

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


lowercase_			=  {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_			=  [
         "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTNeoForCausalLM",
         "GPTNeoForQuestionAnswering",
         "GPTNeoForSequenceClassification",
         "GPTNeoForTokenClassification",
         "GPTNeoModel",
         "GPTNeoPreTrainedModel",
         "load_tf_weights_in_gpt_neo",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_			=  [
         "FlaxGPTNeoForCausalLM",
         "FlaxGPTNeoModel",
         "FlaxGPTNeoPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_neo import (
              GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTNeoForCausalLM,
              GPTNeoForQuestionAnswering,
              GPTNeoForSequenceClassification,
              GPTNeoForTokenClassification,
              GPTNeoModel,
              GPTNeoPreTrainedModel,
              load_tf_weights_in_gpt_neo,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
     import sys

     lowercase_			=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




import numpy as np

from transformers import Pipeline
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int				)							->				int:





    '''simple docstring'''






    A__   				=       np.max(SCREAMING_SNAKE_CASE__   ,      axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)
    A__   				=       np.exp(outputs - maxes				)
    return shifted_exp / shifted_exp.sum(axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Dict,**lowercase_			:	Tuple		)->   Tuple:





        '''simple docstring'''







        A__   				=       {}
        if "second_text" in kwargs:
            A__   				=       kwargs['second_text']
        return preprocess_kwargs, {}, {}


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	int,lowercase_			:	Optional[int]=None		)->   List[str]:





        '''simple docstring'''







        return self.tokenizer(lowercase_,text_pair=lowercase_,return_tensors=self.framework		)


    def 						snake_case__					(				self			:	str,lowercase_			:	Dict		)->   List[str]:





        '''simple docstring'''







        return self.model(**lowercase_		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Optional[int]		)->   Dict:





        '''simple docstring'''







        A__   				=       model_outputs.logits[0].numpy()
        A__   				=       softmax(lowercase_		)

        A__   				=       np.argmax(lowercase_		)
        A__   				=       self.model.config.idalabel[best_class]
        A__   				=       probabilities[best_class].item()
        A__   				=       logits.tolist()
        return {"label": label, "score": score, "logits": logits}

