






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
			import tensorflow as tf

			from transformers.models.swin.modeling_tf_swin import (
			    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFSwinForImageClassification,
			    TFSwinForMaskedImageModeling,
			    TFSwinModel,
			)


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor




class        snake_case_					:

						"""simple docstring"""



						def __init__(self:				int   ,				__UpperCAmelCase:				Optional[int]   ,				__UpperCAmelCase:				List[str]=13   ,				__UpperCAmelCase:				List[str]=32   ,				__UpperCAmelCase:				Tuple=2   ,				__UpperCAmelCase:				Union[str, Any]=3   ,				__UpperCAmelCase:				Optional[int]=16   ,				__UpperCAmelCase:				List[Any]=[1, 2, 1]   ,				__UpperCAmelCase:				List[str]=[2, 2, 4]   ,				__UpperCAmelCase:				List[str]=2   ,				__UpperCAmelCase:				Tuple=2.0   ,				__UpperCAmelCase:				List[Any]=True   ,				__UpperCAmelCase:				List[Any]=0.0   ,				__UpperCAmelCase:				Any=0.0   ,				__UpperCAmelCase:				Dict=0.1   ,				__UpperCAmelCase:				str="gelu"   ,				__UpperCAmelCase:				List[str]=False   ,				__UpperCAmelCase:				List[Any]=True   ,				__UpperCAmelCase:				Optional[int]=0.02   ,				__UpperCAmelCase:				str=1E-5   ,				__UpperCAmelCase:				Optional[Any]=True   ,				__UpperCAmelCase:				List[Any]=None   ,				__UpperCAmelCase:				Optional[int]=True   ,				__UpperCAmelCase:				Union[str, Any]=10   ,				__UpperCAmelCase:				int=8   ,				)						->    None:




											'''simple docstring'''



											__a				:	Any			=				parent
											__a				:	int			=				batch_size
											__a				:	Dict			=				image_size
											__a				:	List[str]			=				patch_size
											__a				:	str			=				num_channels
											__a				:	Optional[int]			=				embed_dim
											__a				:	Optional[int]			=				depths
											__a				:	Tuple			=				num_heads
											__a				:	List[Any]			=				window_size
											__a				:	Any			=				mlp_ratio
											__a				:	str			=				qkv_bias
											__a				:	List[Any]			=				hidden_dropout_prob
											__a				:	List[Any]			=				attention_probs_dropout_prob
											__a				:	Dict			=				drop_path_rate
											__a				:	Dict			=				hidden_act
											__a				:	List[str]			=				use_absolute_embeddings
											__a				:	List[str]			=				patch_norm
											__a				:	str			=				layer_norm_eps
											__a				:	Optional[int]			=				initializer_range
											__a				:	List[Any]			=				is_training
											__a				:	str			=				scope
											__a				:	str			=				use_labels
											__a				:	Optional[Any]			=				type_sequence_label_size
											__a				:	int			=				encoder_stride



						def 					UpperCAmelCase__				(self:				Any      )						->    Union[str, Any]:




											'''simple docstring'''



											__a				:	List[str]			=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

											__a				:	Dict			=				None
											if self.use_labels:
																__a				:	str			=				ids_tensor([self.batch_size]   ,				self.type_sequence_label_size      )

											__a				:	Optional[Any]			=				self.get_config()

											return config, pixel_values, labels



						def 					UpperCAmelCase__				(self:				Tuple      )						->    Dict:




											'''simple docstring'''



											return SwinConfig(
											    image_size=self.image_size   ,				patch_size=self.patch_size   ,				num_channels=self.num_channels   ,				embed_dim=self.embed_dim   ,				depths=self.depths   ,				num_heads=self.num_heads   ,				window_size=self.window_size   ,				mlp_ratio=self.mlp_ratio   ,				qkv_bias=self.qkv_bias   ,				hidden_dropout_prob=self.hidden_dropout_prob   ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,				drop_path_rate=self.drop_path_rate   ,				hidden_act=self.hidden_act   ,				use_absolute_embeddings=self.use_absolute_embeddings   ,				path_norm=self.patch_norm   ,				layer_norm_eps=self.layer_norm_eps   ,				initializer_range=self.initializer_range   ,				encoder_stride=self.encoder_stride   ,				)



						def 					UpperCAmelCase__				(self:				Union[str, Any]   ,				__UpperCAmelCase:				List[str]   ,				__UpperCAmelCase:				str   ,				__UpperCAmelCase:				List[Any]      )						->    List[Any]:




