




'''simple docstring'''





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


lowercase__								=						logging.get_logger(__name__)


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

lowercase__								=						'''
    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     lowercase  ( UpperCAmelCase_):
      __lowercase					=	'''bark_module'''
      __lowercase					=	['''past_key_values''']

      __lowercase					=	{
          '''num_attention_heads''': '''num_heads''',
          '''num_hidden_layers''': '''num_layers''',
          '''vocab_size''': '''input_vocab_size''',
          '''window_size''': '''block_size''',
      }






      def __init__(self			,	a__=1_0_2_4			,	a__=1_0_0_4_8			,	a__=1_0_0_4_8			,	a__=1_2			,	a__=1_2			,	a__=7_6_8			,	a__=0.0			,	a__=True			,	a__=0.02			,	a__=True			,	**a__			,	)							-> str:
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			block_size
         SCREAMING_SNAKE_CASE__      :					str							    =			input_vocab_size
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			output_vocab_size
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			num_layers
         SCREAMING_SNAKE_CASE__      :					Dict							    =			num_heads
         SCREAMING_SNAKE_CASE__      :					Any							    =			hidden_size
         SCREAMING_SNAKE_CASE__      :					Dict							    =			dropout
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			bias
         SCREAMING_SNAKE_CASE__      :					int							    =			use_cache
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			initializer_range

         super().__init__(**a__       )






      @classmethod
      def  A__(cls			,	a__			,	a__ = None			,	a__ = False			,	a__ = False			,	a__ = None			,	a__ = "main"			,	**a__			,	)							-> "PretrainedConfig":
         SCREAMING_SNAKE_CASE__      :					Dict							    =			cache_dir
         SCREAMING_SNAKE_CASE__      :					Any							    =			force_download
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			local_files_only
         SCREAMING_SNAKE_CASE__      :					int							    =			revision

         cls._set_token_in_kwargs(a__			,	a__       )

         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					int							    =			cls.get_config_dict(a__			,	**a__       )

         # get the config dict if we are loading from Bark
         if config_dict.get('''model_type'''       ) == "bark":
            SCREAMING_SNAKE_CASE__      :					Any							    =			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(a__			,	**a__       )






@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     lowercase  ( UpperCAmelCase_):
      __lowercase					=	'''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     lowercase  ( UpperCAmelCase_):
      __lowercase					=	'''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     lowercase  ( UpperCAmelCase_):
      __lowercase					=	'''fine_acoustics'''






      def __init__(self			,	a__=True			,	a__=8			,	a__=1			,	**a__       )							-> int:
         SCREAMING_SNAKE_CASE__      :					str							    =			n_codes_total
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			n_codes_given

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






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	'''bark'''
      __lowercase					=	True






      def __init__(self			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__=0.02			,	**a__			,	)							-> List[str]:
         if semantic_config is None:
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			{}
            logger.info('''semantic_config is None. initializing the semantic model with default values.'''       )

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

         if fine_acoustics_config is None:
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			{}
            logger.info('''fine_acoustics_config is None. initializing the fine model with default values.'''       )

         if codec_config is None:
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			{}
            logger.info('''codec_config is None. initializing the codec model with default values.'''       )

         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			BarkSemanticConfig(**a__       )
         SCREAMING_SNAKE_CASE__      :					str							    =			BarkCoarseConfig(**a__       )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			BarkFineConfig(**a__       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			AutoConfig.for_model(**a__       )

         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			initializer_range

         super().__init__(**a__       )






      @classmethod
      def  A__(cls			,	a__			,	a__			,	a__			,	a__			,	**a__			,	)							-> Union[str, Any]:
         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()			,	**a__			,	)






      def  A__(self       )							-> Tuple:
         SCREAMING_SNAKE_CASE__      :					int							    =			copy.deepcopy(self.__dict__       )

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

         SCREAMING_SNAKE_CASE__      :					Any							    =			self.__class__.model_type
         return output







'''simple docstring'''





import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
       from PIL import Image
else:






       class     lowercase  :






             @staticmethod
             def  A__(*a__			,	**a__       )							-> Union[str, Any]:
                pass






