code: 
  
  
  import unittest
  
  from transformers import ElectraConfig, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, slow, torch_device
  
  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 (
  							    MODEL_FOR_PRETRAINING_MAPPING,
  							    ElectraForCausalLM,
  							    ElectraForMaskedLM,
  							    ElectraForMultipleChoice,
  							    ElectraForPreTraining,
  							    ElectraForQuestionAnswering,
  							    ElectraForSequenceClassification,
  							    ElectraForTokenClassification,
  							    ElectraModel,
  							)
  							from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST
  class _lowerCamelCase :
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(      self	:		List[str]						,  _A	:		Optional[int]						,  _A	:		Union[str, Any]=13						,  _A	:		List[Any]=7						,  _A	:		Tuple=True						,  _A	:		Union[str, Any]=True						,  _A	:		Optional[int]=True						,  _A	:		int=True						,  _A	:		Any=99						,  _A	:		Dict=32						,  _A	:		Optional[int]=5						,  _A	:		List[str]=4						,  _A	:		Any=37						,  _A	:		str="gelu"						,  _A	:		str=0.1						,  _A	:		str=0.1						,  _A	:		Union[str, Any]=512						,  _A	:		str=16						,  _A	:		Tuple=2						,  _A	:		List[Any]=0.02						,  _A	:		Dict=3						,  _A	:		int=4						,  _A	:		Optional[Any]=None						,  )						->				List[Any]:
  			__magic_name__ :					Optional[int]  			=  parent
  			__magic_name__ :					Optional[Any]  			=  batch_size
  			__magic_name__ :					int  			=  seq_length
  			__magic_name__ :					Tuple  			=  is_training
  			__magic_name__ :					List[Any]  			=  use_input_mask
  			__magic_name__ :					Optional[int]  			=  use_token_type_ids
  			__magic_name__ :					int  			=  use_labels
  			__magic_name__ :					int  			=  vocab_size
  			__magic_name__ :					Union[str, Any]  			=  hidden_size
  			__magic_name__ :					List[Any]  			=  num_hidden_layers
  			__magic_name__ :					Union[str, Any]  			=  num_attention_heads
  			__magic_name__ :					Optional[int]  			=  intermediate_size
  			__magic_name__ :					List[Any]  			=  hidden_act
  			__magic_name__ :					List[str]  			=  hidden_dropout_prob
  			__magic_name__ :					Any  			=  attention_probs_dropout_prob
  			__magic_name__ :					List[str]  			=  max_position_embeddings
  			__magic_name__ :					List[Any]  			=  type_vocab_size
  			__magic_name__ :					Optional[int]  			=  type_sequence_label_size
  			__magic_name__ :					List[str]  			=  initializer_range
  			__magic_name__ :					Dict  			=  num_labels
  			__magic_name__ :					Union[str, Any]  			=  num_choices
  			__magic_name__ :					Optional[int]  			=  scope
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict    )						->				Optional[int]:
  			__magic_name__ :					Optional[Any]  			=  ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size    )
  
  			__magic_name__ :					str  			=  None
  			if self.use_input_mask:
  				__magic_name__ :					Dict  			=  random_attention_mask([self.batch_size, self.seq_length]    )
  
  			__magic_name__ :					List[Any]  			=  None
  			if self.use_token_type_ids:
  				__magic_name__ :					List[str]  			=  ids_tensor([self.batch_size, self.seq_length]						,  self.type_vocab_size    )
  
  			__magic_name__ :					str  			=  None
  			__magic_name__ :					List[str]  			=  None
  			__magic_name__ :					Any  			=  None
  			if self.use_labels:
  				__magic_name__ :					List[Any]  			=  ids_tensor([self.batch_size]						,  self.type_sequence_label_size    )
  				__magic_name__ :					List[Any]  			=  ids_tensor([self.batch_size, self.seq_length]						,  self.num_labels    )
  				__magic_name__ :					int  			=  ids_tensor([self.batch_size]						,  self.num_choices    )
  				__magic_name__ :					List[str]  			=  ids_tensor([self.batch_size, self.seq_length]						,  1    )
  
  			__magic_name__ :					List[Any]  			=  self.get_config()
  
  			return (
  			    config,
  			    input_ids,
  			    token_type_ids,
  			    input_mask,
  			    sequence_labels,
  			    token_labels,
  			    choice_labels,
  			    fake_token_labels,
  			)
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict    )						->				List[Any]:
  			return ElectraConfig(
  			    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=lowerCamelCase_						,  initializer_range=self.initializer_range						,  )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Any    )						->				Dict:
  			(
  			    (
  			    __magic_name__							
  			)			,       (
  			    __magic_name__							
  			)			,       (
  			    __magic_name__							
  			)			,       (
  			    __magic_name__							
  			)			,       (
  			    __magic_name__							
  			)			,       (
  			    __magic_name__							
  			)			,       (
  			    __magic_name__							
  			)			,       (
  			    __magic_name__							
  			)			,       							
  			) :					Any  			=  self.prepare_config_and_inputs()
  
