'''simple docstring'''

import copy

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


A_	   = logging.get_logger(__name__)

A_	   = {
    "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 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				List[Any]   							=       """musicgen_decoder"""
			__magic_name__ :				Union[str, Any]   							=       ["""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_				,			):
									snake_case :       Union[str, Any]				=    vocab_size
									snake_case :       int				=    max_position_embeddings
									snake_case :       List[str]				=    hidden_size
									snake_case :       Tuple				=    ffn_dim
									snake_case :       List[str]				=    num_hidden_layers
									snake_case :       Optional[int]				=    num_attention_heads
									snake_case :       Any				=    dropout
									snake_case :       Tuple				=    attention_dropout
									snake_case :       List[str]				=    activation_dropout
									snake_case :       Optional[int]				=    activation_function
									snake_case :       int				=    initializer_factor
									snake_case :       str				=    layerdrop
									snake_case :       Union[str, Any]				=    use_cache
									snake_case :       Any				=    scale_embedding  # scale factor will be sqrt(d_model) if True
									snake_case :       Dict				=    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 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Optional[int]   							=       """musicgen"""
			__magic_name__ :				int   							=       True



			def __init__(							self				,			**snake_case_					):
									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"					)

									snake_case :       Any				=    kwargs.pop("text_encoder"					)
									snake_case :       Any				=    text_encoder_config.pop("model_type"					)

									snake_case :       List[Any]				=    kwargs.pop("audio_encoder"					)
									snake_case :       int				=    audio_encoder_config.pop("model_type"					)

									snake_case :       List[Any]				=    kwargs.pop("decoder"					)

									snake_case :       Any				=    AutoConfig.for_model(snake_case_				,			**snake_case_					)
									snake_case :       int				=    AutoConfig.for_model(snake_case_				,			**snake_case_					)
									snake_case :       Union[str, Any]				=    MusicgenDecoderConfig(**snake_case_					)
									snake_case :       Dict				=    True



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






			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    copy.deepcopy(self.__dict__					)
									snake_case :       List[Any]				=    self.text_encoder.to_dict()
									snake_case :       List[str]				=    self.audio_encoder.to_dict()
									snake_case :       Dict				=    self.decoder.to_dict()
									snake_case :       Dict				=    self.__class__.model_type
									return output






'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Dict   							=       ["""image_processor""", """tokenizer"""]
			__magic_name__ :				Union[str, Any]   							=       """VideoMAEImageProcessor"""
			__magic_name__ :				List[str]   							=       ("""CLIPTokenizer""", """CLIPTokenizerFast""")



			def __init__(							self				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):
									snake_case :       int				=    None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
															    " instead."				,			snake_case_				,			)
															snake_case :       Optional[int]				=    kwargs.pop("feature_extractor"					)

									snake_case :       Union[str, Any]				=    image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("You need to specify an `image_processor`."					)
									if tokenizer is None:
															raise ValueError("You need to specify a `tokenizer`."					)

									super().__init__(snake_case_				,			snake_case_					)
									snake_case :       List[Any]				=    self.image_processor



			def __call__(							self				,			snake_case_=None				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):

									if text is None and videos is None:
															raise ValueError("You have to specify either text or videos. Both cannot be none."					)

									if text is not None:
															snake_case :       List[Any]				=    self.tokenizer(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if videos is not None:
															snake_case :       List[Any]				=    self.image_processor(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if text is not None and videos is not None:
															snake_case :       Tuple				=    image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**snake_case_					)				,			tensor_type=snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.batch_decode(*snake_case_				,			**snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.decode(*snake_case_				,			**snake_case_					)



			@property
			def   lowerCamelCase (							self					):
									return ["input_ids", "attention_mask", "position_ids", "pixel_values"]



			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			snake_case_				,			)
									return self.image_processor_class






			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			snake_case_				,			)
									return self.image_processor





