





"""simple docstring"""



from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 						__snake_case   (						SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	[R"""h\.\d+\.attn\.bias""", R"""h\.\d+\.attn\.masked_bias"""]

 @register_to_config
 def __init__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		__lowerCamelCase = 5_0257   ,		__lowerCamelCase = 1024   ,		__lowerCamelCase = 768   ,		__lowerCamelCase = 12   ,		__lowerCamelCase = 12   ,		__lowerCamelCase = None   ,		__lowerCamelCase = "gelu_new"   ,		__lowerCamelCase = 0.1   ,		__lowerCamelCase = 0.1   ,		__lowerCamelCase = 0.1   ,		__lowerCamelCase = 1e-5   ,		__lowerCamelCase = 0.0_2   ,		__lowerCamelCase = True   ,		__lowerCamelCase = True   ,		__lowerCamelCase = False   ,		__lowerCamelCase = False   ,		):



     '''simple docstring'''




     super().__init__()

     __A       :		List[str]							=	prefix_length

     if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
         raise ValueError(
             F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
             F""" `n_embd`: {n_embd} are not equal."""    )

     __A       :		Union[str, Any]							=	prefix_inner_dim
     __A       :		Dict							=	prefix_hidden_dim

     __A       :		Union[str, Any]							=	(
         nn.Linear(self.prefix_inner_dim   ,		self.prefix_hidden_dim    )
         if self.prefix_hidden_dim is not None
         else nn.Identity()
     )
     __A       :		str							=	(
         nn.Linear(self.prefix_hidden_dim   ,		__lowerCamelCase    ) if self.prefix_hidden_dim is not None else nn.Identity()
     )

     __A       :		List[str]							=	GPTaConfig(
         vocab_size=__lowerCamelCase   ,		n_positions=__lowerCamelCase   ,		n_embd=__lowerCamelCase   ,		n_layer=__lowerCamelCase   ,		n_head=__lowerCamelCase   ,		n_inner=__lowerCamelCase   ,		activation_function=__lowerCamelCase   ,		resid_pdrop=__lowerCamelCase   ,		embd_pdrop=__lowerCamelCase   ,		attn_pdrop=__lowerCamelCase   ,		layer_norm_epsilon=__lowerCamelCase   ,		initializer_range=__lowerCamelCase   ,		scale_attn_weights=__lowerCamelCase   ,		use_cache=__lowerCamelCase   ,		scale_attn_by_inverse_layer_idx=__lowerCamelCase   ,		reorder_and_upcast_attn=__lowerCamelCase   ,		)
     __A       :		List[str]							=	GPTaLMHeadModel(__lowerCamelCase    )

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



     '''simple docstring'''




     __A       :		Optional[int]							=	self.transformer.transformer.wte(__lowerCamelCase    )
     __A       :		Any							=	self.encode_prefix(__lowerCamelCase    )
     __A       :		List[Any]							=	self.decode_prefix(__lowerCamelCase    )
     __A       :		int							=	torch.cat((prefix_embeds, embedding_text)   ,		dim=1    )

     if labels is not None:
         __A       :		List[Any]							=	self.get_dummy_token(input_ids.shape[0]   ,		input_ids.device    )
         __A       :		Union[str, Any]							=	torch.cat((dummy_token, input_ids)   ,		dim=1    )
     __A       :		List[Any]							=	self.transformer(inputs_embeds=__lowerCamelCase   ,		labels=__lowerCamelCase   ,		attention_mask=__lowerCamelCase    )
     if self.prefix_hidden_dim is not None:
         return out, hidden
     else:
         return out

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




     return torch.zeros(__lowerCamelCase   ,		self.prefix_length   ,		dtype=torch.intaa   ,		device=__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     return self.encode_prefix(__lowerCamelCase    )

 @torch.no_grad()
 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		Optional[Any]							=	torch.split(__lowerCamelCase   ,		1   ,		dim=0    )
     __A       :		Optional[int]							=	[]
     __A       :		List[Any]							=	[]
     for feature in features:
         __A       :		Any							=	self.decode_prefix(feature.to(__lowerCamelCase    )    )  # back to the clip feature
         # Only support beam search for now
         __A					,			__A       :		Any							=	self.generate_beam(
             input_embeds=__lowerCamelCase   ,		device=__lowerCamelCase   ,		eos_token_id=__lowerCamelCase    )
         generated_tokens.append(output_tokens[0]    )
         generated_seq_lengths.append(seq_lengths[0]    )
     __A       :		int							=	torch.stack(__lowerCamelCase    )
     __A       :		Union[str, Any]							=	torch.stack(__lowerCamelCase    )
     return generated_tokens, generated_seq_lengths







 @torch.no_grad()
 def    UpperCamelCase__(       self   ,		__lowerCamelCase=None   ,		__lowerCamelCase=None   ,		__lowerCamelCase=None   ,		__lowerCamelCase = 5   ,		__lowerCamelCase = 67   ,		__lowerCamelCase = 1.0   ,		__lowerCamelCase = None   ,		):



     '''simple docstring'''




