import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
    import jax.numpy as jnp

if is_tf_available():
    import tensorflow as tf

if is_torch_available():
    import torch



class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				List[Any]:
         A						:     List[str]         ={
             'task_specific_params': {
                 'summarization': {'length_penalty': 1.0, 'max_length': 1_28, 'min_length': 12, 'num_beams': 4},
                 'summarization_cnn': {'length_penalty': 2.0, 'max_length': 1_42, 'min_length': 56, 'num_beams': 4},
                 'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
             }
         }
         A						:     List[str]         ={
             'task_specific_params.summarization.length_penalty': 1.0,
             'task_specific_params.summarization.max_length': 1_28,
             'task_specific_params.summarization.min_length': 12,
             'task_specific_params.summarization.num_beams': 4,
             'task_specific_params.summarization_cnn.length_penalty': 2.0,
             'task_specific_params.summarization_cnn.max_length': 1_42,
             'task_specific_params.summarization_cnn.min_length': 56,
             'task_specific_params.summarization_cnn.num_beams': 4,
             'task_specific_params.summarization_xsum.length_penalty': 1.0,
             'task_specific_params.summarization_xsum.max_length': 62,
             'task_specific_params.summarization_xsum.min_length': 11,
             'task_specific_params.summarization_xsum.num_beams': 6,
         }

         self.assertEqual(flatten_dict(SCREAMING_SNAKE_CASE__     )      ,	SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Any:
         A						:     str         =np.random.randn(3      ,	4     )
         self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__     )      ,	x.transpose()     )     )

         A						:     Optional[int]         =np.random.randn(3      ,	4      ,	5     )
         self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__      ,	axes=(1, 2, 0)     )      ,	x.transpose((1, 2, 0)     )     )     )







  @require_torch
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Union[str, Any]:
         A						:     int         =np.random.randn(3      ,	4     )
         A						:     List[Any]         =torch.tensor(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__     )      ,	transpose(SCREAMING_SNAKE_CASE__     ).numpy()     )     )

         A						:     Any         =np.random.randn(3      ,	4      ,	5     )
         A						:     Optional[int]         =torch.tensor(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__      ,	axes=(1, 2, 0)     )      ,	transpose(SCREAMING_SNAKE_CASE__      ,	axes=(1, 2, 0)     ).numpy()     )     )







  @require_tf
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				int:
         A						:     Optional[Any]         =np.random.randn(3      ,	4     )
         A						:     List[Any]         =tf.constant(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__     )      ,	transpose(SCREAMING_SNAKE_CASE__     ).numpy()     )     )

         A						:     Union[str, Any]         =np.random.randn(3      ,	4      ,	5     )
         A						:     List[Any]         =tf.constant(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__      ,	axes=(1, 2, 0)     )      ,	transpose(SCREAMING_SNAKE_CASE__      ,	axes=(1, 2, 0)     ).numpy()     )     )







  @require_flax
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				Optional[int]:
         A						:     str         =np.random.randn(3      ,	4     )
         A						:     Tuple         =jnp.array(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__     )      ,	np.asarray(transpose(SCREAMING_SNAKE_CASE__     )     )     )     )

         A						:     Optional[Any]         =np.random.randn(3      ,	4      ,	5     )
         A						:     Union[str, Any]         =jnp.array(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__      ,	axes=(1, 2, 0)     )      ,	np.asarray(transpose(SCREAMING_SNAKE_CASE__      ,	axes=(1, 2, 0)     )     )     )     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Optional[Any]:
         A						:     List[str]         =np.random.randn(3      ,	4     )
         self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__      ,	(4, 3)     )      ,	np.reshape(SCREAMING_SNAKE_CASE__      ,	(4, 3)     )     )     )

         A						:     Union[str, Any]         =np.random.randn(3      ,	4      ,	5     )
         self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__      ,	(12, 5)     )      ,	np.reshape(SCREAMING_SNAKE_CASE__      ,	(12, 5)     )     )     )







  @require_torch
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Dict:
         A						:     Union[str, Any]         =np.random.randn(3      ,	4     )
         A						:     List[Any]         =torch.tensor(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__      ,	(4, 3)     )      ,	reshape(SCREAMING_SNAKE_CASE__      ,	(4, 3)     ).numpy()     )     )