@is_pipeline_test
@require_torch_or_tf
@require_vision
class     lowercase  ( unittest.TestCase):
      __lowercase					=	MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
      __lowercase					=	TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






      def  A__(self			,	a__			,	a__			,	a__       )							-> Dict:
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			ImageClassificationPipeline(model=a__			,	image_processor=a__			,	top_k=2       )
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[
             Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       ),
             '''http://images.cocodataset.org/val2017/000000039769.jpg''',
         ]
         return image_classifier, examples






      def  A__(self			,	a__			,	a__       )							-> List[Any]:
         SCREAMING_SNAKE_CASE__      :					int							    =			image_classifier('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )

         self.assertEqual(
             a__			,	[
                 {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
             ]			,	)

         import datasets

         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''			,	'''image'''			,	split='''test'''       )

         # Accepts URL + PIL.Image + lists
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			image_classifier(
             [
                 Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       ),
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                 # RGBA
                 dataset[0]['''file'''],
                 # LA
                 dataset[1]['''file'''],
                 # L
                 dataset[2]['''file'''],
             ]       )
         self.assertEqual(
             a__			,	[
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
             ]			,	)






      @require_torch
      def  A__(self       )							-> List[str]:
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			'''hf-internal-testing/tiny-random-vit'''
         SCREAMING_SNAKE_CASE__      :					Any							    =			pipeline('''image-classification'''			,	model=a__       )

         SCREAMING_SNAKE_CASE__      :					Any							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}]			,	)

         SCREAMING_SNAKE_CASE__      :					Any							    =			image_classifier(
             [
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
             ]			,	top_k=2			,	)
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
                 [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
             ]			,	)






      @require_tf
      def  A__(self       )							-> Optional[int]:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			'''hf-internal-testing/tiny-random-vit'''
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			pipeline('''image-classification'''			,	model=a__			,	framework='''tf'''       )

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}]			,	)

         SCREAMING_SNAKE_CASE__      :					Dict							    =			image_classifier(
             [
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
             ]			,	top_k=2			,	)
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
                 [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
             ]			,	)






      def  A__(self       )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			PreTrainedTokenizer()

         # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			pipeline(
             '''image-classification'''			,	model='''hf-internal-testing/tiny-random-vit'''			,	tokenizer=a__       )

         self.assertIs(image_classifier.tokenizer			,	a__       )






      @slow
      @require_torch
      def  A__(self       )							-> int:
         # Perceiver is not tested by `run_pipeline_test` properly.
         # That is because the type of feature_extractor and model preprocessor need to be kept
         # in sync, which is not the case in the current design
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			pipeline('''image-classification'''			,	model='''deepmind/vision-perceiver-conv'''       )
         SCREAMING_SNAKE_CASE__      :					str							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 {'''score''': 0.43_85, '''label''': '''tabby, tabby cat'''},
                 {'''score''': 0.3_21, '''label''': '''tiger cat'''},
                 {'''score''': 0.05_02, '''label''': '''Egyptian cat'''},
                 {'''score''': 0.01_37, '''label''': '''crib, cot'''},
                 {'''score''': 0.0_07, '''label''': '''radiator'''},
             ]			,	)

         SCREAMING_SNAKE_CASE__      :					str							    =			pipeline('''image-classification'''			,	model='''deepmind/vision-perceiver-fourier'''       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 {'''score''': 0.56_58, '''label''': '''tabby, tabby cat'''},
                 {'''score''': 0.13_09, '''label''': '''tiger cat'''},
                 {'''score''': 0.07_22, '''label''': '''Egyptian cat'''},
                 {'''score''': 0.07_07, '''label''': '''remote control, remote'''},
                 {'''score''': 0.00_82, '''label''': '''computer keyboard, keypad'''},
             ]			,	)

         SCREAMING_SNAKE_CASE__      :					Dict							    =			pipeline('''image-classification'''			,	model='''deepmind/vision-perceiver-learned'''       )
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 {'''score''': 0.30_22, '''label''': '''tabby, tabby cat'''},
                 {'''score''': 0.23_62, '''label''': '''Egyptian cat'''},
                 {'''score''': 0.18_56, '''label''': '''tiger cat'''},
                 {'''score''': 0.03_24, '''label''': '''remote control, remote'''},
                 {'''score''': 0.00_96, '''label''': '''quilt, comforter, comfort, puff'''},
             ]			,	)