											'''simple docstring'''



											__a				:	Union[str, Any]			=				TFSwinModel(config=__UpperCAmelCase      )
											__a				:	List[str]			=				model(__UpperCAmelCase      )

											__a				:	str			=				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
											__a				:	Optional[Any]			=				int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

											self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, expected_seq_len, expected_dim)      )



						def 					UpperCAmelCase__				(self:				int   ,				__UpperCAmelCase:				Dict   ,				__UpperCAmelCase:				Optional[int]   ,				__UpperCAmelCase:				Union[str, Any]      )						->    str:




											'''simple docstring'''



											__a				:	List[str]			=				TFSwinForMaskedImageModeling(config=__UpperCAmelCase      )
											__a				:	Tuple			=				model(__UpperCAmelCase      )
											self.parent.assertEqual(
											    result.logits.shape   ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

											# test greyscale images
											__a				:	Optional[Any]			=				1
											__a				:	Optional[int]			=				TFSwinForMaskedImageModeling(__UpperCAmelCase      )

											__a				:	List[str]			=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
											__a				:	Optional[int]			=				model(__UpperCAmelCase      )
											self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, 1, self.image_size, self.image_size)      )



						def 					UpperCAmelCase__				(self:				str   ,				__UpperCAmelCase:				int   ,				__UpperCAmelCase:				List[str]   ,				__UpperCAmelCase:				Optional[int]      )						->    Optional[Any]:




											'''simple docstring'''



											__a				:	Optional[int]			=				self.type_sequence_label_size
											__a				:	Union[str, Any]			=				TFSwinForImageClassification(__UpperCAmelCase      )
											__a				:	List[Any]			=				model(__UpperCAmelCase   ,				labels=__UpperCAmelCase      )
											self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)      )

											# test greyscale images
											__a				:	List[str]			=				1
											__a				:	Dict			=				TFSwinForImageClassification(__UpperCAmelCase      )
											__a				:	Any			=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
											__a				:	List[str]			=				model(__UpperCAmelCase      )
											self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)      )



						def 					UpperCAmelCase__				(self:				List[str]      )						->    Any:




											'''simple docstring'''



											__a				:	List[Any]			=				self.prepare_config_and_inputs()
											__a			, __a			, __a				:	List[Any]			=				config_and_inputs
											__a				:	Tuple			=				{"pixel_values": pixel_values}
											return config, inputs_dict




@require_tf
class        snake_case_					(						__UpperCamelCase		,       __UpperCamelCase		,       unittest.TestCase			):

						"""simple docstring"""



						snake_case__   					=			(
						    (
						        TFSwinModel,
						        TFSwinForImageClassification,
						        TFSwinForMaskedImageModeling,
						    )
						    if is_tf_available()
						    else ()
						)
						snake_case__   					=			(
						    {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
						    if is_tf_available()
						    else {}
						)

						snake_case__   					=			False
						snake_case__   					=			False
						snake_case__   					=			False
						snake_case__   					=			False



						def 					UpperCAmelCase__				(self:				str      )						->    Any:




											'''simple docstring'''



											__a				:	Optional[int]			=				TFSwinModelTester(self      )
											__a				:	str			=				ConfigTester(self   ,				config_class=__UpperCAmelCase   ,				embed_dim=37      )



						def 					UpperCAmelCase__				(self:				List[str]      )						->    Optional[Any]:




											'''simple docstring'''



											self.create_and_test_config_common_properties()
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()



						def 					UpperCAmelCase__				(self:				List[Any]      )						->    Dict:




											'''simple docstring'''



											return



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    List[str]:




											'''simple docstring'''



											__a				:	List[str]			=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__UpperCAmelCase      )



						def 					UpperCAmelCase__				(self:				int      )						->    List[str]:




											'''simple docstring'''



											__a				:	Dict			=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase      )



						def 					UpperCAmelCase__				(self:				List[Any]      )						->    List[Any]:




											'''simple docstring'''



											__a				:	Optional[Any]			=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase      )



						@unittest.skip(reason="Swin does not use inputs_embeds"      )
						def 					UpperCAmelCase__				(self:				List[Any]      )						->    Optional[Any]:




											'''simple docstring'''



											pass



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    Optional[int]:




