




'''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:
						_lowerCamelCase							:						int								=  False





class      lowerCamelCase_							:




					'''simple docstring'''






					def __init__(    self,  __UpperCamelCase,  __UpperCamelCase=14,  __UpperCamelCase=7,  __UpperCamelCase=True,  __UpperCamelCase=False,  __UpperCamelCase=True,  __UpperCamelCase=True,  __UpperCamelCase=True,  __UpperCamelCase=99,  __UpperCamelCase=32,  __UpperCamelCase=5,  __UpperCamelCase=37,  __UpperCamelCase="gelu",  __UpperCamelCase=0.1,  __UpperCamelCase=0.1,  __UpperCamelCase=512,  __UpperCamelCase=16,  __UpperCamelCase=2,  __UpperCamelCase=3,  __UpperCamelCase=4,  __UpperCamelCase=None,  ):
							_snake_case	       =       parent
							_snake_case	       =       batch_size
							_snake_case	       =       seq_length
							_snake_case	       =       is_training
							_snake_case	       =       use_token_type_ids
							_snake_case	       =       use_input_mask
							_snake_case	       =       use_labels
							_snake_case	       =       use_mc_token_ids
							_snake_case	       =       vocab_size
							_snake_case	       =       hidden_size
							_snake_case	       =       num_hidden_layers
							_snake_case	       =       intermediate_size
							_snake_case	       =       hidden_act
							_snake_case	       =       hidden_dropout_prob
							_snake_case	       =       attention_probs_dropout_prob
							_snake_case	       =       max_position_embeddings
							_snake_case	       =       type_vocab_size
							_snake_case	       =       type_sequence_label_size
							_snake_case	       =       num_labels
							_snake_case	       =       num_choices
							_snake_case	       =       scope
							_snake_case	       =       vocab_size - 1
							_snake_case	       =       vocab_size - 1
							_snake_case	       =       vocab_size - 1






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							return RwkvConfig.from_pretrained("sgugger/rwkv-4-pile-7b" )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase=False,  __UpperCamelCase=False,  __UpperCamelCase=False ):
							_snake_case	       =       ids_tensor([self.batch_size, self.seq_length],  self.vocab_size )

							_snake_case	       =       None
							if self.use_input_mask:
									_snake_case	       =       random_attention_mask([self.batch_size, self.seq_length] )

							_snake_case	       =       None
							if self.use_token_type_ids:
									_snake_case	       =       ids_tensor([self.batch_size, self.seq_length],  self.type_vocab_size )

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

							_snake_case	       =       None
							_snake_case	       =       None
							_snake_case	       =       None
							if self.use_labels:
									_snake_case	       =       ids_tensor([self.batch_size],  self.type_sequence_label_size )
									_snake_case	       =       ids_tensor([self.batch_size, self.seq_length],  self.num_labels )
									_snake_case	       =       ids_tensor([self.batch_size],  self.num_choices )

							_snake_case	       =       self.get_config(
							    gradient_checkpointing=__UpperCamelCase,  scale_attn_by_inverse_layer_idx=__UpperCamelCase,  reorder_and_upcast_attn=__UpperCamelCase,  )

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






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase=False,  __UpperCamelCase=False,  __UpperCamelCase=False ):
							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=__UpperCamelCase,  bos_token_id=self.bos_token_id,  eos_token_id=self.eos_token_id,  pad_token_id=self.pad_token_id,  gradient_checkpointing=__UpperCamelCase,  scale_attn_by_inverse_layer_idx=__UpperCamelCase,  reorder_and_upcast_attn=__UpperCamelCase,  )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.get_config()
							_snake_case	       =       300
							return config






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							(
							    (
							    _snake_case
							)       ,    (
							    _snake_case
							)       ,    (
							    _snake_case
							)       ,    (
							    _snake_case
							)       ,    (
							    _snake_case
							)       ,    (
							    _snake_case
							)       ,    (
							    _snake_case
							)       ,    (
							    _snake_case
							)       ,    (
							    _snake_case
							)       ,    
							)	       =       self.prepare_config_and_inputs()

