





from collections import OrderedDict
from typing import Mapping

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


_A				:							Any							= logging.get_logger(__name__)

_A				:							List[Any]							= {
    """google/mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"""
}



class 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    lowerCamelCase__						:		Dict							=  MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
    lowerCamelCase__						:		Optional[Any]							=  """mobilebert"""



    def __init__(					self      ,							A_=3_05_22      ,							A_=5_12      ,							A_=24      ,							A_=4      ,							A_=5_12      ,							A_="relu"      ,							A_=0.0      ,							A_=0.1      ,							A_=5_12      ,							A_=2      ,							A_=0.02      ,							A_=1E-12      ,							A_=0      ,							A_=1_28      ,							A_=True      ,							A_=True      ,							A_=1_28      ,							A_=False      ,							A_=True      ,							A_=4      ,							A_="no_norm"      ,							A_=True      ,							A_=None      ,							**A_      ,							):





        '''simple docstring'''

        super().__init__(pad_token_id=A_      ,							**A_    )

        SCREAMING_SNAKE_CASE__       							= vocab_size
        SCREAMING_SNAKE_CASE__       							= hidden_size
        SCREAMING_SNAKE_CASE__       							= num_hidden_layers
        SCREAMING_SNAKE_CASE__       							= num_attention_heads
        SCREAMING_SNAKE_CASE__       							= hidden_act
        SCREAMING_SNAKE_CASE__       							= intermediate_size
        SCREAMING_SNAKE_CASE__       							= hidden_dropout_prob
        SCREAMING_SNAKE_CASE__       							= attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE__       							= max_position_embeddings
        SCREAMING_SNAKE_CASE__       							= type_vocab_size
        SCREAMING_SNAKE_CASE__       							= initializer_range
        SCREAMING_SNAKE_CASE__       							= layer_norm_eps
        SCREAMING_SNAKE_CASE__       							= embedding_size
        SCREAMING_SNAKE_CASE__       							= trigram_input
        SCREAMING_SNAKE_CASE__       							= use_bottleneck
        SCREAMING_SNAKE_CASE__       							= intra_bottleneck_size
        SCREAMING_SNAKE_CASE__       							= use_bottleneck_attention
        SCREAMING_SNAKE_CASE__       							= key_query_shared_bottleneck
        SCREAMING_SNAKE_CASE__       							= num_feedforward_networks
        SCREAMING_SNAKE_CASE__       							= normalization_type
        SCREAMING_SNAKE_CASE__       							= classifier_activation

        if self.use_bottleneck:
            SCREAMING_SNAKE_CASE__       							= intra_bottleneck_size
        else:
            SCREAMING_SNAKE_CASE__       							= hidden_size

        SCREAMING_SNAKE_CASE__       							= classifier_dropout





class 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    @property
    def    lowercase_				(					self    ):





        '''simple docstring'''

        if self.task == "multiple-choice":
            SCREAMING_SNAKE_CASE__       							= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
            SCREAMING_SNAKE_CASE__       							= {0: '''batch''', 1: '''sequence'''}
        return OrderedDict(
            [
                ('''input_ids''', dynamic_axis),
                ('''attention_mask''', dynamic_axis),
                ('''token_type_ids''', dynamic_axis),
            ]    )







import torch

from transformers import AutoTokenizer



class 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    def __init__(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= AutoTokenizer.from_pretrained(A_    )



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        if isinstance(A_      ,							A_    ) and all(isinstance(_x      ,							A_    ) for _x in x    ):
            SCREAMING_SNAKE_CASE__       							= None
            for l in x:
                SCREAMING_SNAKE_CASE__       							= self.tokenizer(
                    A_      ,							padding='''max_length'''      ,							max_length=3_84      ,							truncation=A_      ,							return_tensors='''pt'''      ,							)
                SCREAMING_SNAKE_CASE__       							= torch.tensor([len(A_    )]    )
                if d is not None:
                    for k in d.keys():
                        SCREAMING_SNAKE_CASE__       							= torch.cat((d[k], t[k])      ,							0    )
                else:
                    SCREAMING_SNAKE_CASE__       							= t

            SCREAMING_SNAKE_CASE__       							= torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''    )    )
            SCREAMING_SNAKE_CASE__       							= torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''    )    )

        elif isinstance(A_      ,							A_    ) and all(isinstance(_x      ,							A_    ) for _x in x    ):
            SCREAMING_SNAKE_CASE__       							= self.tokenizer(
                A_      ,							padding='''max_length'''      ,							max_length=3_84      ,							truncation=A_      ,							return_tensors='''pt'''      ,							)

        else:
            raise Exception(
                '''Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'''
                ''' strings` for supports are supported.'''    )

        return d




    def    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_=0.70    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= []
        for idx in range(len(W_query['''input_ids''']    )    ):
            SCREAMING_SNAKE_CASE__       							= SCREAMING_SNAKE_CASE__       							= range(p_start.shape[1]    )

            SCREAMING_SNAKE_CASE__       							= []
            for start_id in start_indexes:
                for end_id in end_indexes:
                    if start_id < end_id:
                        output.append(
                            (
                                start_id,
                                end_id,
                                p_start[idx][start_id].item(),
                                p_end[idx][end_id].item(),
                            )    )

            output.sort(key=lambda A_    : (tup[2] * tup[3])      ,							reverse=A_    )
            SCREAMING_SNAKE_CASE__       							= []
            for k in range(len(A_    )    ):
                if output[k][2] * output[k][3] >= thresh:
                    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= output[k][0], output[k][1]
                    SCREAMING_SNAKE_CASE__       							= self.tokenizer.decode(W_query['''input_ids'''][idx][c_start_pos:c_end_pos]    )
                    temp.append((decoded, output[k][2] * output[k][3])    )

            final_outputs.append(A_    )

        return final_outputs
