
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 :       Union[str, Any]     					=    logging.get_logger(__name__)


_lowerCamelCase :       Optional[int]     					=    {
    '''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 :       List[str]     					=    '''
    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      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





 '''simple docstring'''



 _UpperCAmelCase :	Any					     =      "bark_module"
 _UpperCAmelCase :	Any					     =      ["past_key_values"]

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



 def __init__(						self			:					str    ,						lowercase			:					Optional[Any]=1_024    ,						lowercase			:					Any=10_048    ,						lowercase			:					str=10_048    ,						lowercase			:					List[Any]=12    ,						lowercase			:					Optional[int]=12    ,						lowercase			:					Any=768    ,						lowercase			:					int=0.0    ,						lowercase			:					Dict=True    ,						lowercase			:					List[str]=0.02    ,						lowercase			:					Union[str, Any]=True    ,						**lowercase			:					Any    ,						):


    '''simple docstring'''


    _snake_case						=    block_size
    _snake_case						=    input_vocab_size
    _snake_case						=    output_vocab_size
    _snake_case						=    num_layers
    _snake_case						=    num_heads
    _snake_case						=    hidden_size
    _snake_case						=    dropout
    _snake_case						=    bias
    _snake_case						=    use_cache
    _snake_case						=    initializer_range

    super().__init__(**lowercase			)





 @classmethod
 def 				A							(						cls			:					Optional[int]    ,						lowercase			:					Union[str, os.PathLike]    ,						lowercase			:					Optional[Union[str, os.PathLike]] = None    ,						lowercase			:					bool = False    ,						lowercase			:					bool = False    ,						lowercase			:					Optional[Union[str, bool]] = None    ,						lowercase			:					str = "main"    ,						**lowercase			:					Dict    ,						):


    '''simple docstring'''


    _snake_case						=    cache_dir
    _snake_case						=    force_download
    _snake_case						=    local_files_only
    _snake_case						=    revision

    cls._set_token_in_kwargs(lowercase    ,						lowercase			)

    _snake_case			,      _snake_case						=    cls.get_config_dict(lowercase    ,						**lowercase			)

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





 '''simple docstring'''



 _UpperCAmelCase :	List[str]					     =      "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      ):





 '''simple docstring'''



 _UpperCAmelCase :	Optional[int]					     =      "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      ):





 '''simple docstring'''



 _UpperCAmelCase :	Union[str, Any]					     =      "fine_acoustics"



 def __init__(						self			:					List[str]    ,						lowercase			:					Union[str, Any]=True    ,						lowercase			:					List[Any]=8    ,						lowercase			:					int=1    ,						**lowercase			:					List[Any]			):


    '''simple docstring'''


    _snake_case						=    n_codes_total
    _snake_case						=    n_codes_given

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




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





 '''simple docstring'''



 _UpperCAmelCase :	int					     =      "bark"
 _UpperCAmelCase :	Optional[Any]					     =      True



 def __init__(						self			:					Any    ,						lowercase			:					Dict = None    ,						lowercase			:					Dict = None    ,						lowercase			:					Dict = None    ,						lowercase			:					Dict = None    ,						lowercase			:					Optional[int]=0.02    ,						**lowercase			:					int    ,						):


    '''simple docstring'''


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

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

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

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

    _snake_case						=    BarkSemanticConfig(**lowercase			)
    _snake_case						=    BarkCoarseConfig(**lowercase			)
    _snake_case						=    BarkFineConfig(**lowercase			)
    _snake_case						=    AutoConfig.for_model(**lowercase			)

    _snake_case						=    initializer_range

    super().__init__(**lowercase			)



 @classmethod
 def 				A							(						cls			:					Any    ,						lowercase			:					BarkSemanticConfig    ,						lowercase			:					BarkCoarseConfig    ,						lowercase			:					BarkFineConfig    ,						lowercase			:					AutoConfig    ,						**lowercase			:					Optional[Any]    ,						):


    '''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 				A							(						self			:					Optional[int]			):


    '''simple docstring'''


    _snake_case						=    copy.deepcopy(self.__dict__			)

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

    _snake_case						=    self.__class__.model_type
    return output

from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
			import tensorflow as tf

			from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel




@require_tf
class      SCREAMING_SNAKE_CASE__  :





	'''simple docstring'''



	_UpperCAmelCase :	Optional[int]					     =      MarianConfig
	_UpperCAmelCase :	str					     =      {}
	_UpperCAmelCase :	Union[str, Any]					     =      "gelu"



	def __init__(						self			:					List[Any]    ,						lowercase			:					List[str]    ,						lowercase			:					str=13    ,						lowercase			:					Union[str, Any]=7    ,						lowercase			:					List[Any]=True    ,						lowercase			:					Tuple=False    ,						lowercase			:					Dict=99    ,						lowercase			:					Optional[Any]=32    ,						lowercase			:					Optional[int]=2    ,						lowercase			:					int=4    ,						lowercase			:					List[str]=37    ,						lowercase			:					Tuple=0.1    ,						lowercase			:					Union[str, Any]=0.1    ,						lowercase			:					Optional[int]=20    ,						lowercase			:					List[Any]=2    ,						lowercase			:					Dict=1    ,						lowercase			:					str=0    ,						):


