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


lowercase__		:str   =    logging.get_logger(__name__)

lowercase__		:Tuple   =    {
    "facebook/dpr-ctx_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-single-nq-base": (
        "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-ctx_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-multiset-base": (
        "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
    ),
}







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Tuple       					='''dpr'''




							def __init__(				self   ,A__=3_0_5_2_2   ,A__=7_6_8   ,A__=1_2   ,A__=1_2   ,A__=3_0_7_2   ,A__="gelu"   ,A__=0.1   ,A__=0.1   ,A__=5_1_2   ,A__=2   ,A__=0.02   ,A__=1E-12   ,A__=0   ,A__="absolute"   ,A__ = 0   ,**A__   ,):
												super().__init__(pad_token_id=A__   ,**A__)

												lowercase							=						vocab_size
												lowercase							=						hidden_size
												lowercase							=						num_hidden_layers
												lowercase							=						num_attention_heads
												lowercase							=						hidden_act
												lowercase							=						intermediate_size
												lowercase							=						hidden_dropout_prob
												lowercase							=						attention_probs_dropout_prob
												lowercase							=						max_position_embeddings
												lowercase							=						type_vocab_size
												lowercase							=						initializer_range
												lowercase							=						layer_norm_eps
												lowercase							=						projection_dim
												lowercase							=						position_embedding_type











import numpy as np

from transformers import Pipeline





def lowerCAmelCase__(__snake_case							)				->      Tuple:







	'''simple docstring'''







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






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			def 		__lowerCamelCase				(    self       ,  **__lowerCAmelCase							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=None							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self.model(**__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		model_outputs.logits[0].numpy()
				lowerCamelCase__								=		softmax(__lowerCAmelCase							)

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



