import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def A__  (			lowercase:     int,		lowercase:     str					)				->		Dict:
       assert isinstance(lowercase,		lowercase					)
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('keep_in_memory',		[False, True]					)
def A__  (			lowercase:     Dict,		lowercase:     Tuple,		lowercase:     str					)				->		str:
       A						:     Any         =tmp_path / 'cache'
       A						:     Dict         ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              A						:     Dict         =JsonDatasetReader(lowercase,		cache_dir=lowercase,		keep_in_memory=lowercase					).read()
       _check_json_dataset(lowercase,		lowercase					)



@pytest.mark.parametrize(
    'features',		[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ],		)
def A__  (			lowercase:     Optional[int],		lowercase:     Any,		lowercase:     Union[str, Any]					)				->		Tuple:
       A						:     Tuple         =tmp_path / 'cache'
       A						:     Optional[Any]         ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       A						:     Optional[Any]         =features.copy() if features else default_expected_features
       A						:     Union[str, Any]         =(
           Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
       )
       A						:     str         =JsonDatasetReader(lowercase,		features=lowercase,		cache_dir=lowercase					).read()
       _check_json_dataset(lowercase,		lowercase					)



@pytest.mark.parametrize(
    'features',		[
        None,
        {'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'},
    ],		)
def A__  (			lowercase:     Optional[int],		lowercase:     str,		lowercase:     Dict					)				->		Optional[int]:
       A						:     int         =tmp_path / 'cache'
       A						:     Tuple         ={'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'}
       A						:     int         =features.copy() if features else default_expected_features
       A						:     str         =(
           Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
       )
       A						:     Optional[int]         =JsonDatasetReader(lowercase,		features=lowercase,		cache_dir=lowercase					).read()
       assert isinstance(lowercase,		lowercase					)
       assert dataset.num_rows == 2
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_3", "col_1", "col_2"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype



def A__  (			lowercase:     Optional[Any],		lowercase:     str					)				->		Tuple:
       # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
       A						:     str         ={'col_2': 'int64', 'col_3': 'float64', 'col_1': 'string'}
       A						:     Dict         =features.copy()
       A						:     List[str]         =(
           Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
       )
       A						:     int         =tmp_path / 'cache'
       A						:     Optional[int]         =JsonDatasetReader(lowercase,		features=lowercase,		cache_dir=lowercase					).read()
       assert isinstance(lowercase,		lowercase					)
       assert dataset.num_rows == 2
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_2", "col_3", "col_1"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('split',		[None, NamedSplit('train'					), 'train', 'test']					)
def A__  (			lowercase:     Union[str, Any],		lowercase:     Any,		lowercase:     str					)				->		Optional[Any]:
       A						:     Optional[int]         =tmp_path / 'cache'
       A						:     Optional[Any]         ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       A						:     str         =JsonDatasetReader(lowercase,		cache_dir=lowercase,		split=lowercase					).read()
       _check_json_dataset(lowercase,		lowercase					)
       assert dataset.split == split if split else "train"



@pytest.mark.parametrize('path_type',		[str, list]					)
def A__  (			lowercase:     Optional[Any],		lowercase:     int,		lowercase:     Union[str, Any]					)				->		List[Any]:
       if issubclass(lowercase,		lowercase					):
              A						:     int         =jsonl_path
       elif issubclass(lowercase,		lowercase					):
              A						:     Any         =[jsonl_path]
       A						:     Optional[Any]         =tmp_path / 'cache'
       A						:     Tuple         ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       A						:     List[str]         =JsonDatasetReader(lowercase,		cache_dir=lowercase					).read()
       _check_json_dataset(lowercase,		lowercase					)



def A__  (			lowercase:     List[str],		lowercase:     Tuple,		lowercase:     Optional[Any]=("train",)					)				->		Tuple:
       assert isinstance(lowercase,		lowercase					)
       for split in splits:
              A						:     List[str]         =dataset_dict[split]
              assert dataset.num_rows == 4
              assert dataset.num_columns == 3
              assert dataset.column_names == ["col_1", "col_2", "col_3"]
              for feature, expected_dtype in expected_features.items():
                     assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('keep_in_memory',		[False, True]					)
def A__  (			lowercase:     Tuple,		lowercase:     Optional[int],		lowercase:     Any					)				->		str:
       A						:     List[str]         =tmp_path / 'cache'
       A						:     Union[str, Any]         ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              A						:     str         =JsonDatasetReader({'train': jsonl_path},		cache_dir=lowercase,		keep_in_memory=lowercase					).read()
       _check_json_datasetdict(lowercase,		lowercase					)



@pytest.mark.parametrize(
    'features',		[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ],		)
def A__  (			lowercase:     Optional[int],		lowercase:     Optional[int],		lowercase:     Optional[int]					)				->		Tuple:
       A						:     Any         =tmp_path / 'cache'
       A						:     List[str]         ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       A						:     str         =features.copy() if features else default_expected_features
       A						:     Dict         =(
           Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
       )
       A						:     Optional[Any]         =JsonDatasetReader({'train': jsonl_path},		features=lowercase,		cache_dir=lowercase					).read()
       _check_json_datasetdict(lowercase,		lowercase					)



