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 		_lowerCamelCase		( UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ):
    __a						=   [R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]
    @register_to_config
    def __init__(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = None			,			lowerCAmelCase = 50257			,			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 = 0.02			,			lowerCAmelCase = True			,			lowerCAmelCase = True			,			lowerCAmelCase = False			,			lowerCAmelCase = False			,			)							->       Optional[int]:
     super().__init__()

     SCREAMING_SNAKE_CASE__: 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.' )

     SCREAMING_SNAKE_CASE__: List[str]=   prefix_inner_dim
     SCREAMING_SNAKE_CASE__: Optional[int]=   prefix_hidden_dim

     SCREAMING_SNAKE_CASE__: Dict=   (
         nn.Linear(self.prefix_inner_dim			,			self.prefix_hidden_dim )
         if self.prefix_hidden_dim is not None
         else nn.Identity()
     )
     SCREAMING_SNAKE_CASE__: Dict=   (
         nn.Linear(self.prefix_hidden_dim			,			lowerCAmelCase ) if self.prefix_hidden_dim is not None else nn.Identity()
     )

     SCREAMING_SNAKE_CASE__: Optional[Any]=   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			,			)
     SCREAMING_SNAKE_CASE__: List[Any]=   GPTaLMHeadModel(lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			)							->       Optional[int]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.transformer.transformer.wte(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Any=   self.encode_prefix(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Optional[Any]=   self.decode_prefix(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: str=   torch.cat((prefix_embeds, embedding_text)			,			dim=1 )

     if labels is not None:
      SCREAMING_SNAKE_CASE__: Optional[int]=   self.get_dummy_token(input_ids.shape[0]			,			input_ids.device )
      SCREAMING_SNAKE_CASE__: Tuple=   torch.cat((dummy_token, input_ids)			,			dim=1 )
     SCREAMING_SNAKE_CASE__: Union[str, 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 )							->       torch.Tensor:
     return torch.zeros(lowerCAmelCase			,			self.prefix_length			,			dtype=torch.intaa			,			device=lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       Optional[int]:
     return self.encode_prefix(lowerCAmelCase )
    @torch.no_grad()
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: int=   torch.split(lowerCAmelCase			,			1			,			dim=0 )
     SCREAMING_SNAKE_CASE__: str=   []
     SCREAMING_SNAKE_CASE__: Optional[Any]=   []
     for feature in features:
      SCREAMING_SNAKE_CASE__: Optional[int]=   self.decode_prefix(feature.to(lowerCAmelCase ) )  # back to the clip feature
      # Only support beam search for now
      SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: List[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] )
     SCREAMING_SNAKE_CASE__: Dict=   torch.stack(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Optional[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			,			)							->       Any:
     SCREAMING_SNAKE_CASE__: Tuple=   eos_token_id
     SCREAMING_SNAKE_CASE__: List[Any]=   None
     SCREAMING_SNAKE_CASE__: Any=   None
     SCREAMING_SNAKE_CASE__: str=   torch.ones(lowerCAmelCase			,			device=lowerCAmelCase			,			dtype=torch.int )
     SCREAMING_SNAKE_CASE__: List[Any]=   torch.zeros(lowerCAmelCase			,			device=lowerCAmelCase			,			dtype=torch.bool )

     if input_embeds is not None:
      SCREAMING_SNAKE_CASE__: str=   input_embeds
     else:
      SCREAMING_SNAKE_CASE__: Dict=   self.transformer.transformer.wte(lowerCAmelCase )

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

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

      SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0]			,			1			,			-1 )
      SCREAMING_SNAKE_CASE__: Any=   torch.cat((generated, next_token_embed)			,			dim=1 )
      SCREAMING_SNAKE_CASE__: List[str]=   is_stopped + next_tokens.eq(lowerCAmelCase ).squeeze()
      if is_stopped.all():
       break

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






"""simple docstring"""







import math
import sys

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int ):
						"""simple docstring"""






						if number != int(lowerCamelCase_ ):
												raise ValueError("the value of input must be a natural number" )
						if number < 0:
												raise ValueError("the value of input must not be a negative number" )
						if number == 0:
												return 1
						snake_case   :			List[str]						    = [-1] * (number + 1)
						snake_case   :			str						    = 0
						for i in range(1     ,		number + 1 ):
												snake_case   :			Tuple						    = sys.maxsize
												snake_case   :			Optional[Any]						    = int(math.sqrt(lowerCamelCase_ ) )
												for j in range(1     ,		root + 1 ):
																		snake_case   :			List[str]						    = 1 + answers[i - (j**2)]
																		snake_case   :			List[str]						    = min(lowerCamelCase_     ,		lowerCamelCase_ )
												snake_case   :			Dict						    = answer
						return answers[number]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



