






'''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     a_						(      lowerCamelCase     ,	lowerCamelCase     ,	lowerCamelCase    ):
  lowercase							=				[r"""h\.\d+\.attn\.bias""", r"""h\.\d+\.attn\.masked_bias"""]

  @register_to_config
  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = 50257      ,		_SCREAMING_SNAKE_CASE = 1024      ,		_SCREAMING_SNAKE_CASE = 768      ,		_SCREAMING_SNAKE_CASE = 12      ,		_SCREAMING_SNAKE_CASE = 12      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = "gelu_new"      ,		_SCREAMING_SNAKE_CASE = 0.1      ,		_SCREAMING_SNAKE_CASE = 0.1      ,		_SCREAMING_SNAKE_CASE = 0.1      ,		_SCREAMING_SNAKE_CASE = 1e-5      ,		_SCREAMING_SNAKE_CASE = 0.0_2      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = False      ,		_SCREAMING_SNAKE_CASE = False      ,		)  ->				Optional[Any]:



        """simple docstring"""






        super().__init__()

        UpperCamelCase      = 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."				)

        UpperCamelCase      = prefix_inner_dim
        UpperCamelCase      = prefix_hidden_dim

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

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

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		)  ->				int:



        """simple docstring"""






        UpperCamelCase      = self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = self.encode_prefix(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = self.decode_prefix(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = torch.cat((prefix_embeds, embedding_text)      ,		dim=1				)

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

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				torch.Tensor:



        """simple docstring"""






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

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Tuple:



        """simple docstring"""






        return self.encode_prefix(_SCREAMING_SNAKE_CASE				)

  @torch.no_grad()
  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Tuple:



        """simple docstring"""






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


  @torch.no_grad()
  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE = 5      ,		_SCREAMING_SNAKE_CASE = 67      ,		_SCREAMING_SNAKE_CASE = 1.0      ,		_SCREAMING_SNAKE_CASE = None      ,		)  ->				str:



        """simple docstring"""






        UpperCamelCase      = eos_token_id
        UpperCamelCase      = None
        UpperCamelCase      = None
        UpperCamelCase      = torch.ones(_SCREAMING_SNAKE_CASE      ,		device=_SCREAMING_SNAKE_CASE      ,		dtype=torch.int				)
        UpperCamelCase      = torch.zeros(_SCREAMING_SNAKE_CASE      ,		device=_SCREAMING_SNAKE_CASE      ,		dtype=torch.bool				)

        if input_embeds is not None:
              UpperCamelCase      = input_embeds
        else:
              UpperCamelCase      = self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE				)

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

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

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

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








'''simple docstring'''

from __future__ import annotations

import collections
import pprint
from pathlib import Path







def    lowercase__			(				__UpperCamelCase   )->			str:
      return "".join(sorted(__UpperCamelCase   )   )







def    lowercase__			(				__UpperCamelCase   )->			list[str]:
      return word_by_signature[signature(__UpperCamelCase   )]


SCREAMING_SNAKE_CASE__     =       Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
SCREAMING_SNAKE_CASE__     =       sorted({word.strip().lower() for word in data.splitlines()})

SCREAMING_SNAKE_CASE__     =       collections.defaultdict(list)
for word in word_list:
     word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

     with open('anagrams.txt', 'w') as file:
          file.write('all_anagrams = \n ')
          file.write(pprint.pformat(all_anagrams))
