code: 
  
  
  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:
  		A_														=      False
  
  class 						snake_case			:
  			'''simple docstring'''
  
  			def __init__(	self     :     List[Any]							,							lowerCAmelCase_     :     List[Any]							,							lowerCAmelCase_     :     List[str]=14							,							lowerCAmelCase_     :     int=7							,							lowerCAmelCase_     :     Union[str, Any]=True							,							lowerCAmelCase_     :     Optional[int]=False							,							lowerCAmelCase_     :     Optional[Any]=True							,							lowerCAmelCase_     :     List[str]=True							,							lowerCAmelCase_     :     Any=True							,							lowerCAmelCase_     :     List[str]=99							,							lowerCAmelCase_     :     Optional[Any]=32							,							lowerCAmelCase_     :     int=5							,							lowerCAmelCase_     :     Optional[Any]=37							,							lowerCAmelCase_     :     Union[str, Any]="gelu"							,							lowerCAmelCase_     :     Any=0.1							,							lowerCAmelCase_     :     List[Any]=0.1							,							lowerCAmelCase_     :     Union[str, Any]=512							,							lowerCAmelCase_     :     str=16							,							lowerCAmelCase_     :     List[str]=2							,							lowerCAmelCase_     :     Optional[int]=3							,							lowerCAmelCase_     :     Tuple=4							,							lowerCAmelCase_     :     Tuple=None							,							)					->					Any:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       parent
  								SCREAMING_SNAKE_CASE_	      =       batch_size
  								SCREAMING_SNAKE_CASE_	      =       seq_length
  								SCREAMING_SNAKE_CASE_	      =       is_training
  								SCREAMING_SNAKE_CASE_	      =       use_token_type_ids
  								SCREAMING_SNAKE_CASE_	      =       use_input_mask
  								SCREAMING_SNAKE_CASE_	      =       use_labels
  								SCREAMING_SNAKE_CASE_	      =       use_mc_token_ids
  								SCREAMING_SNAKE_CASE_	      =       vocab_size
  								SCREAMING_SNAKE_CASE_	      =       hidden_size
  								SCREAMING_SNAKE_CASE_	      =       num_hidden_layers
  								SCREAMING_SNAKE_CASE_	      =       intermediate_size
  								SCREAMING_SNAKE_CASE_	      =       hidden_act
  								SCREAMING_SNAKE_CASE_	      =       hidden_dropout_prob
  								SCREAMING_SNAKE_CASE_	      =       attention_probs_dropout_prob
  								SCREAMING_SNAKE_CASE_	      =       max_position_embeddings
  								SCREAMING_SNAKE_CASE_	      =       type_vocab_size
  								SCREAMING_SNAKE_CASE_	      =       type_sequence_label_size
  								SCREAMING_SNAKE_CASE_	      =       num_labels
  								SCREAMING_SNAKE_CASE_	      =       num_choices
  								SCREAMING_SNAKE_CASE_	      =       scope
  								SCREAMING_SNAKE_CASE_	      =       vocab_size - 1
  								SCREAMING_SNAKE_CASE_	      =       vocab_size - 1
  								SCREAMING_SNAKE_CASE_	      =       vocab_size - 1
  
  			def _lowercase (	self     :     int					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return RwkvConfig.from_pretrained('''sgugger/rwkv-4-pile-7b'''					)
  
  			def _lowercase (	self     :     Any							,							lowerCAmelCase_     :     List[str]=False							,							lowerCAmelCase_     :     str=False							,							lowerCAmelCase_     :     Dict=False					)					->					Union[str, Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size					)
  
  								SCREAMING_SNAKE_CASE_	      =       None
  								if self.use_input_mask:
  													SCREAMING_SNAKE_CASE_	      =       random_attention_mask([self.batch_size, self.seq_length]					)
  
  								SCREAMING_SNAKE_CASE_	      =       None
  								if self.use_token_type_ids:
  													SCREAMING_SNAKE_CASE_	      =       ids_tensor([self.batch_size, self.seq_length]							,							self.type_vocab_size					)
  
