

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_  (						__A      ,			__A      ,			__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]







			@register_to_config
			def __init__(	self					:							str				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							Optional[int] = None				,  _UpperCamelCase					:							int = 5_0_2_5_7				,  _UpperCamelCase					:							int = 1_0_2_4				,  _UpperCamelCase					:							int = 7_6_8				,  _UpperCamelCase					:							int = 1_2				,  _UpperCamelCase					:							int = 1_2				,  _UpperCamelCase					:							Optional[int] = None				,  _UpperCamelCase					:							str = "gelu_new"				,  _UpperCamelCase					:							float = 0.1				,  _UpperCamelCase					:							float = 0.1				,  _UpperCamelCase					:							float = 0.1				,  _UpperCamelCase					:							float = 1e-5				,  _UpperCamelCase					:							float = 0.02				,  _UpperCamelCase					:							bool = True				,  _UpperCamelCase					:							bool = True				,  _UpperCamelCase					:							bool = False				,  _UpperCamelCase					:							bool = False				,  ) ->Tuple:
									super().__init__()

									snake_case_  =      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.'''				)

									snake_case_  =      prefix_inner_dim
									snake_case_  =      prefix_hidden_dim

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

									snake_case_  =      GPTaConfig(
									    vocab_size=_UpperCamelCase				,  n_positions=_UpperCamelCase				,  n_embd=_UpperCamelCase				,  n_layer=_UpperCamelCase				,  n_head=_UpperCamelCase				,  n_inner=_UpperCamelCase				,  activation_function=_UpperCamelCase				,  resid_pdrop=_UpperCamelCase				,  embd_pdrop=_UpperCamelCase				,  attn_pdrop=_UpperCamelCase				,  layer_norm_epsilon=_UpperCamelCase				,  initializer_range=_UpperCamelCase				,  scale_attn_weights=_UpperCamelCase				,  use_cache=_UpperCamelCase				,  scale_attn_by_inverse_layer_idx=_UpperCamelCase				,  reorder_and_upcast_attn=_UpperCamelCase				,  )
									snake_case_  =      GPTaLMHeadModel(_UpperCamelCase				)







			def 							snake_case__(	self					:							str				,  _UpperCamelCase					:							torch.Tensor				,  _UpperCamelCase					:							torch.Tensor				,  _UpperCamelCase					:							Optional[torch.Tensor] = None				,  _UpperCamelCase					:							Optional[torch.Tensor] = None				,  ) ->Any:
									snake_case_  =      self.transformer.transformer.wte(_UpperCamelCase				)
									snake_case_  =      self.encode_prefix(_UpperCamelCase				)
									snake_case_  =      self.decode_prefix(_UpperCamelCase				)
									snake_case_  =      torch.cat((prefix_embeds, embedding_text)				,  dim=1				)

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







			def 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							torch.device				) ->torch.Tensor:
									return torch.zeros(_UpperCamelCase				,  self.prefix_length				,  dtype=torch.intaa				,  device=_UpperCamelCase				)







			def 							snake_case__(	self					:							int				,  _UpperCamelCase					:							List[str]				) ->List[Any]:
									return self.encode_prefix(_UpperCamelCase				)







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







			@torch.no_grad()
			def 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							List[Any]=None				,  _UpperCamelCase					:							int=None				,  _UpperCamelCase					:							List[str]=None				,  _UpperCamelCase					:							int = 5				,  _UpperCamelCase					:							int = 6_7				,  _UpperCamelCase					:							float = 1.0				,  _UpperCamelCase					:							Optional[int] = None				,  ) ->List[str]:
									snake_case_  =      eos_token_id
									snake_case_  =      None
									snake_case_  =      None
									snake_case_  =      torch.ones(_UpperCamelCase				,  device=_UpperCamelCase				,  dtype=torch.int				)
									snake_case_  =      torch.zeros(_UpperCamelCase				,  device=_UpperCamelCase				,  dtype=torch.bool				)

									if input_embeds is not None:
															snake_case_  =      input_embeds
									else:
															snake_case_  =      self.transformer.transformer.wte(_UpperCamelCase				)

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

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

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

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






__lowerCamelCase			:	str					    =	6_5521



def 						A_							(	_lowerCAmelCase	)		->					int:
 UpperCamelCase      :   Any			      =  1
 UpperCamelCase      :   str			      =  0
 for plain_chr in plain_text:
  UpperCamelCase      :   List[Any]			      =  (a + ord(_lowerCAmelCase	)) % MOD_ADLER
  UpperCamelCase      :   List[Any]			      =  (b + a) % MOD_ADLER
 return (b << 16) | a
