




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 			a     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       List[Any]   		=  'bark_module'
     lowerCAmelCase				:       Any   		=  ['past_key_values']

     lowerCAmelCase				:       int   		=  {
         'num_attention_heads': 'num_heads',
         'num_hidden_layers': 'num_layers',
         'vocab_size': 'input_vocab_size',
         'window_size': 'block_size',
     }
     def __init__(	self   :			Dict					,    __snake_case   :			str=10_24					,    __snake_case   :			Dict=1_00_48					,    __snake_case   :			str=1_00_48					,    __snake_case   :			int=12					,    __snake_case   :			str=12					,    __snake_case   :			Dict=7_68					,    __snake_case   :			List[str]=0.0					,    __snake_case   :			Dict=True					,    __snake_case   :			int=0.02					,    __snake_case   :			Dict=True					,    **__snake_case   :			Tuple					,    ):
         UpperCAmelCase_							 =     block_size
         UpperCAmelCase_							 =     input_vocab_size
         UpperCAmelCase_							 =     output_vocab_size
         UpperCAmelCase_							 =     num_layers
         UpperCAmelCase_							 =     num_heads
         UpperCAmelCase_							 =     hidden_size
         UpperCAmelCase_							 =     dropout
         UpperCAmelCase_							 =     bias
         UpperCAmelCase_							 =     use_cache
         UpperCAmelCase_							 =     initializer_range

         super().__init__(**__snake_case				)




     @classmethod
     def   lowerCamelCase_   (	cls   :			Union[str, Any]					,    __snake_case   :			Union[str, os.PathLike]					,    __snake_case   :			Optional[Union[str, os.PathLike]] = None					,    __snake_case   :			bool = False					,    __snake_case   :			bool = False					,    __snake_case   :			Optional[Union[str, bool]] = None					,    __snake_case   :			str = "main"					,    **__snake_case   :			Tuple					,    ):
         UpperCAmelCase_							 =     cache_dir
         UpperCAmelCase_							 =     force_download
         UpperCAmelCase_							 =     local_files_only
         UpperCAmelCase_							 =     revision

         cls._set_token_in_kwargs(__snake_case					,    __snake_case				)

         UpperCAmelCase_ ,							UpperCAmelCase_							 =     cls.get_config_dict(__snake_case					,    **__snake_case				)

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



@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 			a     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       int   		=  '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 			a     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       Union[str, Any]   		=  '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 			a     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       str   		=  'fine_acoustics'
     def __init__(	self   :			List[Any]					,    __snake_case   :			Dict=True					,    __snake_case   :			Any=8					,    __snake_case   :			Any=1					,    **__snake_case   :			Optional[int]				):
         UpperCAmelCase_							 =     n_codes_total
         UpperCAmelCase_							 =     n_codes_given

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



class 			a     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       Optional[int]   		=  'bark'
     lowerCAmelCase				:       Dict   		=  True
     def __init__(	self   :			Tuple					,    __snake_case   :			Dict = None					,    __snake_case   :			Dict = None					,    __snake_case   :			Dict = None					,    __snake_case   :			Dict = None					,    __snake_case   :			List[str]=0.02					,    **__snake_case   :			int					,    ):
         if semantic_config is None:
             UpperCAmelCase_							 =     {}
             logger.info('''semantic_config is None. initializing the semantic model with default values.'''				)

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

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

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

         UpperCAmelCase_							 =     BarkSemanticConfig(**__snake_case				)
         UpperCAmelCase_							 =     BarkCoarseConfig(**__snake_case				)
         UpperCAmelCase_							 =     BarkFineConfig(**__snake_case				)
         UpperCAmelCase_							 =     AutoConfig.for_model(**__snake_case				)

         UpperCAmelCase_							 =     initializer_range

         super().__init__(**__snake_case				)
     @classmethod
     def   lowerCamelCase_   (	cls   :			Union[str, Any]					,    __snake_case   :			BarkSemanticConfig					,    __snake_case   :			BarkCoarseConfig					,    __snake_case   :			BarkFineConfig					,    __snake_case   :			AutoConfig					,    **__snake_case   :			str					,    ):
         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()					,    **__snake_case					,    )




     def   lowerCamelCase_   (	self   :			List[str]				):
         UpperCAmelCase_							 =     copy.deepcopy(self.__dict__				)

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

         UpperCAmelCase_							 =     self.__class__.model_type
         return output













"""simple docstring"""







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
			import torch

			from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer


@require_torch
class     __UpperCAmelCase(   unittest.TestCase							):




						"""simple docstring"""




						@slow
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"			)
												model.to(snake_case__			)

												# encoder-decoder outputs
												lowercase__				:      Dict=       torch.tensor([[17, 96208, 103471, 2]]			).to(snake_case__			)
												lowercase__				:      Optional[Any]=       torch.tensor(
												    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]			).to(snake_case__			)
												lowercase__				:      Dict=       model(
												    input_ids=snake_case__       ,  attention_mask=snake_case__       ,  encoder_outputs=snake_case__       ,  decoder_input_ids=snake_case__			)
												lowercase__				:      int=       output[0]
												lowercase__				:      str=       torch.Size((1, 14, 250012)			)
												self.assertEqual(output_predited_logis.shape       ,  snake_case__			)
												lowercase__				:      Union[str, Any]=       torch.tensor(
												    [[[-6.39_86, -8.23_91, 12.51_89], [-6.32_89, -8.08_64, 12.62_11], [-6.24_18, -8.04_45, 12.79_68]]]			).to(snake_case__			)
												self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]       ,  snake_case__       ,  atol=1e-4			)			)

