


import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


UpperCAmelCase__        =				torch.cuda.is_available()
UpperCAmelCase__        =				[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
UpperCAmelCase__        =				["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
UpperCAmelCase__        =				{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1e-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case		)	->     str:







 """simple docstring"""


 _lowercase				 ='\n'.join(UpperCamelCase_		)
 Path(UpperCamelCase_		).open('''w'''		).writelines(UpperCamelCase_		)
def 							UpperCAmelCase_   (				__snake_case		)	->     Union[str, Any]:







 """simple docstring"""


 for split in ["train", "val", "test"]:
  _dump_articles(os.path.join(UpperCamelCase_	,		F"{split}.source"		)	,		UpperCamelCase_		)
  _dump_articles(os.path.join(UpperCamelCase_	,		F"{split}.target"		)	,		UpperCamelCase_		)
 return tmp_dir







class  lowerCamelCase__  (			snake_case_):





     @classmethod
     def 	__A   (cls			)	->  Any:
      return cls





     @require_torch_multi_gpu
     def 	__A   (self			)	->  Optional[Any]:
      _lowercase				 ={
          'no_teacher': True,
          'freeze_encoder': True,
          'gpus': 2,
          'overwrite_output_dir': True,
          'sortish_sampler': True,
      }
      self._test_distiller_cli_fork(lowercase_						,	check_contents=lowercase_			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase=True			)	->  List[str]:
      _lowercase				 ={
          'label_smoothing': 0.0,
          'early_stopping_patience': -1,
          'train_batch_size': 1,
          'eval_batch_size': 2,
          'max_epochs': 2,
          'alpha_mlm': 0.2,
          'alpha_ce': 0.8,
          'do_predict': True,
          'model_name_or_path': 'sshleifer/tinier_bart',
          'teacher': CHEAP_ARGS['model_name_or_path'],
          'val_check_interval': 0.5,
      }
      default_updates.update(lowercase_			)
      _lowercase				 =CHEAP_ARGS.copy()
      _lowercase				 =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
      _lowercase				 =self.get_auto_remove_tmp_dir()
      args_d.update(data_dir=lowercase_						,	output_dir=lowercase_						,	**lowercase_			)

      def convert(UpperCAmelCase						,	UpperCAmelCase			):
       if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
        return ""
       if v is False or v is None:
        return ""
       if v is True:  # or len(str(v))==0:
        return f"--{k}"
       return f"--{k}={v}"

      _lowercase				 =[x for x in (convert(lowercase_						,	lowercase_			) for k, v in args_d.items()) if len(lowercase_			)]
      _lowercase				 =[sys.executable, f"{self.test_file_dir}/distillation.py"] + cli_args
      execute_subprocess_async(lowercase_						,	env=self.get_env()			)

      _lowercase				 =os.listdir(lowercase_			)
      _lowercase				 ={os.path.basename(lowercase_			) for p in contents}
      _lowercase				 =[p for p in contents if p.endswith('''ckpt'''			)]
      assert len(lowercase_			) > 0

      self.assertIn('''test_generations.txt'''						,	lowercase_			)
      self.assertIn('''test_results.txt'''						,	lowercase_			)

      # get the following from the module, (we don't have access to `model` here)
      _lowercase				 =os.path.join(lowercase_						,	'''metrics.json'''			)
      _lowercase				 ='rouge2'

      _lowercase				 =load_json(lowercase_			)
      # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
      print(lowercase_			)
      _lowercase				 =metrics['val'][-1]
      self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']						,	0.01			)
      self.assertIsInstance(last_step_stats[f"val_avg_{val_metric}"]						,	lowercase_			)
      self.assertEqual(len(metrics['''test''']			)						,	1			)
      _lowercase				 =int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1			)
      self.assertEqual(len(metrics['''val''']			)						,	lowercase_			)