  								SCREAMING_SNAKE_CASE_	      =       None
  								if self.use_mc_token_ids:
  													SCREAMING_SNAKE_CASE_	      =       ids_tensor([self.batch_size, self.num_choices]							,							self.seq_length					)
  
  								SCREAMING_SNAKE_CASE_	      =       None
  								SCREAMING_SNAKE_CASE_	      =       None
  								SCREAMING_SNAKE_CASE_	      =       None
  								if self.use_labels:
  													SCREAMING_SNAKE_CASE_	      =       ids_tensor([self.batch_size]							,							self.type_sequence_label_size					)
  													SCREAMING_SNAKE_CASE_	      =       ids_tensor([self.batch_size, self.seq_length]							,							self.num_labels					)
  													SCREAMING_SNAKE_CASE_	      =       ids_tensor([self.batch_size]							,							self.num_choices					)
  
  								SCREAMING_SNAKE_CASE_	      =       self.get_config(
  								    gradient_checkpointing=lowerCAmelCase_							,							scale_attn_by_inverse_layer_idx=lowerCAmelCase_							,							reorder_and_upcast_attn=lowerCAmelCase_							,							)
  
  								return (
  								    config,
  								    input_ids,
  								    input_mask,
  								    None,
  								    token_type_ids,
  								    mc_token_ids,
  								    sequence_labels,
  								    token_labels,
  								    choice_labels,
  								)
  
  			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     List[str]=False							,							lowerCAmelCase_     :     Any=False							,							lowerCAmelCase_     :     Optional[Any]=False					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								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=lowerCAmelCase_							,							bos_token_id=self.bos_token_id							,							eos_token_id=self.eos_token_id							,							pad_token_id=self.pad_token_id							,							gradient_checkpointing=lowerCAmelCase_							,							scale_attn_by_inverse_layer_idx=lowerCAmelCase_							,							reorder_and_upcast_attn=lowerCAmelCase_							,							)
  
  			def _lowercase (	self     :     int					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.get_config()
  								SCREAMING_SNAKE_CASE_	      =       300
  								return config
  
  			def _lowercase (	self     :     Dict					)					->					Any:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								(
  								    (
  								    SCREAMING_SNAKE_CASE_
  								)      ,(
  								    SCREAMING_SNAKE_CASE_
  								)      ,(
  								    SCREAMING_SNAKE_CASE_
  								)      ,(
  								    SCREAMING_SNAKE_CASE_
  								)      ,(
  								    SCREAMING_SNAKE_CASE_
  								)      ,(
  								    SCREAMING_SNAKE_CASE_
  								)      ,(
  								    SCREAMING_SNAKE_CASE_
  								)      ,(
  								    SCREAMING_SNAKE_CASE_
  								)      ,(
  								    SCREAMING_SNAKE_CASE_
  								)      ,
  								)	      =       self.prepare_config_and_inputs()
  
  								SCREAMING_SNAKE_CASE_	      =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
  								SCREAMING_SNAKE_CASE_	      =       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 _lowercase (	self     :     str							,							lowerCAmelCase_     :     Optional[int]							,							lowerCAmelCase_     :     List[str]							,							lowerCAmelCase_     :     Any							,							lowerCAmelCase_     :     Any							,							lowerCAmelCase_     :     Tuple							,							*lowerCAmelCase_     :     Dict					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       True
  								SCREAMING_SNAKE_CASE_	      =       RwkvModel(config=lowerCAmelCase_					)
  								model.to(lowerCAmelCase_					)
  								model.eval()
  
  								SCREAMING_SNAKE_CASE_	      =       model(lowerCAmelCase_					)
  
  								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 _lowercase (	self     :     Optional[int]							,							lowerCAmelCase_     :     List[str]							,							lowerCAmelCase_     :     Optional[int]							,							lowerCAmelCase_     :     Dict							,							lowerCAmelCase_     :     Optional[Any]							,							lowerCAmelCase_     :     Optional[Any]							,							*lowerCAmelCase_     :     List[str]					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       RwkvForCausalLM(lowerCAmelCase_					)
  								model.to(lowerCAmelCase_					)
  								model.eval()
  
