num_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer architecture.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use bias in the linear layers and layer norm layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
'''





class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  List[str]		="""bark_module"""
     _SCREAMING_SNAKE_CASE  :  str		=["""past_key_values"""]

     _SCREAMING_SNAKE_CASE  :  Tuple		={
         """num_attention_heads""": """num_heads""",
         """num_hidden_layers""": """num_layers""",
         """vocab_size""": """input_vocab_size""",
         """window_size""": """block_size""",
     }



     def __init__(       self					,   lowerCAmelCase__=1024					,   lowerCAmelCase__=10048					,   lowerCAmelCase__=10048					,   lowerCAmelCase__=12					,   lowerCAmelCase__=12					,   lowerCAmelCase__=768					,   lowerCAmelCase__=0.0					,   lowerCAmelCase__=True					,   lowerCAmelCase__=0.02					,   lowerCAmelCase__=True					,   **lowerCAmelCase__					,   ):
            _A=						block_size
            _A=						input_vocab_size
            _A=						output_vocab_size
            _A=						num_layers
            _A=						num_heads
            _A=						hidden_size
            _A=						dropout
            _A=						bias
            _A=						use_cache
            _A=						initializer_range

            super().__init__(**lowerCAmelCase__							)





     @classmethod
     def       a__	(       cls					,   lowerCAmelCase__					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = "main"					,   **lowerCAmelCase__					,   ):
            _A=						cache_dir
            _A=						force_download
            _A=						local_files_only
            _A=						revision

            cls._set_token_in_kwargs(lowerCAmelCase__					,   lowerCAmelCase__							)

            _A,      _A=						cls.get_config_dict(lowerCAmelCase__					,   **lowerCAmelCase__							)

            # get the config dict if we are loading from Bark
            if config_dict.get('model_type'							) == "bark":
                   _A=						config_dict[f"{cls.model_type}_config"]

            if "model_type" in config_dict and hasattr(cls					,   'model_type'							) and config_dict["model_type"] != cls.model_type:
                   logger.warning(
                       f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                       f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."							)

            return cls.from_dict(lowerCAmelCase__					,   **lowerCAmelCase__							)





@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkSemanticConfig"""      ,		model="""BarkSemanticModel"""      )      ,		"""
    Example:

    ```python
    >>> from transformers import BarkSemanticConfig, BarkSemanticModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkSemanticConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkSemanticModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""      ,		)
class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  Union[str, Any]		="""semantic"""





@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkCoarseConfig"""      ,		model="""BarkCoarseModel"""      )      ,		"""
    Example:

    ```python
    >>> from transformers import BarkCoarseConfig, BarkCoarseModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkCoarseConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkCoarseModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""      ,		)
class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  str		="""coarse_acoustics"""





@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkFineConfig"""      ,		model="""BarkFineModel"""      )      ,		"""
        n_codes_total (`int`, *optional*, defaults to 8):
            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
        n_codes_given (`int`, *optional*, defaults to 1):
            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
            sub-models.
    Example:

    ```python
    >>> from transformers import BarkFineConfig, BarkFineModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkFineConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkFineModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""      ,		)
class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  str		="""fine_acoustics"""



     def __init__(       self					,   lowerCAmelCase__=True					,   lowerCAmelCase__=8					,   lowerCAmelCase__=1					,   **lowerCAmelCase__							):
            _A=						n_codes_total
            _A=						n_codes_given

            super().__init__(tie_word_embeddings=lowerCAmelCase__					,   **lowerCAmelCase__							)





class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  Optional[Any]		="""bark"""
     _SCREAMING_SNAKE_CASE  :  Any		=True



     def __init__(       self					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__=0.02					,   **lowerCAmelCase__					,   ):
            if semantic_config is None:
                   _A=						{}
                   logger.info('semantic_config is None. initializing the semantic model with default values.'							)

            if coarse_acoustics_config is None:
                   _A=						{}
                   logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.'							)

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

            if codec_config is None:
                   _A=						{}
                   logger.info('codec_config is None. initializing the codec model with default values.'							)

            _A=						BarkSemanticConfig(**lowerCAmelCase__							)
            _A=						BarkCoarseConfig(**lowerCAmelCase__							)
            _A=						BarkFineConfig(**lowerCAmelCase__							)
            _A=						AutoConfig.for_model(**lowerCAmelCase__							)

            _A=						initializer_range

            super().__init__(**lowerCAmelCase__							)



     @classmethod
     def       a__	(       cls					,   lowerCAmelCase__					,   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()					,   codec_config=codec_config.to_dict()					,   **lowerCAmelCase__					,   )





     def       a__	(       self							):
            _A=						copy.deepcopy(self.__dict__							)

            _A=						self.semantic_config.to_dict()
            _A=						self.coarse_acoustics_config.to_dict()
            _A=						self.fine_acoustics_config.to_dict()
            _A=						self.codec_config.to_dict()

            _A=						self.__class__.model_type
            return output