






import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


UpperCamelCase 				=	1E-4

if is_torch_available():
		import torch

		from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
		from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder



@require_torch
class       __UpperCAmelCase		:







			def __init__(	self:  List[Any]      ,	UpperCAmelCase_:  List[Any]      ,	UpperCAmelCase_:  int=16      ,	UpperCAmelCase_:  List[Any]=13      ,	UpperCAmelCase_:  Union[str, Any]=7      ,	UpperCAmelCase_:  List[Any]=14      ,	UpperCAmelCase_:  List[str]=10      ,	UpperCAmelCase_:  Dict=19      ,	UpperCAmelCase_:  Tuple=5      ,	UpperCAmelCase_:  Union[str, Any]=4      ,	UpperCAmelCase_:  Dict=True      ,	UpperCAmelCase_:  List[Any]=16      ,	UpperCAmelCase_:  List[Any]=2      ,	UpperCAmelCase_:  Dict=4      ,	UpperCAmelCase_:  str=4      ,	UpperCAmelCase_:  Union[str, Any]="gelu"      ,	UpperCAmelCase_:  Union[str, Any]=0.1      ,	UpperCAmelCase_:  Tuple=0.1      ,	UpperCAmelCase_:  List[Any]=[1, 2, 3, 4, 5]      ,	UpperCAmelCase_:  Optional[Any]=25      ,	UpperCAmelCase_:  List[Any]=5      ,	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  d_model
									_SCREAMING_SNAKE_CASE	      =  parent
									_SCREAMING_SNAKE_CASE	      =  batch_size
									_SCREAMING_SNAKE_CASE	      =  prediction_length
									_SCREAMING_SNAKE_CASE	      =  context_length
									_SCREAMING_SNAKE_CASE	      =  cardinality
									_SCREAMING_SNAKE_CASE	      =  num_time_features
									_SCREAMING_SNAKE_CASE	      =  lags_sequence
									_SCREAMING_SNAKE_CASE	      =  embedding_dimension
									_SCREAMING_SNAKE_CASE	      =  is_training
									_SCREAMING_SNAKE_CASE	      =  hidden_size
									_SCREAMING_SNAKE_CASE	      =  num_hidden_layers
									_SCREAMING_SNAKE_CASE	      =  num_attention_heads
									_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	      =  context_length
									_SCREAMING_SNAKE_CASE	      =  prediction_length + label_length
									_SCREAMING_SNAKE_CASE	      =  label_length

									_SCREAMING_SNAKE_CASE	      =  moving_average
									_SCREAMING_SNAKE_CASE	      =  autocorrelation_factor







			def 							UpperCamelCase      (	self:  Optional[int]     ):







									'''simple docstring'''




									return AutoformerConfig(
									    d_model=self.d_model      ,	encoder_layers=self.num_hidden_layers      ,	decoder_layers=self.num_hidden_layers      ,	encoder_attention_heads=self.num_attention_heads      ,	decoder_attention_heads=self.num_attention_heads      ,	encoder_ffn_dim=self.intermediate_size      ,	decoder_ffn_dim=self.intermediate_size      ,	dropout=self.hidden_dropout_prob      ,	attention_dropout=self.attention_probs_dropout_prob      ,	prediction_length=self.prediction_length      ,	context_length=self.context_length      ,	label_length=self.label_length      ,	lags_sequence=self.lags_sequence      ,	num_time_features=self.num_time_features      ,	num_static_categorical_features=1      ,	cardinality=[self.cardinality]      ,	embedding_dimension=[self.embedding_dimension]      ,	moving_average=self.moving_average      ,	)







			def 							UpperCamelCase      (	self:  Dict      ,	UpperCAmelCase_:  Dict     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  config.context_length + max(config.lags_sequence     )

									_SCREAMING_SNAKE_CASE	      =  ids_tensor([self.batch_size, 1]      ,	config.cardinality[0]     )
									_SCREAMING_SNAKE_CASE	      =  floats_tensor([self.batch_size, _past_length, config.num_time_features]     )
									_SCREAMING_SNAKE_CASE	      =  floats_tensor([self.batch_size, _past_length]     )
									_SCREAMING_SNAKE_CASE	      =  floats_tensor([self.batch_size, _past_length]     ) > 0.5

									# decoder inputs
									_SCREAMING_SNAKE_CASE	      =  floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]     )
									_SCREAMING_SNAKE_CASE	      =  floats_tensor([self.batch_size, config.prediction_length]     )