  								SCREAMING_SNAKE_CASE_	      =       model(lowerCAmelCase_							,							labels=lowerCAmelCase_					)
  								self.parent.assertEqual(result.loss.shape							,							()					)
  								self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.vocab_size)					)
  
  			def _lowercase (	self     :     Dict							,							lowerCAmelCase_     :     Optional[Any]							,							lowerCAmelCase_     :     Dict							,							lowerCAmelCase_     :     Optional[int]							,							lowerCAmelCase_     :     Optional[int]							,							lowerCAmelCase_     :     int							,							*lowerCAmelCase_     :     Tuple					)					->					Union[str, Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       RwkvModel(config=lowerCAmelCase_					)
  								model.to(lowerCAmelCase_					)
  								model.eval()
  
  								SCREAMING_SNAKE_CASE_	      =       model(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       outputs.last_hidden_state
  
  								SCREAMING_SNAKE_CASE_	      =       model(input_ids[:, :2]					)
  								SCREAMING_SNAKE_CASE_	      =       outputs.last_hidden_state
  
  								# Using the state computed on the first inputs, we will get the same output
  								SCREAMING_SNAKE_CASE_	      =       model(input_ids[:, 2:]							,							state=outputs.state					)
  								SCREAMING_SNAKE_CASE_	      =       outputs.last_hidden_state
  
  								self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]							,							dim=1					)							,							lowerCAmelCase_							,							atol=1e-5					)					)
  
  			def _lowercase (	self     :     Union[str, Any]							,							lowerCAmelCase_     :     int							,							lowerCAmelCase_     :     Tuple							,							lowerCAmelCase_     :     List[Any]							,							lowerCAmelCase_     :     List[str]							,							lowerCAmelCase_     :     Union[str, Any]							,							*lowerCAmelCase_     :     int							,							lowerCAmelCase_     :     str=False					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       RwkvForCausalLM(lowerCAmelCase_					)
  								model.to(lowerCAmelCase_					)
  								if gradient_checkpointing:
  													model.gradient_checkpointing_enable()
  
  								SCREAMING_SNAKE_CASE_	      =       model(lowerCAmelCase_							,							labels=lowerCAmelCase_					)
  								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 _lowercase (	self     :     List[Any]					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.prepare_config_and_inputs()
  
  								(
  								    (
  								    SCREAMING_SNAKE_CASE_
  								)      ,(
  								    SCREAMING_SNAKE_CASE_
  								)      ,(
  								    SCREAMING_SNAKE_CASE_
  								)      ,(
  								    SCREAMING_SNAKE_CASE_
  								)      ,(
  								    SCREAMING_SNAKE_CASE_
  								)      ,(
  								    SCREAMING_SNAKE_CASE_
  								)      ,(
  								    SCREAMING_SNAKE_CASE_
  								)      ,(
  								    SCREAMING_SNAKE_CASE_
  								)      ,(
  								    SCREAMING_SNAKE_CASE_
  								)      ,
  								)	      =       config_and_inputs
  
  								SCREAMING_SNAKE_CASE_	      =       {'''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 						snake_case			(       lowerCAmelCase__	, lowerCAmelCase__	, lowerCAmelCase__	, unittest.TestCase	):
  			'''simple docstring'''
  
  			UpperCAmelCase       :       str       = (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
  			UpperCAmelCase       :       Union[str, Any]       = (
  			    {"""feature-extraction""": RwkvModel, """text-generation""": RwkvForCausalLM} if is_torch_available() else {}
  			)
  			# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
  			UpperCAmelCase       :       Optional[Any]       = False
  			UpperCAmelCase       :       Optional[Any]       = False
  			UpperCAmelCase       :       int       = False
  			UpperCAmelCase       :       List[Any]       = False
  			UpperCAmelCase       :       Tuple       = False  # Rwkv does not support head masking
  
