



"""simple docstring"""


import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer







class       A_			:



						"""simple docstring"""






						def __init__(					self :Optional[Any]   ,  lowercase_ :List[Any]   ,  lowercase_ :Tuple=13   ,  lowercase_ :Union[str, Any]=7   ,  lowercase_ :Optional[Any]=True   ,  lowercase_ :Optional[int]=True   ,  lowercase_ :str=False   ,  lowercase_ :Dict=True   ,  lowercase_ :Optional[int]=99   ,  lowercase_ :List[str]=32   ,  lowercase_ :List[Any]=5   ,  lowercase_ :int=4   ,  lowercase_ :str=37   ,  lowercase_ :Optional[int]="gelu"   ,  lowercase_ :Union[str, Any]=0.1   ,  lowercase_ :Optional[int]=0.1   ,  lowercase_ :Tuple=5_12   ,  lowercase_ :Optional[int]=16   ,  lowercase_ :Optional[int]=2   ,  lowercase_ :int=0.02   ,  lowercase_ :Union[str, Any]=3   ,  lowercase_ :Tuple=4   ,  lowercase_ :Any=None   ,  )				->			str:
												UpperCAmelCase             = parent
												UpperCAmelCase             = batch_size
												UpperCAmelCase             = seq_length
												UpperCAmelCase             = is_training
												UpperCAmelCase             = use_input_mask
												UpperCAmelCase             = use_token_type_ids
												UpperCAmelCase             = use_labels
												UpperCAmelCase             = vocab_size
												UpperCAmelCase             = hidden_size
												UpperCAmelCase             = num_hidden_layers
												UpperCAmelCase             = num_attention_heads
												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             = initializer_range
												UpperCAmelCase             = num_labels
												UpperCAmelCase             = num_choices
												UpperCAmelCase             = scope






						def 	UpperCAmelCase__     (					self :Dict					)				->			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
												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()

												return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






						def 	UpperCAmelCase__     (					self :int					)				->			Optional[Any]:
												return LlamaConfig(
												    vocab_size=self.vocab_size   ,  hidden_size=self.hidden_size   ,  num_hidden_layers=self.num_hidden_layers   ,  num_attention_heads=self.num_attention_heads   ,  intermediate_size=self.intermediate_size   ,  hidden_act=self.hidden_act   ,  hidden_dropout_prob=self.hidden_dropout_prob   ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,  max_position_embeddings=self.max_position_embeddings   ,  type_vocab_size=self.type_vocab_size   ,  is_decoder=lowercase_   ,  initializer_range=self.initializer_range   ,  )






						def 	UpperCAmelCase__     (					self :List[Any]   ,  lowercase_ :int   ,  lowercase_ :Union[str, Any]   ,  lowercase_ :List[str]   ,  lowercase_ :List[Any]   ,  lowercase_ :Tuple   ,  lowercase_ :Dict   ,  lowercase_ :Union[str, Any]					)				->			int:
												UpperCAmelCase             = LlamaModel(config=lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_					)
												UpperCAmelCase             = model(lowercase_					)
												self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, self.seq_length, self.hidden_size)					)






						def 	UpperCAmelCase__     (					self :Dict   ,  lowercase_ :Dict   ,  lowercase_ :Union[str, Any]   ,  lowercase_ :Optional[int]   ,  lowercase_ :List[Any]   ,  lowercase_ :List[str]   ,  lowercase_ :Optional[int]   ,  lowercase_ :int   ,  lowercase_ :Dict   ,  lowercase_ :str   ,  )				->			Dict:
												UpperCAmelCase             = True
												UpperCAmelCase             = LlamaModel(lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = model(
												    lowercase_   ,  attention_mask=lowercase_   ,  encoder_hidden_states=lowercase_   ,  encoder_attention_mask=lowercase_   ,  )
												UpperCAmelCase             = model(
												    lowercase_   ,  attention_mask=lowercase_   ,  encoder_hidden_states=lowercase_   ,  )
												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_					)
												self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, self.seq_length, self.hidden_size)					)






						def 	UpperCAmelCase__     (					self :Dict   ,  lowercase_ :Optional[int]   ,  lowercase_ :Union[str, Any]   ,  lowercase_ :Any   ,  lowercase_ :List[Any]   ,  lowercase_ :List[str]   ,  lowercase_ :Dict   ,  lowercase_ :Optional[Any]   ,  lowercase_ :Tuple   ,  lowercase_ :Union[str, Any]   ,  )				->			List[str]:
												UpperCAmelCase             = LlamaForCausalLM(config=lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_   ,  labels=lowercase_					)
												self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.seq_length, self.vocab_size)					)






						def 	UpperCAmelCase__     (					self :str   ,  lowercase_ :List[Any]   ,  lowercase_ :str   ,  lowercase_ :Optional[int]   ,  lowercase_ :Optional[int]   ,  lowercase_ :Optional[int]   ,  lowercase_ :Dict   ,  lowercase_ :Tuple   ,  lowercase_ :Optional[int]   ,  lowercase_ :Any   ,  )				->			List[str]:
												UpperCAmelCase             = True
												UpperCAmelCase             = True
												UpperCAmelCase             = LlamaForCausalLM(config=lowercase_					)
												model.to(lowercase_					)
												model.eval()