				'''simple docstring'''


				_snake_case						=    parent
				_snake_case						=    batch_size
				_snake_case						=    seq_length
				_snake_case						=    is_training
				_snake_case						=    use_labels
				_snake_case						=    vocab_size
				_snake_case						=    hidden_size
				_snake_case						=    num_hidden_layers
				_snake_case						=    num_attention_heads
				_snake_case						=    intermediate_size

				_snake_case						=    hidden_dropout_prob
				_snake_case						=    attention_probs_dropout_prob
				_snake_case						=    max_position_embeddings
				_snake_case						=    eos_token_id
				_snake_case						=    pad_token_id
				_snake_case						=    bos_token_id



	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				_snake_case						=    ids_tensor([self.batch_size, self.seq_length - 1]    ,						self.vocab_size			)
				_snake_case						=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)    ,						1			)
				_snake_case						=    tf.concat([input_ids, eos_tensor]    ,						axis=1			)

				_snake_case						=    ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size			)

				_snake_case						=    self.config_cls(
				    vocab_size=self.vocab_size    ,						d_model=self.hidden_size    ,						encoder_layers=self.num_hidden_layers    ,						decoder_layers=self.num_hidden_layers    ,						encoder_attention_heads=self.num_attention_heads    ,						decoder_attention_heads=self.num_attention_heads    ,						encoder_ffn_dim=self.intermediate_size    ,						decoder_ffn_dim=self.intermediate_size    ,						dropout=self.hidden_dropout_prob    ,						attention_dropout=self.attention_probs_dropout_prob    ,						max_position_embeddings=self.max_position_embeddings    ,						eos_token_ids=[2]    ,						bos_token_id=self.bos_token_id    ,						pad_token_id=self.pad_token_id    ,						decoder_start_token_id=self.pad_token_id    ,						**self.config_updates    ,						)
				_snake_case						=    prepare_marian_inputs_dict(lowercase    ,						lowercase    ,						lowercase			)
				return config, inputs_dict





	def 				A							(						self			:					List[Any]    ,						lowercase			:					List[str]    ,						lowercase			:					Optional[Any]			):


				'''simple docstring'''


				_snake_case						=    TFMarianModel(config=lowercase			).get_decoder()
				_snake_case						=    inputs_dict['input_ids']

				_snake_case						=    input_ids[:1, :]
				_snake_case						=    inputs_dict['attention_mask'][:1, :]
				_snake_case						=    inputs_dict['head_mask']
				_snake_case						=    1

				# first forward pass
				_snake_case						=    model(lowercase    ,						attention_mask=lowercase    ,						head_mask=lowercase    ,						use_cache=lowercase			)

				_snake_case			,      _snake_case						=    outputs.to_tuple()

				# create hypothetical next token and extent to next_input_ids
				_snake_case						=    ids_tensor((self.batch_size, 3)    ,						config.vocab_size			)
				_snake_case						=    tf.cast(ids_tensor((self.batch_size, 3)    ,						2			)    ,						tf.inta			)

				# append to next input_ids and
				_snake_case						=    tf.concat([input_ids, next_tokens]    ,						axis=-1			)
				_snake_case						=    tf.concat([attention_mask, next_attn_mask]    ,						axis=-1			)

				_snake_case						=    model(lowercase    ,						attention_mask=lowercase			)[0]
				_snake_case						=    model(lowercase    ,						attention_mask=lowercase    ,						past_key_values=lowercase			)[0]

				self.parent.assertEqual(next_tokens.shape[1]    ,						output_from_past.shape[1]			)

				# select random slice
				_snake_case						=    int(ids_tensor((1,)    ,						output_from_past.shape[-1]			)			)
				_snake_case						=    output_from_no_past[:, -3:, random_slice_idx]
				_snake_case						=    output_from_past[:, :, random_slice_idx]

				# test that outputs are equal for slice
				tf.debugging.assert_near(lowercase    ,						lowercase    ,						rtol=1E-3			)






def  a_	( __lowercase     :			List[str] ,      __lowercase     :			List[str] ,      __lowercase     :			Any ,      __lowercase     :			int=None ,      __lowercase     :			Tuple=None ,      __lowercase     :			str=None ,      __lowercase     :			Tuple=None ,      __lowercase     :			str=None ,      )				->						List[str]:
			if attention_mask is None:
						_snake_case						=    tf.cast(tf.math.not_equal(__lowercase ,      config.pad_token_id				) ,      tf.inta				)
			if decoder_attention_mask is None:
						_snake_case						=    tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape ,      dtype=tf.inta				),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,      config.pad_token_id				) ,      tf.inta				),
						    ] ,      axis=-1 ,      )
			if head_mask is None:
						_snake_case						=    tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
			if decoder_head_mask is None:
						_snake_case						=    tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
			if cross_attn_head_mask is None:
						_snake_case						=    tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}




