

"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
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 import TF_MODEL_FOR_PRETRAINING_MAPPING
						from transformers.models.albert.modeling_tf_albert import (
						    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFAlbertForMaskedLM,
						    TFAlbertForMultipleChoice,
						    TFAlbertForPreTraining,
						    TFAlbertForQuestionAnswering,
						    TFAlbertForSequenceClassification,
						    TFAlbertForTokenClassification,
						    TFAlbertModel,
						)





class 				lowerCamelCase  :

			def __init__(   self			:  List[str]      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  List[str]=1_3      ,			__UpperCAmelCase			:  Union[str, Any]=7      ,			__UpperCAmelCase			:  Optional[Any]=True      ,			__UpperCAmelCase			:  Dict=True      ,			__UpperCAmelCase			:  List[Any]=True      ,			__UpperCAmelCase			:  Any=True      ,			__UpperCAmelCase			:  List[str]=9_9      ,			__UpperCAmelCase			:  Any=1_6      ,			__UpperCAmelCase			:  Union[str, Any]=3_2      ,			__UpperCAmelCase			:  Tuple=2      ,			__UpperCAmelCase			:  Union[str, Any]=4      ,			__UpperCAmelCase			:  Optional[int]=3_7      ,			__UpperCAmelCase			:  List[str]="gelu"      ,			__UpperCAmelCase			:  str=0.1      ,			__UpperCAmelCase			:  Dict=0.1      ,			__UpperCAmelCase			:  Dict=5_1_2      ,			__UpperCAmelCase			:  str=1_6      ,			__UpperCAmelCase			:  Optional[int]=2      ,			__UpperCAmelCase			:  List[Any]=0.02      ,			__UpperCAmelCase			:  Union[str, Any]=3      ,			__UpperCAmelCase			:  Union[str, Any]=4      ,			__UpperCAmelCase			:  Dict=None      ,			)				->      int:
						SCREAMING_SNAKE_CASE__									= parent
						SCREAMING_SNAKE_CASE__									= 1_3
						SCREAMING_SNAKE_CASE__									= 7
						SCREAMING_SNAKE_CASE__									= True
						SCREAMING_SNAKE_CASE__									= True
						SCREAMING_SNAKE_CASE__									= True
						SCREAMING_SNAKE_CASE__									= True
						SCREAMING_SNAKE_CASE__									= 9_9
						SCREAMING_SNAKE_CASE__									= 1_6
						SCREAMING_SNAKE_CASE__									= 3_2
						SCREAMING_SNAKE_CASE__									= 2
						SCREAMING_SNAKE_CASE__									= 4
						SCREAMING_SNAKE_CASE__									= 3_7
						SCREAMING_SNAKE_CASE__									= "gelu"
						SCREAMING_SNAKE_CASE__									= 0.1
						SCREAMING_SNAKE_CASE__									= 0.1
						SCREAMING_SNAKE_CASE__									= 5_1_2
						SCREAMING_SNAKE_CASE__									= 1_6
						SCREAMING_SNAKE_CASE__									= 2
						SCREAMING_SNAKE_CASE__									= 0.02
						SCREAMING_SNAKE_CASE__									= 3
						SCREAMING_SNAKE_CASE__									= 4
						SCREAMING_SNAKE_CASE__									= None

			def    SCREAMING_SNAKE_CASE			(   self			:  List[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
						if self.use_token_type_ids:
									SCREAMING_SNAKE_CASE__									= ids_tensor([self.batch_size, self.seq_length]      ,			self.type_vocab_size    )

						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__									= AlbertConfig(
						    vocab_size=self.vocab_size      ,			hidden_size=self.hidden_size      ,			embedding_size=self.embedding_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      ,			)

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

			def    SCREAMING_SNAKE_CASE			(   self			:  Any      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Union[str, Any]      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  List[str]      ,			__UpperCAmelCase			:  str    )				->      str:
						SCREAMING_SNAKE_CASE__									= TFAlbertModel(config=SCREAMING_SNAKE_CASE__    )
						# inputs = {'input_ids': input_ids,
						#           'attention_mask': input_mask,
						#           'token_type_ids': token_type_ids}
						# sequence_output, pooled_output = model(**inputs)
						SCREAMING_SNAKE_CASE__									= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
						SCREAMING_SNAKE_CASE__									= model(SCREAMING_SNAKE_CASE__    )