@pytest.mark.parametrize('split',		[None, NamedSplit('train'					), 'train', 'test']					)
def A__  (			lowercase:     Any,		lowercase:     List[Any],		lowercase:     List[Any]					)				->		Tuple:
       if split:
              A						:     Optional[int]         ={split: jsonl_path}
       else:
              A						:     Dict         ='train'
              A						:     Optional[Any]         ={'train': jsonl_path, 'test': jsonl_path}
       A						:     Tuple         =tmp_path / 'cache'
       A						:     List[str]         ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       A						:     List[Any]         =JsonDatasetReader(lowercase,		cache_dir=lowercase					).read()
       _check_json_datasetdict(lowercase,		lowercase,		splits=list(path.keys()					)					)
       assert all(dataset[split].split == split for split in path.keys()					)



def A__  (			lowercase:     List[Any]					)				->		Tuple:
       return json.load(lowercase					)



def A__  (			lowercase:     List[Any]					)				->		Tuple:
       return [json.loads(lowercase					) for line in buffer]



class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  @pytest.mark.parametrize('lines, load_json_function'      ,	[(True, load_json_lines), (False, load_json)]     )
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				Any:
         with io.BytesIO() as buffer:
                JsonDatasetWriter(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	lines=SCREAMING_SNAKE_CASE__     ).write()
                buffer.seek(0     )
                A						:     int         =load_json_function(SCREAMING_SNAKE_CASE__     )
         assert isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
         assert isinstance(exported_content[0]      ,	SCREAMING_SNAKE_CASE__     )
         assert len(SCREAMING_SNAKE_CASE__     ) == 10







  @pytest.mark.parametrize(
      'orient, container, keys, len_at'      ,	[
          ('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
          ('split', dict, {'columns', 'data'}, 'data'),
          ('index', dict, set('0123456789'     ), None),
          ('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
          ('values', list, None, None),
          ('table', dict, {'schema', 'data'}, 'data'),
      ]      ,	)
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Any     )    ->				Optional[Any]:
         with io.BytesIO() as buffer:
                JsonDatasetWriter(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	lines=SCREAMING_SNAKE_CASE__      ,	orient=SCREAMING_SNAKE_CASE__     ).write()
                buffer.seek(0     )
                A						:     Any         =load_json(SCREAMING_SNAKE_CASE__     )
         assert isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
         if keys:
                if container is dict:
                       assert exported_content.keys() == keys
                else:
                       assert exported_content[0].keys() == keys
         else:
                assert not hasattr(SCREAMING_SNAKE_CASE__      ,	'keys'     ) and not hasattr(exported_content[0]      ,	'keys'     )
         if len_at:
                assert len(exported_content[len_at]     ) == 10
         else:
                assert len(SCREAMING_SNAKE_CASE__     ) == 10







  @pytest.mark.parametrize('lines, load_json_function'      ,	[(True, load_json_lines), (False, load_json)]     )
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				Optional[int]:
         with io.BytesIO() as buffer:
                JsonDatasetWriter(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	lines=SCREAMING_SNAKE_CASE__      ,	num_proc=2     ).write()
                buffer.seek(0     )
                A						:     int         =load_json_function(SCREAMING_SNAKE_CASE__     )
         assert isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
         assert isinstance(exported_content[0]      ,	SCREAMING_SNAKE_CASE__     )
         assert len(SCREAMING_SNAKE_CASE__     ) == 10







  @pytest.mark.parametrize(
      'orient, container, keys, len_at'      ,	[
          ('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
          ('split', dict, {'columns', 'data'}, 'data'),
          ('index', dict, set('0123456789'     ), None),
          ('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
          ('values', list, None, None),
          ('table', dict, {'schema', 'data'}, 'data'),
      ]      ,	)
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				Optional[Any]:
         with io.BytesIO() as buffer:
                JsonDatasetWriter(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	lines=SCREAMING_SNAKE_CASE__      ,	orient=SCREAMING_SNAKE_CASE__      ,	num_proc=2     ).write()
                buffer.seek(0     )
                A						:     List[Any]         =load_json(SCREAMING_SNAKE_CASE__     )
         assert isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
         if keys:
                if container is dict:
                       assert exported_content.keys() == keys
                else:
                       assert exported_content[0].keys() == keys
         else:
                assert not hasattr(SCREAMING_SNAKE_CASE__      ,	'keys'     ) and not hasattr(exported_content[0]      ,	'keys'     )
         if len_at:
                assert len(exported_content[len_at]     ) == 10
         else:
                assert len(SCREAMING_SNAKE_CASE__     ) == 10







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				List[Any]:
         with pytest.raises(SCREAMING_SNAKE_CASE__     ):
                with io.BytesIO() as buffer:
                       JsonDatasetWriter(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	num_proc=0     )







  @pytest.mark.parametrize('compression, extension'      ,	[('gzip', 'gz'), ('bz2', 'bz2'), ('xz', 'xz')]     )
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Dict     )    ->				str:
         A						:     Union[str, Any]         =tmp_path_factory.mktemp('data'     ) / f'test.json.{extension}'
         A						:     Union[str, Any]         =str(shared_datadir / f'test_file.json.{extension}'     )
         JsonDatasetWriter(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	compression=SCREAMING_SNAKE_CASE__     ).write()

         with fsspec.open(SCREAMING_SNAKE_CASE__      ,	'rb'      ,	compression='infer'     ) as f:
                A						:     str         =f.read()
         with fsspec.open(SCREAMING_SNAKE_CASE__      ,	'rb'      ,	compression='infer'     ) as f:
                A						:     List[str]         =f.read()
         assert exported_content == original_content



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






