"""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         =		False
class 	__UpperCAmelCase						:







			'''simple docstring'''





			def __init__( self      ,			snake_case_      ,			snake_case_=14      ,			snake_case_=7      ,			snake_case_=True      ,			snake_case_=False      ,			snake_case_=True      ,			snake_case_=True      ,			snake_case_=True      ,			snake_case_=99      ,			snake_case_=32      ,			snake_case_=5      ,			snake_case_=37      ,			snake_case_="gelu"      ,			snake_case_=0.1      ,			snake_case_=0.1      ,			snake_case_=512      ,			snake_case_=16      ,			snake_case_=2      ,			snake_case_=3      ,			snake_case_=4      ,			snake_case_=None      ,			):







										'''simple docstring'''


										A__					:    str														= parent
										A__					:    Any														= batch_size
										A__					:    List[str]														= seq_length
										A__					:    int														= is_training
										A__					:    List[str]														= use_token_type_ids
										A__					:    List[Any]														= use_input_mask
										A__					:    Union[str, Any]														= use_labels
										A__					:    Any														= use_mc_token_ids
										A__					:    Dict														= vocab_size
										A__					:    Optional[Any]														= hidden_size
										A__					:    Optional[Any]														= num_hidden_layers
										A__					:    Dict														= intermediate_size
										A__					:    int														= hidden_act
										A__					:    List[Any]														= hidden_dropout_prob
										A__					:    Union[str, Any]														= attention_probs_dropout_prob
										A__					:    Tuple														= max_position_embeddings
										A__					:    int														= type_vocab_size
										A__					:    List[Any]														= type_sequence_label_size
										A__					:    Optional[int]														= num_labels
										A__					:    Union[str, Any]														= num_choices
										A__					:    Dict														= scope
										A__					:    Union[str, Any]														= vocab_size - 1
										A__					:    Dict														= vocab_size - 1
										A__					:    Any														= vocab_size - 1





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return RwkvConfig.from_pretrained("""sgugger/rwkv-4-pile-7b"""       )





			def 		lowerCamelCase				( self      ,			snake_case_=False      ,			snake_case_=False      ,			snake_case_=False       ):







										'''simple docstring'''


										A__					:    List[str]														= ids_tensor([self.batch_size, self.seq_length]      ,			self.vocab_size       )

										A__					:    List[Any]														= None
										if self.use_input_mask:
																	A__					:    int														= random_attention_mask([self.batch_size, self.seq_length]       )

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

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

										A__					:    Tuple														= 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__					:    int														= ids_tensor([self.batch_size, self.seq_length]      ,			self.num_labels       )
																	A__					:    Any														= ids_tensor([self.batch_size]      ,			self.num_choices       )

										A__					:    int														= self.get_config(
										    gradient_checkpointing=snake_case_      ,			scale_attn_by_inverse_layer_idx=snake_case_      ,			reorder_and_upcast_attn=snake_case_      ,			)

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





			def 		lowerCamelCase				( self      ,			snake_case_=False      ,			snake_case_=False      ,			snake_case_=False       ):







										'''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=snake_case_      ,			bos_token_id=self.bos_token_id      ,			eos_token_id=self.eos_token_id      ,			pad_token_id=self.pad_token_id      ,			gradient_checkpointing=snake_case_      ,			scale_attn_by_inverse_layer_idx=snake_case_      ,			reorder_and_upcast_attn=snake_case_      ,			)





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    int														= self.get_config()
										A__					:    Union[str, Any]														= 300
										return config





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										(
										    (
										    A__
										)							,       (
										    A__
										)							,       (
										    A__
										)							,       (
										    A__
										)							,       (
										    A__
										)							,       (
										    A__
										)							,       (
										    A__
										)							,       (
										    A__
										)							,       (
										    A__
										)							,       
										)					:    str														= self.prepare_config_and_inputs()

										A__					:    Tuple														= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
										A__					:    int														= 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 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			*snake_case_       ):







										'''simple docstring'''


