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:
  				__lowerCAmelCase     =			False
  
  
  
  
  class   _UpperCAmelCase	:
  
  
  
  
  					def __init__(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase=14    ,      __lowerCAmelCase=7    ,      __lowerCAmelCase=True    ,      __lowerCAmelCase=False    ,      __lowerCAmelCase=True    ,      __lowerCAmelCase=True    ,      __lowerCAmelCase=True    ,      __lowerCAmelCase=99    ,      __lowerCAmelCase=32    ,      __lowerCAmelCase=5    ,      __lowerCAmelCase=37    ,      __lowerCAmelCase="gelu"    ,      __lowerCAmelCase=0.1    ,      __lowerCAmelCase=0.1    ,      __lowerCAmelCase=512    ,      __lowerCAmelCase=16    ,      __lowerCAmelCase=2    ,      __lowerCAmelCase=3    ,      __lowerCAmelCase=4    ,      __lowerCAmelCase=None    ,      )						->  Any:
  								UpperCamelCase__         =	parent
  								UpperCamelCase__         =	batch_size
  								UpperCamelCase__         =	seq_length
  								UpperCamelCase__         =	is_training
  								UpperCamelCase__         =	use_token_type_ids
  								UpperCamelCase__         =	use_input_mask
  								UpperCamelCase__         =	use_labels
  								UpperCamelCase__         =	use_mc_token_ids
  								UpperCamelCase__         =	vocab_size
  								UpperCamelCase__         =	hidden_size
  								UpperCamelCase__         =	num_hidden_layers
  								UpperCamelCase__         =	intermediate_size
  								UpperCamelCase__         =	hidden_act
  								UpperCamelCase__         =	hidden_dropout_prob
  								UpperCamelCase__         =	attention_probs_dropout_prob
  								UpperCamelCase__         =	max_position_embeddings
  								UpperCamelCase__         =	type_vocab_size
  								UpperCamelCase__         =	type_sequence_label_size
  								UpperCamelCase__         =	num_labels
  								UpperCamelCase__         =	num_choices
  								UpperCamelCase__         =	scope
  								UpperCamelCase__         =	vocab_size - 1
  								UpperCamelCase__         =	vocab_size - 1
  								UpperCamelCase__         =	vocab_size - 1
  
  
  
  
  					def 	snake_case_		(						self       )						->  Optional[Any]:
  								return RwkvConfig.from_pretrained("""sgugger/rwkv-4-pile-7b"""       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase=False    ,      __lowerCAmelCase=False    ,      __lowerCAmelCase=False       )						->  Union[str, Any]:
  								UpperCamelCase__         =	ids_tensor([self.batch_size, self.seq_length]    ,      self.vocab_size       )
  
  								UpperCamelCase__         =	None
  								if self.use_input_mask:
  											UpperCamelCase__         =	random_attention_mask([self.batch_size, self.seq_length]       )
  
  								UpperCamelCase__         =	None
  								if self.use_token_type_ids:
  											UpperCamelCase__         =	ids_tensor([self.batch_size, self.seq_length]    ,      self.type_vocab_size       )
  
  								UpperCamelCase__         =	None
  								if self.use_mc_token_ids:
  											UpperCamelCase__         =	ids_tensor([self.batch_size, self.num_choices]    ,      self.seq_length       )
  
  								UpperCamelCase__         =	None
  								UpperCamelCase__         =	None
  								UpperCamelCase__         =	None
  								if self.use_labels:
  											UpperCamelCase__         =	ids_tensor([self.batch_size]    ,      self.type_sequence_label_size       )
  											UpperCamelCase__         =	ids_tensor([self.batch_size, self.seq_length]    ,      self.num_labels       )
  											UpperCamelCase__         =	ids_tensor([self.batch_size]    ,      self.num_choices       )
  