         A						:     Optional[int]         =np.random.randn(3      ,	4      ,	5     )
         A						:     Any         =torch.tensor(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__      ,	(12, 5)     )      ,	reshape(SCREAMING_SNAKE_CASE__      ,	(12, 5)     ).numpy()     )     )







  @require_tf
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				List[str]:
         A						:     str         =np.random.randn(3      ,	4     )
         A						:     int         =tf.constant(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__      ,	(4, 3)     )      ,	reshape(SCREAMING_SNAKE_CASE__      ,	(4, 3)     ).numpy()     )     )

         A						:     List[Any]         =np.random.randn(3      ,	4      ,	5     )
         A						:     List[str]         =tf.constant(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__      ,	(12, 5)     )      ,	reshape(SCREAMING_SNAKE_CASE__      ,	(12, 5)     ).numpy()     )     )







  @require_flax
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				str:
         A						:     Any         =np.random.randn(3      ,	4     )
         A						:     List[str]         =jnp.array(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__      ,	(4, 3)     )      ,	np.asarray(reshape(SCREAMING_SNAKE_CASE__      ,	(4, 3)     )     )     )     )

         A						:     str         =np.random.randn(3      ,	4      ,	5     )
         A						:     Tuple         =jnp.array(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__      ,	(12, 5)     )      ,	np.asarray(reshape(SCREAMING_SNAKE_CASE__      ,	(12, 5)     )     )     )     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				Optional[Any]:
         A						:     List[str]         =np.random.randn(1      ,	3      ,	4     )
         self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__     )      ,	np.squeeze(SCREAMING_SNAKE_CASE__     )     )     )

         A						:     Dict         =np.random.randn(1      ,	4      ,	1      ,	5     )
         self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__      ,	axis=2     )      ,	np.squeeze(SCREAMING_SNAKE_CASE__      ,	axis=2     )     )     )







  @require_torch
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				List[Any]:
         A						:     int         =np.random.randn(1      ,	3      ,	4     )
         A						:     Optional[int]         =torch.tensor(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__     )      ,	squeeze(SCREAMING_SNAKE_CASE__     ).numpy()     )     )

         A						:     Tuple         =np.random.randn(1      ,	4      ,	1      ,	5     )
         A						:     Any         =torch.tensor(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__      ,	axis=2     )      ,	squeeze(SCREAMING_SNAKE_CASE__      ,	axis=2     ).numpy()     )     )







  @require_tf
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Optional[Any]:
         A						:     Optional[Any]         =np.random.randn(1      ,	3      ,	4     )
         A						:     Union[str, Any]         =tf.constant(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__     )      ,	squeeze(SCREAMING_SNAKE_CASE__     ).numpy()     )     )

         A						:     Any         =np.random.randn(1      ,	4      ,	1      ,	5     )
         A						:     Optional[Any]         =tf.constant(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__      ,	axis=2     )      ,	squeeze(SCREAMING_SNAKE_CASE__      ,	axis=2     ).numpy()     )     )







  @require_flax
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				int:
         A						:     List[str]         =np.random.randn(1      ,	3      ,	4     )
         A						:     int         =jnp.array(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__     )      ,	np.asarray(squeeze(SCREAMING_SNAKE_CASE__     )     )     )     )

         A						:     List[Any]         =np.random.randn(1      ,	4      ,	1      ,	5     )
         A						:     Any         =jnp.array(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__      ,	axis=2     )      ,	np.asarray(squeeze(SCREAMING_SNAKE_CASE__      ,	axis=2     )     )     )     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				int:
         A						:     Tuple         =np.random.randn(3      ,	4     )
         self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__      ,	axis=1     )      ,	np.expand_dims(SCREAMING_SNAKE_CASE__      ,	axis=1     )     )     )







  @require_torch
  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Tuple:
         A						:     Union[str, Any]         =np.random.randn(3      ,	4     )
         A						:     Optional[Any]         =torch.tensor(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__      ,	axis=1     )      ,	expand_dims(SCREAMING_SNAKE_CASE__      ,	axis=1     ).numpy()     )     )