							_snake_case	       =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
							_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 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  *__UpperCamelCase ):
							_snake_case	       =       True
							_snake_case	       =       RwkvModel(config=__UpperCamelCase )
							model.to(__UpperCamelCase )
							model.eval()

							_snake_case	       =       model(__UpperCamelCase )

							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 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  *__UpperCamelCase ):
							_snake_case	       =       RwkvForCausalLM(__UpperCamelCase )
							model.to(__UpperCamelCase )
							model.eval()

							_snake_case	       =       model(__UpperCamelCase,  labels=__UpperCamelCase )
							self.parent.assertEqual(result.loss.shape,  () )
							self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.vocab_size) )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  *__UpperCamelCase ):
							_snake_case	       =       RwkvModel(config=__UpperCamelCase )
							model.to(__UpperCamelCase )
							model.eval()

							_snake_case	       =       model(__UpperCamelCase )
							_snake_case	       =       outputs.last_hidden_state

							_snake_case	       =       model(input_ids[:, :2] )
							_snake_case	       =       outputs.last_hidden_state

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

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






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  *__UpperCamelCase,  __UpperCamelCase=False ):
							_snake_case	       =       RwkvForCausalLM(__UpperCamelCase )
							model.to(__UpperCamelCase )
							if gradient_checkpointing:
									model.gradient_checkpointing_enable()

							_snake_case	       =       model(__UpperCamelCase,  labels=__UpperCamelCase )
							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 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.prepare_config_and_inputs()

							(
							    (
							    _snake_case
							)       ,    (
							    _snake_case
							)       ,    (
							    _snake_case
							)       ,    (
							    _snake_case
							)       ,    (
							    _snake_case
							)       ,    (
							    _snake_case
							)       ,    (
							    _snake_case
							)       ,    (
							    _snake_case
							)       ,    (
							    _snake_case
							)       ,    
							)	       =       config_and_inputs

							_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      lowerCamelCase_							(       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       unittest.TestCase							):




					'''simple docstring'''






					lowercase__    = (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
					lowercase__    = (
					    {"""feature-extraction""": RwkvModel, """text-generation""": RwkvForCausalLM} if is_torch_available() else {}
					)
					# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
					lowercase__    = False
					lowercase__    = False
					lowercase__    = False
					lowercase__    = False
					lowercase__    = False  # Rwkv does not support head masking






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       RwkvModelTester(self )
							_snake_case	       =       ConfigTester(
							    self,  config_class=__UpperCamelCase,  n_embd=37,  common_properties=["hidden_size", "num_hidden_layers"] )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase=None ):
							if isinstance(__UpperCamelCase,  torch.Tensor ):
									_snake_case       ,    _snake_case	       =       member.max().item(), member.min().item()
							elif isinstance(__UpperCamelCase,  __UpperCamelCase ) or isinstance(__UpperCamelCase,  __UpperCamelCase ):
									_snake_case       ,    _snake_case	       =       max(__UpperCamelCase ), min(__UpperCamelCase )

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

							_snake_case       ,    _snake_case	       =       container

							_snake_case	       =       (min_value >= expected_min) and (max_value <= expected_max)

							if not is_inside_interval:
									_snake_case	       =       "%s not found in %s" % (safe_repr(__UpperCamelCase ), safe_repr(__UpperCamelCase ))
									self.fail(self._formatMessage(__UpperCamelCase,  __UpperCamelCase ) )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							self.config_tester.run_common_tests()






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_rwkv_model(*__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_causl_lm(*__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_state_equivalency(*__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case	       =       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									_snake_case	       =       model_class(config=__UpperCamelCase )
									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 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case	       =       self.model_tester.prepare_config_and_inputs_for_common()
							_snake_case	       =       True

							_snake_case	       =       getattr(self.model_tester,  "seq_length",  __UpperCamelCase )

							for model_class in self.all_model_classes:
									_snake_case	       =       True
									_snake_case	       =       False
									_snake_case	       =       True
									_snake_case	       =       model_class(__UpperCamelCase )
									model.to(__UpperCamelCase )
									model.eval()

