




import unittest

from transformers import 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          RemBertConfig,
          RemBertForCausalLM,
          RemBertForMaskedLM,
          RemBertForMultipleChoice,
          RemBertForQuestionAnswering,
          RemBertForSequenceClassification,
          RemBertForTokenClassification,
          RemBertModel,
      )
      from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST






class 	A	:
     """simple docstring"""

     def __init__(			self     :      Tuple		,     A__     :      str		,     A__     :      List[Any]=1_3		,     A__     :      Any=7		,     A__     :      Optional[Any]=True		,     A__     :      Union[str, Any]=True		,     A__     :      Optional[int]=True		,     A__     :      str=True		,     A__     :      Union[str, Any]=9_9		,     A__     :      List[str]=3_2		,     A__     :      Optional[int]=1_8		,     A__     :      Any=4_3		,     A__     :      Dict=5		,     A__     :      str=4		,     A__     :      List[str]=3_7		,     A__     :      List[Any]="gelu"		,     A__     :      int=0.1		,     A__     :      Optional[Any]=0.1		,     A__     :      Optional[int]=5_1_2		,     A__     :      Optional[int]=1_6		,     A__     :      Dict=2		,     A__     :      int=0.02		,     A__     :      Dict=3		,     A__     :      str=4		,     A__     :      int=None		,     ) ->				List[Any]:
           lowerCamelCase__					     = parent
           lowerCamelCase__					     = batch_size
           lowerCamelCase__					     = seq_length
           lowerCamelCase__					     = is_training
           lowerCamelCase__					     = use_input_mask
           lowerCamelCase__					     = use_token_type_ids
           lowerCamelCase__					     = use_labels
           lowerCamelCase__					     = vocab_size
           lowerCamelCase__					     = hidden_size
           lowerCamelCase__					     = input_embedding_size
           lowerCamelCase__					     = output_embedding_size
           lowerCamelCase__					     = num_hidden_layers
           lowerCamelCase__					     = num_attention_heads
           lowerCamelCase__					     = intermediate_size
           lowerCamelCase__					     = hidden_act
           lowerCamelCase__					     = hidden_dropout_prob
           lowerCamelCase__					     = attention_probs_dropout_prob
           lowerCamelCase__					     = max_position_embeddings
           lowerCamelCase__					     = type_vocab_size
           lowerCamelCase__					     = type_sequence_label_size
           lowerCamelCase__					     = initializer_range
           lowerCamelCase__					     = num_labels
           lowerCamelCase__					     = num_choices
           lowerCamelCase__					     = scope




     def        __snake_case      (			self     :      str	) ->				int:
           lowerCamelCase__					     = ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size	)

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

           lowerCamelCase__					     = None
           if self.use_token_type_ids:
                 lowerCamelCase__					     = ids_tensor([self.batch_size, self.seq_length]		,     self.type_vocab_size	)

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

           lowerCamelCase__					     = RemBertConfig(
               vocab_size=self.vocab_size		,     hidden_size=self.hidden_size		,     input_embedding_size=self.input_embedding_size		,     output_embedding_size=self.output_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		,     is_decoder=A__		,     initializer_range=self.initializer_range		,     )

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




     def        __snake_case      (			self     :      Union[str, Any]	) ->				str:
           (
               (
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						
           )					     = self.prepare_config_and_inputs()

           lowerCamelCase__					     = True
           lowerCamelCase__					     = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
           lowerCamelCase__					     = ids_tensor([self.batch_size, self.seq_length]		,     vocab_size=2	)

           return (
               config,
               input_ids,
               token_type_ids,
               input_mask,
               sequence_labels,
               token_labels,
               choice_labels,
               encoder_hidden_states,
               encoder_attention_mask,
           )




     def        __snake_case      (			self     :      List[str]		,     A__     :      List[str]		,     A__     :      str		,     A__     :      Optional[Any]		,     A__     :      List[Any]		,     A__     :      Optional[int]		,     A__     :      Union[str, Any]		,     A__     :      Optional[int]	) ->				Any:
           lowerCamelCase__					     = RemBertModel(config=A__	)
           model.to(A__	)
           model.eval()
           lowerCamelCase__					     = model(A__		,     attention_mask=A__		,     token_type_ids=A__	)
           lowerCamelCase__					     = model(A__		,     token_type_ids=A__	)
           lowerCamelCase__					     = model(A__	)
           self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)	)