									_SCREAMING_SNAKE_CASE	      =  {
									    'past_values': past_values,
									    'static_categorical_features': static_categorical_features,
									    'past_time_features': past_time_features,
									    'past_observed_mask': past_observed_mask,
									    'future_time_features': future_time_features,
									    'future_values': future_values,
									}
									return inputs_dict







			def 							UpperCamelCase      (	self:  List[str]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.get_config()
									_SCREAMING_SNAKE_CASE	      =  self.prepare_autoformer_inputs_dict(UpperCAmelCase_     )
									return config, inputs_dict







			def 							UpperCamelCase      (	self:  Any     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.prepare_config_and_inputs()
									return config, inputs_dict







			def 							UpperCamelCase      (	self:  int      ,	UpperCAmelCase_:  Union[str, Any]      ,	UpperCAmelCase_:  Optional[Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  AutoformerModel(config=UpperCAmelCase_     ).to(UpperCAmelCase_     ).eval()
									_SCREAMING_SNAKE_CASE	      =  model(**UpperCAmelCase_     )

									_SCREAMING_SNAKE_CASE	      =  outputs.encoder_last_hidden_state
									_SCREAMING_SNAKE_CASE	      =  outputs.last_hidden_state

									with tempfile.TemporaryDirectory() as tmpdirname:
															_SCREAMING_SNAKE_CASE	      =  model.get_encoder()
															encoder.save_pretrained(UpperCAmelCase_     )
															_SCREAMING_SNAKE_CASE	      =  AutoformerEncoder.from_pretrained(UpperCAmelCase_     ).to(UpperCAmelCase_     )

									_SCREAMING_SNAKE_CASE	      =  model.create_network_inputs(**UpperCAmelCase_     )
									_SCREAMING_SNAKE_CASE	      =  model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]     )

									_SCREAMING_SNAKE_CASE	      =  torch.cat(
									    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])      ,	dim=-1      ,	)
									_SCREAMING_SNAKE_CASE	      =  encoder(inputs_embeds=UpperCAmelCase_     )[0]
									self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3     )

									_SCREAMING_SNAKE_CASE	      =  (
									    torch.mean(transformer_inputs[:, : config.context_length, ...]      ,	dim=1     )
									    .unsqueeze(1     )
									    .repeat(1      ,	config.prediction_length      ,	1     )
									)
									_SCREAMING_SNAKE_CASE	      =  torch.zeros(
									    [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]      ,	device=enc_input.device      ,	)

									_SCREAMING_SNAKE_CASE	      =  torch.cat(
									    (
									        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)      ,	dim=1     ),
									        feature[:, config.context_length - config.label_length :, ...],
									    )      ,	dim=-1      ,	)
									_SCREAMING_SNAKE_CASE	      =  torch.cat(
									    (
									        torch.cat((trend_input[:, -config.label_length :, ...], mean)      ,	dim=1     ),
									        feature[:, config.context_length - config.label_length :, ...],
									    )      ,	dim=-1      ,	)

									with tempfile.TemporaryDirectory() as tmpdirname:
															_SCREAMING_SNAKE_CASE	      =  model.get_decoder()
															decoder.save_pretrained(UpperCAmelCase_     )
															_SCREAMING_SNAKE_CASE	      =  AutoformerDecoder.from_pretrained(UpperCAmelCase_     ).to(UpperCAmelCase_     )

									_SCREAMING_SNAKE_CASE	      =  decoder(
									    trend=UpperCAmelCase_      ,	inputs_embeds=UpperCAmelCase_      ,	encoder_hidden_states=UpperCAmelCase_      ,	)[0]

									self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3     )



@require_torch
class       __UpperCAmelCase		(snake_case_       ,snake_case_       ,unittest.TestCase    ):
			__snake_case				:      Any 						=							(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
			__snake_case				:      str 						=							(AutoformerForPrediction,) if is_torch_available() else ()
			__snake_case				:      Tuple 						=							{"feature-extraction": AutoformerModel} if is_torch_available() else {}
			__snake_case				:      str 						=							False
			__snake_case				:      List[str] 						=							False
			__snake_case				:      Tuple 						=							False
			__snake_case				:      Tuple 						=							False
			__snake_case				:      List[str] 						=							False
			__snake_case				:      str 						=							False







			def 							UpperCamelCase      (	self:  Optional[Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  AutoformerModelTester(self     )
									_SCREAMING_SNAKE_CASE	      =  ConfigTester(self      ,	config_class=UpperCAmelCase_      ,	has_text_modality=UpperCAmelCase_     )







			def 							UpperCamelCase      (	self:  List[str]     ):







