




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__		=						"\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     __SCREAMING_SNAKE_CASE							(		UpperCAmelCase__  ):
					_lowerCamelCase		  =			'bark_module'
					_lowerCamelCase		  =			['past_key_values']

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

					def __init__(    self			,	__lowercase=1_024			,	__lowercase=10_048			,	__lowercase=10_048			,	__lowercase=12			,	__lowercase=12			,	__lowercase=768			,	__lowercase=0.0			,	__lowercase=True			,	__lowercase=0.02			,	__lowercase=True			,	**__lowercase			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							       =							block_size
						lowerCamelCase_							:		Optional[Any]							       =							input_vocab_size
						lowerCamelCase_							:		Optional[int]							       =							output_vocab_size
						lowerCamelCase_							:		int							       =							num_layers
						lowerCamelCase_							:		str							       =							num_heads
						lowerCamelCase_							:		Dict							       =							hidden_size
						lowerCamelCase_							:		Union[str, Any]							       =							dropout
						lowerCamelCase_							:		Union[str, Any]							       =							bias
						lowerCamelCase_							:		Dict							       =							use_cache
						lowerCamelCase_							:		Optional[Any]							       =							initializer_range

						super().__init__(**__lowercase )





					@classmethod
					def      lowercase_							(    cls			,	__lowercase			,	__lowercase = None			,	__lowercase = False			,	__lowercase = False			,	__lowercase = None			,	__lowercase = "main"			,	**__lowercase			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							cache_dir
						lowerCamelCase_							:		Tuple							       =							force_download
						lowerCamelCase_							:		Any							       =							local_files_only
						lowerCamelCase_							:		Tuple							       =							revision

						cls._set_token_in_kwargs(__lowercase			,	__lowercase )

						lowerCamelCase_       ,      lowerCamelCase_							:		Union[str, Any]							       =							cls.get_config_dict(__lowercase			,	**__lowercase )

						# get the config dict if we are loading from Bark
						if config_dict.get('model_type' ) == "bark":
							lowerCamelCase_							:		List[str]							       =							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(__lowercase			,	**__lowercase )


@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     __SCREAMING_SNAKE_CASE							(		UpperCAmelCase__  ):
					_lowerCamelCase		  =			'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     __SCREAMING_SNAKE_CASE							(		UpperCAmelCase__  ):
					_lowerCamelCase		  =			'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     __SCREAMING_SNAKE_CASE							(		UpperCAmelCase__  ):
					_lowerCamelCase		  =			'fine_acoustics'

					def __init__(    self			,	__lowercase=True			,	__lowercase=8			,	__lowercase=1			,	**__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							n_codes_total
						lowerCamelCase_							:		Tuple							       =							n_codes_given

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


class     __SCREAMING_SNAKE_CASE							(		UpperCAmelCase__  ):
					_lowerCamelCase		  =			'bark'
					_lowerCamelCase		  =			True

					def __init__(    self			,	__lowercase = None			,	__lowercase = None			,	__lowercase = None			,	__lowercase = None			,	__lowercase=0.02			,	**__lowercase			,	):







						'''simple docstring'''




						if semantic_config is None:
							lowerCamelCase_							:		Tuple							       =							{}
							logger.info('semantic_config is None. initializing the semantic model with default values.' )

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

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

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

						lowerCamelCase_							:		Union[str, Any]							       =							BarkSemanticConfig(**__lowercase )
						lowerCamelCase_							:		Dict							       =							BarkCoarseConfig(**__lowercase )
						lowerCamelCase_							:		List[Any]							       =							BarkFineConfig(**__lowercase )
						lowerCamelCase_							:		Union[str, Any]							       =							AutoConfig.for_model(**__lowercase )

						lowerCamelCase_							:		str							       =							initializer_range

						super().__init__(**__lowercase )

					@classmethod
					def      lowercase_							(    cls			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	**__lowercase			,	):







						'''simple docstring'''




						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()			,	**__lowercase			,	)





					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							copy.deepcopy(self.__dict__ )