  			__magic_name__ :					List[Any]  			=  True
  			__magic_name__ :					Union[str, Any]  			=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
  			__magic_name__ :					str  			=  ids_tensor([self.batch_size, self.seq_length]						,  vocab_size=2    )
  
  			return (
  			    config,
  			    input_ids,
  			    token_type_ids,
  			    input_mask,
  			    sequence_labels,
  			    token_labels,
  			    choice_labels,
  			    encoder_hidden_states,
  			    encoder_attention_mask,
  			)
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int						,  _A	:		List[str]						,  _A	:		List[str]						,  _A	:		Any						,  _A	:		Dict						,  _A	:		Union[str, Any]						,  _A	:		List[str]						,  _A	:		int						,  _A	:		Any						,  )						->				Any:
  			__magic_name__ :					Optional[Any]  			=  ElectraModel(config=lowerCamelCase_    )
  			model.to(lowerCamelCase_    )
  			model.eval()
  			__magic_name__ :					int  			=  model(lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_    )
  			__magic_name__ :					Optional[int]  			=  model(lowerCamelCase_						,  token_type_ids=lowerCamelCase_    )
  			__magic_name__ :					str  			=  model(lowerCamelCase_    )
  			self.parent.assertEqual(result.last_hidden_state.shape						,  (self.batch_size, self.seq_length, self.hidden_size)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict						,  _A	:		Any						,  _A	:		int						,  _A	:		List[str]						,  _A	:		Optional[Any]						,  _A	:		List[str]						,  _A	:		Tuple						,  _A	:		List[Any]						,  _A	:		str						,  _A	:		Optional[int]						,  )						->				int:
  			__magic_name__ :					int  			=  True
  			__magic_name__ :					Optional[int]  			=  ElectraModel(lowerCamelCase_    )
  			model.to(lowerCamelCase_    )
  			model.eval()
  			__magic_name__ :					str  			=  model(
  			    lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  encoder_hidden_states=lowerCamelCase_						,  encoder_attention_mask=lowerCamelCase_						,  )
  			__magic_name__ :					str  			=  model(
  			    lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  encoder_hidden_states=lowerCamelCase_						,  )
  			__magic_name__ :					Optional[int]  			=  model(lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_    )
  			self.parent.assertEqual(result.last_hidden_state.shape						,  (self.batch_size, self.seq_length, self.hidden_size)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict						,  _A	:		Optional[Any]						,  _A	:		List[str]						,  _A	:		List[str]						,  _A	:		List[str]						,  _A	:		Dict						,  _A	:		List[str]						,  _A	:		Any						,  _A	:		Tuple						,  )						->				Dict:
  			__magic_name__ :					Optional[Any]  			=  ElectraForMaskedLM(config=lowerCamelCase_    )
  			model.to(lowerCamelCase_    )
  			model.eval()
  			__magic_name__ :					List[str]  			=  model(lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  labels=lowerCamelCase_    )
  			self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.vocab_size)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]						,  _A	:		List[Any]						,  _A	:		List[str]						,  _A	:		Optional[int]						,  _A	:		Dict						,  _A	:		int						,  _A	:		List[str]						,  _A	:		List[str]						,  _A	:		int						,  _A	:		Optional[Any]						,  )						->				Optional[Any]:
  			__magic_name__ :					int  			=  ElectraForCausalLM(config=lowerCamelCase_    )
  			model.to(lowerCamelCase_    )
  			model.eval()
  			__magic_name__ :					int  			=  model(lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  labels=lowerCamelCase_    )
  			self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.vocab_size)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]						,  _A	:		str						,  _A	:		Any						,  _A	:		Optional[int]						,  _A	:		List[Any]						,  _A	:		Union[str, Any]						,  _A	:		Optional[Any]						,  _A	:		int						,  _A	:		Dict						,  )						->				Tuple:
  			__magic_name__ :					Tuple  			=  self.num_labels
  			__magic_name__ :					int  			=  ElectraForTokenClassification(config=lowerCamelCase_    )
  			model.to(lowerCamelCase_    )
  			model.eval()
  			__magic_name__ :					str  			=  model(lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  labels=lowerCamelCase_    )
  			self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.num_labels)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]						,  _A	:		Tuple						,  _A	:		Optional[Any]						,  _A	:		List[str]						,  _A	:		Tuple						,  _A	:		List[str]						,  _A	:		Any						,  _A	:		Optional[Any]						,  _A	:		Dict						,  )						->				Any:
  			__magic_name__ :					int  			=  self.num_labels
  			__magic_name__ :					Dict  			=  ElectraForPreTraining(config=lowerCamelCase_    )
  			model.to(lowerCamelCase_    )
  			model.eval()
  			__magic_name__ :					Tuple  			=  model(lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  labels=lowerCamelCase_    )