									'''simple docstring'''




									self.config_tester.run_common_tests()







			def 							UpperCamelCase      (	self:  Tuple     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE	      =  model_class(UpperCAmelCase_     )

															with tempfile.TemporaryDirectory() as tmpdirname:
																					model.save_pretrained(UpperCAmelCase_     )
																					_SCREAMING_SNAKE_CASE	      =  model_class.from_pretrained(UpperCAmelCase_      ,	output_loading_info=UpperCAmelCase_     )
															self.assertEqual(info["""missing_keys"""]      ,	[]     )







			def 							UpperCamelCase      (	self:  Union[str, Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_encoder_decoder_model_standalone(*UpperCAmelCase_     )







			@unittest.skip(reason="""Model has no tokens embeddings"""     )
			def 							UpperCamelCase      (	self:  Optional[Any]     ):







									'''simple docstring'''




									pass







			def 							UpperCamelCase      (	self:  List[Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  inspect.signature(getattr(UpperCAmelCase_      ,	"""forward"""     )     )
									# The main input is the name of the argument after `self`
									_SCREAMING_SNAKE_CASE	      =  list(model_signature.parameters.keys()     )[1]
									self.assertEqual(AutoformerModel.main_input_name      ,	UpperCAmelCase_     )







			def 							UpperCamelCase      (	self:  Dict     ):







									'''simple docstring'''




									_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(UpperCAmelCase_     )
															_SCREAMING_SNAKE_CASE	      =  inspect.signature(model.forward     )
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															_SCREAMING_SNAKE_CASE	      =  [*signature.parameters.keys()]

															_SCREAMING_SNAKE_CASE	      =  [
															    'past_values',
															    'past_time_features',
															    'past_observed_mask',
															    'static_categorical_features',
															    'static_real_features',
															    'future_values',
															    'future_time_features',
															]

															if model.__class__.__name__ in ["AutoformerForPrediction"]:
																					expected_arg_names.append("""future_observed_mask"""     )

															expected_arg_names.extend(
															    [
															        """decoder_attention_mask""",
															        """head_mask""",
															        """decoder_head_mask""",
															        """cross_attn_head_mask""",
															        """encoder_outputs""",
															        """past_key_values""",
															        """output_hidden_states""",
															        """output_attentions""",
															        """use_cache""",
															        """return_dict""",
															    ]     )

															self.assertListEqual(arg_names[: len(UpperCAmelCase_     )]      ,	UpperCAmelCase_     )







			def 							UpperCamelCase      (	self:  Optional[int]     ):







									'''simple docstring'''




									_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"""      ,	UpperCAmelCase_     )
									_SCREAMING_SNAKE_CASE	      =  getattr(self.model_tester      ,	"""decoder_seq_length"""      ,	UpperCAmelCase_     )
									_SCREAMING_SNAKE_CASE	      =  getattr(self.model_tester      ,	"""encoder_seq_length"""      ,	UpperCAmelCase_     )
									_SCREAMING_SNAKE_CASE	      =  getattr(self.model_tester      ,	"""d_model"""      ,	UpperCAmelCase_     )
									_SCREAMING_SNAKE_CASE	      =  getattr(self.model_tester      ,	"""num_attention_heads"""      ,	UpperCAmelCase_     )
									_SCREAMING_SNAKE_CASE	      =  d_model // num_attention_heads

									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(UpperCAmelCase_     )
															model.to(UpperCAmelCase_     )
															model.eval()
															with torch.no_grad():
																					_SCREAMING_SNAKE_CASE	      =  model(**self._prepare_for_class(UpperCAmelCase_      ,	UpperCAmelCase_     )     )
															_SCREAMING_SNAKE_CASE	      =  outputs.encoder_attentions if config.is_encoder_decoder else 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"]
															_SCREAMING_SNAKE_CASE	      =  True
															_SCREAMING_SNAKE_CASE	      =  model_class(UpperCAmelCase_     )
															model.to(UpperCAmelCase_     )
															model.eval()
															with torch.no_grad():
																					_SCREAMING_SNAKE_CASE	      =  model(**self._prepare_for_class(UpperCAmelCase_      ,	UpperCAmelCase_     )     )
															_SCREAMING_SNAKE_CASE	      =  outputs.encoder_attentions
															self.assertEqual(len(UpperCAmelCase_     )      ,	self.model_tester.num_hidden_layers     )

															self.assertListEqual(
															    list(attentions[0].shape[-3:]     )      ,	[self.model_tester.num_attention_heads, encoder_seq_length, dim]      ,	)
															_SCREAMING_SNAKE_CASE	      =  len(UpperCAmelCase_     )

