


'''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_     (__UpperCAmelCase			,		__UpperCAmelCase			,		__UpperCAmelCase   ):
					UpperCAmelCase__      :					List[str]					   =    [R'''h\.\d+\.attn\.bias''', R'''h\.\d+\.attn\.masked_bias''']
					@register_to_config
					def __init__(   self     :int    ,__snake_case     :int    ,__snake_case     :int    ,__snake_case     :Optional[int] = None    ,__snake_case     :int = 5_02_57    ,__snake_case     :int = 10_24    ,__snake_case     :int = 7_68    ,__snake_case     :int = 12    ,__snake_case     :int = 12    ,__snake_case     :Optional[int] = None    ,__snake_case     :str = "gelu_new"    ,__snake_case     :float = 0.1    ,__snake_case     :float = 0.1    ,__snake_case     :float = 0.1    ,__snake_case     :float = 1E-5    ,__snake_case     :float = 0.02    ,__snake_case     :bool = True    ,__snake_case     :bool = True    ,__snake_case     :bool = False    ,__snake_case     :bool = False    ,)      ->					List[Any]:
												super().__init__()

												a__    =  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__    =  prefix_inner_dim
												a__    =  prefix_hidden_dim

												a__    =  (
												    nn.Linear(self.prefix_inner_dim    ,self.prefix_hidden_dim   )
												    if self.prefix_hidden_dim is not None
												    else nn.Identity()
												)
												a__    =  (
												    nn.Linear(self.prefix_hidden_dim    ,lowerCAmelCase_   ) if self.prefix_hidden_dim is not None else nn.Identity()
												)

												a__    =  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__    =  GPTaLMHeadModel(lowerCAmelCase_   )
					def 				lowerCamelCase__(   self     :int    ,__snake_case     :torch.Tensor    ,__snake_case     :torch.Tensor    ,__snake_case     :Optional[torch.Tensor] = None    ,__snake_case     :Optional[torch.Tensor] = None    ,)      ->					Tuple:
												a__    =  self.transformer.transformer.wte(lowerCAmelCase_   )
												a__    =  self.encode_prefix(lowerCAmelCase_   )
												a__    =  self.decode_prefix(lowerCAmelCase_   )
												a__    =  torch.cat((prefix_embeds, embedding_text)    ,dim=1   )

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



					@torch.no_grad()
					def 				lowerCamelCase__(   self     :int    ,__snake_case     :List[str]=None    ,__snake_case     :Tuple=None    ,__snake_case     :str=None    ,__snake_case     :int = 5    ,__snake_case     :int = 67    ,__snake_case     :float = 1.0    ,__snake_case     :Optional[int] = None    ,)      ->					Union[str, Any]:
												a__    =  eos_token_id
												a__    =  None
												a__    =  None
												a__    =  torch.ones(lowerCAmelCase_    ,device=lowerCAmelCase_    ,dtype=torch.int   )
												a__    =  torch.zeros(lowerCAmelCase_    ,device=lowerCAmelCase_    ,dtype=torch.bool   )

												if input_embeds is not None:
																			a__    =  input_embeds
												else:
																			a__    =  self.transformer.transformer.wte(lowerCAmelCase_   )

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

																			if scores is None:
																										a__   ,      a__    =  logits.topk(lowerCAmelCase_    ,-1   )
																										a__    =  generated.expand(lowerCAmelCase_    ,*generated.shape[1:]   )
																										a__   ,      a__    =  next_tokens.permute(1    ,0   ), scores.squeeze(0   )
																										if tokens is None:
																																	a__    =  next_tokens
																										else:
																																	a__    =  tokens.expand(lowerCAmelCase_    ,*tokens.shape[1:]   )
																																	a__    =  torch.cat((tokens, next_tokens)    ,dim=1   )
																			else:
																										a__    =  -float(np.inf   )
																										a__    =  0
																										a__    =  scores[:, None] + logits
																										seq_lengths[~is_stopped] += 1
																										a__    =  scores_sum / seq_lengths[:, None]
																										a__   ,      a__    =  scores_sum_average.view(-1   ).topk(lowerCAmelCase_    ,-1   )
																										a__    =  next_tokens // scores_sum.shape[1]
																										a__    =  seq_lengths[next_tokens_source]
																										a__    =  next_tokens % scores_sum.shape[1]
																										a__    =  next_tokens.unsqueeze(1   )
																										a__    =  tokens[next_tokens_source]
																										a__    =  torch.cat((tokens, next_tokens)    ,dim=1   )
																										a__    =  generated[next_tokens_source]
																										a__    =  scores_sum_average * seq_lengths
																										a__    =  is_stopped[next_tokens_source]

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

												a__    =  scores / seq_lengths
												a__    =  scores.argsort(descending=lowerCAmelCase_   )
												# tokens tensors are already padded to max_seq_length
												a__    =  [tokens[i] for i in order]
												a__    =  torch.stack(lowerCAmelCase_    ,dim=0   )
												a__    =  torch.tensor([seq_lengths[i] for i in order]    ,dtype=seq_lengths.dtype   )
												return output_texts, seq_lengths








def 							__lowercase     (				__lowerCAmelCase							:       int = 2_0_0     ):
							a__    =  [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
							a__    =  [0] * (pence + 1)
							a__    =  1  # base case: 1 way to make 0 pence

							for coin in coins:
														for i in range(__lowerCAmelCase      ,					pence + 1      ,					1     ):
																					number_of_ways[i] += number_of_ways[i - coin]
							return number_of_ways[pence]


if __name__ == "__main__":
						assert solution(2_00) == 7_36_82




