





'''simple docstring'''





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


_A				:							Optional[Any]							= logging.get_logger(__name__)

_A				:							Optional[Any]							= {
    """google/reformer-crime-and-punishment""": (
        """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"""
    ),
    """google/reformer-enwik8""": """https://huggingface.co/google/reformer-enwik8/resolve/main/config.json""",
}



class 				__snake_case  (     lowerCamelCase_						):


    '''simple docstring'''







    lowerCamelCase__						:		Dict							=  """reformer"""
    lowerCamelCase__						:		Tuple							=  ["""past_buckets_states"""]
    lowerCamelCase__						:		Optional[int]							=  {}



    def __init__(					self      ,							A_=64      ,							A_=["local", "lsh", "local", "lsh", "local", "lsh"]      ,							A_=1.0      ,							A_=True      ,							A_=[64, 64]      ,							A_=[64, 1_92]      ,							A_=0      ,							A_=2      ,							A_=5_12      ,							A_=None      ,							A_="relu"      ,							A_=0.05      ,							A_=2_56      ,							A_=0.02      ,							A_=False      ,							A_=1E-12      ,							A_=1      ,							A_=0      ,							A_=0.05      ,							A_=64      ,							A_=64      ,							A_=0.0      ,							A_=1      ,							A_=0      ,							A_=40_96      ,							A_=12      ,							A_=None      ,							A_=1      ,							A_=0      ,							A_=3_20      ,							A_=False      ,							A_=True      ,							A_=None      ,							**A_      ,							):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= hash_seed
        SCREAMING_SNAKE_CASE__       							= vocab_size
        SCREAMING_SNAKE_CASE__       							= attention_head_size
        SCREAMING_SNAKE_CASE__       							= hidden_size
        SCREAMING_SNAKE_CASE__       							= num_attention_heads
        SCREAMING_SNAKE_CASE__       							= num_hashes
        SCREAMING_SNAKE_CASE__       							= len(A_    )
        SCREAMING_SNAKE_CASE__       							= tuple(A_    ) if isinstance(A_      ,							A_    ) else num_buckets
        SCREAMING_SNAKE_CASE__       							= lsh_attn_chunk_length
        SCREAMING_SNAKE_CASE__       							= local_attn_chunk_length
        SCREAMING_SNAKE_CASE__       							= lsh_num_chunks_after
        SCREAMING_SNAKE_CASE__       							= lsh_num_chunks_before
        SCREAMING_SNAKE_CASE__       							= local_num_chunks_after
        SCREAMING_SNAKE_CASE__       							= local_num_chunks_before
        SCREAMING_SNAKE_CASE__       							= hidden_act
        SCREAMING_SNAKE_CASE__       							= feed_forward_size
        SCREAMING_SNAKE_CASE__       							= hidden_dropout_prob
        SCREAMING_SNAKE_CASE__       							= lsh_attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE__       							= local_attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE__       							= max_position_embeddings
        SCREAMING_SNAKE_CASE__       							= initializer_range
        SCREAMING_SNAKE_CASE__       							= layer_norm_eps
        SCREAMING_SNAKE_CASE__       							= axial_pos_embds
        SCREAMING_SNAKE_CASE__       							= tuple(A_    )
        SCREAMING_SNAKE_CASE__       							= tuple(A_    )
        SCREAMING_SNAKE_CASE__       							= axial_norm_std
        SCREAMING_SNAKE_CASE__       							= chunk_size_lm_head
        SCREAMING_SNAKE_CASE__       							= attn_layers
        SCREAMING_SNAKE_CASE__       							= use_cache
        SCREAMING_SNAKE_CASE__       							= classifier_dropout
        super().__init__(
            pad_token_id=A_      ,							eos_token_id=A_      ,							is_decoder=A_      ,							tie_word_embeddings=A_      ,							**A_      ,							)







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