						SCREAMING_SNAKE_CASE__									= [input_ids, input_mask]
						SCREAMING_SNAKE_CASE__									= model(SCREAMING_SNAKE_CASE__    )

						SCREAMING_SNAKE_CASE__									= model(SCREAMING_SNAKE_CASE__    )

						self.parent.assertEqual(result.last_hidden_state.shape      ,			(self.batch_size, self.seq_length, self.hidden_size)    )
						self.parent.assertEqual(result.pooler_output.shape      ,			(self.batch_size, self.hidden_size)    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  List[str]      ,			__UpperCAmelCase			:  Tuple      ,			__UpperCAmelCase			:  Union[str, Any]      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  List[Any]    )				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= self.num_labels
						SCREAMING_SNAKE_CASE__									= TFAlbertForPreTraining(config=SCREAMING_SNAKE_CASE__    )
						SCREAMING_SNAKE_CASE__									= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
						SCREAMING_SNAKE_CASE__									= model(SCREAMING_SNAKE_CASE__    )
						self.parent.assertEqual(result.prediction_logits.shape      ,			(self.batch_size, self.seq_length, self.vocab_size)    )
						self.parent.assertEqual(result.sop_logits.shape      ,			(self.batch_size, self.num_labels)    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Any      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  str      ,			__UpperCAmelCase			:  List[str]      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  str      ,			__UpperCAmelCase			:  Union[str, Any]    )				->      str:
						SCREAMING_SNAKE_CASE__									= TFAlbertForMaskedLM(config=SCREAMING_SNAKE_CASE__    )
						SCREAMING_SNAKE_CASE__									= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
						SCREAMING_SNAKE_CASE__									= model(SCREAMING_SNAKE_CASE__    )
						self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.seq_length, self.vocab_size)    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]      ,			__UpperCAmelCase			:  Tuple      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  str      ,			__UpperCAmelCase			:  str      ,			__UpperCAmelCase			:  Optional[int]    )				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= self.num_labels
						SCREAMING_SNAKE_CASE__									= TFAlbertForSequenceClassification(config=SCREAMING_SNAKE_CASE__    )
						SCREAMING_SNAKE_CASE__									= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
						SCREAMING_SNAKE_CASE__									= model(SCREAMING_SNAKE_CASE__    )
						self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.num_labels)    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  Optional[Any]    )				->      Union[str, Any]:
						SCREAMING_SNAKE_CASE__									= TFAlbertForQuestionAnswering(config=SCREAMING_SNAKE_CASE__    )
						SCREAMING_SNAKE_CASE__									= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
						SCREAMING_SNAKE_CASE__									= model(SCREAMING_SNAKE_CASE__    )
						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			:  Optional[Any]      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Any    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= self.num_choices
						SCREAMING_SNAKE_CASE__									= TFAlbertForMultipleChoice(config=SCREAMING_SNAKE_CASE__    )
						SCREAMING_SNAKE_CASE__									= tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__      ,			1    )      ,			(1, self.num_choices, 1)    )
						SCREAMING_SNAKE_CASE__									= tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__      ,			1    )      ,			(1, self.num_choices, 1)    )
						SCREAMING_SNAKE_CASE__									= tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__      ,			1    )      ,			(1, self.num_choices, 1)    )
						SCREAMING_SNAKE_CASE__									= {
						    "input_ids": multiple_choice_inputs_ids,
						    "attention_mask": multiple_choice_input_mask,
						    "token_type_ids": multiple_choice_token_type_ids,
						}
						SCREAMING_SNAKE_CASE__									= model(SCREAMING_SNAKE_CASE__    )
						self.parent.assertListEqual(list(result["""logits"""].shape    )      ,			[self.batch_size, self.num_choices]    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]      ,			__UpperCAmelCase			:  Tuple      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  Union[str, Any]      ,			__UpperCAmelCase			:  Tuple      ,			__UpperCAmelCase			:  Union[str, Any]      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  str    )				->      Any:
						SCREAMING_SNAKE_CASE__									= self.num_labels
						SCREAMING_SNAKE_CASE__									= TFAlbertForTokenClassification(config=SCREAMING_SNAKE_CASE__    )
						SCREAMING_SNAKE_CASE__									= {
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						}
						SCREAMING_SNAKE_CASE__									= model(SCREAMING_SNAKE_CASE__    )
						self.parent.assertListEqual(list(result["""logits"""].shape    )      ,			[self.batch_size, self.seq_length, self.num_labels]    )