										A__					:    List[Any]														= True
										A__					:    int														= RwkvModel(config=snake_case_       )
										model.to(snake_case_       )
										model.eval()

										A__					:    Optional[Any]														= model(snake_case_       )

										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 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			*snake_case_       ):







										'''simple docstring'''


										A__					:    Tuple														= RwkvForCausalLM(snake_case_       )
										model.to(snake_case_       )
										model.eval()

										A__					:    int														= model(snake_case_      ,			labels=snake_case_       )
										self.parent.assertEqual(result.loss.shape      ,			()       )
										self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.seq_length, self.vocab_size)       )





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			*snake_case_       ):







										'''simple docstring'''


										A__					:    Optional[Any]														= RwkvModel(config=snake_case_       )
										model.to(snake_case_       )
										model.eval()

										A__					:    Union[str, Any]														= model(snake_case_       )
										A__					:    Union[str, Any]														= outputs.last_hidden_state

										A__					:    Any														= model(input_ids[:, :2]       )
										A__					:    int														= outputs.last_hidden_state

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

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





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			*snake_case_      ,			snake_case_=False       ):







										'''simple docstring'''


										A__					:    Dict														= RwkvForCausalLM(snake_case_       )
										model.to(snake_case_       )
										if gradient_checkpointing:
																	model.gradient_checkpointing_enable()

										A__					:    Dict														= model(snake_case_      ,			labels=snake_case_       )
										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 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Union[str, Any]														= self.prepare_config_and_inputs()

										(
										    (
										    A__
										)							,       (
										    A__
										)							,       (
										    A__
										)							,       (
										    A__
										)							,       (
										    A__
										)							,       (
										    A__
										)							,       (
										    A__
										)							,       (
										    A__
										)							,       (
										    A__
										)							,       
										)					:    str														= config_and_inputs

										A__					:    Optional[Any]														= {"""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						(__A       ,      __A       ,      __A       ,      unittest.TestCase    ):







			'''simple docstring'''
			_UpperCamelCase     :	str				    =							(RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
			_UpperCamelCase     :	Optional[int]				    =							(
			    {'feature-extraction': RwkvModel, 'text-generation': RwkvForCausalLM} if is_torch_available() else {}
			)
			# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
			_UpperCamelCase     :	List[Any]				    =							False
			_UpperCamelCase     :	str				    =							False
			_UpperCamelCase     :	Any				    =							False
			_UpperCamelCase     :	int				    =							False
			_UpperCamelCase     :	Union[str, Any]				    =							False  # Rwkv does not support head masking





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    str														= RwkvModelTester(self       )
										A__					:    Optional[int]														= ConfigTester(
										    self      ,			config_class=snake_case_      ,			n_embd=37      ,			common_properties=["""hidden_size""", """num_hidden_layers"""]       )





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_=None       ):







										'''simple docstring'''


										if isinstance(snake_case_      ,			torch.Tensor       ):
																	A__							,       A__					:    str														= member.max().item(), member.min().item()
										elif isinstance(snake_case_      ,			snake_case_       ) or isinstance(snake_case_      ,			snake_case_       ):
																	A__							,       A__					:    str														= max(snake_case_       ), min(snake_case_       )

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

										A__							,       A__					:    int														= container

										A__					:    Optional[Any]														= (min_value >= expected_min) and (max_value <= expected_max)

										if not is_inside_interval:
																	A__					:    Optional[Any]														= """%s not found in %s""" % (safe_repr(snake_case_       ), safe_repr(snake_case_       ))
																	self.fail(self._formatMessage(snake_case_      ,			snake_case_       )       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										self.config_tester.run_common_tests()





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Union[str, Any]														= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_rwkv_model(*snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    str														= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_causl_lm(*snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[Any]														= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_state_equivalency(*snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__							,       A__					:    str														= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	A__					:    int														= model_class(config=snake_case_       )
																	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 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__							,       A__					:    Tuple														= self.model_tester.prepare_config_and_inputs_for_common()
										A__					:    Optional[Any]														= True

