


'''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   UpperCAmelCase_    (_UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   Any					=     [r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']







    @register_to_config
    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = 5_02_57     ,	SCREAMING_SNAKE_CASE_ = 10_24     ,	SCREAMING_SNAKE_CASE_ = 7_68     ,	SCREAMING_SNAKE_CASE_ = 12     ,	SCREAMING_SNAKE_CASE_ = 12     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = "gelu_new"     ,	SCREAMING_SNAKE_CASE_ = 0.1     ,	SCREAMING_SNAKE_CASE_ = 0.1     ,	SCREAMING_SNAKE_CASE_ = 0.1     ,	SCREAMING_SNAKE_CASE_ = 1E-5     ,	SCREAMING_SNAKE_CASE_ = 0.0_2     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = False     ,	)							->	Tuple:
         super().__init__()

         __lowerCamelCase  :      int														=						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.'      )

         __lowerCamelCase  :      List[Any]														=						prefix_inner_dim
         __lowerCamelCase  :      Any														=						prefix_hidden_dim

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

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







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	)							->	List[str]:
         __lowerCamelCase  :      Any														=						self.transformer.transformer.wte(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Tuple														=						self.encode_prefix(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      List[str]														=						self.decode_prefix(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Any														=						torch.cat((prefix_embeds, embedding_text)     ,	dim=1      )

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







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	torch.Tensor:
         return torch.zeros(SCREAMING_SNAKE_CASE_     ,	self.prefix_length     ,	dtype=torch.intaa     ,	device=SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         return self.encode_prefix(SCREAMING_SNAKE_CASE_      )







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







    @torch.no_grad()
    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_ = 5     ,	SCREAMING_SNAKE_CASE_ = 67     ,	SCREAMING_SNAKE_CASE_ = 1.0     ,	SCREAMING_SNAKE_CASE_ = None     ,	)							->	Union[str, Any]:
         __lowerCamelCase  :      Dict														=						eos_token_id
         __lowerCamelCase  :      Dict														=						None
         __lowerCamelCase  :      List[str]														=						None
         __lowerCamelCase  :      Tuple														=						torch.ones(SCREAMING_SNAKE_CASE_     ,	device=SCREAMING_SNAKE_CASE_     ,	dtype=torch.int      )
         __lowerCamelCase  :      Dict														=						torch.zeros(SCREAMING_SNAKE_CASE_     ,	device=SCREAMING_SNAKE_CASE_     ,	dtype=torch.bool      )

         if input_embeds is not None:
              __lowerCamelCase  :      Any														=						input_embeds
         else:
              __lowerCamelCase  :      Dict														=						self.transformer.transformer.wte(SCREAMING_SNAKE_CASE_      )

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

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

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

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












class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     {}



 def  lowerCAmelCase_    (				self    :						List[str]		):
       print(self.vertex		)
       for i in self.vertex:
             print(_lowerCAmelCase							,							' -> '							,							' -> '.join([str(_lowerCAmelCase		) for j in self.vertex[i]]		)		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						int		):
       # check if vertex is already present,
       if from_vertex in self.vertex:
             self.vertex[from_vertex].append(_lowerCAmelCase		)
       else:
             # else make a new vertex
             SCREAMING_SNAKE_CASE_      		=     [to_vertex]



 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       # visited array for storing already visited nodes
       SCREAMING_SNAKE_CASE_      		=     [False] * len(self.vertex		)

       # call the recursive helper function
       for i in range(len(self.vertex		)		):
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase							,							_lowerCAmelCase		)




 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						list		):
       # mark start vertex as visited
       SCREAMING_SNAKE_CASE_      		=     True

       print(_lowerCAmelCase							,							end=' '		)

       # Recur for all the vertices that are adjacent to this node
       for i in self.vertex:
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase							,							_lowerCAmelCase		)


if __name__ == "__main__":
   lowerCamelCase__					:  List[Any]      							=				Graph()
   g.add_edge(0, 1)
   g.add_edge(0, 2)
   g.add_edge(1, 2)
   g.add_edge(2, 0)
   g.add_edge(2, 3)
   g.add_edge(3, 3)

   g.print_graph()
   print('DFS:')
   g.dfs()

   # OUTPUT:
   # 0  ->  1 -> 2
   # 1  ->  2
   # 2  ->  0 -> 3
   # 3  ->  3
   # DFS:
   #  0 1 2 3