

"""simple docstring"""




import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    ErnieMForInformationExtraction,
						    ErnieMForMultipleChoice,
						    ErnieMForQuestionAnswering,
						    ErnieMForSequenceClassification,
						    ErnieMForTokenClassification,
						    ErnieMModel,
						)
						from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST





class 				lowerCamelCase  :

			def __init__(   self			:  Any      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  Any=1_3      ,			__UpperCAmelCase			:  Any=7      ,			__UpperCAmelCase			:  int=True      ,			__UpperCAmelCase			:  List[str]=True      ,			__UpperCAmelCase			:  Optional[int]=True      ,			__UpperCAmelCase			:  Tuple=9_9      ,			__UpperCAmelCase			:  Tuple=3_2      ,			__UpperCAmelCase			:  Optional[int]=5      ,			__UpperCAmelCase			:  Union[str, Any]=4      ,			__UpperCAmelCase			:  Tuple=3_7      ,			__UpperCAmelCase			:  Optional[Any]="gelu"      ,			__UpperCAmelCase			:  Union[str, Any]=0.1      ,			__UpperCAmelCase			:  List[Any]=0.1      ,			__UpperCAmelCase			:  Optional[Any]=5_1_2      ,			__UpperCAmelCase			:  Optional[int]=1_6      ,			__UpperCAmelCase			:  Tuple=2      ,			__UpperCAmelCase			:  Optional[int]=0.02      ,			__UpperCAmelCase			:  Optional[int]=3      ,			__UpperCAmelCase			:  str=4      ,			__UpperCAmelCase			:  Dict=None      ,			)				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= parent
						SCREAMING_SNAKE_CASE__									= batch_size
						SCREAMING_SNAKE_CASE__									= seq_length
						SCREAMING_SNAKE_CASE__									= is_training
						SCREAMING_SNAKE_CASE__									= use_input_mask
						SCREAMING_SNAKE_CASE__									= use_labels
						SCREAMING_SNAKE_CASE__									= vocab_size
						SCREAMING_SNAKE_CASE__									= hidden_size
						SCREAMING_SNAKE_CASE__									= num_hidden_layers
						SCREAMING_SNAKE_CASE__									= num_attention_heads
						SCREAMING_SNAKE_CASE__									= intermediate_size
						SCREAMING_SNAKE_CASE__									= hidden_act
						SCREAMING_SNAKE_CASE__									= hidden_dropout_prob
						SCREAMING_SNAKE_CASE__									= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__									= max_position_embeddings
						SCREAMING_SNAKE_CASE__									= type_vocab_size
						SCREAMING_SNAKE_CASE__									= type_sequence_label_size
						SCREAMING_SNAKE_CASE__									= initializer_range
						SCREAMING_SNAKE_CASE__									= num_labels
						SCREAMING_SNAKE_CASE__									= num_choices
						SCREAMING_SNAKE_CASE__									= scope

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Union[str, Any]:
						SCREAMING_SNAKE_CASE__									= ids_tensor([self.batch_size, self.seq_length]      ,			self.vocab_size    )

						SCREAMING_SNAKE_CASE__									= None
						if self.use_input_mask:
									SCREAMING_SNAKE_CASE__									= random_attention_mask([self.batch_size, self.seq_length]    )

						SCREAMING_SNAKE_CASE__									= None
						SCREAMING_SNAKE_CASE__									= None
						SCREAMING_SNAKE_CASE__									= None
						if self.use_labels:
									SCREAMING_SNAKE_CASE__									= ids_tensor([self.batch_size]      ,			self.type_sequence_label_size    )
									SCREAMING_SNAKE_CASE__									= ids_tensor([self.batch_size, self.seq_length]      ,			self.num_labels    )
									SCREAMING_SNAKE_CASE__									= ids_tensor([self.batch_size]      ,			self.num_choices    )

						SCREAMING_SNAKE_CASE__									= self.get_config()

						return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= ids_tensor([self.batch_size, self.seq_length]      ,			self.vocab_size    )

						SCREAMING_SNAKE_CASE__									= None
						if self.use_input_mask:
									SCREAMING_SNAKE_CASE__									= random_attention_mask([self.batch_size, self.seq_length]    )
						SCREAMING_SNAKE_CASE__									= self.get_config()

						return config, input_ids, input_mask

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      Optional[int]:
						return ErnieMConfig(
						    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      ,			type_vocab_size=self.type_vocab_size      ,			initializer_range=self.initializer_range      ,			)