  			def _lowercase (	self     :     Optional[Any]					)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       RwkvModelTester(self					)
  								SCREAMING_SNAKE_CASE_	      =       ConfigTester(
  								    self							,							config_class=lowerCAmelCase_							,							n_embd=37							,							common_properties=['''hidden_size''', '''num_hidden_layers''']					)
  
  			def _lowercase (	self     :     Optional[int]							,							lowerCAmelCase_     :     List[Any]							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     int=None					)					->					Union[str, Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if isinstance(lowerCAmelCase_							,							torch.Tensor					):
  													SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       member.max().item(), member.min().item()
  								elif isinstance(lowerCAmelCase_							,							lowerCAmelCase_					) or isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
  													SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       max(lowerCAmelCase_					), min(lowerCAmelCase_					)
  
  								if not isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
  													raise TypeError('''container should be a list or tuple'''					)
  								elif len(lowerCAmelCase_					) != 2:
  													raise ValueError('''container should have 2 elements'''					)
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       container
  
  								SCREAMING_SNAKE_CASE_	      =       (min_value >= expected_min) and (max_value <= expected_max)
  
  								if not is_inside_interval:
  													SCREAMING_SNAKE_CASE_	      =       '''%s not found in %s''' % (safe_repr(lowerCAmelCase_					), safe_repr(lowerCAmelCase_					))
  													self.fail(self._formatMessage(lowerCAmelCase_							,							lowerCAmelCase_					)					)
  
  			def _lowercase (	self     :     List[str]					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								self.config_tester.run_common_tests()
  
  			def _lowercase (	self     :     Dict					)					->					Any:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_rwkv_model(*lowerCAmelCase_					)
  
  			def _lowercase (	self     :     Union[str, Any]					)					->					Dict:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_causl_lm(*lowerCAmelCase_					)
  
  			def _lowercase (	self     :     List[str]					)					->					Any:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_state_equivalency(*lowerCAmelCase_					)
  
  			def _lowercase (	self     :     List[Any]					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.model_tester.prepare_config_and_inputs_for_common()
  
  								for model_class in self.all_model_classes:
  													SCREAMING_SNAKE_CASE_	      =       model_class(config=lowerCAmelCase_					)
  													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 _lowercase (	self     :     List[str]					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.model_tester.prepare_config_and_inputs_for_common()
  								SCREAMING_SNAKE_CASE_	      =       True
  
  								SCREAMING_SNAKE_CASE_	      =       getattr(self.model_tester							,							'''seq_length'''							,							lowerCAmelCase_					)
  
  								for model_class in self.all_model_classes:
  													SCREAMING_SNAKE_CASE_	      =       True
  													SCREAMING_SNAKE_CASE_	      =       False
  													SCREAMING_SNAKE_CASE_	      =       True
  													SCREAMING_SNAKE_CASE_	      =       model_class(lowerCAmelCase_					)
  													model.to(lowerCAmelCase_					)
  													model.eval()
  
  													SCREAMING_SNAKE_CASE_	      =       self._prepare_for_class(lowerCAmelCase_							,							lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       inputs['''input_ids'''].shape[0]
  													with torch.no_grad():
  																		SCREAMING_SNAKE_CASE_	      =       model(**lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       outputs.attentions
  													self.assertEqual(len(lowerCAmelCase_					)							,							self.model_tester.num_hidden_layers					)
  
  													# check that output_attentions also work using config
  													del inputs_dict["output_attentions"]
  													SCREAMING_SNAKE_CASE_	      =       True
  													SCREAMING_SNAKE_CASE_	      =       model_class(lowerCAmelCase_					)
  													model.to(lowerCAmelCase_					)
  													model.eval()
  
  													SCREAMING_SNAKE_CASE_	      =       self._prepare_for_class(lowerCAmelCase_							,							lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       inputs['''input_ids'''].shape[0]
  													with torch.no_grad():
  																		SCREAMING_SNAKE_CASE_	      =       model(**lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       outputs.attentions
  													self.assertEqual(len(lowerCAmelCase_					)							,							self.model_tester.num_hidden_layers					)
  
  													self.assertListEqual(
  													    list(attentions[0].shape[-3:]					)							,							[batch_size, seq_len, config.hidden_size]							,							)
  													SCREAMING_SNAKE_CASE_	      =       len(lowerCAmelCase_					)
  