     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      Optional[Any]		,     A__     :      str		,     A__     :      Tuple		,     A__     :      Optional[int]		,     A__     :      Union[str, Any]		,     A__     :      int		,     A__     :      List[Any]		,     A__     :      str		,     A__     :      Dict		,     ) ->				Optional[Any]:
           lowerCamelCase__					     = True
           lowerCamelCase__					     = RemBertModel(A__	)
           model.to(A__	)
           model.eval()
           lowerCamelCase__					     = model(
               A__		,     attention_mask=A__		,     token_type_ids=A__		,     encoder_hidden_states=A__		,     encoder_attention_mask=A__		,     )
           lowerCamelCase__					     = model(
               A__		,     attention_mask=A__		,     token_type_ids=A__		,     encoder_hidden_states=A__		,     )
           lowerCamelCase__					     = model(A__		,     attention_mask=A__		,     token_type_ids=A__	)
           self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)	)




     def        __snake_case      (			self     :      Tuple		,     A__     :      Union[str, Any]		,     A__     :      str		,     A__     :      Optional[Any]		,     A__     :      int		,     A__     :      Union[str, Any]		,     A__     :      Tuple		,     A__     :      Tuple		,     A__     :      Any		,     A__     :      Optional[Any]		,     ) ->				List[str]:
           lowerCamelCase__					     = RemBertForCausalLM(config=A__	)
           model.to(A__	)
           model.eval()
           lowerCamelCase__					     = model(A__		,     attention_mask=A__		,     token_type_ids=A__		,     labels=A__	)
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size)	)




     def        __snake_case      (			self     :      Any		,     A__     :      str		,     A__     :      Dict		,     A__     :      Any		,     A__     :      Union[str, Any]		,     A__     :      Union[str, Any]		,     A__     :      Any		,     A__     :      List[Any]	) ->				Dict:
           lowerCamelCase__					     = RemBertForMaskedLM(config=A__	)
           model.to(A__	)
           model.eval()
           lowerCamelCase__					     = model(A__		,     attention_mask=A__		,     token_type_ids=A__		,     labels=A__	)
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size)	)




     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      Union[str, Any]		,     A__     :      Tuple		,     A__     :      Dict		,     A__     :      Dict		,     A__     :      int		,     A__     :      str		,     A__     :      Dict		,     A__     :      str		,     A__     :      List[Any]		,     ) ->				Dict:
           lowerCamelCase__					     = True
           lowerCamelCase__					     = True
           lowerCamelCase__					     = RemBertForCausalLM(config=A__	)
           model.to(A__	)
           model.eval()

           # first forward pass
           lowerCamelCase__					     = model(
               A__		,     attention_mask=A__		,     encoder_hidden_states=A__		,     encoder_attention_mask=A__		,     use_cache=A__		,     )
           lowerCamelCase__					     = outputs.past_key_values

           # create hypothetical multiple next token and extent to next_input_ids
           lowerCamelCase__					     = ids_tensor((self.batch_size, 3)		,     config.vocab_size	)
           lowerCamelCase__					     = ids_tensor((self.batch_size, 3)		,     vocab_size=2	)

           # append to next input_ids and
           lowerCamelCase__					     = torch.cat([input_ids, next_tokens]		,     dim=-1	)
           lowerCamelCase__					     = torch.cat([input_mask, next_mask]		,     dim=-1	)

           lowerCamelCase__					     = model(
               A__		,     attention_mask=A__		,     encoder_hidden_states=A__		,     encoder_attention_mask=A__		,     output_hidden_states=A__		,     )['hidden_states'][0]
           lowerCamelCase__					     = model(
               A__		,     attention_mask=A__		,     encoder_hidden_states=A__		,     encoder_attention_mask=A__		,     past_key_values=A__		,     output_hidden_states=A__		,     )['hidden_states'][0]

