
import copy

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


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

A  :			Optional[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     __A(				a    ):
				snake_case_		     =					'''musicgen_decoder'''
				snake_case_		     =					['''past_key_values''']




				def __init__(  self	,  _snake_case=2_048	,  _snake_case=2_048	,  _snake_case=24	,  _snake_case=4_096	,  _snake_case=16	,  _snake_case=0.0	,  _snake_case=True	,  _snake_case="gelu"	,  _snake_case=1_024	,  _snake_case=0.1	,  _snake_case=0.0	,  _snake_case=0.0	,  _snake_case=0.02	,  _snake_case=False	,  _snake_case=4	,  _snake_case=2_048	,  _snake_case=2_048	,  _snake_case=None	,  _snake_case=False	,  **_snake_case	,  )					->   Tuple:


								'''simple docstring'''




								__a    							=					vocab_size
								__a    							=					max_position_embeddings
								__a    							=					hidden_size
								__a    							=					ffn_dim
								__a    							=					num_hidden_layers
								__a    							=					num_attention_heads
								__a    							=					dropout
								__a    							=					attention_dropout
								__a    							=					activation_dropout
								__a    							=					activation_function
								__a    							=					initializer_factor
								__a    							=					layerdrop
								__a    							=					use_cache
								__a    							=					scale_embedding  # scale factor will be sqrt(d_model) if True
								__a    							=					num_codebooks
								super().__init__(
								    pad_token_id=_snake_case	,  bos_token_id=_snake_case	,  eos_token_id=_snake_case	,  tie_word_embeddings=_snake_case	,  **_snake_case	,  )







class     __A(				a    ):
				snake_case_		     =					'''musicgen'''
				snake_case_		     =					True




				def __init__(  self	,  **_snake_case       )					->   Any:


								'''simple docstring'''




								super().__init__(**_snake_case       )
								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    							=					kwargs.pop('''text_encoder'''       )
								__a    							=					text_encoder_config.pop('''model_type'''       )

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

								__a    							=					kwargs.pop('''decoder'''       )

								__a    							=					AutoConfig.for_model(_snake_case	,  **_snake_case       )
								__a    							=					AutoConfig.for_model(_snake_case	,  **_snake_case       )
								__a    							=					MusicgenDecoderConfig(**_snake_case       )
								__a    							=					True




				@classmethod
				def        SCREAMING_SNAKE_CASE_	(  cls	,  _snake_case	,  _snake_case	,  _snake_case	,  **_snake_case	,  )					->   Optional[int]:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[Any]:


								'''simple docstring'''




								__a    							=					copy.deepcopy(self.__dict__       )
								__a    							=					self.text_encoder.to_dict()
								__a    							=					self.audio_encoder.to_dict()
								__a    							=					self.decoder.to_dict()
								__a    							=					self.__class__.model_type
								return output








# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
					import tensorflow as tf







class     __A(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]    ):




				def __init__(  self	,  _snake_case=None	,  **_snake_case       )					->   Dict:


								'''simple docstring'''




								super().__init__(features=_snake_case       )
								__a    							=					tf_tensor_kwargs
								import tensorflow as tf  # noqa: import tf at initialization




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if isinstance(_snake_case	,  _snake_case       ) and column:
												if all(
												    isinstance(_snake_case	,  tf.Tensor       ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column       ):
																return tf.stack(_snake_case       )
												elif all(
												    isinstance(_snake_case	,  (tf.Tensor, tf.RaggedTensor)       ) and x.ndim == 1 and x.dtype == column[0].dtype
												    for x in column       ):
																# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
																return tf.ragged.stack(_snake_case       )

								return column




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if value is None:
												return value

								__a    							=					{}

								if isinstance(_snake_case	,  (np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,  np.integer       ):
												__a    							=					{'''dtype''': tf.intaa}
								elif isinstance(_snake_case	,  (np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,  np.floating       ):
												__a    							=					{'''dtype''': tf.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
												import PIL.Image

												if isinstance(_snake_case	,  PIL.Image.Image       ):
																__a    							=					np.asarray(_snake_case       )

								return tf.convert_to_tensor(_snake_case	,  **{**default_dtype, **self.tf_tensor_kwargs}       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   str:


								'''simple docstring'''




								import tensorflow as tf

								# support for torch, tf, jax etc.
								if config.TORCH_AVAILABLE and "torch" in sys.modules:
												import torch

												if isinstance(_snake_case	,  torch.Tensor       ):
																return self._tensorize(data_struct.detach().cpu().numpy()[()]       )
								if hasattr(_snake_case	,  '''__array__'''       ) and not isinstance(_snake_case	,  tf.Tensor       ):
												__a    							=					data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(_snake_case	,  np.ndarray       ):
												if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
																return self._consolidate([self.recursive_tensorize(_snake_case       ) for substruct in data_struct]       )
								elif isinstance(_snake_case	,  (list, tuple)       ):
												return self._consolidate([self.recursive_tensorize(_snake_case       ) for substruct in data_struct]       )
								return self._tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tuple:


								'''simple docstring'''




								return map_nested(self._recursive_tensorize	,  _snake_case	,  map_list=_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_row(_snake_case       )
								__a    							=					self.python_features_decoder.decode_row(_snake_case       )
								return self.recursive_tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   "tf.Tensor":


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_column(_snake_case       )
								__a    							=					self.python_features_decoder.decode_column(_snake_case	,  pa_table.column_names[0]       )
								__a    							=					self.recursive_tensorize(_snake_case       )
								__a    							=					self._consolidate(_snake_case       )
								return column




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_batch(_snake_case       )
								__a    							=					self.python_features_decoder.decode_batch(_snake_case       )
								__a    							=					self.recursive_tensorize(_snake_case       )
								for column_name in batch:
												__a    							=					self._consolidate(batch[column_name]       )
								return batch