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  Tuple      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  List[Any]    )				->      Union[str, Any]:
						SCREAMING_SNAKE_CASE__									= ErnieMModel(config=__UpperCAmelCase    )
						model.to(__UpperCAmelCase    )
						model.eval()
						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase      ,			return_dict=__UpperCAmelCase    )
						self.parent.assertEqual(result.last_hidden_state.shape      ,			(self.batch_size, self.seq_length, self.hidden_size)    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple      ,			__UpperCAmelCase			:  Union[str, Any]      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  Union[str, Any]      ,			__UpperCAmelCase			:  Tuple      ,			__UpperCAmelCase			:  List[str]      ,			__UpperCAmelCase			:  Tuple    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__									= ErnieMForQuestionAnswering(config=__UpperCAmelCase    )
						model.to(__UpperCAmelCase    )
						model.eval()
						SCREAMING_SNAKE_CASE__									= model(
						    __UpperCAmelCase      ,			attention_mask=__UpperCAmelCase      ,			start_positions=__UpperCAmelCase      ,			end_positions=__UpperCAmelCase      ,			)
						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    SCREAMING_SNAKE_CASE			(   self			:  Any      ,			__UpperCAmelCase			:  Union[str, Any]      ,			__UpperCAmelCase			:  Union[str, Any]      ,			__UpperCAmelCase			:  Union[str, Any]      ,			__UpperCAmelCase			:  Union[str, Any]      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  Optional[int]    )				->      List[str]:
						SCREAMING_SNAKE_CASE__									= ErnieMForInformationExtraction(config=__UpperCAmelCase    )
						model.to(__UpperCAmelCase    )
						model.eval()
						SCREAMING_SNAKE_CASE__									= torch.ones_like(__UpperCAmelCase      ,			dtype=torch.floataa    )
						SCREAMING_SNAKE_CASE__									= model(
						    __UpperCAmelCase      ,			attention_mask=__UpperCAmelCase      ,			start_positions=__UpperCAmelCase      ,			end_positions=__UpperCAmelCase      ,			)
						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    SCREAMING_SNAKE_CASE			(   self			:  str      ,			__UpperCAmelCase			:  Tuple      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  str      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  str    )				->      Dict:
						SCREAMING_SNAKE_CASE__									= self.num_labels
						SCREAMING_SNAKE_CASE__									= ErnieMForSequenceClassification(__UpperCAmelCase    )
						model.to(__UpperCAmelCase    )
						model.eval()
						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase      ,			attention_mask=__UpperCAmelCase      ,			labels=__UpperCAmelCase    )
						self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.num_labels)    )

			def    SCREAMING_SNAKE_CASE			(   self			:  int      ,			__UpperCAmelCase			:  str      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  Union[str, Any]      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Tuple    )				->      Dict:
						SCREAMING_SNAKE_CASE__									= self.num_labels
						SCREAMING_SNAKE_CASE__									= ErnieMForTokenClassification(config=__UpperCAmelCase    )
						model.to(__UpperCAmelCase    )
						model.eval()
						input_ids.to(__UpperCAmelCase    )
						input_mask.to(__UpperCAmelCase    )
						token_labels.to(__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase      ,			attention_mask=__UpperCAmelCase      ,			labels=__UpperCAmelCase    )

						self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.seq_length, self.num_labels)    )

			def    SCREAMING_SNAKE_CASE			(   self			:  str      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  List[str]    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= self.num_choices
						SCREAMING_SNAKE_CASE__									= ErnieMForMultipleChoice(config=__UpperCAmelCase    )
						model.to(__UpperCAmelCase    )
						model.eval()
						SCREAMING_SNAKE_CASE__									= input_ids.unsqueeze(1    ).expand(-1      ,			self.num_choices      ,			-1    ).contiguous()
						SCREAMING_SNAKE_CASE__									= input_mask.unsqueeze(1    ).expand(-1      ,			self.num_choices      ,			-1    ).contiguous()
						SCREAMING_SNAKE_CASE__									= model(
						    __UpperCAmelCase      ,			attention_mask=__UpperCAmelCase      ,			labels=__UpperCAmelCase      ,			)
						self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.num_choices)    )





			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__									= self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE__
						)							,						(
						    SCREAMING_SNAKE_CASE__
						)							,						(
						    SCREAMING_SNAKE_CASE__
						)							,						(
						    SCREAMING_SNAKE_CASE__
						)							,						(
						    SCREAMING_SNAKE_CASE__
						)							,						(
						    SCREAMING_SNAKE_CASE__
						)							,						
						)									= config_and_inputs
						SCREAMING_SNAKE_CASE__									= {"""input_ids""": input_ids, """attention_mask""": input_mask}
						return config, inputs_dict





