


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


lowerCAmelCase__   	=				logging.get_logger(__name__)


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

lowerCAmelCase__   	=				'\n    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model\n    according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)\n    architecture.\n\n    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n    documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        block_size (`int`, *optional*, defaults to 1024):\n            The maximum sequence length that this model might ever be used with. Typically set this to something large\n            just in case (e.g., 512 or 1024 or 2048).\n        input_vocab_size (`int`, *optional*, defaults to 10_048):\n            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the\n            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with\n            regards to the chosen sub-model.\n        output_vocab_size (`int`, *optional*, defaults to 10_048):\n            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented\n            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought\n            with regards to the chosen sub-model.\n        num_layers (`int`, *optional*, defaults to 12):\n            Number of hidden layers in the given sub-model.\n        num_heads (`int`, *optional*, defaults to 12):\n            Number of attention heads for each attention layer in the Transformer architecture.\n        hidden_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.\n        dropout (`float`, *optional*, defaults to 0.0):\n            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.\n        bias (`bool`, *optional*, defaults to `True`):\n            Whether or not to use bias in the linear layers and layer norm layers.\n        initializer_range (`float`, *optional*, defaults to 0.02):\n            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n'







class   lowerCAmelCase__ (       a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				"bark_module"
     __SCREAMING_SNAKE_CASE						    =				["past_key_values"]

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


     def __init__(		self				,		__lowerCamelCase=1_0_2_4				,		__lowerCamelCase=1_0_0_4_8				,		__lowerCamelCase=1_0_0_4_8				,		__lowerCamelCase=1_2				,		__lowerCamelCase=1_2				,		__lowerCamelCase=7_6_8				,		__lowerCamelCase=0.0				,		__lowerCamelCase=True				,		__lowerCamelCase=0.0_2				,		__lowerCamelCase=True				,		**__lowerCamelCase				,		)    ->       Union[str, Any]:
            _A   : Optional[int]       					=				block_size
            _A   : int       					=				input_vocab_size
            _A   : List[Any]       					=				output_vocab_size
            _A   : Optional[Any]       					=				num_layers
            _A   : str       					=				num_heads
            _A   : List[str]       					=				hidden_size
            _A   : Tuple       					=				dropout
            _A   : str       					=				bias
            _A   : List[str]       					=				use_cache
            _A   : Optional[int]       					=				initializer_range

            super().__init__(**__lowerCamelCase)


     @classmethod
     def 				_lowerCamelCase				(		cls				,		__lowerCamelCase				,		__lowerCamelCase = None				,		__lowerCamelCase = False				,		__lowerCamelCase = False				,		__lowerCamelCase = None				,		__lowerCamelCase = "main"				,		**__lowerCamelCase				,		)    ->       "PretrainedConfig":
            _A   : Optional[int]       					=				cache_dir
            _A   : Tuple       					=				force_download
            _A   : Optional[int]       					=				local_files_only
            _A   : Any       					=				revision

            cls._set_token_in_kwargs(__lowerCamelCase				,		__lowerCamelCase)

            _A					,    _A   : str       					=				cls.get_config_dict(__lowerCamelCase				,		**__lowerCamelCase)

            # get the config dict if we are loading from Bark
            if config_dict.get("model_type") == "bark":
                   _A   : Optional[int]       					=				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")  ,					"\n    Example:\n\n    ```python\n    >>> from transformers import BarkSemanticConfig, BarkSemanticModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkSemanticConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkSemanticModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"  ,					)
class   lowerCAmelCase__ (       a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				"semantic"







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkCoarseConfig"  ,					model="BarkCoarseModel")  ,					"\n    Example:\n\n    ```python\n    >>> from transformers import BarkCoarseConfig, BarkCoarseModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkCoarseConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkCoarseModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"  ,					)
class   lowerCAmelCase__ (       a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				"coarse_acoustics"







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkFineConfig"  ,					model="BarkFineModel")  ,					"\n        n_codes_total (`int`, *optional*, defaults to 8):\n            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.\n        n_codes_given (`int`, *optional*, defaults to 1):\n            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics\n            sub-models.\n    Example:\n\n    ```python\n    >>> from transformers import BarkFineConfig, BarkFineModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkFineConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkFineModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"  ,					)
class   lowerCAmelCase__ (       a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				"fine_acoustics"


     def __init__(		self				,		__lowerCamelCase=True				,		__lowerCamelCase=8				,		__lowerCamelCase=1				,		**__lowerCamelCase)    ->       Optional[Any]:
            _A   : Tuple       					=				n_codes_total
            _A   : Optional[int]       					=				n_codes_given

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







class   lowerCAmelCase__ (       a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				"bark"
     __SCREAMING_SNAKE_CASE						    =				True


     def __init__(		self				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase=0.0_2				,		**__lowerCamelCase				,		)    ->       Any:
            if semantic_config is None:
                   _A   : List[Any]       					=				{}
                   logger.info("semantic_config is None. initializing the semantic model with default values.")

            if coarse_acoustics_config is None:
                   _A   : List[Any]       					=				{}
                   logger.info("coarse_acoustics_config is None. initializing the coarse model with default values.")

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

            if codec_config is None:
                   _A   : Union[str, Any]       					=				{}
                   logger.info("codec_config is None. initializing the codec model with default values.")