											'''simple docstring'''



											__a			, __a				:	Optional[int]			=				self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																__a				:	Any			=				model_class(__UpperCAmelCase      )
																self.assertIsInstance(model.get_input_embeddings()   ,				tf.keras.layers.Layer      )
																__a				:	Any			=				model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(__UpperCAmelCase   ,				tf.keras.layers.Dense      )      )



						def 					UpperCAmelCase__				(self:				str      )						->    Tuple:




											'''simple docstring'''



											__a			, __a				:	Optional[Any]			=				self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																__a				:	Dict			=				model_class(__UpperCAmelCase      )
																__a				:	str			=				inspect.signature(model.call      )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																__a				:	Optional[Any]			=				[*signature.parameters.keys()]

																__a				:	Any			=				["pixel_values"]
																self.assertListEqual(arg_names[:1]   ,				__UpperCAmelCase      )



						def 					UpperCAmelCase__				(self:				Tuple      )						->    str:




											'''simple docstring'''



											__a			, __a				:	Optional[int]			=				self.model_tester.prepare_config_and_inputs_for_common()
											__a				:	Dict			=				True

											for model_class in self.all_model_classes:
																__a				:	Union[str, Any]			=				True
																__a				:	Tuple			=				False
																__a				:	Tuple			=				True
																__a				:	str			=				model_class(__UpperCAmelCase      )
																__a				:	Tuple			=				model(**self._prepare_for_class(__UpperCAmelCase   ,				__UpperCAmelCase      )      )
																__a				:	Optional[Any]			=				outputs.attentions
																__a				:	str			=				len(self.model_tester.depths      )
																self.assertEqual(len(__UpperCAmelCase      )   ,				__UpperCAmelCase      )

																# check that output_attentions also work using config
																del inputs_dict["output_attentions"]
																__a				:	str			=				True
																__a				:	int			=				config.window_size**2
																__a				:	str			=				model_class(__UpperCAmelCase      )
																__a				:	Optional[Any]			=				model(**self._prepare_for_class(__UpperCAmelCase   ,				__UpperCAmelCase      )      )
																__a				:	Tuple			=				outputs.attentions
																self.assertEqual(len(__UpperCAmelCase      )   ,				__UpperCAmelCase      )

																self.assertListEqual(
																    list(attentions[0].shape[-3:]      )   ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,				)
																__a				:	List[Any]			=				len(__UpperCAmelCase      )

																# Check attention is always last and order is fine
																__a				:	List[Any]			=				True
																__a				:	Union[str, Any]			=				True
																__a				:	Optional[Any]			=				model_class(__UpperCAmelCase      )
																__a				:	int			=				model(**self._prepare_for_class(__UpperCAmelCase   ,				__UpperCAmelCase      )      )

																if hasattr(self.model_tester   ,				"num_hidden_states_types"      ):
																					__a				:	List[Any]			=				self.model_tester.num_hidden_states_types
																else:
																					# also another +1 for reshaped_hidden_states
																					__a				:	Any			=				2
																self.assertEqual(out_len + added_hidden_states   ,				len(__UpperCAmelCase      )      )

																__a				:	List[Any]			=				outputs.attentions

																self.assertEqual(len(__UpperCAmelCase      )   ,				__UpperCAmelCase      )

																self.assertListEqual(
																    list(self_attentions[0].shape[-3:]      )   ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,				)



						def 					UpperCAmelCase__				(self:				Optional[Any]   ,				__UpperCAmelCase:				Tuple   ,				__UpperCAmelCase:				List[Any]   ,				__UpperCAmelCase:				str   ,				__UpperCAmelCase:				List[str]      )						->    List[Any]:




											'''simple docstring'''



											__a				:	str			=				model_class(__UpperCAmelCase      )
											__a				:	Tuple			=				model(**self._prepare_for_class(__UpperCAmelCase   ,				__UpperCAmelCase      )      )
											__a				:	List[Any]			=				outputs.hidden_states

											__a				:	Optional[Any]			=				getattr(
											    self.model_tester   ,				"expected_num_hidden_layers"   ,				len(self.model_tester.depths      ) + 1      )
											self.assertEqual(len(__UpperCAmelCase      )   ,				__UpperCAmelCase      )