  @require_tf
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				str:
         A						:     str         =np.random.randn(3      ,	4     )
         A						:     int         =tf.constant(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__      ,	axis=1     )      ,	expand_dims(SCREAMING_SNAKE_CASE__      ,	axis=1     ).numpy()     )     )







  @require_flax
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				Any:
         A						:     Optional[int]         =np.random.randn(3      ,	4     )
         A						:     Optional[int]         =jnp.array(SCREAMING_SNAKE_CASE__     )
         self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__      ,	axis=1     )      ,	np.asarray(expand_dims(SCREAMING_SNAKE_CASE__      ,	axis=1     )     )     )     )







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  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						lowerCAmelCase_   ,						lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Tuple            =				[r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]







  @register_to_config
  def __init__(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					int = 5_02_57      ,	SCREAMING_SNAKE_CASE__    :					int = 10_24      ,	SCREAMING_SNAKE_CASE__    :					int = 7_68      ,	SCREAMING_SNAKE_CASE__    :					int = 12      ,	SCREAMING_SNAKE_CASE__    :					int = 12      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					str = "gelu_new"      ,	SCREAMING_SNAKE_CASE__    :					float = 0.1      ,	SCREAMING_SNAKE_CASE__    :					float = 0.1      ,	SCREAMING_SNAKE_CASE__    :					float = 0.1      ,	SCREAMING_SNAKE_CASE__    :					float = 1e-5      ,	SCREAMING_SNAKE_CASE__    :					float = 0.0_2      ,	SCREAMING_SNAKE_CASE__    :					bool = True      ,	SCREAMING_SNAKE_CASE__    :					bool = True      ,	SCREAMING_SNAKE_CASE__    :					bool = False      ,	SCREAMING_SNAKE_CASE__    :					bool = False      ,	)    ->				List[str]:
         super().__init__()

         A						:     str         =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[Any]         =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						:     Optional[int]         =(
             nn.Linear(self.prefix_hidden_dim      ,	SCREAMING_SNAKE_CASE__     ) if self.prefix_hidden_dim is not None else nn.Identity()
         )

         A						:     Dict         =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__      ,	)
         A						:     Dict         =GPTaLMHeadModel(SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					torch.Tensor      ,	SCREAMING_SNAKE_CASE__    :					torch.Tensor      ,	SCREAMING_SNAKE_CASE__    :					Optional[torch.Tensor] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[torch.Tensor] = None      ,	)    ->				Optional[Any]:
         A						:     str         =self.transformer.transformer.wte(SCREAMING_SNAKE_CASE__     )
         A						:     Any         =self.encode_prefix(SCREAMING_SNAKE_CASE__     )
         A						:     Optional[Any]         =self.decode_prefix(SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         =torch.cat((prefix_embeds, embedding_text)      ,	dim=1     )

         if labels is not None:
                A						:     int         =self.get_dummy_token(input_ids.shape[0]      ,	input_ids.device     )
                A						:     Optional[int]         =torch.cat((dummy_token, input_ids)      ,	dim=1     )
         A						:     Dict         =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 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					torch.device     )    ->				torch.Tensor:
         return torch.zeros(SCREAMING_SNAKE_CASE__      ,	self.prefix_length      ,	dtype=torch.intaa      ,	device=SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]     )    ->				List[str]:
         return self.encode_prefix(SCREAMING_SNAKE_CASE__     )







  @torch.no_grad()
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Any     )    ->				Dict:
         A						:     Dict         =torch.split(SCREAMING_SNAKE_CASE__      ,	1      ,	dim=0     )
         A						:     int         =[]
         A						:     Optional[int]         =[]
         for feature in features:
                A						:     int         =self.decode_prefix(feature.to(SCREAMING_SNAKE_CASE__     )     )  # back to the clip feature
                # Only support beam search for now
                A  ,  A						:     Dict         =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]     )
         A						:     str         =torch.stack(SCREAMING_SNAKE_CASE__     )
         A						:     int         =torch.stack(SCREAMING_SNAKE_CASE__     )
         return generated_tokens, generated_seq_lengths







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

         if input_embeds is not None:
                A						:     Any         =input_embeds
         else:
                A						:     List[Any]         =self.transformer.transformer.wte(SCREAMING_SNAKE_CASE__     )

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

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

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

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