															_SCREAMING_SNAKE_CASE	      =  7

															if "last_hidden_state" in outputs:
																					correct_outlen += 1

															if "trend" in outputs:
																					correct_outlen += 1

															if "past_key_values" in outputs:
																					correct_outlen += 1  # past_key_values have been returned

															if "loss" in outputs:
																					correct_outlen += 1

															if "params" in outputs:
																					correct_outlen += 1

															self.assertEqual(UpperCAmelCase_      ,	UpperCAmelCase_     )

															# decoder attentions
															_SCREAMING_SNAKE_CASE	      =  outputs.decoder_attentions
															self.assertIsInstance(UpperCAmelCase_      ,	(list, tuple)     )
															self.assertEqual(len(UpperCAmelCase_     )      ,	self.model_tester.num_hidden_layers     )
															self.assertListEqual(
															    list(decoder_attentions[0].shape[-3:]     )      ,	[self.model_tester.num_attention_heads, decoder_seq_length, dim]      ,	)

															# cross attentions
															_SCREAMING_SNAKE_CASE	      =  outputs.cross_attentions
															self.assertIsInstance(UpperCAmelCase_      ,	(list, tuple)     )
															self.assertEqual(len(UpperCAmelCase_     )      ,	self.model_tester.num_hidden_layers     )
															self.assertListEqual(
															    list(cross_attentions[0].shape[-3:]     )      ,	[self.model_tester.num_attention_heads, decoder_seq_length, dim]      ,	)

									# Check attention is always last and order is fine
									_SCREAMING_SNAKE_CASE	      =  True
									_SCREAMING_SNAKE_CASE	      =  True
									_SCREAMING_SNAKE_CASE	      =  model_class(UpperCAmelCase_     )
									model.to(UpperCAmelCase_     )
									model.eval()
									with torch.no_grad():
															_SCREAMING_SNAKE_CASE	      =  model(**self._prepare_for_class(UpperCAmelCase_      ,	UpperCAmelCase_     )     )

									self.assertEqual(out_len + 2      ,	len(UpperCAmelCase_     )     )

									_SCREAMING_SNAKE_CASE	      =  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

									self.assertEqual(len(UpperCAmelCase_     )      ,	self.model_tester.num_hidden_layers     )
									self.assertListEqual(
									    list(self_attentions[0].shape[-3:]     )      ,	[self.model_tester.num_attention_heads, encoder_seq_length, dim]      ,	)







			@is_flaky()
			def 							UpperCamelCase      (	self:  Optional[Any]     ):







									'''simple docstring'''




									super().test_retain_grad_hidden_states_attentions()

def        __lowerCamelCase			(		snake_case__="train-batch.pt"		)							->  List[str]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch"""       ,filename=snake_case__       ,repo_type="""dataset"""		)
						_SCREAMING_SNAKE_CASE	      =  torch.load(snake_case__       ,map_location=snake_case__		)
						return batch




@require_torch
@slow
class       __UpperCAmelCase		(unittest.TestCase    ):







			def 							UpperCamelCase      (	self:  Union[str, Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly"""     ).to(UpperCAmelCase_     )
									_SCREAMING_SNAKE_CASE	      =  prepare_batch()

									with torch.no_grad():
															_SCREAMING_SNAKE_CASE	      =  model(
															    past_values=batch["""past_values"""]      ,	past_time_features=batch["""past_time_features"""]      ,	past_observed_mask=batch["""past_observed_mask"""]      ,	static_categorical_features=batch["""static_categorical_features"""]      ,	future_values=batch["""future_values"""]      ,	future_time_features=batch["""future_time_features"""]      ,	)[0]

									_SCREAMING_SNAKE_CASE	      =  torch.Size(
									    (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)     )
									self.assertEqual(output.shape      ,	UpperCAmelCase_     )