@require_torch
class 				lowerCamelCase  (A__     ,A__     ,unittest.TestCase     ):
			lowerCamelCase__				:						Optional[int]   						=							(
			    (
			        ErnieMModel,
			        ErnieMForMultipleChoice,
			        ErnieMForQuestionAnswering,
			        ErnieMForSequenceClassification,
			        ErnieMForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__				:						Optional[int]   						=							()
			lowerCamelCase__				:						Optional[Any]   						=							(
			    {
			        'feature-extraction': ErnieMModel,
			        'question-answering': ErnieMForQuestionAnswering,
			        'text-classification': ErnieMForSequenceClassification,
			        'token-classification': ErnieMForTokenClassification,
			        'zero-shot': ErnieMForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__				:						List[Any]   						=							False

			def    SCREAMING_SNAKE_CASE			(   self			:  int      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  Tuple      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  List[str]    )				->      Any:
						if pipeline_test_casse_name == "QAPipelineTests":
									return True

						return False

			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      str:
						SCREAMING_SNAKE_CASE__									= ErnieMModelTester(self    )
						SCREAMING_SNAKE_CASE__									= ConfigTester(self      ,			config_class=__UpperCAmelCase      ,			hidden_size=3_7    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      str:
						self.config_tester.run_common_tests()

			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      str:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      Union[str, Any]:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
									SCREAMING_SNAKE_CASE__									= type
									self.model_tester.create_and_check_model(*__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      str:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      Any:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      str:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_information_extraction(*__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      int:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase    )





			@slow
			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      Optional[int]:
						for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__									= ErnieMModel.from_pretrained(__UpperCAmelCase    )
									self.assertIsNotNone(__UpperCAmelCase    )





@require_torch
class 				lowerCamelCase  (unittest.TestCase     ):

			@slow
			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      int:
						SCREAMING_SNAKE_CASE__									= ErnieMModel.from_pretrained("""susnato/ernie-m-base_pytorch"""    )
						model.eval()
						SCREAMING_SNAKE_CASE__									= torch.tensor([[0, 1, 2, 3, 4, 5]]    )
						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase    )[0]

						# TODO Replace vocab size
						SCREAMING_SNAKE_CASE__									= 7_6_8

						SCREAMING_SNAKE_CASE__									= torch.Size((1, 6, hidden_size)    )
						self.assertEqual(output.shape      ,			__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= torch.tensor(
						    [[[-0.0_012, 0.1_245, -0.0_214], [-0.0_742, 0.0_244, -0.0_771], [-0.0_333, 0.1_164, -0.1_554]]]    )

						self.assertTrue(torch.allclose(output[:, :3, :3]      ,			__UpperCAmelCase      ,			atol=1e-3    )    )





"""simple docstring"""




import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


A_							:     str     =	logging.get_logger(__name__)





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Any   						=							'semantic'

			def __init__(   self			:  int      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_0_0_0      ,			__UpperCAmelCase			:  List[str]=True      ,			__UpperCAmelCase			:  Tuple=7_6_8      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Optional[int]=False      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Any=0.7      ,			__UpperCAmelCase			:  Optional[int]=True      ,			__UpperCAmelCase			:  str=1_0_0_4_8      ,			__UpperCAmelCase			:  Tuple=1_2_9_5_9_5      ,			__UpperCAmelCase			:  List[Any]=1_2_9_5_9_9      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_0_0_0      ,			__UpperCAmelCase			:  Dict=2_5_6      ,			__UpperCAmelCase			:  int=49.9      ,			**__UpperCAmelCase			:  Any      ,			)				->      List[Any]:
						super().__init__(
						    temperature=__UpperCAmelCase      ,			do_sample=__UpperCAmelCase      ,			eos_token_id=__UpperCAmelCase      ,			renormalize_logits=__UpperCAmelCase      ,			max_new_tokens=__UpperCAmelCase      ,			output_scores=__UpperCAmelCase      ,			return_dict_in_generate=__UpperCAmelCase      ,			output_hidden_states=__UpperCAmelCase      ,			output_attentions=__UpperCAmelCase      ,			**__UpperCAmelCase      ,			)

						SCREAMING_SNAKE_CASE__									= text_encoding_offset
						SCREAMING_SNAKE_CASE__									= text_pad_token
						SCREAMING_SNAKE_CASE__									= eos_token_id
						SCREAMING_SNAKE_CASE__									= semantic_infer_token
						SCREAMING_SNAKE_CASE__									= semantic_vocab_size
						SCREAMING_SNAKE_CASE__									= max_input_semantic_length
						SCREAMING_SNAKE_CASE__									= semantic_rate_hz