  			self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]						,  _A	:		Union[str, Any]						,  _A	:		Any						,  _A	:		Dict						,  _A	:		str						,  _A	:		List[str]						,  _A	:		Union[str, Any]						,  _A	:		Dict						,  _A	:		List[Any]						,  )						->				Dict:
  			__magic_name__ :					List[str]  			=  self.num_labels
  			__magic_name__ :					int  			=  ElectraForSequenceClassification(lowerCamelCase_    )
  			model.to(lowerCamelCase_    )
  			model.eval()
  			__magic_name__ :					str  			=  model(lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  labels=lowerCamelCase_    )
  			self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_labels)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]						,  _A	:		Any						,  _A	:		int						,  _A	:		Union[str, Any]						,  _A	:		Union[str, Any]						,  _A	:		Union[str, Any]						,  _A	:		Optional[int]						,  _A	:		Any						,  _A	:		Optional[Any]						,  )						->				int:
  			__magic_name__ :					Optional[int]  			=  ElectraForQuestionAnswering(config=lowerCamelCase_    )
  			model.to(lowerCamelCase_    )
  			model.eval()
  			__magic_name__ :					Any  			=  model(
  			    lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  start_positions=lowerCamelCase_						,  end_positions=lowerCamelCase_						,  )
  			self.parent.assertEqual(result.start_logits.shape						,  (self.batch_size, self.seq_length)    )
  			self.parent.assertEqual(result.end_logits.shape						,  (self.batch_size, self.seq_length)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]						,  _A	:		Optional[int]						,  _A	:		Any						,  _A	:		str						,  _A	:		str						,  _A	:		Any						,  _A	:		Union[str, Any]						,  _A	:		Union[str, Any]						,  _A	:		Tuple						,  )						->				List[Any]:
  			__magic_name__ :					Optional[int]  			=  self.num_choices
  			__magic_name__ :					Optional[Any]  			=  ElectraForMultipleChoice(config=lowerCamelCase_    )
  			model.to(lowerCamelCase_    )
  			model.eval()
  			__magic_name__ :					Union[str, Any]  			=  input_ids.unsqueeze(1    ).expand(-1						,  self.num_choices						,  -1    ).contiguous()
  			__magic_name__ :					Optional[Any]  			=  token_type_ids.unsqueeze(1    ).expand(-1						,  self.num_choices						,  -1    ).contiguous()
  			__magic_name__ :					Dict  			=  input_mask.unsqueeze(1    ).expand(-1						,  self.num_choices						,  -1    ).contiguous()
  			__magic_name__ :					Tuple  			=  model(
  			    lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  labels=lowerCamelCase_						,  )
  			self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_choices)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				List[str]:
  			__magic_name__ :					Tuple  			=  self.prepare_config_and_inputs()
  			(
  			    (
  			    __magic_name__							
  			)			,       (
  			    __magic_name__							
  			)			,       (
  			    __magic_name__							
  			)			,       (
  			    __magic_name__							
  			)			,       (
  			    __magic_name__							
  			)			,       (
  			    __magic_name__							
  			)			,       (
  			    __magic_name__							
  			)			,       (
  			    __magic_name__							
  			)			,       							
  			) :					Optional[int]  			=  config_and_inputs
  			__magic_name__ :					Tuple  			=  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
  			return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class _lowerCamelCase (		lowerCAmelCase_	,  lowerCAmelCase_	,  unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Optional[int]   					=       (
  		    (
  		        ElectraModel,
  		        ElectraForPreTraining,
  		        ElectraForMaskedLM,
  		        ElectraForCausalLM,
  		        ElectraForMultipleChoice,
  		        ElectraForTokenClassification,
  		        ElectraForSequenceClassification,
  		        ElectraForQuestionAnswering,
  		    )
  		    if is_torch_available()
  		    else ()
  		)
  		A_			:    Optional[Any]   					=       (
  		    {
  		        'feature-extraction': ElectraModel,
  		        'fill-mask': ElectraForMaskedLM,
  		        'question-answering': ElectraForQuestionAnswering,
  		        'text-classification': ElectraForSequenceClassification,
  		        'text-generation': ElectraForCausalLM,
  		        'token-classification': ElectraForTokenClassification,
  		        'zero-shot': ElectraForSequenceClassification,
  		    }
  		    if is_torch_available()
  		    else {}
  		)
  		A_			:    Optional[int]   					=       True
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int						,  _A	:		List[str]						,  _A	:		int						,  _A	:		List[str]=False    )						->				Tuple:
  			__magic_name__ :					Optional[Any]  			=  super()._prepare_for_class(lowerCamelCase_						,  lowerCamelCase_						,  return_labels=lowerCamelCase_    )
  