											# Swin has a different seq_length
											__a				:	Optional[Any]			=				to_atuple(config.patch_size      )

											__a				:	List[str]			=				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]      )   ,				[num_patches, self.model_tester.embed_dim]   ,				)

											__a				:	str			=				outputs.reshaped_hidden_states
											self.assertEqual(len(__UpperCAmelCase      )   ,				__UpperCAmelCase      )

											__a			, __a			, __a			, __a				:	Optional[Any]			=				reshaped_hidden_states[0].shape

											__a				:	List[Any]			=				tf.reshape(reshaped_hidden_states[0]   ,				(batch_size, num_channels, height * width)      )
											__a				:	Optional[Any]			=				tf.transpose(__UpperCAmelCase   ,				(0, 2, 1)      )

											self.assertListEqual(
											    list(reshaped_hidden_states.shape[-2:]      )   ,				[num_patches, self.model_tester.embed_dim]   ,				)



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    str:




											'''simple docstring'''



											__a			, __a				:	List[Any]			=				self.model_tester.prepare_config_and_inputs_for_common()

											__a				:	str			=				to_atuple(self.model_tester.image_size      )

											for model_class in self.all_model_classes:
																__a				:	Any			=				True
																self.check_hidden_states_output(__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      )

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																__a				:	str			=				True

																self.check_hidden_states_output(__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      )



						def 					UpperCAmelCase__				(self:				Any      )						->    Any:




											'''simple docstring'''



											__a			, __a				:	Tuple			=				self.model_tester.prepare_config_and_inputs_for_common()
											__a				:	List[Any]			=				3

											__a				:	Optional[int]			=				to_atuple(self.model_tester.image_size      )
											__a				:	Any			=				to_atuple(config.patch_size      )

											__a				:	Optional[int]			=				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
											__a				:	List[str]			=				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

											for model_class in self.all_model_classes:
																__a				:	int			=				True
																self.check_hidden_states_output(__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase   ,				(padded_height, padded_width)      )

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																__a				:	str			=				True
																self.check_hidden_states_output(__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase   ,				(padded_height, padded_width)      )



						@slow
						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    Tuple:




											'''simple docstring'''



											for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__a				:	str			=				TFSwinModel.from_pretrained(__UpperCAmelCase      )
																self.assertIsNotNone(__UpperCAmelCase      )




@require_vision
@require_tf
class        snake_case_					(						unittest.TestCase			):

						"""simple docstring"""



						@cached_property
						def 					UpperCAmelCase__				(self:				List[Any]      )						->    Dict:




											'''simple docstring'''



											return (
											    AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"      )
											    if is_vision_available()
											    else None
											)



						@slow
						def 					UpperCAmelCase__				(self:				Union[str, Any]      )						->    Union[str, Any]:




											'''simple docstring'''



											__a				:	Optional[Any]			=				TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"      )
											__a				:	List[str]			=				self.default_image_processor

											__a				:	List[Any]			=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
											__a				:	Union[str, Any]			=				image_processor(images=__UpperCAmelCase   ,				return_tensors="tf"      )

											# forward pass
											__a				:	List[Any]			=				model(__UpperCAmelCase      )

											# verify the logits
											__a				:	List[str]			=				tf.TensorShape((1, 1000)      )
											self.assertEqual(outputs.logits.shape   ,				__UpperCAmelCase      )
											__a				:	Union[str, Any]			=				tf.constant([-0.09_48, -0.64_54, -0.09_21]      )
											self.assertTrue(np.allclose(outputs.logits[0, :3]   ,				__UpperCAmelCase   ,				atol=1E-4      )      )













import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


UpperCAmelCase__			 =	logging.get_logger(__name__)

UpperCAmelCase__			 =	{
    '''facebook/musicgen-small''': '''https://huggingface.co/facebook/musicgen-small/resolve/main/config.json''',
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}




class        snake_case_					(						__UpperCamelCase			):