												# first forward pass
												UpperCAmelCase             = model(
												    lowercase_   ,  attention_mask=lowercase_   ,  encoder_hidden_states=lowercase_   ,  encoder_attention_mask=lowercase_   ,  use_cache=lowercase_   ,  )
												UpperCAmelCase             = outputs.past_key_values

												# create hypothetical multiple next token and extent to next_input_ids
												UpperCAmelCase             = ids_tensor((self.batch_size, 3)   ,  config.vocab_size					)
												UpperCAmelCase             = ids_tensor((self.batch_size, 3)   ,  vocab_size=2					)

												# append to next input_ids and
												UpperCAmelCase             = torch.cat([input_ids, next_tokens]   ,  dim=-1					)
												UpperCAmelCase             = torch.cat([input_mask, next_mask]   ,  dim=-1					)

												UpperCAmelCase             = model(
												    lowercase_   ,  attention_mask=lowercase_   ,  encoder_hidden_states=lowercase_   ,  encoder_attention_mask=lowercase_   ,  output_hidden_states=lowercase_   ,  )['hidden_states'][0]
												UpperCAmelCase             = model(
												    lowercase_   ,  attention_mask=lowercase_   ,  encoder_hidden_states=lowercase_   ,  encoder_attention_mask=lowercase_   ,  past_key_values=lowercase_   ,  output_hidden_states=lowercase_   ,  )['hidden_states'][0]

												# select random slice
												UpperCAmelCase             = ids_tensor((1,)   ,  output_from_past.shape[-1]					).item()
												UpperCAmelCase             = output_from_no_past[:, -3:, random_slice_idx].detach()
												UpperCAmelCase             = output_from_past[:, :, random_slice_idx].detach()

												self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

												# test that outputs are equal for slice
												self.parent.assertTrue(torch.allclose(lowercase_   ,  lowercase_   ,  atol=1E-3					)					)






						def 	UpperCAmelCase__     (					self :Union[str, Any]					)				->			Union[str, Any]:
												UpperCAmelCase             = self.prepare_config_and_inputs()
												(
												    (
												    UpperCAmelCase
												)      ,    (
												    UpperCAmelCase
												)      ,    (
												    UpperCAmelCase
												)      ,    (
												    UpperCAmelCase
												)      ,    (
												    UpperCAmelCase
												)      ,    (
												    UpperCAmelCase
												)      ,    (
												    UpperCAmelCase
												)      ,    
												)             = config_and_inputs
												UpperCAmelCase             = {'input_ids': input_ids, 'attention_mask': input_mask}
												return config, inputs_dict







@require_torch
class       A_			(     SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_	,   unittest.TestCase				):



						"""simple docstring"""
						__UpperCamelCase						=							(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
						__UpperCamelCase						=							(LlamaForCausalLM,) if is_torch_available() else ()
						__UpperCamelCase						=							(
						    {
						        """feature-extraction""": LlamaModel,
						        """text-classification""": LlamaForSequenceClassification,
						        """text-generation""": LlamaForCausalLM,
						        """zero-shot""": LlamaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						__UpperCamelCase						=							False
						__UpperCamelCase						=							False






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			str:
												UpperCAmelCase             = LlamaModelTester(self					)
												UpperCAmelCase             = ConfigTester(self   ,  config_class=lowercase_   ,  hidden_size=37					)






						def 	UpperCAmelCase__     (					self :str					)				->			Optional[int]:
												self.config_tester.run_common_tests()






						def 	UpperCAmelCase__     (					self :List[Any]					)				->			Dict:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*lowercase_					)






						def 	UpperCAmelCase__     (					self :Tuple					)				->			List[str]:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																		UpperCAmelCase             = type
																		self.model_tester.create_and_check_model(*lowercase_					)