										A__					:    Dict														= getattr(self.model_tester      ,			"""seq_length"""      ,			snake_case_       )

										for model_class in self.all_model_classes:
																	A__					:    Tuple														= True
																	A__					:    Tuple														= False
																	A__					:    Any														= True
																	A__					:    Any														= model_class(snake_case_       )
																	model.to(snake_case_       )
																	model.eval()

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

																	# check that output_attentions also work using config
																	del inputs_dict["output_attentions"]
																	A__					:    Optional[Any]														= True
																	A__					:    Union[str, Any]														= model_class(snake_case_       )
																	model.to(snake_case_       )
																	model.eval()

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

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

																	# Check attention is always last and order is fine
																	A__					:    Optional[int]														= True
																	A__					:    Union[str, Any]														= True
																	A__					:    List[Any]														= model_class(snake_case_       )
																	model.to(snake_case_       )
																	model.eval()

																	A__					:    Any														= self._prepare_for_class(snake_case_      ,			snake_case_       )
																	A__					:    List[Any]														= inputs["""input_ids"""].shape[0]
																	with torch.no_grad():
																								A__					:    List[str]														= model(**snake_case_       )

																	A__					:    List[Any]														= 1
																	self.assertEqual(out_len + added_hidden_states      ,			len(snake_case_       )       )

																	A__					:    str														= outputs.attentions

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





			@slow
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	A__					:    List[str]														= RwkvModel.from_pretrained(snake_case_       )
																	self.assertIsNotNone(snake_case_       )





@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    ):







			'''simple docstring'''





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Dict														= """RWKV/rwkv-4-169m-pile"""
										A__					:    Optional[int]														= AutoTokenizer.from_pretrained(self.model_id       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Optional[int]														= """Hello my name is Jasmine and I am a newbie to the"""
										A__					:    Union[str, Any]														= RwkvForCausalLM.from_pretrained(self.model_id       ).to(snake_case_       )

										A__					:    Optional[int]														= self.tokenizer("""Hello my name is"""      ,			return_tensors="""pt"""       ).input_ids.to(snake_case_       )
										A__					:    Union[str, Any]														= model.generate(snake_case_      ,			max_new_tokens=10       )
										A__					:    Tuple														= self.tokenizer.decode(output[0].tolist()       )

										self.assertEqual(snake_case_      ,			snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Optional[int]														= """Hello my name is Jasmine and I am a newbie to the"""

										A__					:    List[Any]														= self.tokenizer("""Hello my name is"""      ,			return_tensors="""pt"""       ).input_ids.to(snake_case_       )
										A__					:    str														= RwkvForCausalLM.from_pretrained(self.model_id      ,			torch_dtype=torch.bfloataa       ).to(snake_case_       )

										A__					:    Union[str, Any]														= model.generate(snake_case_      ,			max_new_tokens=10       )
										A__					:    Optional[int]														= self.tokenizer.decode(output[0].tolist()       )

										self.assertEqual(snake_case_      ,			snake_case_       )






"""simple docstring"""



def    _A(		lowerCAmelCase       ):
							A__					:    str														= len(lowerCAmelCase       )
							A__					:    List[Any]														= [0] * length
							A__					:    Union[str, Any]														= [[False for i in range(lowerCAmelCase       )] for j in range(lowerCAmelCase       )]
							for i, c in enumerate(lowerCAmelCase       ):
														A__					:    Dict														= i
														for j in range(i + 1       ):
																					if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
																												A__					:    List[str]														= True
																												A__					:    Optional[Any]														= min(lowerCAmelCase ,     0 if j == 0 else (cut[j - 1] + 1)       )
														A__					:    Optional[int]														= mincut
							return cut[length - 1]


if __name__ == "__main__":
				_UpperCamelCase         =		input("Enter the string: ").strip()
				_UpperCamelCase         =		find_minimum_partitions(s)
				print(F'Minimum number of partitions required for the \'{s}\' is {ans}')