						"""simple docstring"""



						snake_case__   					=			"""musicgen_decoder"""
						snake_case__   					=			["""past_key_values"""]



						def __init__(self:				str   ,				__UpperCAmelCase:				Union[str, Any]=2048   ,				__UpperCAmelCase:				Union[str, Any]=2048   ,				__UpperCAmelCase:				List[Any]=24   ,				__UpperCAmelCase:				Dict=4096   ,				__UpperCAmelCase:				int=16   ,				__UpperCAmelCase:				Dict=0.0   ,				__UpperCAmelCase:				str=True   ,				__UpperCAmelCase:				Tuple="gelu"   ,				__UpperCAmelCase:				Optional[Any]=1024   ,				__UpperCAmelCase:				Optional[Any]=0.1   ,				__UpperCAmelCase:				List[Any]=0.0   ,				__UpperCAmelCase:				int=0.0   ,				__UpperCAmelCase:				List[Any]=0.02   ,				__UpperCAmelCase:				Union[str, Any]=False   ,				__UpperCAmelCase:				Any=4   ,				__UpperCAmelCase:				Tuple=2048   ,				__UpperCAmelCase:				Tuple=2048   ,				__UpperCAmelCase:				Dict=None   ,				__UpperCAmelCase:				Dict=False   ,				**__UpperCAmelCase:				Any   ,				)						->    Optional[Any]:




											'''simple docstring'''



											__a				:	Dict			=				vocab_size
											__a				:	List[str]			=				max_position_embeddings
											__a				:	int			=				hidden_size
											__a				:	Union[str, Any]			=				ffn_dim
											__a				:	List[Any]			=				num_hidden_layers
											__a				:	List[Any]			=				num_attention_heads
											__a				:	List[Any]			=				dropout
											__a				:	str			=				attention_dropout
											__a				:	Union[str, Any]			=				activation_dropout
											__a				:	Optional[int]			=				activation_function
											__a				:	List[str]			=				initializer_factor
											__a				:	Union[str, Any]			=				layerdrop
											__a				:	Optional[int]			=				use_cache
											__a				:	List[Any]			=				scale_embedding  # scale factor will be sqrt(d_model) if True
											__a				:	str			=				num_codebooks
											super().__init__(
											    pad_token_id=__UpperCAmelCase   ,				bos_token_id=__UpperCAmelCase   ,				eos_token_id=__UpperCAmelCase   ,				tie_word_embeddings=__UpperCAmelCase   ,				**__UpperCAmelCase   ,				)




class        snake_case_					(						__UpperCamelCase			):

						"""simple docstring"""



						snake_case__   					=			"""musicgen"""
						snake_case__   					=			True



						def __init__(self:				Union[str, Any]   ,				**__UpperCAmelCase:				Optional[int]      )						->    List[str]:




											'''simple docstring'''



											super().__init__(**__UpperCAmelCase      )
											if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
																raise ValueError("Config has to be initialized with text_encoder, audio_encoder and decoder config"      )

											__a				:	List[Any]			=				kwargs.pop("text_encoder"      )
											__a				:	Optional[Any]			=				text_encoder_config.pop("model_type"      )

											__a				:	Union[str, Any]			=				kwargs.pop("audio_encoder"      )
											__a				:	Dict			=				audio_encoder_config.pop("model_type"      )

											__a				:	Optional[Any]			=				kwargs.pop("decoder"      )

											__a				:	Optional[int]			=				AutoConfig.for_model(__UpperCAmelCase   ,				**__UpperCAmelCase      )
											__a				:	int			=				AutoConfig.for_model(__UpperCAmelCase   ,				**__UpperCAmelCase      )
											__a				:	List[Any]			=				MusicgenDecoderConfig(**__UpperCAmelCase      )
											__a				:	Optional[int]			=				True



						@classmethod
						def 					UpperCAmelCase__				(cls:				Union[str, Any]   ,				__UpperCAmelCase:				PretrainedConfig   ,				__UpperCAmelCase:				PretrainedConfig   ,				__UpperCAmelCase:				MusicgenDecoderConfig   ,				**__UpperCAmelCase:				Any   ,				)						->    Tuple:




											'''simple docstring'''



											return cls(
											    text_encoder=text_encoder_config.to_dict()   ,				audio_encoder=audio_encoder_config.to_dict()   ,				decoder=decoder_config.to_dict()   ,				**__UpperCAmelCase   ,				)



						def 					UpperCAmelCase__				(self:				List[str]      )						->    int:




											'''simple docstring'''



											__a				:	Dict			=				copy.deepcopy(self.__dict__      )
											__a				:	Optional[Any]			=				self.text_encoder.to_dict()
											__a				:	Optional[Any]			=				self.audio_encoder.to_dict()
											__a				:	Tuple			=				self.decoder.to_dict()
											__a				:	List[Any]			=				self.__class__.model_type
											return output