						def 	UpperCAmelCase__     (					self :str					)				->			List[str]:
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase             = 3
												UpperCAmelCase             = input_dict['input_ids']
												UpperCAmelCase             = input_ids.ne(1					).to(lowercase_					)
												UpperCAmelCase             = ids_tensor([self.model_tester.batch_size]   ,  self.model_tester.type_sequence_label_size					)
												UpperCAmelCase             = LlamaForSequenceClassification(lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_   ,  labels=lowercase_					)
												self.assertEqual(result.logits.shape   ,  (self.model_tester.batch_size, self.model_tester.num_labels)					)






						def 	UpperCAmelCase__     (					self :Union[str, Any]					)				->			Union[str, Any]:
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase             = 3
												UpperCAmelCase             = 'single_label_classification'
												UpperCAmelCase             = input_dict['input_ids']
												UpperCAmelCase             = input_ids.ne(1					).to(lowercase_					)
												UpperCAmelCase             = ids_tensor([self.model_tester.batch_size]   ,  self.model_tester.type_sequence_label_size					)
												UpperCAmelCase             = LlamaForSequenceClassification(lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_   ,  labels=lowercase_					)
												self.assertEqual(result.logits.shape   ,  (self.model_tester.batch_size, self.model_tester.num_labels)					)






						def 	UpperCAmelCase__     (					self :List[str]					)				->			Tuple:
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase             = 3
												UpperCAmelCase             = 'multi_label_classification'
												UpperCAmelCase             = input_dict['input_ids']
												UpperCAmelCase             = input_ids.ne(1					).to(lowercase_					)
												UpperCAmelCase             = ids_tensor(
												    [self.model_tester.batch_size, config.num_labels]   ,  self.model_tester.type_sequence_label_size					).to(torch.float					)
												UpperCAmelCase             = LlamaForSequenceClassification(lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_   ,  labels=lowercase_					)
												self.assertEqual(result.logits.shape   ,  (self.model_tester.batch_size, self.model_tester.num_labels)					)






						@unittest.skip('LLaMA buffers include complex numbers, which breaks this test'					)
						def 	UpperCAmelCase__     (					self :Dict					)				->			List[str]:
												pass






						@parameterized.expand([('linear',), ('dynamic',)]					)
						def 	UpperCAmelCase__     (					self :Optional[int]   ,  lowercase_ :Optional[int]					)				->			int:
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase             = ids_tensor([1, 10]   ,  config.vocab_size					)
												UpperCAmelCase             = ids_tensor([1, int(config.max_position_embeddings * 1.5					)]   ,  config.vocab_size					)

												set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
												UpperCAmelCase             = LlamaModel(lowercase_					)
												original_model.to(lowercase_					)
												original_model.eval()
												UpperCAmelCase             = original_model(lowercase_					).last_hidden_state
												UpperCAmelCase             = original_model(lowercase_					).last_hidden_state

												set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
												UpperCAmelCase             = {'type': scaling_type, 'factor': 10.0}
												UpperCAmelCase             = LlamaModel(lowercase_					)
												scaled_model.to(lowercase_					)
												scaled_model.eval()
												UpperCAmelCase             = scaled_model(lowercase_					).last_hidden_state
												UpperCAmelCase             = scaled_model(lowercase_					).last_hidden_state

												# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
												# maximum sequence length, so the outputs for the short input should match.
												if scaling_type == "dynamic":
																		self.assertTrue(torch.allclose(lowercase_   ,  lowercase_   ,  atol=1E-5					)					)
												else:
																		self.assertFalse(torch.allclose(lowercase_   ,  lowercase_   ,  atol=1E-5					)					)

												# The output should be different for long inputs
												self.assertFalse(torch.allclose(lowercase_   ,  lowercase_   ,  atol=1E-5					)					)







@require_torch
class       A_			(     unittest.TestCase				):



						"""simple docstring"""






						@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'					)
						@slow
						def 	UpperCAmelCase__     (					self :Any					)				->			Union[str, Any]:
												UpperCAmelCase             = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
												UpperCAmelCase             = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'   ,  device_map='auto'					)
												UpperCAmelCase             = model(torch.tensor([input_ids]					)					)
												# Expected mean on dim = -1
												UpperCAmelCase             = torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]					)
												torch.testing.assert_close(out.mean(-1					)   ,  lowercase_   ,  atol=1E-2   ,  rtol=1E-2					)
												# slicing logits[0, 0, 0:30]
												# fmt: off
												UpperCAmelCase             = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]					)
												# fmt: on
												torch.testing.assert_close(out[0, 0, :30]   ,  lowercase_   ,  atol=1E-5   ,  rtol=1E-5					)