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

           self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]	)

           # test that outputs are equal for slice
           self.parent.assertTrue(torch.allclose(A__		,     A__		,     atol=1E-3	)	)




     def        __snake_case      (			self     :      str		,     A__     :      int		,     A__     :      Tuple		,     A__     :      Union[str, Any]		,     A__     :      List[Any]		,     A__     :      Tuple		,     A__     :      Optional[Any]		,     A__     :      Dict	) ->				Optional[Any]:
           lowerCamelCase__					     = RemBertForQuestionAnswering(config=A__	)
           model.to(A__	)
           model.eval()
           lowerCamelCase__					     = model(
               A__		,     attention_mask=A__		,     token_type_ids=A__		,     start_positions=A__		,     end_positions=A__		,     )
           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        __snake_case      (			self     :      Tuple		,     A__     :      Tuple		,     A__     :      Tuple		,     A__     :      Optional[Any]		,     A__     :      Any		,     A__     :      Any		,     A__     :      Any		,     A__     :      Dict	) ->				Dict:
           lowerCamelCase__					     = self.num_labels
           lowerCamelCase__					     = RemBertForSequenceClassification(A__	)
           model.to(A__	)
           model.eval()
           lowerCamelCase__					     = model(A__		,     attention_mask=A__		,     token_type_ids=A__		,     labels=A__	)
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)	)




     def        __snake_case      (			self     :      str		,     A__     :      str		,     A__     :      List[str]		,     A__     :      int		,     A__     :      int		,     A__     :      str		,     A__     :      str		,     A__     :      Any	) ->				Optional[int]:
           lowerCamelCase__					     = self.num_labels
           lowerCamelCase__					     = RemBertForTokenClassification(config=A__	)
           model.to(A__	)
           model.eval()
           lowerCamelCase__					     = model(A__		,     attention_mask=A__		,     token_type_ids=A__		,     labels=A__	)
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.num_labels)	)




     def        __snake_case      (			self     :      int		,     A__     :      Optional[int]		,     A__     :      Union[str, Any]		,     A__     :      Optional[int]		,     A__     :      List[Any]		,     A__     :      Any		,     A__     :      Union[str, Any]		,     A__     :      List[str]	) ->				Optional[Any]:
           lowerCamelCase__					     = self.num_choices
           lowerCamelCase__					     = RemBertForMultipleChoice(config=A__	)
           model.to(A__	)
           model.eval()
           lowerCamelCase__					     = input_ids.unsqueeze(1	).expand(-1		,     self.num_choices		,     -1	).contiguous()
           lowerCamelCase__					     = token_type_ids.unsqueeze(1	).expand(-1		,     self.num_choices		,     -1	).contiguous()
           lowerCamelCase__					     = input_mask.unsqueeze(1	).expand(-1		,     self.num_choices		,     -1	).contiguous()
           lowerCamelCase__					     = model(
               A__		,     attention_mask=A__		,     token_type_ids=A__		,     labels=A__		,     )
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_choices)	)




     def        __snake_case      (			self     :      Tuple	) ->				Optional[Any]:
           lowerCamelCase__					     = self.prepare_config_and_inputs()
           (
               (
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						
           )					     = config_and_inputs
           lowerCamelCase__					     = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
           return config, inputs_dict






@require_torch
class 	A	(						_lowerCAmelCase	,  _lowerCAmelCase	,  unittest.TestCase						):
     """simple docstring"""