  								UpperCamelCase__         =	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 	snake_case_		(						self    ,      __lowerCAmelCase=False    ,      __lowerCAmelCase=False    ,      __lowerCAmelCase=False       )						->  List[str]:
  								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 	snake_case_		(						self       )						->  List[Any]:
  								UpperCamelCase__         =	self.get_config()
  								UpperCamelCase__         =	300
  								return config
  
  
  
  
  					def 	snake_case_		(						self       )						->  Any:
  								(
  								    (
  								    UpperCamelCase__
  								)  ,			(
  								    UpperCamelCase__
  								)  ,			(
  								    UpperCamelCase__
  								)  ,			(
  								    UpperCamelCase__
  								)  ,			(
  								    UpperCamelCase__
  								)  ,			(
  								    UpperCamelCase__
  								)  ,			(
  								    UpperCamelCase__
  								)  ,			(
  								    UpperCamelCase__
  								)  ,			(
  								    UpperCamelCase__
  								)  ,			
  								)         =	self.prepare_config_and_inputs()
  
  								UpperCamelCase__         =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
  								UpperCamelCase__         =	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 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      *__lowerCAmelCase       )						->  List[Any]:
  								UpperCamelCase__         =	True
  								UpperCamelCase__         =	RwkvModel(config=lowerCAmelCase_       )
  								model.to(lowerCAmelCase_       )
  								model.eval()
  
  								UpperCamelCase__         =	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 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      *__lowerCAmelCase       )						->  str:
  								UpperCamelCase__         =	RwkvForCausalLM(lowerCAmelCase_       )
  								model.to(lowerCAmelCase_       )
  								model.eval()
  
  								UpperCamelCase__         =	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 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      *__lowerCAmelCase       )						->  Union[str, Any]:
  								UpperCamelCase__         =	RwkvModel(config=lowerCAmelCase_       )
  								model.to(lowerCAmelCase_       )
  								model.eval()
  
  								UpperCamelCase__         =	model(lowerCAmelCase_       )
  								UpperCamelCase__         =	outputs.last_hidden_state
  
  								UpperCamelCase__         =	model(input_ids[:, :2]       )
  								UpperCamelCase__         =	outputs.last_hidden_state
  
  								# Using the state computed on the first inputs, we will get the same output
  								UpperCamelCase__         =	model(input_ids[:, 2:]    ,      state=outputs.state       )
  								UpperCamelCase__         =	outputs.last_hidden_state
  
  								self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]    ,      dim=1       )    ,      lowerCAmelCase_    ,      atol=1E-5       )       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      *__lowerCAmelCase    ,      __lowerCAmelCase=False       )						->  Optional[Any]:
  								UpperCamelCase__         =	RwkvForCausalLM(lowerCAmelCase_       )
  								model.to(lowerCAmelCase_       )
  								if gradient_checkpointing:
  											model.gradient_checkpointing_enable()
  
  								UpperCamelCase__         =	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 	snake_case_		(						self       )						->  int:
  								UpperCamelCase__         =	self.prepare_config_and_inputs()
  
  								(
  								    (
  								    UpperCamelCase__
  								)  ,			(
  								    UpperCamelCase__
  								)  ,			(
  								    UpperCamelCase__
  								)  ,			(
  								    UpperCamelCase__
  								)  ,			(
  								    UpperCamelCase__
  								)  ,			(
  								    UpperCamelCase__
  								)  ,			(
  								    UpperCamelCase__
  								)  ,			(
  								    UpperCamelCase__
  								)  ,			(
  								    UpperCamelCase__
  								)  ,			
  								)         =	config_and_inputs
  
  								UpperCamelCase__         =	{"""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   _UpperCAmelCase	(   lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__ ,       unittest.TestCase					):
  					__A          =   (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
  					__A          =   (
  					    {"""feature-extraction""": RwkvModel, """text-generation""": RwkvForCausalLM} if is_torch_available() else {}
  					)
  					# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
  					__A          =   False
  					__A          =   False
  					__A          =   False
  					__A          =   False
  					__A          =   False  # Rwkv does not support head masking
  