									_SCREAMING_SNAKE_CASE	      =  torch.tensor(
									    [[0.35_93, -1.33_98, 0.63_30], [0.22_79, 1.53_96, -0.17_92], [0.04_50, 1.32_25, -0.23_35]]      ,	device=UpperCAmelCase_     )
									self.assertTrue(torch.allclose(output[0, :3, :3]      ,	UpperCAmelCase_      ,	atol=UpperCAmelCase_     )     )







			def 							UpperCamelCase      (	self:  int     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""     ).to(UpperCAmelCase_     )
									_SCREAMING_SNAKE_CASE	      =  prepare_batch("""val-batch.pt"""     )
									with torch.no_grad():
															_SCREAMING_SNAKE_CASE	      =  model(
															    past_values=batch["""past_values"""]      ,	past_time_features=batch["""past_time_features"""]      ,	past_observed_mask=batch["""past_observed_mask"""]      ,	static_categorical_features=batch["""static_categorical_features"""]      ,	).encoder_last_hidden_state
									_SCREAMING_SNAKE_CASE	      =  torch.Size((64, model.config.context_length, model.config.d_model)     )
									self.assertEqual(output.shape      ,	UpperCAmelCase_     )

									_SCREAMING_SNAKE_CASE	      =  torch.tensor(
									    [[-0.07_34, -0.90_36, 0.83_58], [4.71_86, 2.41_13, 1.95_81], [1.79_53, 2.35_58, 1.29_70]]      ,	device=UpperCAmelCase_     )
									self.assertTrue(torch.allclose(output[0, :3, :3]      ,	UpperCAmelCase_      ,	atol=UpperCAmelCase_     )     )







			def 							UpperCamelCase      (	self:  Optional[int]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""     ).to(UpperCAmelCase_     )
									_SCREAMING_SNAKE_CASE	      =  prepare_batch("""val-batch.pt"""     )
									with torch.no_grad():
															_SCREAMING_SNAKE_CASE	      =  model.generate(
															    static_categorical_features=batch["""static_categorical_features"""]      ,	past_time_features=batch["""past_time_features"""]      ,	past_values=batch["""past_values"""]      ,	future_time_features=batch["""future_time_features"""]      ,	past_observed_mask=batch["""past_observed_mask"""]      ,	)
									_SCREAMING_SNAKE_CASE	      =  torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)     )
									self.assertEqual(outputs.sequences.shape      ,	UpperCAmelCase_     )

									_SCREAMING_SNAKE_CASE	      =  torch.tensor([31_30.67_63, 40_56.52_93, 70_53.07_86]      ,	device=UpperCAmelCase_     )
									_SCREAMING_SNAKE_CASE	      =  outputs.sequences.mean(dim=1     )
									self.assertTrue(torch.allclose(mean_prediction[0, -3:]      ,	UpperCAmelCase_      ,	rtol=1E-1     )     )


def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     str	):

 def get_matched_characters(lowerCamelCase					:     str  ,  lowerCamelCase					:     str	) -> str:
  UpperCamelCase_					:						Tuple =					[]
  UpperCamelCase_					:						List[Any] =					min(len(_stra	)  ,  len(_stra	)	) // 2
  for i, l in enumerate(_stra	):
   UpperCamelCase_					:						int =					int(max(0  ,  i - limit	)	)
   UpperCamelCase_					:						Dict =					int(min(i + limit + 1  ,  len(_stra	)	)	)
   if l in _stra[left:right]:
    matched.append(lowerCamelCase	)
    UpperCamelCase_					:						Dict =					F"{_stra[0:_stra.index(lowerCamelCase	)]} {_stra[_stra.index(lowerCamelCase	) + 1:]}"

  return "".join(lowerCamelCase	)

 # matching characters
 UpperCamelCase_					:						str =					get_matched_characters(lowerCamelCase  ,  lowerCamelCase	)
 UpperCamelCase_					:						str =					get_matched_characters(lowerCamelCase  ,  lowerCamelCase	)
 UpperCamelCase_					:						Union[str, Any] =					len(lowerCamelCase	)

 # transposition
 UpperCamelCase_					:						int =					(
     len([(ca, ca) for ca, ca in zip(lowerCamelCase  ,  lowerCamelCase	) if ca != ca]	) // 2
 )

 if not match_count:
  UpperCamelCase_					:						Union[str, Any] =					0.0
 else:
  UpperCamelCase_					:						str =					(
      1
      / 3
      * (
          match_count / len(lowerCamelCase	)
          + match_count / len(lowerCamelCase	)
          + (match_count - transpositions) / match_count
      )
  )

 # common prefix up to 4 characters
 UpperCamelCase_					:						Dict =					0
 for ca, ca in zip(stra[:4]  ,  stra[:4]	):
  if ca == ca:
   prefix_len += 1
  else:
   break

 return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(jaro_winkler('hello', 'world'))