"""simple docstring"""


import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
       RwkvForCausalLM,
       RwkvModel,
   )
   from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
   _UpperCamelCase :    Union[str, Any]      =			False






class __a :



  def __init__(self	:		str					,      lowercase_	:		Dict					,      lowercase_	:		str=14					,      lowercase_	:		List[str]=7					,      lowercase_	:		Any=True					,      lowercase_	:		List[str]=False					,      lowercase_	:		int=True					,      lowercase_	:		List[str]=True					,      lowercase_	:		str=True					,      lowercase_	:		Union[str, Any]=99					,      lowercase_	:		str=32					,      lowercase_	:		List[Any]=5					,      lowercase_	:		List[str]=37					,      lowercase_	:		str="gelu"					,      lowercase_	:		Any=0.1					,      lowercase_	:		Dict=0.1					,      lowercase_	:		List[Any]=512					,      lowercase_	:		Any=16					,      lowercase_	:		Union[str, Any]=2					,      lowercase_	:		Tuple=3					,      lowercase_	:		Optional[int]=4					,      lowercase_	:		int=None					,      )		->				Tuple:
   __A						:							Any      =						parent
   __A						:							int      =						batch_size
   __A						:							int      =						seq_length
   __A						:							int      =						is_training
   __A						:							int      =						use_token_type_ids
   __A						:							Union[str, Any]      =						use_input_mask
   __A						:							Any      =						use_labels
   __A						:							Union[str, Any]      =						use_mc_token_ids
   __A						:							List[Any]      =						vocab_size
   __A						:							List[str]      =						hidden_size
   __A						:							List[Any]      =						num_hidden_layers
   __A						:							Union[str, Any]      =						intermediate_size
   __A						:							Union[str, Any]      =						hidden_act
   __A						:							int      =						hidden_dropout_prob
   __A						:							Optional[Any]      =						attention_probs_dropout_prob
   __A						:							List[str]      =						max_position_embeddings
   __A						:							List[Any]      =						type_vocab_size
   __A						:							Tuple      =						type_sequence_label_size
   __A						:							Union[str, Any]      =						num_labels
   __A						:							Tuple      =						num_choices
   __A						:							int      =						scope
   __A						:							Union[str, Any]      =						vocab_size - 1
   __A						:							int      =						vocab_size - 1
   __A						:							Optional[Any]      =						vocab_size - 1



  def     A_					(self	:		List[str] )		->				Optional[int]:
   return RwkvConfig.from_pretrained('sgugger/rwkv-4-pile-7b' )



  def     A_					(self	:		Dict					,      lowercase_	:		str=False					,      lowercase_	:		Optional[int]=False					,      lowercase_	:		int=False )		->				List[str]:
   __A						:							List[Any]      =						ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size )

   __A						:							Optional[Any]      =						None
   if self.use_input_mask:
    __A						:							Tuple      =						random_attention_mask([self.batch_size, self.seq_length] )

   __A						:							Tuple      =						None
   if self.use_token_type_ids:
    __A						:							Optional[int]      =						ids_tensor([self.batch_size, self.seq_length]					,      self.type_vocab_size )

   __A						:							str      =						None
   if self.use_mc_token_ids:
    __A						:							int      =						ids_tensor([self.batch_size, self.num_choices]					,      self.seq_length )

   __A						:							List[Any]      =						None
   __A						:							int      =						None
   __A						:							Dict      =						None
   if self.use_labels:
    __A						:							List[str]      =						ids_tensor([self.batch_size]					,      self.type_sequence_label_size )
    __A						:							Dict      =						ids_tensor([self.batch_size, self.seq_length]					,      self.num_labels )
    __A						:							Tuple      =						ids_tensor([self.batch_size]					,      self.num_choices )

   __A						:							Any      =						self.get_config(
       gradient_checkpointing=lowercase_					,      scale_attn_by_inverse_layer_idx=lowercase_					,      reorder_and_upcast_attn=lowercase_					,      )

   return (
       config,
       input_ids,
       input_mask,
       None,
       token_type_ids,
       mc_token_ids,
       sequence_labels,
       token_labels,
       choice_labels,
   )



  def     A_					(self	:		Optional[int]					,      lowercase_	:		Optional[int]=False					,      lowercase_	:		Dict=False					,      lowercase_	:		List[str]=False )		->				Any:
   return RwkvConfig(
       vocab_size=self.vocab_size					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      intermediate_size=self.intermediate_size					,      activation_function=self.hidden_act					,      resid_pdrop=self.hidden_dropout_prob					,      attn_pdrop=self.attention_probs_dropout_prob					,      n_positions=self.max_position_embeddings					,      type_vocab_size=self.type_vocab_size					,      use_cache=lowercase_					,      bos_token_id=self.bos_token_id					,      eos_token_id=self.eos_token_id					,      pad_token_id=self.pad_token_id					,      gradient_checkpointing=lowercase_					,      scale_attn_by_inverse_layer_idx=lowercase_					,      reorder_and_upcast_attn=lowercase_					,      )