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





@require_tf
class 				lowerCamelCase  (_a     ,_a     ,unittest.TestCase     ):
			lowerCamelCase__				:						Tuple   						=							(
			    (
			        TFAlbertModel,
			        TFAlbertForPreTraining,
			        TFAlbertForMaskedLM,
			        TFAlbertForSequenceClassification,
			        TFAlbertForQuestionAnswering,
			        TFAlbertForTokenClassification,
			        TFAlbertForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			lowerCamelCase__				:						Optional[Any]   						=							(
			    {
			        '''feature-extraction''': TFAlbertModel,
			        '''fill-mask''': TFAlbertForMaskedLM,
			        '''question-answering''': TFAlbertForQuestionAnswering,
			        '''text-classification''': TFAlbertForSequenceClassification,
			        '''token-classification''': TFAlbertForTokenClassification,
			        '''zero-shot''': TFAlbertForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			lowerCamelCase__				:						List[Any]   						=							False
			lowerCamelCase__				:						str   						=							False

			def    SCREAMING_SNAKE_CASE			(   self			:  int      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  List[str]      ,			__UpperCAmelCase			:  List[Any]=False    )				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= super()._prepare_for_class(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			return_labels=SCREAMING_SNAKE_CASE__    )

						if return_labels:
									if model_class in get_values(SCREAMING_SNAKE_CASE__    ):
												SCREAMING_SNAKE_CASE__									= tf.zeros(self.model_tester.batch_size      ,			dtype=tf.intaa    )

						return inputs_dict

			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= TFAlbertModelTester(self    )
						SCREAMING_SNAKE_CASE__									= ConfigTester(self      ,			config_class=SCREAMING_SNAKE_CASE__      ,			hidden_size=3_7    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      str:
						self.config_tester.run_common_tests()

			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      str:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_albert_model(*SCREAMING_SNAKE_CASE__    )

			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_albert_for_pretraining(*SCREAMING_SNAKE_CASE__    )

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

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

			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_albert_for_sequence_classification(*SCREAMING_SNAKE_CASE__    )

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





			@slow
			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      Any:
						for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__									= TFAlbertModel.from_pretrained(SCREAMING_SNAKE_CASE__    )
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__    )





@require_tf
class 				lowerCamelCase  (unittest.TestCase     ):

			@slow
			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      int:
						SCREAMING_SNAKE_CASE__									= TFAlbertForPreTraining.from_pretrained("""albert-base-v2"""    )
						SCREAMING_SNAKE_CASE__									= tf.constant([[0, 1, 2, 3, 4, 5]]    )
						SCREAMING_SNAKE_CASE__									= model(SCREAMING_SNAKE_CASE__    )[0]

						SCREAMING_SNAKE_CASE__									= [1, 6, 3_0_0_0_0]
						self.assertEqual(output.shape      ,			SCREAMING_SNAKE_CASE__    )

						SCREAMING_SNAKE_CASE__									= tf.constant(
						    [
						        [
						            [4.595_668, 0.74_462_754, -1.818_147],
						            [4.5_954_347, 0.7_454_184, -1.8_188_258],
						            [4.5_954_905, 0.7_448_235, -1.8_182_316],
						        ]
						    ]    )
						tf.debugging.assert_near(output[:, :3, :3]      ,			SCREAMING_SNAKE_CASE__      ,			atol=1e-4    )





"""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