     _snake_case:		Tuple   =(
         (
             RemBertModel,
             RemBertForMaskedLM,
             RemBertForCausalLM,
             RemBertForMultipleChoice,
             RemBertForQuestionAnswering,
             RemBertForSequenceClassification,
             RemBertForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     _snake_case:		Union[str, Any]   =(RemBertForCausalLM,) if is_torch_available() else ()
     _snake_case:		Optional[int]   =(
         {
             "feature-extraction": RemBertModel,
             "fill-mask": RemBertForMaskedLM,
             "question-answering": RemBertForQuestionAnswering,
             "text-classification": RemBertForSequenceClassification,
             "text-generation": RemBertForCausalLM,
             "token-classification": RemBertForTokenClassification,
             "zero-shot": RemBertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )




     def        __snake_case      (			self     :      int	) ->				Optional[Any]:
           lowerCamelCase__					     = RemBertModelTester(self	)
           lowerCamelCase__					     = ConfigTester(self		,     config_class=A__		,     hidden_size=3_7	)




     def        __snake_case      (			self     :      str	) ->				Optional[Any]:
           self.config_tester.run_common_tests()




     def        __snake_case      (			self     :      Any	) ->				str:
           lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A__	)




     def        __snake_case      (			self     :      int	) ->				Dict:
           lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                 lowerCamelCase__					     = type
                 self.model_tester.create_and_check_model(*A__	)




     def        __snake_case      (			self     :      Optional[Any]	) ->				List[Any]:
           lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*A__	)




     def        __snake_case      (			self     :      Tuple	) ->				Optional[Any]:
           lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*A__	)




     def        __snake_case      (			self     :      Optional[Any]	) ->				Optional[Any]:
           lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_decoder_model_past_large_inputs(*A__	)




     def        __snake_case      (			self     :      Any	) ->				str:
           lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*A__	)




     def        __snake_case      (			self     :      Optional[Any]	) ->				Union[str, Any]:
           lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*A__	)




     def        __snake_case      (			self     :      Optional[Any]	) ->				Optional[Any]:
           lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*A__	)




     def        __snake_case      (			self     :      str	) ->				str:
           lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_model_as_decoder(*A__	)




     def        __snake_case      (			self     :      List[Any]	) ->				Dict:
           # This regression test was failing with PyTorch < 1.3
           (
               (
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						(
               lowerCamelCase__
           )     ,						
           )					     = self.model_tester.prepare_config_and_inputs_for_decoder()

           lowerCamelCase__					     = None

           self.model_tester.create_and_check_model_as_decoder(
               A__		,     A__		,     A__		,     A__		,     A__		,     A__		,     A__		,     A__		,     A__		,     )




     @slow
     def        __snake_case      (			self     :      int	) ->				Tuple:
           for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase__					     = RemBertModel.from_pretrained(A__	)
                 self.assertIsNotNone(A__	)






@require_torch
class 	A	(						unittest.TestCase						):
     """simple docstring"""

     @slow
     def        __snake_case      (			self     :      Any	) ->				List[Any]:
           # Test exact values at the last hidden layer
           lowerCamelCase__					     = RemBertModel.from_pretrained('google/rembert'	)
           lowerCamelCase__					     = torch.tensor([[3_1_2, 5_6_4_9_8, 3_1_3, 2_1_2_5, 3_1_3]]	)
           lowerCamelCase__					     = torch.tensor([[0, 0, 0, 1, 1]]	)
           with torch.no_grad():
                 lowerCamelCase__					     = model(A__		,     token_type_ids=A__		,     output_hidden_states=A__	)

           lowerCamelCase__					     = 1_1_5_2

           lowerCamelCase__					     = torch.Size((1, 5, hidden_size)	)
           self.assertEqual(output['last_hidden_state'].shape		,     A__	)

           lowerCamelCase__					     = torch.tensor(
               [
                   [
                       [0.0754, -0.2022, 0.1904],
                       [-0.3354, -0.3692, -0.4791],
                       [-0.2314, -0.6729, -0.0749],
                       [-0.0396, -0.3105, -0.4234],
                       [-0.1571, -0.0525, 0.5353],
                   ]
               ]	)

           # Running on the original tf implementation gives slightly different results here.
           # Not clear why this variations is present
           # TODO: Find reason for discrepancy
           # expected_original_implementation = [[
           #     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
           #     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
           #     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
           #     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
           #     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
           # ]]

           self.assertTrue(torch.allclose(output['last_hidden_state'][:, :, :3]		,     A__		,     atol=1E-4	)	)


import copy

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


UpperCamelCase__     :					Optional[Any]								=			logging.get_logger(__name__)

