






def    __magic_name__    (	SCREAMING_SNAKE_CASE = 200				)     ->    int:
							_lowercase							:   Optional[Any] 							=   [1, 2, 5, 10, 20, 50, 100, 200]
							_lowercase							:   List[str] 							=   [0] * (pence + 1)
							_lowercase							:   Any 							=   1  # base case: 1 way to make 0 pence

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


if __name__ == "__main__":
							assert solution(200) == 73_682










"""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 lowerCAmelCase__      (	lowercase,      lowercase,      lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     [R"""h\.\d+\.attn\.bias""", R"""h\.\d+\.attn\.masked_bias"""]
			@register_to_config
			def __init__(    self    , lowercase    , lowercase    , lowercase = None    , lowercase = 50257    , lowercase = 1024    , lowercase = 768    , lowercase = 12    , lowercase = 12    , lowercase = None    , lowercase = "gelu_new"    , lowercase = 0.1    , lowercase = 0.1    , lowercase = 0.1    , lowercase = 1E-5    , lowercase = 0.02    , lowercase = True    , lowercase = True    , lowercase = False    , lowercase = False    , ):
							super().__init__()

							_lowerCamelCase	:     Tuple       =       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	:     Optional[Any]       =       prefix_inner_dim
							_lowerCamelCase	:     Tuple       =       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	:     Tuple       =       (
							    nn.Linear(self.prefix_hidden_dim    , lowercase    ) if self.prefix_hidden_dim is not None else nn.Identity()
							)

							_lowerCamelCase	:     Any       =       GPTaConfig(
							    vocab_size=lowercase    , n_positions=lowercase    , n_embd=lowercase    , n_layer=lowercase    , n_head=lowercase    , n_inner=lowercase    , activation_function=lowercase    , resid_pdrop=lowercase    , embd_pdrop=lowercase    , attn_pdrop=lowercase    , layer_norm_epsilon=lowercase    , initializer_range=lowercase    , scale_attn_weights=lowercase    , use_cache=lowercase    , scale_attn_by_inverse_layer_idx=lowercase    , reorder_and_upcast_attn=lowercase    , )
							_lowerCamelCase	:     Dict       =       GPTaLMHeadModel(lowercase    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase = None    , lowercase = None    , ):
							_lowerCamelCase	:     List[Any]       =       self.transformer.transformer.wte(lowercase    )
							_lowerCamelCase	:     str       =       self.encode_prefix(lowercase    )
							_lowerCamelCase	:     int       =       self.decode_prefix(lowercase    )
							_lowerCamelCase	:     Optional[int]       =       torch.cat((prefix_embeds, embedding_text)    , dim=1    )

							if labels is not None:
											_lowerCamelCase	:     Any       =       self.get_dummy_token(input_ids.shape[0]    , input_ids.device    )
											_lowerCamelCase	:     int       =       torch.cat((dummy_token, input_ids)    , dim=1    )
							_lowerCamelCase	:     List[Any]       =       self.transformer(inputs_embeds=lowercase    , labels=lowercase    , attention_mask=lowercase    )
							if self.prefix_hidden_dim is not None:
											return out, hidden
							else:
											return out
			def    A_       (    self    , lowercase    , lowercase    ):
							return torch.zeros(lowercase    , self.prefix_length    , dtype=torch.intaa    , device=lowercase    )
			def    A_       (    self    , lowercase    ):
							return self.encode_prefix(lowercase    )
			@torch.no_grad()
			def    A_       (    self    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Dict       =       torch.split(lowercase    , 1    , dim=0    )
							_lowerCamelCase	:     Any       =       []
							_lowerCamelCase	:     str       =       []
							for feature in features:
											_lowerCamelCase	:     Any       =       self.decode_prefix(feature.to(lowercase    )    )  # back to the clip feature
											# Only support beam search for now
											_lowerCamelCase,					_lowerCamelCase	:     Dict       =       self.generate_beam(
											    input_embeds=lowercase    , device=lowercase    , eos_token_id=lowercase    )
											generated_tokens.append(output_tokens[0]    )
											generated_seq_lengths.append(seq_lengths[0]    )
							_lowerCamelCase	:     List[Any]       =       torch.stack(lowercase    )
							_lowerCamelCase	:     Optional[int]       =       torch.stack(lowercase    )
							return generated_tokens, generated_seq_lengths






			@torch.no_grad()
			def    A_       (    self    , lowercase=None    , lowercase=None    , lowercase=None    , lowercase = 5    , lowercase = 67    , lowercase = 1.0    , lowercase = None    , ):
							_lowerCamelCase	:     Any       =       eos_token_id
							_lowerCamelCase	:     List[str]       =       None
							_lowerCamelCase	:     List[Any]       =       None
							_lowerCamelCase	:     List[str]       =       torch.ones(lowercase    , device=lowercase    , dtype=torch.int    )
							_lowerCamelCase	:     Optional[int]       =       torch.zeros(lowercase    , device=lowercase    , dtype=torch.bool    )

							if input_embeds is not None:
											_lowerCamelCase	:     Optional[int]       =       input_embeds
							else:
											_lowerCamelCase	:     Union[str, Any]       =       self.transformer.transformer.wte(lowercase    )

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

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

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

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