						@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'					)
						@slow
						def 	UpperCAmelCase__     (					self :Optional[Any]					)				->			Any:
												UpperCAmelCase             = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
												UpperCAmelCase             = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'   ,  device_map='auto'					)
												UpperCAmelCase             = model(torch.tensor(lowercase_					)					)
												# Expected mean on dim = -1
												UpperCAmelCase             = torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]					)
												torch.testing.assert_close(out.mean(-1					)   ,  lowercase_   ,  atol=1E-2   ,  rtol=1E-2					)
												# slicing logits[0, 0, 0:30]
												# fmt: off
												UpperCAmelCase             = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]					)
												# fmt: on
												torch.testing.assert_close(out[0, 0, :30]   ,  lowercase_   ,  atol=1E-5   ,  rtol=1E-5					)






						@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'					)
						@slow
						def 	UpperCAmelCase__     (					self :List[str]					)				->			List[str]:
												UpperCAmelCase             = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
												UpperCAmelCase             = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'   ,  device_map='auto'					)
												UpperCAmelCase             = model(torch.tensor(lowercase_					)					)
												# Expected mean on dim = -1
												UpperCAmelCase             = torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]					)
												torch.testing.assert_close(out.mean(-1					)   ,  lowercase_   ,  atol=1E-2   ,  rtol=1E-2					)
												# slicing logits[0, 0, 0:30]
												# fmt: off
												UpperCAmelCase             = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]					)
												# fmt: on
												torch.testing.assert_close(out.mean(-1					)   ,  lowercase_   ,  atol=1E-2   ,  rtol=1E-2					)






						@unittest.skip(
						    'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'					)
						@slow
						def 	UpperCAmelCase__     (					self :int					)				->			Optional[int]:
												UpperCAmelCase             = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
												UpperCAmelCase             = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'   ,  device_map='auto'					)
												UpperCAmelCase             = model(torch.tensor(lowercase_					)					)

												UpperCAmelCase             = torch.tensor(
												    [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]   ,  dtype=torch.floataa					)
												torch.testing.assert_close(out.mean(-1					)   ,  lowercase_   ,  atol=1E-2   ,  rtol=1E-2					)
												# fmt: off
												UpperCAmelCase             = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]					)
												# fmt: on
												torch.testing.assert_close(out[0, 0, :30]   ,  lowercase_   ,  atol=1E-5   ,  rtol=1E-5					)






						@unittest.skip('Model is curently gated'					)
						@slow
						def 	UpperCAmelCase__     (					self :Any					)				->			int:
												UpperCAmelCase             = 'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
												UpperCAmelCase             = 'Simply put, the theory of relativity states that '
												UpperCAmelCase             = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'					)
												UpperCAmelCase             = tokenizer.encode(lowercase_   ,  return_tensors='pt'					)
												UpperCAmelCase             = LlamaForCausalLM.from_pretrained(
												    'meta-llama/Llama-2-13b-chat-hf'   ,  device_map='sequential'   ,  use_safetensors=lowercase_					)

												# greedy generation outputs
												UpperCAmelCase             = model.generate(lowercase_   ,  max_new_tokens=64   ,  top_p=lowercase_   ,  temperature=1   ,  do_sample=lowercase_					)
												UpperCAmelCase             = tokenizer.decode(generated_ids[0]   ,  skip_special_tokens=lowercase_					)
												self.assertEqual(lowercase_   ,  lowercase_					)






def    lowerCAmelCase_ (     __A,  __A							)     ->				float:







      '''simple docstring'''


      def get_matched_characters(__A,  __A							) -> str:
            UpperCAmelCase__								=  []
            UpperCAmelCase__								=  min(len(_stra							),  len(_stra							)							) // 2
            for i, l in enumerate(_stra							):
                  UpperCAmelCase__								=  int(max(0,  i - limit							)							)
                  UpperCAmelCase__								=  int(min(i + limit + 1,  len(_stra							)							)							)
                  if l in _stra[left:right]:
                        matched.append(__A							)
                        UpperCAmelCase__								=  f"""{_stra[0:_stra.index(__A							)]} {_stra[_stra.index(__A							) + 1:]}"""

            return "".join(__A							)

      # matching characters
      UpperCAmelCase__								=  get_matched_characters(__A,  __A							)
      UpperCAmelCase__								=  get_matched_characters(__A,  __A							)
      UpperCAmelCase__								=  len(__A							)

      # transposition
      UpperCAmelCase__								=  (
          len([(ca, ca) for ca, ca in zip(__A,  __A							) if ca != ca]							) // 2
      )

      if not match_count:
            UpperCAmelCase__								=  0.0
      else:
            UpperCAmelCase__								=  (
                1
                / 3
                * (
                    match_count / len(__A							)
                    + match_count / len(__A							)
                    + (match_count - transpositions) / match_count
                )
            )

      # common prefix up to 4 characters
      UpperCAmelCase__								=  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'))