  def     A_					(self	:		Optional[int] )		->				Dict:
   __A						:							Any      =						self.get_config()
   __A						:							Any      =						300
   return config



  def     A_					(self	:		Tuple )		->				Dict:
   (
       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       
   )						:							Tuple      =						self.prepare_config_and_inputs()

   __A						:							Tuple      =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
   __A						:							List[str]      =						ids_tensor([self.batch_size, self.seq_length]					,      vocab_size=2 )

   return (
       config,
       input_ids,
       input_mask,
       head_mask,
       token_type_ids,
       sequence_labels,
       token_labels,
       choice_labels,
       encoder_hidden_states,
       encoder_attention_mask,
   )



  def     A_					(self	:		Any					,      lowercase_	:		Any					,      lowercase_	:		Union[str, Any]					,      lowercase_	:		Optional[Any]					,      lowercase_	:		List[Any]					,      lowercase_	:		str					,      *lowercase_	:		Any )		->				Tuple:
   __A						:							List[str]      =						True
   __A						:							str      =						RwkvModel(config=lowercase_ )
   model.to(lowercase_ )
   model.eval()

   __A						:							List[Any]      =						model(lowercase_ )

   self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size) )
   self.parent.assertEqual(len(result.hidden_states )					,      config.num_hidden_layers + 1 )



  def     A_					(self	:		List[str]					,      lowercase_	:		Dict					,      lowercase_	:		List[str]					,      lowercase_	:		Union[str, Any]					,      lowercase_	:		Optional[int]					,      lowercase_	:		List[Any]					,      *lowercase_	:		int )		->				Union[str, Any]:
   __A						:							Any      =						RwkvForCausalLM(lowercase_ )
   model.to(lowercase_ )
   model.eval()

   __A						:							Union[str, Any]      =						model(lowercase_					,      labels=lowercase_ )
   self.parent.assertEqual(result.loss.shape					,      () )
   self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size) )



  def     A_					(self	:		str					,      lowercase_	:		List[Any]					,      lowercase_	:		Dict					,      lowercase_	:		Tuple					,      lowercase_	:		str					,      lowercase_	:		Dict					,      *lowercase_	:		Optional[Any] )		->				Union[str, Any]:
   __A						:							Any      =						RwkvModel(config=lowercase_ )
   model.to(lowercase_ )
   model.eval()

   __A						:							Tuple      =						model(lowercase_ )
   __A						:							str      =						outputs.last_hidden_state

   __A						:							Optional[int]      =						model(input_ids[:, :2] )
   __A						:							Dict      =						outputs.last_hidden_state

   # Using the state computed on the first inputs, we will get the same output
   __A						:							Optional[int]      =						model(input_ids[:, 2:]					,      state=outputs.state )
   __A						:							Any      =						outputs.last_hidden_state

   self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]					,      dim=1 )					,      lowercase_					,      atol=1E-5 ) )



  def     A_					(self	:		List[str]					,      lowercase_	:		Optional[int]					,      lowercase_	:		List[str]					,      lowercase_	:		int					,      lowercase_	:		int					,      lowercase_	:		Union[str, Any]					,      *lowercase_	:		Dict					,      lowercase_	:		List[Any]=False )		->				str:
   __A						:							Tuple      =						RwkvForCausalLM(lowercase_ )
   model.to(lowercase_ )
   if gradient_checkpointing:
    model.gradient_checkpointing_enable()

   __A						:							Optional[int]      =						model(lowercase_					,      labels=lowercase_ )
   self.parent.assertEqual(result.loss.shape					,      () )
   self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size) )
   result.loss.backward()



  def     A_					(self	:		int )		->				Tuple:
   __A						:							Union[str, Any]      =						self.prepare_config_and_inputs()

   (
       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       
   )						:							int      =						config_and_inputs

