



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_     (lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_   ):
					UpperCAmelCase__      :					str					   =    [R'''h\.\d+\.attn\.bias''', R'''h\.\d+\.attn\.masked_bias''']
					@register_to_config
					def __init__(   self     :Dict    ,__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    ,)      ->					int:
												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    ,__snake_case   ) if self.prefix_hidden_dim is not None else nn.Identity()
												)

												a__    =  GPTaConfig(
												    vocab_size=__snake_case    ,n_positions=__snake_case    ,n_embd=__snake_case    ,n_layer=__snake_case    ,n_head=__snake_case    ,n_inner=__snake_case    ,activation_function=__snake_case    ,resid_pdrop=__snake_case    ,embd_pdrop=__snake_case    ,attn_pdrop=__snake_case    ,layer_norm_epsilon=__snake_case    ,initializer_range=__snake_case    ,scale_attn_weights=__snake_case    ,use_cache=__snake_case    ,scale_attn_by_inverse_layer_idx=__snake_case    ,reorder_and_upcast_attn=__snake_case    ,)
												a__    =  GPTaLMHeadModel(__snake_case   )
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :torch.Tensor    ,__snake_case     :torch.Tensor    ,__snake_case     :Optional[torch.Tensor] = None    ,__snake_case     :Optional[torch.Tensor] = None    ,)      ->					Union[str, Any]:
												a__    =  self.transformer.transformer.wte(__snake_case   )
												a__    =  self.encode_prefix(__snake_case   )
												a__    =  self.decode_prefix(__snake_case   )
												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=__snake_case    ,labels=__snake_case    ,attention_mask=__snake_case   )
												if self.prefix_hidden_dim is not None:
																			return out, hidden
												else:
																			return out
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :int    ,__snake_case     :torch.device   )      ->					torch.Tensor:
												return torch.zeros(__snake_case    ,self.prefix_length    ,dtype=torch.intaa    ,device=__snake_case   )
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :Optional[Any]   )      ->					Tuple:
												return self.encode_prefix(__snake_case   )
					@torch.no_grad()
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :Tuple    ,__snake_case     :str    ,__snake_case     :Any   )      ->					Dict:
												a__    =  torch.split(__snake_case    ,1    ,dim=0   )
												a__    =  []
												a__    =  []
												for feature in features:
																			a__    =  self.decode_prefix(feature.to(__snake_case   )   )  # back to the clip feature
																			# Only support beam search for now
																			a__   ,      a__    =  self.generate_beam(
																			    input_embeds=__snake_case    ,device=__snake_case    ,eos_token_id=__snake_case   )
																			generated_tokens.append(output_tokens[0]   )
																			generated_seq_lengths.append(seq_lengths[0]   )
												a__    =  torch.stack(__snake_case   )
												a__    =  torch.stack(__snake_case   )
												return generated_tokens, generated_seq_lengths



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

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

												for i in range(__snake_case   ):
																			a__    =  self.transformer(inputs_embeds=__snake_case   )
																			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(__snake_case    ,-1   )
																										a__    =  generated.expand(__snake_case    ,*generated.shape[1:]   )
																										a__   ,      a__    =  next_tokens.permute(1    ,0   ), scores.squeeze(0   )
																										if tokens is None:
																																	a__    =  next_tokens
																										else:
																																	a__    =  tokens.expand(__snake_case    ,*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(__snake_case    ,-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(__snake_case   ).squeeze()
																			if is_stopped.all():
																										break

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








from math import pi






def 							__lowercase     (				__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       int     ):
							return 2 * pi * radius * (angle / 3_6_0)


if __name__ == "__main__":
						print(arc_length(90, 10))




