


'''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 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_    ,					SCREAMING_SNAKE_CASE_    ,					SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    [R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]







     @register_to_config
     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = 5_0257      ,	snake_case__ = 1024      ,	snake_case__ = 768      ,	snake_case__ = 12      ,	snake_case__ = 12      ,	snake_case__ = None      ,	snake_case__ = "gelu_new"      ,	snake_case__ = 0.1      ,	snake_case__ = 0.1      ,	snake_case__ = 0.1      ,	snake_case__ = 1E-5      ,	snake_case__ = 0.02      ,	snake_case__ = True      ,	snake_case__ = True      ,	snake_case__ = False      ,	snake_case__ = False      ,	):

            '''simple docstring'''

            super().__init__()

            _lowerCAmelCase    :							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.'   )

            _lowerCAmelCase    :							Any   			=       prefix_inner_dim
            _lowerCAmelCase    :							Any   			=       prefix_hidden_dim

            _lowerCAmelCase    :							str   			=       (
                nn.Linear(self.prefix_inner_dim      ,	self.prefix_hidden_dim   )
                if self.prefix_hidden_dim is not None
                else nn.Identity()
            )
            _lowerCAmelCase    :							Optional[Any]   			=       (
                nn.Linear(self.prefix_hidden_dim      ,	snake_case__   ) if self.prefix_hidden_dim is not None else nn.Identity()
            )

            _lowerCAmelCase    :							Union[str, Any]   			=       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__      ,	)
            _lowerCAmelCase    :							List[str]   			=       GPTaLMHeadModel(snake_case__   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.transformer.transformer.wte(snake_case__   )
            _lowerCAmelCase    :							str   			=       self.encode_prefix(snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       self.decode_prefix(snake_case__   )
            _lowerCAmelCase    :							Optional[int]   			=       torch.cat((prefix_embeds, embedding_text)      ,	dim=1   )

            if labels is not None:
                   _lowerCAmelCase    :							List[str]   			=       self.get_dummy_token(input_ids.shape[0]      ,	input_ids.device   )
                   _lowerCAmelCase    :							Union[str, Any]   			=       torch.cat((dummy_token, input_ids)      ,	dim=1   )
            _lowerCAmelCase    :							int   			=       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 a	( self      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            return torch.zeros(snake_case__      ,	self.prefix_length      ,	dtype=torch.intaa      ,	device=snake_case__   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            return self.encode_prefix(snake_case__   )







     @torch.no_grad()
     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       torch.split(snake_case__      ,	1      ,	dim=0   )
            _lowerCAmelCase    :							Dict   			=       []
            _lowerCAmelCase    :							str   			=       []
            for feature in features:
                   _lowerCAmelCase    :							List[Any]   			=       self.decode_prefix(feature.to(snake_case__   )   )  # back to the clip feature
                   # Only support beam search for now
                   _lowerCAmelCase			,     _lowerCAmelCase    :							List[Any]   			=       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]   )
            _lowerCAmelCase    :							Tuple   			=       torch.stack(snake_case__   )
            _lowerCAmelCase    :							List[Any]   			=       torch.stack(snake_case__   )
            return generated_tokens, generated_seq_lengths







     @torch.no_grad()
     def a	( self      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__ = 5      ,	snake_case__ = 67      ,	snake_case__ = 1.0      ,	snake_case__ = None      ,	):

            '''simple docstring'''

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

            if input_embeds is not None:
                   _lowerCAmelCase    :							int   			=       input_embeds
            else:
                   _lowerCAmelCase    :							List[Any]   			=       self.transformer.transformer.wte(snake_case__   )

            for i in range(snake_case__   ):
                   _lowerCAmelCase    :							str   			=       self.transformer(inputs_embeds=snake_case__   )
                   _lowerCAmelCase    :							Tuple   			=       outputs.logits
                   _lowerCAmelCase    :							Optional[Any]   			=       logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
                   _lowerCAmelCase    :							Any   			=       logits.softmax(-1   ).log()

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

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

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





'''simple docstring'''







lowerCAmelCase    :     Optional[int]					=						[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]






def 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							str   			=       0
       while number:
              # Increased Speed Slightly by checking every 5 digits together.
              sum_of_digits_squared += DIGITS_SQUARED[number % 1_0_0_0_0_0]
              number //= 1_0_0_0_0_0

       return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
lowerCAmelCase    :     list[bool | None]					=						[None] * 10_00_00_00
lowerCAmelCase    :     List[str]					=						True
lowerCAmelCase    :     Union[str, Any]					=						False






def 			lowercase				(_A					):







       """simple docstring"""







       if CHAINS[number - 1] is not None:
              return CHAINS[number - 1]  # type: ignore

       _lowerCAmelCase    :							Any   			=       chain(next_number(_A					)					)
       _lowerCAmelCase    :							List[str]   			=       number_chain

       while number < 1_0_0_0_0_0_0_0:
              _lowerCAmelCase    :							Tuple   			=       number_chain
              number *= 1_0

       return number_chain






def 			lowercase				(_A = 1_0_0_0_0_0_0_0					):







       """simple docstring"""







       for i in range(1   ,						_A					):
              if CHAINS[i] is None:
                     chain(i + 1					)

       return CHAINS[:number].count(_A					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(F'''{solution() = }''')