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Union[str, Any]   						=							'coarse_acoustics'

			def __init__(   self			:  Optional[int]      ,			__UpperCAmelCase			:  Tuple=True      ,			__UpperCAmelCase			:  Optional[Any]=False      ,			__UpperCAmelCase			:  List[Any]=False      ,			__UpperCAmelCase			:  Tuple=False      ,			__UpperCAmelCase			:  Optional[Any]=False      ,			__UpperCAmelCase			:  Dict=0.7      ,			__UpperCAmelCase			:  Dict=True      ,			__UpperCAmelCase			:  List[str]=1_2_0_4_8      ,			__UpperCAmelCase			:  Optional[Any]=7_5      ,			__UpperCAmelCase			:  List[str]=2      ,			__UpperCAmelCase			:  List[str]=1_2_0_5_0      ,			__UpperCAmelCase			:  Optional[Any]=2_5_6      ,			__UpperCAmelCase			:  int = 6_3_0      ,			__UpperCAmelCase			:  int = 6_0      ,			**__UpperCAmelCase			:  Any      ,			)				->      Dict:
						super().__init__(
						    temperature=__UpperCAmelCase      ,			do_sample=__UpperCAmelCase      ,			renormalize_logits=__UpperCAmelCase      ,			output_scores=__UpperCAmelCase      ,			return_dict_in_generate=__UpperCAmelCase      ,			output_hidden_states=__UpperCAmelCase      ,			output_attentions=__UpperCAmelCase      ,			**__UpperCAmelCase      ,			)

						SCREAMING_SNAKE_CASE__									= coarse_semantic_pad_token
						SCREAMING_SNAKE_CASE__									= coarse_rate_hz
						SCREAMING_SNAKE_CASE__									= n_coarse_codebooks
						SCREAMING_SNAKE_CASE__									= coarse_infer_token
						SCREAMING_SNAKE_CASE__									= max_coarse_input_length
						SCREAMING_SNAKE_CASE__									= max_coarse_history
						SCREAMING_SNAKE_CASE__									= sliding_window_len





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Union[str, Any]   						=							'fine_acoustics'

			def __init__(   self			:  List[Any]      ,			__UpperCAmelCase			:  Union[str, Any]=0.5      ,			__UpperCAmelCase			:  List[Any]=5_1_2      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_2_4      ,			__UpperCAmelCase			:  Optional[Any]=8      ,			**__UpperCAmelCase			:  Tuple      ,			)				->      Optional[Any]:
						super().__init__(temperature=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= max_fine_history_length
						SCREAMING_SNAKE_CASE__									= max_fine_input_length
						SCREAMING_SNAKE_CASE__									= n_fine_codebooks





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Tuple   						=							'bark'
			lowerCamelCase__				:						Dict   						=							True

			def __init__(   self			:  int      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  List[Any]=2_4_0_0_0      ,			__UpperCAmelCase			:  Any=1_0_2_4      ,			**__UpperCAmelCase			:  int      ,			)				->      Any:
						if semantic_config is None:
									SCREAMING_SNAKE_CASE__									= {}
									logger.info("""semantic_config is None. initializing the semantic model with default values."""    )

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

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

						SCREAMING_SNAKE_CASE__									= BarkSemanticGenerationConfig(**__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= BarkCoarseGenerationConfig(**__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= BarkFineGenerationConfig(**__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= sample_rate
						SCREAMING_SNAKE_CASE__									= codebook_size

			@classmethod
			def    SCREAMING_SNAKE_CASE			(   cls			:  int      ,			__UpperCAmelCase			:  BarkSemanticGenerationConfig      ,			__UpperCAmelCase			:  BarkCoarseGenerationConfig      ,			__UpperCAmelCase			:  BarkFineGenerationConfig      ,			**__UpperCAmelCase			:  Optional[int]      ,			)				->      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()      ,			**__UpperCAmelCase      ,			)





			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Any:
						SCREAMING_SNAKE_CASE__									= copy.deepcopy(self.__dict__    )

						SCREAMING_SNAKE_CASE__									= self.semantic_config.to_dict()
						SCREAMING_SNAKE_CASE__									= self.coarse_acoustics_config.to_dict()
						SCREAMING_SNAKE_CASE__									= self.fine_acoustics_config.to_dict()

						SCREAMING_SNAKE_CASE__									= self.__class__.model_type
						return output


