
'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "google/electra-small-generator": "https://huggingface.co/google/electra-small-generator/resolve/main/config.json",
    "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/config.json",
    "google/electra-large-generator": "https://huggingface.co/google/electra-large-generator/resolve/main/config.json",
    "google/electra-small-discriminator": (
        "https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"
    ),
    "google/electra-base-discriminator": (
        "https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"
    ),
    "google/electra-large-discriminator": (
        "https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"
    ),
}

class     a (					__magic_name__     ):
				_snake_case		     =			'''electra'''




				def __init__(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Optional[Any]=3_05_22,     SCREAMING_SNAKE_CASE_						:					Tuple=1_28,     SCREAMING_SNAKE_CASE_						:					int=2_56,     SCREAMING_SNAKE_CASE_						:					List[Any]=12,     SCREAMING_SNAKE_CASE_						:					Optional[int]=4,     SCREAMING_SNAKE_CASE_						:					List[str]=10_24,     SCREAMING_SNAKE_CASE_						:					Optional[Any]="gelu",     SCREAMING_SNAKE_CASE_						:					Optional[int]=0.1,     SCREAMING_SNAKE_CASE_						:					Dict=0.1,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=5_12,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=2,     SCREAMING_SNAKE_CASE_						:					str=0.02,     SCREAMING_SNAKE_CASE_						:					int=1e-12,     SCREAMING_SNAKE_CASE_						:					str="first",     SCREAMING_SNAKE_CASE_						:					Optional[Any]=True,     SCREAMING_SNAKE_CASE_						:					str="gelu",     SCREAMING_SNAKE_CASE_						:					str=0.1,     SCREAMING_SNAKE_CASE_						:					int=0,     SCREAMING_SNAKE_CASE_						:					List[str]="absolute",     SCREAMING_SNAKE_CASE_						:					List[Any]=True,     SCREAMING_SNAKE_CASE_						:					Dict=None,     **SCREAMING_SNAKE_CASE_						:					str,     ):
										super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )

										snake_case							:  Dict			=     vocab_size
										snake_case							:  Optional[Any]			=     embedding_size
										snake_case							:  Optional[Any]			=     hidden_size
										snake_case							:  Tuple			=     num_hidden_layers
										snake_case							:  Union[str, Any]			=     num_attention_heads
										snake_case							:  str			=     intermediate_size
										snake_case							:  Any			=     hidden_act
										snake_case							:  Optional[Any]			=     hidden_dropout_prob
										snake_case							:  Tuple			=     attention_probs_dropout_prob
										snake_case							:  Union[str, Any]			=     max_position_embeddings
										snake_case							:  str			=     type_vocab_size
										snake_case							:  List[Any]			=     initializer_range
										snake_case							:  int			=     layer_norm_eps

										snake_case							:  Any			=     summary_type
										snake_case							:  Tuple			=     summary_use_proj
										snake_case							:  Optional[Any]			=     summary_activation
										snake_case							:  Optional[int]			=     summary_last_dropout
										snake_case							:  List[str]			=     position_embedding_type
										snake_case							:  List[Any]			=     use_cache
										snake_case							:  List[str]			=     classifier_dropout




class     a (					__magic_name__     ):




				@property
				def 						__snake_case				(				self						:					int   ):
										if self.task == "multiple-choice":
																snake_case							:  int			=     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
																snake_case							:  str			=     {0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										        ('''token_type_ids''', dynamic_axis),
										    ]   )



'''simple docstring'''





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     a (					__magic_name__     ):
				_snake_case		     =			'''musicgen_decoder'''
				_snake_case		     =			['''past_key_values''']




				def __init__(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Dict=20_48,     SCREAMING_SNAKE_CASE_						:					List[str]=20_48,     SCREAMING_SNAKE_CASE_						:					int=24,     SCREAMING_SNAKE_CASE_						:					Dict=40_96,     SCREAMING_SNAKE_CASE_						:					str=16,     SCREAMING_SNAKE_CASE_						:					int=0.0,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=True,     SCREAMING_SNAKE_CASE_						:					str="gelu",     SCREAMING_SNAKE_CASE_						:					List[Any]=10_24,     SCREAMING_SNAKE_CASE_						:					int=0.1,     SCREAMING_SNAKE_CASE_						:					List[Any]=0.0,     SCREAMING_SNAKE_CASE_						:					int=0.0,     SCREAMING_SNAKE_CASE_						:					str=0.02,     SCREAMING_SNAKE_CASE_						:					Any=False,     SCREAMING_SNAKE_CASE_						:					List[str]=4,     SCREAMING_SNAKE_CASE_						:					str=20_48,     SCREAMING_SNAKE_CASE_						:					int=20_48,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=False,     **SCREAMING_SNAKE_CASE_						:					Tuple,     ):
										snake_case							:  Optional[int]			=     vocab_size
										snake_case							:  Optional[int]			=     max_position_embeddings
										snake_case							:  Dict			=     hidden_size
										snake_case							:  Any			=     ffn_dim
										snake_case							:  Optional[Any]			=     num_hidden_layers
										snake_case							:  Tuple			=     num_attention_heads
										snake_case							:  int			=     dropout
										snake_case							:  Union[str, Any]			=     attention_dropout
										snake_case							:  Optional[int]			=     activation_dropout
										snake_case							:  Union[str, Any]			=     activation_function
										snake_case							:  Tuple			=     initializer_factor
										snake_case							:  List[Any]			=     layerdrop
										snake_case							:  Optional[int]			=     use_cache
										snake_case							:  Dict			=     scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case							:  Tuple			=     num_codebooks
										super().__init__(
										    pad_token_id=SCREAMING_SNAKE_CASE_,     bos_token_id=SCREAMING_SNAKE_CASE_,     eos_token_id=SCREAMING_SNAKE_CASE_,     tie_word_embeddings=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     )




class     a (					__magic_name__     ):
				_snake_case		     =			'''musicgen'''
				_snake_case		     =			True




				def __init__(				self						:					Dict,     **SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										super().__init__(**SCREAMING_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							:  List[Any]			=     text_encoder_config.pop('''model_type'''   )

										snake_case							:  Union[str, Any]			=     kwargs.pop('''audio_encoder'''   )
										snake_case							:  Tuple			=     audio_encoder_config.pop('''model_type'''   )

										snake_case							:  List[str]			=     kwargs.pop('''decoder'''   )

										snake_case							:  List[str]			=     AutoConfig.for_model(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[Any]			=     AutoConfig.for_model(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[Any]			=     MusicgenDecoderConfig(**SCREAMING_SNAKE_CASE_   )
										snake_case							:  Any			=     True




				@classmethod
				def 						__snake_case				(				cls						:					Tuple,     SCREAMING_SNAKE_CASE_						:					PretrainedConfig,     SCREAMING_SNAKE_CASE_						:					PretrainedConfig,     SCREAMING_SNAKE_CASE_						:					MusicgenDecoderConfig,     **SCREAMING_SNAKE_CASE_						:					Optional[Any],     ):
										return cls(
										    text_encoder=text_encoder_config.to_dict(),     audio_encoder=audio_encoder_config.to_dict(),     decoder=decoder_config.to_dict(),     **SCREAMING_SNAKE_CASE_,     )




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