   __A						:							Tuple      =						{'input_ids': input_ids}

   return config, inputs_dict






@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0			,reason='See https://github.com/huggingface/transformers/pull/24204'					)
@require_torch
class __a ( snake_case_			,snake_case_			,snake_case_			,unittest.TestCase					):
  __lowercase     :     Any       						=       (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
  __lowercase     :     Dict       						=       (
      {'feature-extraction': RwkvModel, 'text-generation': RwkvForCausalLM} if is_torch_available() else {}
  )
  # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
  __lowercase     :     List[str]       						=       False
  __lowercase     :     int       						=       False
  __lowercase     :     Optional[Any]       						=       False
  __lowercase     :     Union[str, Any]       						=       False
  __lowercase     :     Union[str, Any]       						=       False  # Rwkv does not support head masking



  def     A_					(self	:		List[str] )		->				Any:
   __A						:							List[str]      =						RwkvModelTester(self )
   __A						:							List[Any]      =						ConfigTester(
       self					,      config_class=lowercase_					,      n_embd=37					,      common_properties=['hidden_size', 'num_hidden_layers'] )



  def     A_					(self	:		int					,      lowercase_	:		Any					,      lowercase_	:		Optional[int]					,      lowercase_	:		Optional[Any]=None )		->				Tuple:
   if isinstance(lowercase_					,      torch.Tensor ):
    __A     ,       __A						:							str      =						member.max().item(), member.min().item()
   elif isinstance(lowercase_					,      lowercase_ ) or isinstance(lowercase_					,      lowercase_ ):
    __A     ,       __A						:							List[str]      =						max(lowercase_ ), min(lowercase_ )

   if not isinstance(lowercase_					,      lowercase_ ):
    raise TypeError('container should be a list or tuple' )
   elif len(lowercase_ ) != 2:
    raise ValueError('container should have 2 elements' )

   __A     ,       __A						:							Optional[Any]      =						container

   __A						:							List[str]      =						(min_value >= expected_min) and (max_value <= expected_max)

   if not is_inside_interval:
    __A						:							Optional[int]      =						'%s not found in %s' % (safe_repr(lowercase_ ), safe_repr(lowercase_ ))
    self.fail(self._formatMessage(lowercase_					,      lowercase_ ) )



  def     A_					(self	:		int )		->				Optional[Any]:
   self.config_tester.run_common_tests()



  def     A_					(self	:		Tuple )		->				str:
   __A						:							Dict      =						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_rwkv_model(*lowercase_ )



  def     A_					(self	:		List[Any] )		->				Union[str, Any]:
   __A						:							Any      =						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_causl_lm(*lowercase_ )



  def     A_					(self	:		Any )		->				Optional[int]:
   __A						:							Optional[int]      =						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_state_equivalency(*lowercase_ )



  def     A_					(self	:		List[str] )		->				int:
   __A     ,       __A						:							Optional[Any]      =						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __A						:							Optional[int]      =						model_class(config=lowercase_ )
    for name, param in model.named_parameters():
     if "time_decay" in name:
      if param.requires_grad:
       self.assertTrue(param.data.max().item() == 3.0 )
       self.assertTrue(param.data.min().item() == -5.0 )
     elif "time_first" in name:
      if param.requires_grad:
       # check if it's a ones like
       self.assertTrue(torch.allclose(param.data					,      torch.ones_like(param.data )					,      atol=1E-5					,      rtol=1E-5 ) )
     elif any(x in name for x in ['time_mix_key', 'time_mix_receptance'] ):
      if param.requires_grad:
       self.assertInterval(
           param.data					,      [0.0, 1.0]					,      msg=f"Parameter {name} of model {model_class} seems not properly initialized"					,      )
     elif "time_mix_value" in name:
      if param.requires_grad:
       self.assertInterval(
           param.data					,      [0.0, 1.3]					,      msg=f"Parameter {name} of model {model_class} seems not properly initialized"					,      )



  def     A_					(self	:		str )		->				Any:
   __A     ,       __A						:							List[str]      =						self.model_tester.prepare_config_and_inputs_for_common()
   __A						:							Optional[Any]      =						True

   __A						:							List[str]      =						getattr(self.model_tester					,      'seq_length'					,      lowercase_ )

   for model_class in self.all_model_classes:
    __A						:							str      =						True
    __A						:							str      =						False
    __A						:							List[str]      =						True
    __A						:							Optional[Any]      =						model_class(lowercase_ )
    model.to(lowercase_ )
    model.eval()