@require_tf
class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase							,UpperCAmelCase							,unittest.TestCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	int					     =      (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
	_UpperCAmelCase :	Tuple					     =      (TFMarianMTModel,) if is_tf_available() else ()
	_UpperCAmelCase :	Optional[Any]					     =      (
	    {
	        "conversational": TFMarianMTModel,
	        "feature-extraction": TFMarianModel,
	        "summarization": TFMarianMTModel,
	        "text2text-generation": TFMarianMTModel,
	        "translation": TFMarianMTModel,
	    }
	    if is_tf_available()
	    else {}
	)
	_UpperCAmelCase :	Union[str, Any]					     =      True
	_UpperCAmelCase :	Union[str, Any]					     =      False
	_UpperCAmelCase :	Tuple					     =      False



	def 				A							(						self			:					int			):


				'''simple docstring'''


				_snake_case						=    TFMarianModelTester(self			)
				_snake_case						=    ConfigTester(self    ,						config_class=lowercase			)



	def 				A							(						self			:					Any			):


				'''simple docstring'''


				self.config_tester.run_common_tests()





	def 				A							(						self			:					Tuple			):


				'''simple docstring'''


				_snake_case						=    self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*lowercase			)




@require_tf
class      SCREAMING_SNAKE_CASE__  (			unittest.TestCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	List[Any]					     =      1_0_0_0  # show more chars for failing integration tests



	@classmethod
	def 				A							(						cls			:					Tuple			):


				'''simple docstring'''


				_snake_case						=    f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
				return cls



	@cached_property
	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


				return AutoTokenizer.from_pretrained(self.model_name			)



	@property
	def 				A							(						self			:					Tuple			):


				'''simple docstring'''


				return self.tokenizer.eos_token_id



	@cached_property
	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				warnings.simplefilter('error'			)
				_snake_case						=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
				assert isinstance(lowercase    ,						lowercase			)
				_snake_case						=    model.config
				self.assertListEqual(c.bad_words_ids    ,						[[c.pad_token_id]]			)
				self.assertEqual(c.max_length    ,						512			)
				self.assertEqual(c.decoder_start_token_id    ,						c.pad_token_id			)
				return model



	def 				A							(						self			:					Optional[Any]    ,						**lowercase			:					int			):


				'''simple docstring'''


				_snake_case						=    self.translate_src_text(**lowercase			)
				self.assertListEqual(self.expected_text    ,						lowercase			)





	def 				A							(						self			:					Optional[int]    ,						**lowercase			:					List[Any]			):


				'''simple docstring'''


				_snake_case						=    self.tokenizer(self.src_text    ,						**lowercase    ,						padding=lowercase    ,						return_tensors='tf'			)
				_snake_case						=    self.model.generate(
				    model_inputs.input_ids    ,						attention_mask=model_inputs.attention_mask    ,						num_beams=2    ,						max_length=128			)
				_snake_case						=    self.tokenizer.batch_decode(generated_ids.numpy()    ,						skip_special_tokens=lowercase			)
				return generated_words




@require_sentencepiece
@require_tokenizers
@require_tf
class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Union[str, Any]					     =      "mt"
	_UpperCAmelCase :	List[str]					     =      "en"
	_UpperCAmelCase :	str					     =      ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
	_UpperCAmelCase :	Any					     =      ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]



	@unittest.skip('Skipping until #12647 is resolved.'			)
	@slow
	def 				A							(						self			:					Union[str, Any]			):


				'''simple docstring'''


				self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Tuple					     =      "en"
	_UpperCAmelCase :	Any					     =      "zh"
	_UpperCAmelCase :	int					     =      ["My name is Wolfgang and I live in Berlin"]
	_UpperCAmelCase :	List[str]					     =      ["我叫沃尔夫冈 我住在柏林"]



	@unittest.skip('Skipping until #12647 is resolved.'			)
	@slow
	def 				A							(						self			:					Optional[Any]			):


				'''simple docstring'''


				self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Any					     =      "en"
	_UpperCAmelCase :	Any					     =      "ROMANCE"
	_UpperCAmelCase :	int					     =      [
	    ">>fr<< Don't spend so much time watching TV.",
	    ">>pt<< Your message has been sent.",
	    ">>es<< He's two years older than me.",
	]
	_UpperCAmelCase :	Tuple					     =      [
	    "Ne passez pas autant de temps à regarder la télé.",
	    "A sua mensagem foi enviada.",
	    "Es dos años más viejo que yo.",
	]



	@unittest.skip('Skipping until #12647 is resolved.'			)
	@slow
	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


				self._assert_generated_batch_equal_expected()





	@unittest.skip('Skipping until #12647 is resolved.'			)
	@slow
	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				_snake_case						=    TranslationPipeline(self.model    ,						self.tokenizer    ,						framework='tf'			)
				_snake_case						=    pipeline(self.src_text			)
				self.assertEqual(self.expected_text    ,						[x['translation_text'] for x in output]			)