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

						lowerCamelCase_							:		Tuple							       =							self.__class__.model_type
						return output






from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
							import tensorflow as tf

							from transformers.models.mpnet.modeling_tf_mpnet import (
							    TFMPNetForMaskedLM,
							    TFMPNetForMultipleChoice,
							    TFMPNetForQuestionAnswering,
							    TFMPNetForSequenceClassification,
							    TFMPNetForTokenClassification,
							    TFMPNetModel,
							)


class     __SCREAMING_SNAKE_CASE							:

					def __init__(    self			,	__lowercase			,	__lowercase=13			,	__lowercase=7			,	__lowercase=True			,	__lowercase=True			,	__lowercase=False			,	__lowercase=True			,	__lowercase=99			,	__lowercase=64			,	__lowercase=2			,	__lowercase=4			,	__lowercase=64			,	__lowercase="gelu"			,	__lowercase=0.1			,	__lowercase=0.1			,	__lowercase=512			,	__lowercase=16			,	__lowercase=2			,	__lowercase=0.02			,	__lowercase=3			,	__lowercase=4			,	__lowercase=None			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							parent
						lowerCamelCase_							:		Optional[Any]							       =							batch_size
						lowerCamelCase_							:		Union[str, Any]							       =							seq_length
						lowerCamelCase_							:		Optional[int]							       =							is_training
						lowerCamelCase_							:		Optional[int]							       =							use_input_mask
						lowerCamelCase_							:		str							       =							use_token_type_ids
						lowerCamelCase_							:		Optional[int]							       =							use_labels
						lowerCamelCase_							:		Dict							       =							vocab_size
						lowerCamelCase_							:		List[str]							       =							hidden_size
						lowerCamelCase_							:		Any							       =							num_hidden_layers
						lowerCamelCase_							:		int							       =							num_attention_heads
						lowerCamelCase_							:		Any							       =							intermediate_size
						lowerCamelCase_							:		Union[str, Any]							       =							hidden_act
						lowerCamelCase_							:		Any							       =							hidden_dropout_prob
						lowerCamelCase_							:		str							       =							attention_probs_dropout_prob
						lowerCamelCase_							:		Optional[Any]							       =							max_position_embeddings
						lowerCamelCase_							:		Dict							       =							type_vocab_size
						lowerCamelCase_							:		Any							       =							type_sequence_label_size
						lowerCamelCase_							:		Any							       =							initializer_range
						lowerCamelCase_							:		Tuple							       =							num_labels
						lowerCamelCase_							:		List[str]							       =							num_choices
						lowerCamelCase_							:		Optional[Any]							       =							scope

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							ids_tensor([self.batch_size, self.seq_length]			,	self.vocab_size )

						lowerCamelCase_							:		Optional[int]							       =							None
						if self.use_input_mask:
							lowerCamelCase_							:		List[Any]							       =							random_attention_mask([self.batch_size, self.seq_length] )

						lowerCamelCase_							:		Optional[Any]							       =							None
						lowerCamelCase_							:		Any							       =							None
						lowerCamelCase_							:		int							       =							None
						if self.use_labels:
							lowerCamelCase_							:		int							       =							ids_tensor([self.batch_size]			,	self.type_sequence_label_size )
							lowerCamelCase_							:		Optional[Any]							       =							ids_tensor([self.batch_size, self.seq_length]			,	self.num_labels )
							lowerCamelCase_							:		str							       =							ids_tensor([self.batch_size]			,	self.num_choices )