            _A   : Dict       					=				BarkSemanticConfig(**__lowerCamelCase)
            _A   : Optional[int]       					=				BarkCoarseConfig(**__lowerCamelCase)
            _A   : Union[str, Any]       					=				BarkFineConfig(**__lowerCamelCase)
            _A   : Optional[int]       					=				AutoConfig.for_model(**__lowerCamelCase)

            _A   : Any       					=				initializer_range

            super().__init__(**__lowerCamelCase)


     @classmethod
     def 				_lowerCamelCase				(		cls				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		**__lowerCamelCase				,		)    ->       Tuple:
            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 				_lowerCamelCase				(		self)    ->       Any:
            _A   : Union[str, Any]       					=				copy.deepcopy(self.__dict__)

            _A   : int       					=				self.semantic_config.to_dict()
            _A   : Any       					=				self.coarse_acoustics_config.to_dict()
            _A   : List[str]       					=				self.fine_acoustics_config.to_dict()
            _A   : int       					=				self.codec_config.to_dict()

            _A   : Union[str, Any]       					=				self.__class__.model_type
            return output




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import MobileNetVaImageProcessor







class   lowerCAmelCase__ (       unittest.TestCase):




     '''simple docstring'''


     def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase=7				,		__lowerCamelCase=3				,		__lowerCamelCase=1_8				,		__lowerCamelCase=3_0				,		__lowerCamelCase=4_0_0				,		__lowerCamelCase=True				,		__lowerCamelCase=None				,		__lowerCamelCase=True				,		__lowerCamelCase=None				,		)    ->       Tuple:
            _A   : int       					=				size if size is not None else {"shortest_edge": 2_0}
            _A   : Tuple       					=				crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
            _A   : str       					=				parent
            _A   : Any       					=				batch_size
            _A   : Dict       					=				num_channels
            _A   : int       					=				image_size
            _A   : Union[str, Any]       					=				min_resolution
            _A   : Tuple       					=				max_resolution
            _A   : Any       					=				do_resize
            _A   : Optional[int]       					=				size
            _A   : Tuple       					=				do_center_crop
            _A   : List[str]       					=				crop_size


     def 				_lowerCamelCase				(		self)    ->       str:
            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
            }







@require_torch
@require_vision
class   lowerCAmelCase__ (       a  ,					unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				MobileNetVaImageProcessor if is_vision_available() else None


     def 				_lowerCamelCase				(		self)    ->       Any:
            _A   : Dict       					=				MobileNetVaImageProcessingTester(self)


     @property
     def 				_lowerCamelCase				(		self)    ->       Dict:
            return self.image_processor_tester.prepare_image_processor_dict()


     def 				_lowerCamelCase				(		self)    ->       Tuple:
            _A   : List[Any]       					=				self.image_processing_class(**self.image_processor_dict)
            self.assertTrue(hasattr(__lowerCamelCase				,		"do_resize"))
            self.assertTrue(hasattr(__lowerCamelCase				,		"size"))
            self.assertTrue(hasattr(__lowerCamelCase				,		"do_center_crop"))
            self.assertTrue(hasattr(__lowerCamelCase				,		"center_crop"))


     def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
            _A   : int       					=				self.image_processing_class.from_dict(self.image_processor_dict)
            self.assertEqual(image_processor.size				,		{"shortest_edge": 2_0})
            self.assertEqual(image_processor.crop_size				,		{"height": 1_8, "width": 1_8})

            _A   : Any       					=				self.image_processing_class.from_dict(self.image_processor_dict				,		size=4_2				,		crop_size=8_4)
            self.assertEqual(image_processor.size				,		{"shortest_edge": 4_2})
            self.assertEqual(image_processor.crop_size				,		{"height": 8_4, "width": 8_4})


     def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
            pass


     def 				_lowerCamelCase				(		self)    ->       List[Any]:
            # Initialize image_processing
            _A   : Optional[int]       					=				self.image_processing_class(**self.image_processor_dict)
            # create random PIL images
            _A   : List[Any]       					=				prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__lowerCamelCase)
            for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase				,		Image.Image)

            # Test not batched input
            _A   : str       					=				image_processing(image_inputs[0]				,		return_tensors="pt").pixel_values
            self.assertEqual(
                encoded_images.shape				,		(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )				,		)

            # Test batched
            _A   : Dict       					=				image_processing(__lowerCamelCase				,		return_tensors="pt").pixel_values
            self.assertEqual(
                encoded_images.shape				,		(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )				,		)


     def 				_lowerCamelCase				(		self)    ->       str:
            # Initialize image_processing
            _A   : List[Any]       					=				self.image_processing_class(**self.image_processor_dict)
            # create random numpy tensors
            _A   : int       					=				prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__lowerCamelCase				,		numpify=__lowerCamelCase)
            for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase				,		np.ndarray)

            # Test not batched input
            _A   : Tuple       					=				image_processing(image_inputs[0]				,		return_tensors="pt").pixel_values
            self.assertEqual(
                encoded_images.shape				,		(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )				,		)

            # Test batched
            _A   : str       					=				image_processing(__lowerCamelCase				,		return_tensors="pt").pixel_values
            self.assertEqual(
                encoded_images.shape				,		(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )				,		)


     def 				_lowerCamelCase				(		self)    ->       Tuple:
            # Initialize image_processing
            _A   : str       					=				self.image_processing_class(**self.image_processor_dict)
            # create random PyTorch tensors
            _A   : List[str]       					=				prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__lowerCamelCase				,		torchify=__lowerCamelCase)
            for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase				,		torch.Tensor)

            # Test not batched input
            _A   : str       					=				image_processing(image_inputs[0]				,		return_tensors="pt").pixel_values
            self.assertEqual(
                encoded_images.shape				,		(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )				,		)

            # Test batched
            _A   : Any       					=				image_processing(__lowerCamelCase				,		return_tensors="pt").pixel_values
            self.assertEqual(
                encoded_images.shape				,		(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )				,		)