UpperCamelCase__     :					List[Any]								=			{
    "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     (			lowerCAmelCase__				):
  __lowerCAmelCase   :		Any         =     'musicgen_decoder'
  __lowerCAmelCase   :		Optional[Any]         =     ['past_key_values']

  def __init__(    self  ,						_A=2048  ,						_A=2048  ,						_A=24  ,						_A=4096  ,						_A=16  ,						_A=0.0  ,						_A=True  ,						_A="gelu"  ,						_A=1024  ,						_A=0.1  ,						_A=0.0  ,						_A=0.0  ,						_A=0.0_2  ,						_A=False  ,						_A=4  ,						_A=2048  ,						_A=2048  ,						_A=None  ,						_A=False  ,						**_A  ,						):
      SCREAMING_SNAKE_CASE_													=  vocab_size
      SCREAMING_SNAKE_CASE_													=  max_position_embeddings
      SCREAMING_SNAKE_CASE_													=  hidden_size
      SCREAMING_SNAKE_CASE_													=  ffn_dim
      SCREAMING_SNAKE_CASE_													=  num_hidden_layers
      SCREAMING_SNAKE_CASE_													=  num_attention_heads
      SCREAMING_SNAKE_CASE_													=  dropout
      SCREAMING_SNAKE_CASE_													=  attention_dropout
      SCREAMING_SNAKE_CASE_													=  activation_dropout
      SCREAMING_SNAKE_CASE_													=  activation_function
      SCREAMING_SNAKE_CASE_													=  initializer_factor
      SCREAMING_SNAKE_CASE_													=  layerdrop
      SCREAMING_SNAKE_CASE_													=  use_cache
      SCREAMING_SNAKE_CASE_													=  scale_embedding  # scale factor will be sqrt(d_model) if True
      SCREAMING_SNAKE_CASE_													=  num_codebooks
      super().__init__(
          pad_token_id=_A  ,						bos_token_id=_A  ,						eos_token_id=_A  ,						tie_word_embeddings=_A  ,						**_A  ,						)






class 					__snake_case     (			lowerCAmelCase__				):
  __lowerCAmelCase   :		int         =     'musicgen'
  __lowerCAmelCase   :		Optional[Any]         =     True

  def __init__(    self  ,						**_A):
      super().__init__(**_A)
      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')

      SCREAMING_SNAKE_CASE_													=  kwargs.pop('text_encoder')
      SCREAMING_SNAKE_CASE_													=  text_encoder_config.pop('model_type')

      SCREAMING_SNAKE_CASE_													=  kwargs.pop('audio_encoder')
      SCREAMING_SNAKE_CASE_													=  audio_encoder_config.pop('model_type')

      SCREAMING_SNAKE_CASE_													=  kwargs.pop('decoder')

      SCREAMING_SNAKE_CASE_													=  AutoConfig.for_model(_A  ,						**_A)
      SCREAMING_SNAKE_CASE_													=  AutoConfig.for_model(_A  ,						**_A)
      SCREAMING_SNAKE_CASE_													=  MusicgenDecoderConfig(**_A)
      SCREAMING_SNAKE_CASE_													=  True

  @classmethod
  def   lowerCAmelCase__   (    cls  ,						_A  ,						_A  ,						_A  ,						**_A  ,						):
      return cls(
          text_encoder=text_encoder_config.to_dict()  ,						audio_encoder=audio_encoder_config.to_dict()  ,						decoder=decoder_config.to_dict()  ,						**_A  ,						)


  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  copy.deepcopy(self.__dict__)
      SCREAMING_SNAKE_CASE_													=  self.text_encoder.to_dict()
      SCREAMING_SNAKE_CASE_													=  self.audio_encoder.to_dict()
      SCREAMING_SNAKE_CASE_													=  self.decoder.to_dict()
      SCREAMING_SNAKE_CASE_													=  self.__class__.model_type
      return output