						lowerCamelCase_							:		Any							       =							MPNetConfig(
						    vocab_size=self.vocab_size			,	hidden_size=self.hidden_size			,	num_hidden_layers=self.num_hidden_layers			,	num_attention_heads=self.num_attention_heads			,	intermediate_size=self.intermediate_size			,	hidden_act=self.hidden_act			,	hidden_dropout_prob=self.hidden_dropout_prob			,	attention_probs_dropout_prob=self.attention_probs_dropout_prob			,	max_position_embeddings=self.max_position_embeddings			,	initializer_range=self.initializer_range			,	)
						return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							TFMPNetModel(config=__lowercase )
						lowerCamelCase_							:		str							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		str							       =							model(__lowercase )
						lowerCamelCase_							:		Dict							       =							[input_ids, input_mask]
						lowerCamelCase_							:		Dict							       =							model(__lowercase )
						self.parent.assertEqual(result.last_hidden_state.shape			,	(self.batch_size, self.seq_length, self.hidden_size) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							TFMPNetForMaskedLM(config=__lowercase )
						lowerCamelCase_							:		List[Any]							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		int							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.vocab_size) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							TFMPNetForQuestionAnswering(config=__lowercase )
						lowerCamelCase_							:		Tuple							       =							{
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						}
						lowerCamelCase_							:		Optional[Any]							       =							model(__lowercase )
						self.parent.assertEqual(result.start_logits.shape			,	(self.batch_size, self.seq_length) )
						self.parent.assertEqual(result.end_logits.shape			,	(self.batch_size, self.seq_length) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							self.num_labels
						lowerCamelCase_							:		Union[str, Any]							       =							TFMPNetForSequenceClassification(__lowercase )
						lowerCamelCase_							:		Union[str, Any]							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		List[Any]							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.num_labels) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							self.num_choices
						lowerCamelCase_							:		Tuple							       =							TFMPNetForMultipleChoice(__lowercase )
						lowerCamelCase_							:		Dict							       =							tf.tile(tf.expand_dims(__lowercase			,	1 )			,	(1, self.num_choices, 1) )
						lowerCamelCase_							:		Any							       =							tf.tile(tf.expand_dims(__lowercase			,	1 )			,	(1, self.num_choices, 1) )
						lowerCamelCase_							:		Optional[Any]							       =							{
						    'input_ids': multiple_choice_inputs_ids,
						    'attention_mask': multiple_choice_input_mask,
						}
						lowerCamelCase_							:		Union[str, Any]							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.num_choices) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							self.num_labels
						lowerCamelCase_							:		str							       =							TFMPNetForTokenClassification(__lowercase )
						lowerCamelCase_							:		Any							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		str							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.num_labels) )





					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.prepare_config_and_inputs()
						((lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_))							:		Tuple							       =							config_and_inputs
						lowerCamelCase_							:		str							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						return config, inputs_dict


@require_tf
class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase       ,					lowerCamelCase       ,					unittest.TestCase  ):
					_lowerCamelCase		  =			(
					    (
					        TFMPNetForMaskedLM,
					        TFMPNetForMultipleChoice,
					        TFMPNetForQuestionAnswering,
					        TFMPNetForSequenceClassification,
					        TFMPNetForTokenClassification,
					        TFMPNetModel,
					    )
					    if is_tf_available()
					    else ()
					)
					_lowerCamelCase		  =			(
					    {
					        """feature-extraction""": TFMPNetModel,
					        """fill-mask""": TFMPNetForMaskedLM,
					        """question-answering""": TFMPNetForQuestionAnswering,
					        """text-classification""": TFMPNetForSequenceClassification,
					        """token-classification""": TFMPNetForTokenClassification,
					        """zero-shot""": TFMPNetForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					_lowerCamelCase		  =			False
					_lowerCamelCase		  =			False

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							TFMPNetModelTester(self )
						lowerCamelCase_							:		str							       =							ConfigTester(self			,	config_class=__lowercase			,	hidden_size=37 )

					def      lowercase_							(    self ):







						'''simple docstring'''




						self.config_tester.run_common_tests()

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_model(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_masked_lm(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_question_answering(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_token_classification(*__lowercase )





					@slow
					def      lowercase_							(    self ):







						'''simple docstring'''




						for model_name in ["microsoft/mpnet-base"]:
							lowerCamelCase_							:		str							       =							TFMPNetModel.from_pretrained(__lowercase )
							self.assertIsNotNone(__lowercase )


@require_tf
class     __SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					@slow
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							TFMPNetModel.from_pretrained('microsoft/mpnet-base' )
						lowerCamelCase_							:		Optional[int]							       =							tf.constant([[0, 1, 2, 3, 4, 5]] )
						lowerCamelCase_							:		str							       =							model(__lowercase )[0]

						lowerCamelCase_							:		List[Any]							       =							[1, 6, 768]
						self.assertEqual(output.shape			,	__lowercase )

						lowerCamelCase_							:		Any							       =							tf.constant(
						    [
						        [
						            [-0.1_06_71_72, 0.08_21_64_73, 0.0_02_45_43],
						            [-0.03_46_58_79, 0.8_35_41_18, -0.03_25_22_88],
						            [-0.06_56_94_76, -0.12_42_41_11, -0.0_49_44_36],
						        ]
						    ] )
						tf.debugging.assert_near(output[:, :3, :3]			,	__lowercase			,	atol=1E-4 )