												# encoder outputs
												lowercase__				:      Tuple=       model.prophetnet.encoder(snake_case__			)[0]
												lowercase__				:      Union[str, Any]=       torch.tensor(
												    [[[-1.42_60, -0.76_28, 0.84_53], [-1.47_19, -0.13_91, 0.78_07], [-1.76_78, 0.01_14, 0.46_46]]]			).to(snake_case__			)
												lowercase__				:      List[Any]=       torch.Size((1, 4, 1024)			)
												self.assertEqual(encoder_outputs.shape       ,  snake_case__			)
												self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]       ,  snake_case__       ,  atol=1e-4			)			)

												# decoder outputs
												lowercase__				:      int=       model.prophetnet.decoder(
												    snake_case__       ,  encoder_hidden_states=snake_case__       ,  )
												lowercase__				:      Union[str, Any]=       decoder_outputs[1].view(1       ,  model.config.ngram       ,  14       ,  -1			)
												lowercase__				:      List[Any]=       model.lm_head(snake_case__			)
												lowercase__				:      str=       predicting_streams_logits[:, 0]
												self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]       ,  snake_case__       ,  atol=1e-4			)			)




						@slow
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       XLMProphetNetForConditionalGeneration.from_pretrained(
												    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"			)
												model.to(snake_case__			)

												lowercase__				:      Dict=       torch.tensor([[17, 96208, 103471, 2]]			).to(snake_case__			)
												lowercase__				:      List[str]=       torch.tensor(
												    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]			).to(snake_case__			)
												lowercase__				:      List[Any]=       model(
												    input_ids=snake_case__       ,  attention_mask=snake_case__       ,  encoder_outputs=snake_case__       ,  decoder_input_ids=snake_case__			)
												lowercase__				:      str=       output[0]
												lowercase__				:      Optional[Any]=       torch.Size((1, 14, 250012)			)
												self.assertEqual(output_predited_logis.shape       ,  snake_case__			)
												# compare the actual values for a slice.
												lowercase__				:      str=       torch.tensor(
												    [[[-9.22_53, -9.71_73, -6.35_29], [-7.67_01, -9.01_45, -1.93_82], [-8.01_95, -7.00_04, -0.15_23]]]			).to(snake_case__			)

												self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]       ,  snake_case__       ,  atol=1e-4			)			)




						@slow
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Tuple=       XLMProphetNetForConditionalGeneration.from_pretrained(
												    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"			)
												model.to(snake_case__			)
												lowercase__				:      Optional[int]=       512

												lowercase__				:      int=       XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"			)

												lowercase__				:      str=       (
												    "Microsoft Corporation intends to officially end free support for the Windows 7 operating system after"
												    " January 14, 2020, according to the official portal of the organization. From that day, users of this"
												    " system will not be able to receive security updates, which could make their computers vulnerable to"
												    " cyber attacks."
												)
												lowercase__				:      Tuple=       (
												    "орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7"
												    " после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи"
												    " этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми"
												    " к кибератакам."
												)
												lowercase__				:      Optional[int]=       (
												    "根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。"
												)

												lowercase__				:      Dict=       tokenizer(
												    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]       ,  padding=snake_case__       ,  max_length=255       ,  return_tensors="pt"			).input_ids
												lowercase__				:      Any=       input_ids.to(snake_case__			)

												lowercase__				:      List[Any]=       model.generate(
												    snake_case__       ,  num_beams=10       ,  length_penalty=1.0       ,  no_repeat_ngram_size=3       ,  early_stopping=snake_case__			)
												lowercase__				:      str=       [tokenizer.decode(snake_case__       ,  skip_special_tokens=snake_case__			) for g in summary_ids]
												lowercase__				:      Dict=       "Microsoft to end Windows 7 free support after January 14, 2020"
												lowercase__				:      int=       "Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
												lowercase__				:      List[str]=       "微软打算终止对Windows 7操作系统的免费支持"
												self.assertListEqual(
												    [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]       ,  snake_case__       ,  )

												lowercase__				:      List[str]=       model.generate(
												    snake_case__       ,  num_beams=1       ,  length_penalty=1.0       ,  no_repeat_ngram_size=3       ,  early_stopping=snake_case__			)
												lowercase__				:      Optional[Any]=       [
												    tokenizer.convert_ids_to_tokens(snake_case__       ,  skip_special_tokens=snake_case__			) for g in summary_ids_beama
												]
												lowercase__				:      List[str]=       "▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7".split(" "			)
												lowercase__				:      List[Any]=       "▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года".split(
												    " "			)
												lowercase__				:      int=       "微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持".split(" "			)
												self.assertListEqual(
												    [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]       ,  snake_case__       ,  )






