






from typing import Dict

from .base import GenericTensor, Pipeline



class 							_UpperCamelCase			(a_			):


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						**__UpperCamelCase	)->	Union[str, Any]:
           if tokenize_kwargs is None:
                __lowerCAmelCase								=					{}

           if truncation is not None:
                if "truncation" in tokenize_kwargs:
                     raise ValueError(
                         "truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"	)
                __lowerCAmelCase								=					truncation

           __lowerCAmelCase								=					tokenize_kwargs

           __lowerCAmelCase								=					{}
           if return_tensors is not None:
                __lowerCAmelCase								=					return_tensors

           return preprocess_params, {}, postprocess_params


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						**__UpperCamelCase	)->	Dict[str, GenericTensor]:
           __lowerCAmelCase								=					self.framework
           __lowerCAmelCase								=					self.tokenizer(__UpperCamelCase					,						return_tensors=__UpperCamelCase					,						**__UpperCamelCase	)
           return model_inputs


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	Tuple:
           __lowerCAmelCase								=					self.model(**__UpperCamelCase	)
           return model_outputs


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase=False	)->	Optional[int]:
           # [0] is the first available tensor, logits or last_hidden_state.
           if return_tensors:
                return model_outputs[0]
           if self.framework == "pt":
                return model_outputs[0].tolist()
           elif self.framework == "tf":
                return model_outputs[0].numpy().tolist()


      def __call__(	self					,						*__UpperCamelCase					,						**__UpperCamelCase	)->	List[Any]:
           return super().__call__(*__UpperCamelCase					,						**__UpperCamelCase	)














from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase			:					List[str]         =	logging.get_logger(__name__)

lowerCamelCase			:					Dict         =	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""biogpt"""


      def __init__(	self					,						__UpperCamelCase=4_2_3_8_4					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=2_4					,						__UpperCamelCase=1_6					,						__UpperCamelCase=4_0_9_6					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.1					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=1e-12					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=1					,						__UpperCamelCase=0					,						__UpperCamelCase=2					,						**__UpperCamelCase					,						)->	Optional[Any]:
           __lowerCAmelCase								=					vocab_size
           __lowerCAmelCase								=					max_position_embeddings
           __lowerCAmelCase								=					hidden_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								=					initializer_range
           __lowerCAmelCase								=					layer_norm_eps
           __lowerCAmelCase								=					scale_embedding
           __lowerCAmelCase								=					use_cache
           __lowerCAmelCase								=					layerdrop
           __lowerCAmelCase								=					activation_dropout
           super().__init__(pad_token_id=__UpperCamelCase					,						bos_token_id=__UpperCamelCase					,						eos_token_id=__UpperCamelCase					,						**__UpperCamelCase	)