     __A       :		Optional[Any]							=	eos_token_id
     __A       :		int							=	None
     __A       :		str							=	None
     __A       :		int							=	torch.ones(__lowerCamelCase   ,		device=__lowerCamelCase   ,		dtype=torch.int    )
     __A       :		List[Any]							=	torch.zeros(__lowerCamelCase   ,		device=__lowerCamelCase   ,		dtype=torch.bool    )

     if input_embeds is not None:
         __A       :		List[Any]							=	input_embeds
     else:
         __A       :		str							=	self.transformer.transformer.wte(__lowerCamelCase    )

     for i in range(__lowerCamelCase    ):
         __A       :		int							=	self.transformer(inputs_embeds=__lowerCamelCase    )
         __A       :		Any							=	outputs.logits
         __A       :		Any							=	logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
         __A       :		List[Any]							=	logits.softmax(-1    ).log()

         if scores is None:
             __A					,			__A       :		int							=	logits.topk(__lowerCamelCase   ,		-1    )
             __A       :		Optional[Any]							=	generated.expand(__lowerCamelCase   ,		*generated.shape[1:]    )
             __A					,			__A       :		List[str]							=	next_tokens.permute(1   ,		0    ), scores.squeeze(0    )
             if tokens is None:
                 __A       :		Any							=	next_tokens
             else:
                 __A       :		Dict							=	tokens.expand(__lowerCamelCase   ,		*tokens.shape[1:]    )
                 __A       :		List[Any]							=	torch.cat((tokens, next_tokens)   ,		dim=1    )
         else:
             __A       :		int							=	-float(np.inf    )
             __A       :		Union[str, Any]							=	0
             __A       :		Optional[Any]							=	scores[:, None] + logits
             seq_lengths[~is_stopped] += 1
             __A       :		Dict							=	scores_sum / seq_lengths[:, None]
             __A					,			__A       :		Any							=	scores_sum_average.view(-1    ).topk(__lowerCamelCase   ,		-1    )
             __A       :		List[Any]							=	next_tokens // scores_sum.shape[1]
             __A       :		str							=	seq_lengths[next_tokens_source]
             __A       :		Dict							=	next_tokens % scores_sum.shape[1]
             __A       :		Optional[int]							=	next_tokens.unsqueeze(1    )
             __A       :		int							=	tokens[next_tokens_source]
             __A       :		Tuple							=	torch.cat((tokens, next_tokens)   ,		dim=1    )
             __A       :		Any							=	generated[next_tokens_source]
             __A       :		Optional[int]							=	scores_sum_average * seq_lengths
             __A       :		Any							=	is_stopped[next_tokens_source]

         __A       :		Optional[Any]							=	self.transformer.transformer.wte(next_tokens.squeeze()    ).view(generated.shape[0]   ,		1   ,		-1    )
         __A       :		Optional[Any]							=	torch.cat((generated, next_token_embed)   ,		dim=1    )
         __A       :		Optional[Any]							=	is_stopped + next_tokens.eq(__lowerCamelCase    ).squeeze()
         if is_stopped.all():
             break

     __A       :		Dict							=	scores / seq_lengths
     __A       :		Optional[Any]							=	scores.argsort(descending=__lowerCamelCase    )
     # tokens tensors are already padded to max_seq_length
     __A       :		List[str]							=	[tokens[i] for i in order]
     __A       :		List[str]							=	torch.stack(__lowerCamelCase   ,		dim=0    )
     __A       :		List[str]							=	torch.tensor([seq_lengths[i] for i in order]   ,		dtype=seq_lengths.dtype    )
     return output_texts, seq_lengths











"""simple docstring"""



def 						__lowercase	(  snake_case_    :    str							,snake_case_    :    str		)  ->float:



    '''simple docstring'''







    def get_matched_characters(snake_case_    :    str							,snake_case_    :    str		) -> str:
        __A       :		Any							=	[]
        __A       :		Any							=	min(len(_stra		)							,len(_stra		)		) // 2
        for i, l in enumerate(_stra		):
            __A       :		Dict							=	int(max(0							,i - limit		)		)
            __A       :		Tuple							=	int(min(i + limit + 1							,len(_stra		)		)		)
            if l in _stra[left:right]:
                matched.append(snake_case_		)
                __A       :		Any							=	F"""{_stra[0:_stra.index(snake_case_		)]} {_stra[_stra.index(snake_case_		) + 1:]}"""

        return "".join(snake_case_		)

    # matching characters
    __A       :		int							=	get_matched_characters(snake_case_							,snake_case_		)
    __A       :		Tuple							=	get_matched_characters(snake_case_							,snake_case_		)
    __A       :		str							=	len(snake_case_		)

    # transposition
    __A       :		Dict							=	(
        len([(ca, ca) for ca, ca in zip(snake_case_							,snake_case_		) if ca != ca]		) // 2
    )

    if not match_count:
        __A       :		List[str]							=	0.0
    else:
        __A       :		Tuple							=	(
            1
            / 3
            * (
                match_count / len(snake_case_		)
                + match_count / len(snake_case_		)
                + (match_count - transpositions) / match_count
            )
        )

    # common prefix up to 4 characters
    __A       :		Tuple							=	0
    for ca, ca in zip(stra[:4]							,stra[:4]		):
        if ca == ca:
            prefix_len += 1
        else:
            break

    return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(jaro_winkler("""hello""", """world"""))




