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						(					__A					,       __A					,       __A  ):


				'''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 = 5_0_2_5_7		,	lowercase = 1_0_2_4		,	lowercase = 7_6_8		,	lowercase = 1_2		,	lowercase = 1_2		,	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		,	):



						"""simple docstring"""





						super().__init__()

						A_		:				Optional[Any]     =      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_		:				List[str]     =      prefix_inner_dim
						A_		:				Dict     =      prefix_hidden_dim

						A_		:				List[str]     =      (
						    nn.Linear(self.prefix_inner_dim		,	self.prefix_hidden_dim					)
						    if self.prefix_hidden_dim is not None
						    else nn.Identity()
						)
						A_		:				int     =      (
						    nn.Linear(self.prefix_hidden_dim		,	__A					) if self.prefix_hidden_dim is not None else nn.Identity()
						)

						A_		:				Union[str, Any]     =      GPTaConfig(
						    vocab_size=__A		,	n_positions=__A		,	n_embd=__A		,	n_layer=__A		,	n_head=__A		,	n_inner=__A		,	activation_function=__A		,	resid_pdrop=__A		,	embd_pdrop=__A		,	attn_pdrop=__A		,	layer_norm_epsilon=__A		,	initializer_range=__A		,	scale_attn_weights=__A		,	use_cache=__A		,	scale_attn_by_inverse_layer_idx=__A		,	reorder_and_upcast_attn=__A		,	)
						A_		:				int     =      GPTaLMHeadModel(__A					)
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase		,	lowercase = None		,	lowercase = None		,	):



						"""simple docstring"""





						A_		:				Dict     =      self.transformer.transformer.wte(__A					)
						A_		:				List[Any]     =      self.encode_prefix(__A					)
						A_		:				str     =      self.decode_prefix(__A					)
						A_		:				List[str]     =      torch.cat((prefix_embeds, embedding_text)		,	dim=1					)

						if labels is not None:
								A_		:				Union[str, Any]     =      self.get_dummy_token(input_ids.shape[0]		,	input_ids.device					)
								A_		:				Dict     =      torch.cat((dummy_token, input_ids)		,	dim=1					)
						A_		:				Any     =      self.transformer(inputs_embeds=__A		,	labels=__A		,	attention_mask=__A					)
						if self.prefix_hidden_dim is not None:
								return out, hidden
						else:
								return out
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase					):



						"""simple docstring"""





						return torch.zeros(__A		,	self.prefix_length		,	dtype=torch.intaa		,	device=__A					)
				def   lowerCAmelCase_   (						self		,	lowercase					):



						"""simple docstring"""





						return self.encode_prefix(__A					)
				@torch.no_grad()
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase		,	lowercase					):



						"""simple docstring"""





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







				@torch.no_grad()
				def   lowerCAmelCase_   (						self		,	lowercase=None		,	lowercase=None		,	lowercase=None		,	lowercase = 5		,	lowercase = 6_7		,	lowercase = 1.0		,	lowercase = None		,	):



						"""simple docstring"""





						A_		:				Optional[int]     =      eos_token_id
						A_		:				Optional[int]     =      None
						A_		:				Optional[int]     =      None
						A_		:				List[Any]     =      torch.ones(__A		,	device=__A		,	dtype=torch.int					)
						A_		:				int     =      torch.zeros(__A		,	device=__A		,	dtype=torch.bool					)

						if input_embeds is not None:
								A_		:				Tuple     =      input_embeds
						else:
								A_		:				str     =      self.transformer.transformer.wte(__A					)

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

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

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

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










def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if divisor % 5 == 0 or divisor % 2 == 0:
    return 0
  lowerCamelCase :					List[Any]    =				1
  lowerCamelCase :					Union[str, Any]    =				1
  while repunit:
    lowerCamelCase :					Union[str, Any]    =				(10 * repunit + 1) % divisor
    repunit_index += 1
  return repunit_index






def 		lowercase_(  SCREAMING_SNAKE_CASE_ = 1000000	):


  '''simple docstring'''

  lowerCamelCase :					List[str]    =				limit - 1
  if divisor % 2 == 0:
    divisor += 1
  while least_divisible_repunit(SCREAMING_SNAKE_CASE_	) <= limit:
    divisor += 2
  return divisor


if __name__ == "__main__":
   print(f'''{solution() = }''')