  
  
  
  					def 	snake_case_		(						self       )						->  Tuple:
  								UpperCamelCase__         =	RwkvModelTester(self       )
  								UpperCamelCase__         =	ConfigTester(
  								    self    ,      config_class=lowerCAmelCase_    ,      n_embd=37    ,      common_properties=["""hidden_size""", """num_hidden_layers"""]       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase=None       )						->  Union[str, Any]:
  
  
  
  
  
  
  								if isinstance(lowerCAmelCase_    ,      torch.Tensor       ):
  											UpperCamelCase__  ,			UpperCamelCase__         =	member.max().item(), member.min().item()
  								elif isinstance(lowerCAmelCase_    ,      lowerCAmelCase_       ) or isinstance(lowerCAmelCase_    ,      lowerCAmelCase_       ):
  											UpperCamelCase__  ,			UpperCamelCase__         =	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"""       )
  
  								UpperCamelCase__  ,			UpperCamelCase__         =	container
  
  								UpperCamelCase__         =	(min_value >= expected_min) and (max_value <= expected_max)
  
  								if not is_inside_interval:
  											UpperCamelCase__         =	"""%s not found in %s""" % (safe_repr(lowerCAmelCase_       ), safe_repr(lowerCAmelCase_       ))
  											self.fail(self._formatMessage(lowerCAmelCase_    ,      lowerCAmelCase_       )       )
  
  
  
  
  					def 	snake_case_		(						self       )						->  str:
  								self.config_tester.run_common_tests()
  
  
  
  
  					def 	snake_case_		(						self       )						->  Any:
  								UpperCamelCase__         =	self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_rwkv_model(*lowerCAmelCase_       )
  
  
  
  
  					def 	snake_case_		(						self       )						->  Dict:
  								UpperCamelCase__         =	self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_causl_lm(*lowerCAmelCase_       )
  
  
  
  
  					def 	snake_case_		(						self       )						->  Any:
  								UpperCamelCase__         =	self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_state_equivalency(*lowerCAmelCase_       )
  
  
  
  
  					def 	snake_case_		(						self       )						->  Optional[Any]:
  								UpperCamelCase__  ,			UpperCamelCase__         =	self.model_tester.prepare_config_and_inputs_for_common()
  
  								for model_class in self.all_model_classes:
  											UpperCamelCase__         =	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 	snake_case_		(						self       )						->  Optional[Any]:
  								UpperCamelCase__  ,			UpperCamelCase__         =	self.model_tester.prepare_config_and_inputs_for_common()
  								UpperCamelCase__         =	True
  
  								UpperCamelCase__         =	getattr(self.model_tester    ,      """seq_length"""    ,      lowerCAmelCase_       )
  
  								for model_class in self.all_model_classes:
  											UpperCamelCase__         =	True
  											UpperCamelCase__         =	False
  											UpperCamelCase__         =	True
  											UpperCamelCase__         =	model_class(lowerCAmelCase_       )
  											model.to(lowerCAmelCase_       )
  											model.eval()
  
  											UpperCamelCase__         =	self._prepare_for_class(lowerCAmelCase_    ,      lowerCAmelCase_       )
  											UpperCamelCase__         =	inputs["""input_ids"""].shape[0]
  											with torch.no_grad():
  														UpperCamelCase__         =	model(**lowerCAmelCase_       )
  											UpperCamelCase__         =	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"]
  											UpperCamelCase__         =	True
  											UpperCamelCase__         =	model_class(lowerCAmelCase_       )
  											model.to(lowerCAmelCase_       )
  											model.eval()
  
  											UpperCamelCase__         =	self._prepare_for_class(lowerCAmelCase_    ,      lowerCAmelCase_       )
  											UpperCamelCase__         =	inputs["""input_ids"""].shape[0]
  											with torch.no_grad():
  														UpperCamelCase__         =	model(**lowerCAmelCase_       )
  											UpperCamelCase__         =	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]    ,      )
  											UpperCamelCase__         =	len(lowerCAmelCase_       )
  