    __A						:							List[str]      =						self._prepare_for_class(lowercase_					,      lowercase_ )
    __A						:							Optional[int]      =						inputs['input_ids'].shape[0]
    with torch.no_grad():
     __A						:							Optional[Any]      =						model(**lowercase_ )
    __A						:							Union[str, Any]      =						outputs.attentions
    self.assertEqual(len(lowercase_ )					,      self.model_tester.num_hidden_layers )

    # check that output_attentions also work using config
    del inputs_dict["output_attentions"]
    __A						:							Optional[int]      =						True
    __A						:							Dict      =						model_class(lowercase_ )
    model.to(lowercase_ )
    model.eval()

    __A						:							Any      =						self._prepare_for_class(lowercase_					,      lowercase_ )
    __A						:							Any      =						inputs['input_ids'].shape[0]
    with torch.no_grad():
     __A						:							List[Any]      =						model(**lowercase_ )
    __A						:							Optional[Any]      =						outputs.attentions
    self.assertEqual(len(lowercase_ )					,      self.model_tester.num_hidden_layers )

    self.assertListEqual(
        list(attentions[0].shape[-3:] )					,      [batch_size, seq_len, config.hidden_size]					,      )
    __A						:							Tuple      =						len(lowercase_ )

    # Check attention is always last and order is fine
    __A						:							Any      =						True
    __A						:							str      =						True
    __A						:							List[str]      =						model_class(lowercase_ )
    model.to(lowercase_ )
    model.eval()

    __A						:							List[Any]      =						self._prepare_for_class(lowercase_					,      lowercase_ )
    __A						:							Union[str, Any]      =						inputs['input_ids'].shape[0]
    with torch.no_grad():
     __A						:							Dict      =						model(**lowercase_ )

    __A						:							Dict      =						1
    self.assertEqual(out_len + added_hidden_states					,      len(lowercase_ ) )

    __A						:							Optional[Any]      =						outputs.attentions

    self.assertEqual(len(lowercase_ )					,      self.model_tester.num_hidden_layers )
    self.assertListEqual(
        list(self_attentions[0].shape[-3:] )					,      [batch_size, seq_len, config.hidden_size]					,      )



  @slow
  def     A_					(self	:		Optional[Any] )		->				Any:
   for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __A						:							Optional[int]      =						RwkvModel.from_pretrained(lowercase_ )
    self.assertIsNotNone(lowercase_ )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0			,reason='See https://github.com/huggingface/transformers/pull/24204'					)
@slow
class __a ( unittest.TestCase					):



  def     A_					(self	:		str )		->				Optional[Any]:
   __A						:							Optional[int]      =						'RWKV/rwkv-4-169m-pile'
   __A						:							Tuple      =						AutoTokenizer.from_pretrained(self.model_id )



  def     A_					(self	:		Optional[Any] )		->				str:
   __A						:							int      =						'Hello my name is Jasmine and I am a newbie to the'
   __A						:							str      =						RwkvForCausalLM.from_pretrained(self.model_id ).to(lowercase_ )

   __A						:							Union[str, Any]      =						self.tokenizer('Hello my name is'					,      return_tensors='pt' ).input_ids.to(lowercase_ )
   __A						:							List[str]      =						model.generate(lowercase_					,      max_new_tokens=10 )
   __A						:							List[str]      =						self.tokenizer.decode(output[0].tolist() )

   self.assertEqual(lowercase_					,      lowercase_ )



  def     A_					(self	:		Any )		->				Optional[Any]:
   __A						:							Optional[int]      =						'Hello my name is Jasmine and I am a newbie to the'

   __A						:							Any      =						self.tokenizer('Hello my name is'					,      return_tensors='pt' ).input_ids.to(lowercase_ )
   __A						:							int      =						RwkvForCausalLM.from_pretrained(self.model_id					,      torch_dtype=torch.bfloataa ).to(lowercase_ )

   __A						:							str      =						model.generate(lowercase_					,      max_new_tokens=10 )
   __A						:							Union[str, Any]      =						self.tokenizer.decode(output[0].tolist() )

   self.assertEqual(lowercase_					,      lowercase_ )