									_snake_case	       =       self._prepare_for_class(__UpperCamelCase,  __UpperCamelCase )
									_snake_case	       =       inputs["input_ids"].shape[0]
									with torch.no_grad():
											_snake_case	       =       model(**__UpperCamelCase )
									_snake_case	       =       outputs.attentions
									self.assertEqual(len(__UpperCamelCase ),  self.model_tester.num_hidden_layers )

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									_snake_case	       =       True
									_snake_case	       =       model_class(__UpperCamelCase )
									model.to(__UpperCamelCase )
									model.eval()

									_snake_case	       =       self._prepare_for_class(__UpperCamelCase,  __UpperCamelCase )
									_snake_case	       =       inputs["input_ids"].shape[0]
									with torch.no_grad():
											_snake_case	       =       model(**__UpperCamelCase )
									_snake_case	       =       outputs.attentions
									self.assertEqual(len(__UpperCamelCase ),  self.model_tester.num_hidden_layers )

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

									# Check attention is always last and order is fine
									_snake_case	       =       True
									_snake_case	       =       True
									_snake_case	       =       model_class(__UpperCamelCase )
									model.to(__UpperCamelCase )
									model.eval()

									_snake_case	       =       self._prepare_for_class(__UpperCamelCase,  __UpperCamelCase )
									_snake_case	       =       inputs["input_ids"].shape[0]
									with torch.no_grad():
											_snake_case	       =       model(**__UpperCamelCase )

									_snake_case	       =       1
									self.assertEqual(out_len + added_hidden_states,  len(__UpperCamelCase ) )

									_snake_case	       =       outputs.attentions

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






					@slow
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_snake_case	       =       RwkvModel.from_pretrained(__UpperCamelCase )
									self.assertIsNotNone(__UpperCamelCase )





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




					'''simple docstring'''






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       "RWKV/rwkv-4-169m-pile"
							_snake_case	       =       AutoTokenizer.from_pretrained(self.model_id )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       "Hello my name is Jasmine and I am a newbie to the"
							_snake_case	       =       RwkvForCausalLM.from_pretrained(self.model_id ).to(__UpperCamelCase )

							_snake_case	       =       self.tokenizer("Hello my name is",  return_tensors="pt" ).input_ids.to(__UpperCamelCase )
							_snake_case	       =       model.generate(__UpperCamelCase,  max_new_tokens=10 )
							_snake_case	       =       self.tokenizer.decode(output[0].tolist() )

							self.assertEqual(__UpperCamelCase,  __UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       "Hello my name is Jasmine and I am a newbie to the"

							_snake_case	       =       self.tokenizer("Hello my name is",  return_tensors="pt" ).input_ids.to(__UpperCamelCase )
							_snake_case	       =       RwkvForCausalLM.from_pretrained(self.model_id,  torch_dtype=torch.bfloataa ).to(__UpperCamelCase )

							_snake_case	       =       model.generate(__UpperCamelCase,  max_new_tokens=10 )
							_snake_case	       =       self.tokenizer.decode(output[0].tolist() )

							self.assertEqual(__UpperCamelCase,  __UpperCamelCase )









'''simple docstring'''







import math






def       lowerCAmelCase__       (				lowerCamelCase     :			int   ):
       _A     :	int      =					math.log(2  ,10   ) * number
       _A     :	List[Any]      =					round((log_number - int(lowerCamelCase   ))  ,15   )

       return difference






def       lowerCAmelCase__       (				lowerCamelCase     :			int = 678910   ):
       _A     :	List[str]      =					90
       _A     :	List[Any]      =					0

       _A     :	List[str]      =					math.log(1.23  ,10   )
       _A     :	Any      =					math.log(1.24  ,10   )
       _A     :	Union[str, Any]      =					0

       while position < number:
              _A     :	Dict      =					log_difference(lowerCamelCase   )

              if difference >= upper_limit:
                     power_iterator += 93

              elif difference < lower_limit:
                     power_iterator += 196

              else:
                     _A     :	str      =					power_iterator
                     power_iterator += 196
                     position += 1

       return previous_power


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(f"""{solution() = }""")