  													# Check attention is always last and order is fine
  													SCREAMING_SNAKE_CASE_	      =       True
  													SCREAMING_SNAKE_CASE_	      =       True
  													SCREAMING_SNAKE_CASE_	      =       model_class(lowerCAmelCase_					)
  													model.to(lowerCAmelCase_					)
  													model.eval()
  
  													SCREAMING_SNAKE_CASE_	      =       self._prepare_for_class(lowerCAmelCase_							,							lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       inputs['''input_ids'''].shape[0]
  													with torch.no_grad():
  																		SCREAMING_SNAKE_CASE_	      =       model(**lowerCAmelCase_					)
  
  													SCREAMING_SNAKE_CASE_	      =       1
  													self.assertEqual(out_len + added_hidden_states							,							len(lowerCAmelCase_					)					)
  
  													SCREAMING_SNAKE_CASE_	      =       outputs.attentions
  
  													self.assertEqual(len(lowerCAmelCase_					)							,							self.model_tester.num_hidden_layers					)
  													self.assertListEqual(
  													    list(self_attentions[0].shape[-3:]					)							,							[batch_size, seq_len, config.hidden_size]							,							)
  
  
  
  
  			@slow
  			def _lowercase (	self     :     List[str]					)					->					Union[str, Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  													SCREAMING_SNAKE_CASE_	      =       RwkvModel.from_pretrained(lowerCAmelCase_					)
  													self.assertIsNotNone(lowerCAmelCase_					)
  
  @unittest.skipIf(
      not is_torch_greater_or_equal_than_2_0	, reason="""See https://github.com/huggingface/transformers/pull/24204"""	)
  @slow
  class 						snake_case			(       unittest.TestCase	):
  			'''simple docstring'''
  
  			def _lowercase (	self     :     Dict					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       '''RWKV/rwkv-4-169m-pile'''
  								SCREAMING_SNAKE_CASE_	      =       AutoTokenizer.from_pretrained(self.model_id					)
  
  			def _lowercase (	self     :     List[str]					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       '''Hello my name is Jasmine and I am a newbie to the'''
  								SCREAMING_SNAKE_CASE_	      =       RwkvForCausalLM.from_pretrained(self.model_id					).to(lowerCAmelCase_					)
  
  								SCREAMING_SNAKE_CASE_	      =       self.tokenizer('''Hello my name is'''							,							return_tensors='''pt'''					).input_ids.to(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       model.generate(lowerCAmelCase_							,							max_new_tokens=10					)
  								SCREAMING_SNAKE_CASE_	      =       self.tokenizer.decode(output[0].tolist()					)
  
  								self.assertEqual(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  
  
  
  			def _lowercase (	self     :     Optional[int]					)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       '''Hello my name is Jasmine and I am a newbie to the'''
  
  								SCREAMING_SNAKE_CASE_	      =       self.tokenizer('''Hello my name is'''							,							return_tensors='''pt'''					).input_ids.to(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       RwkvForCausalLM.from_pretrained(self.model_id							,							torch_dtype=torch.bfloataa					).to(lowerCAmelCase_					)
  
  								SCREAMING_SNAKE_CASE_	      =       model.generate(lowerCAmelCase_							,							max_new_tokens=10					)
  								SCREAMING_SNAKE_CASE_	      =       self.tokenizer.decode(output[0].tolist()					)
  
  								self.assertEqual(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  
  
style_context: 
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase      )->		bool:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					if not isinstance(UpperCAmelCase				,UpperCAmelCase      ):
  										SCREAMING_SNAKE_CASE_	      =       f'''Input value of [number={number}] must be an integer'''
  										raise TypeError(UpperCAmelCase      )
  					if number < 0 or number % 2 == 1:
  										return False
  					SCREAMING_SNAKE_CASE_	      =       int(number**0.5      )
  					return number == number_sqrt * (number_sqrt + 1)
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
  