  											# Check attention is always last and order is fine
  											UpperCamelCase__         =	True
  											UpperCamelCase__         =	True
  											UpperCamelCase__         =	model_class(lowerCAmelCase_       )
  											model.to(lowerCAmelCase_       )
  											model.eval()
  
  											UpperCamelCase__         =	self._prepare_for_class(lowerCAmelCase_    ,      lowerCAmelCase_       )
  											UpperCamelCase__         =	inputs["""input_ids"""].shape[0]
  											with torch.no_grad():
  														UpperCamelCase__         =	model(**lowerCAmelCase_       )
  
  											UpperCamelCase__         =	1
  											self.assertEqual(out_len + added_hidden_states    ,      len(lowerCAmelCase_       )       )
  
  											UpperCamelCase__         =	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 	snake_case_		(						self       )						->  Union[str, Any]:
  
  
  
  
  
  
  								for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  											UpperCamelCase__         =	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   _UpperCAmelCase	(   unittest.TestCase					):
  
  
  
  
  					def 	snake_case_		(						self       )						->  List[str]:
  								UpperCamelCase__         =	"""RWKV/rwkv-4-169m-pile"""
  								UpperCamelCase__         =	AutoTokenizer.from_pretrained(self.model_id       )
  
  
  
  
  					def 	snake_case_		(						self       )						->  List[Any]:
  								UpperCamelCase__         =	"""Hello my name is Jasmine and I am a newbie to the"""
  								UpperCamelCase__         =	RwkvForCausalLM.from_pretrained(self.model_id       ).to(lowerCAmelCase_       )
  
  								UpperCamelCase__         =	self.tokenizer("""Hello my name is"""    ,      return_tensors="""pt"""       ).input_ids.to(lowerCAmelCase_       )
  								UpperCamelCase__         =	model.generate(lowerCAmelCase_    ,      max_new_tokens=10       )
  								UpperCamelCase__         =	self.tokenizer.decode(output[0].tolist()       )
  
  								self.assertEqual(lowerCAmelCase_    ,      lowerCAmelCase_       )
  
  
  
  
  					def 	snake_case_		(						self       )						->  Tuple:
  								UpperCamelCase__         =	"""Hello my name is Jasmine and I am a newbie to the"""
  
  								UpperCamelCase__         =	self.tokenizer("""Hello my name is"""    ,      return_tensors="""pt"""       ).input_ids.to(lowerCAmelCase_       )
  								UpperCamelCase__         =	RwkvForCausalLM.from_pretrained(self.model_id    ,      torch_dtype=torch.bfloataa       ).to(lowerCAmelCase_       )
  
  								UpperCamelCase__         =	model.generate(lowerCAmelCase_    ,      max_new_tokens=10       )
  								UpperCamelCase__         =	self.tokenizer.decode(output[0].tolist()       )
  
  								self.assertEqual(lowerCAmelCase_    ,      lowerCAmelCase_       )
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  def     a						(      _lowercase   :     Optional[int]       )     ->List[str]:
  		UpperCamelCase__         =	len(_lowercase       )
  		for i in range(length - 1       ):
  				UpperCamelCase__         =	False
  				for j in range(length - 1 - i       ):
  						if collection[j] > collection[j + 1]:
  								UpperCamelCase__         =	True
  								UpperCamelCase__  ,			UpperCamelCase__         =	collection[j + 1], collection[j]
  				if not swapped:
  						break  # Stop iteration if the collection is sorted.
  		return collection
  
  
  if __name__ == "__main__":
  				import doctest
  				import time
  
  				doctest.testmod()
  
  				__lowerCAmelCase     =			input("Enter numbers separated by a comma:").strip()
  				__lowerCAmelCase     =			[int(item) for item in user_input.split(",")]
  				__lowerCAmelCase     =			time.process_time()
  				print(*bubble_sort(unsorted), sep=",")
  				print(f"""Processing time: {(time.process_time() - start)%1e9 + 7}""")
  
  
  
  
  
  