  			if return_labels:
  				if model_class in get_values(lowerCamelCase_    ):
  					__magic_name__ :					Union[str, Any]  			=  torch.zeros(
  					    (self.model_tester.batch_size, self.model_tester.seq_length)						,  dtype=torch.long						,  device=lowerCamelCase_    )
  			return inputs_dict
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				List[Any]:
  			__magic_name__ :					Union[str, Any]  			=  ElectraModelTester(self    )
  			__magic_name__ :					List[str]  			=  ConfigTester(self						,  config_class=lowerCamelCase_						,  hidden_size=37    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str    )						->				int:
  			self.config_tester.run_common_tests()
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Optional[Any]:
  			__magic_name__ :					Tuple  			=  self.model_tester.prepare_config_and_inputs()
  			self.model_tester.create_and_check_electra_model(*lowerCamelCase_    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict    )						->				List[str]:
  			__magic_name__ :					List[str]  			=  self.model_tester.prepare_config_and_inputs_for_decoder()
  			self.model_tester.create_and_check_electra_model_as_decoder(*lowerCamelCase_    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				Optional[int]:
  			__magic_name__ :					Optional[Any]  			=  self.model_tester.prepare_config_and_inputs()
  			for type in ["absolute", "relative_key", "relative_key_query"]:
  				__magic_name__ :					Optional[Any]  			=  type
  				self.model_tester.create_and_check_electra_model(*lowerCamelCase_    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict    )						->				List[str]:
  			__magic_name__ :					int  			=  self.model_tester.prepare_config_and_inputs()
  			self.model_tester.create_and_check_electra_for_masked_lm(*lowerCamelCase_    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str    )						->				Union[str, Any]:
  			__magic_name__ :					Tuple  			=  self.model_tester.prepare_config_and_inputs()
  			self.model_tester.create_and_check_electra_for_token_classification(*lowerCamelCase_    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Any:
  			__magic_name__ :					Union[str, Any]  			=  self.model_tester.prepare_config_and_inputs()
  			self.model_tester.create_and_check_electra_for_pretraining(*lowerCamelCase_    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				Dict:
  			__magic_name__ :					Tuple  			=  self.model_tester.prepare_config_and_inputs()
  			self.model_tester.create_and_check_electra_for_sequence_classification(*lowerCamelCase_    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str    )						->				Tuple:
  			__magic_name__ :					Any  			=  self.model_tester.prepare_config_and_inputs()
  			self.model_tester.create_and_check_electra_for_question_answering(*lowerCamelCase_    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict    )						->				Union[str, Any]:
  			__magic_name__ :					Dict  			=  self.model_tester.prepare_config_and_inputs()
  			self.model_tester.create_and_check_electra_for_multiple_choice(*lowerCamelCase_    )
  
  
  
  
  
  
  
  		@slow
  		def      __lowerCAmelCase	(      self	:		str    )						->				List[str]:
  
  			for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  				__magic_name__ :					Optional[int]  			=  ElectraModel.from_pretrained(lowerCamelCase_    )
  				self.assertIsNotNone(lowerCamelCase_    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				str:
  			__magic_name__ :					str  			=  self.model_tester.prepare_config_and_inputs_for_decoder()
  			self.model_tester.create_and_check_electra_for_causal_lm(*lowerCamelCase_    )
  
  
  
  
  
  
  
  @require_torch
  class _lowerCamelCase (		unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		@slow
  		def      __lowerCAmelCase	(      self	:		int    )						->				int:
  			__magic_name__ :					Optional[Any]  			=  ElectraModel.from_pretrained('google/electra-small-discriminator'    )
  			__magic_name__ :					List[str]  			=  torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]    )
  			__magic_name__ :					List[str]  			=  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
  			__magic_name__ :					List[Any]  			=  model(lowerCamelCase_						,  attention_mask=lowerCamelCase_    )[0]
  			__magic_name__ :					Optional[Any]  			=  torch.Size((1, 11, 256)    )
  			self.assertEqual(output.shape						,  lowerCamelCase_    )
  			__magic_name__ :					List[str]  			=  torch.tensor(
  			    [[[0.4471, 0.6821, -0.3265], [0.4627, 0.5255, -0.3668], [0.4532, 0.3313, -0.4344]]]    )
  
  			self.assertTrue(torch.allclose(output[:, 1:4, 1:4]						,  lowerCamelCase_						,  atol=1E-4    )    )
style_context: 
  
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			list[int | float]	,	lowerCAmelCase       :			int	,	lowerCAmelCase       :			int	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if len(lowerCAmelCase	) == 0:
  		raise ValueError('find_min() arg is an empty sequence'	)
  	if (
  	    left >= len(lowerCAmelCase	)
  	    or left < -len(lowerCAmelCase	)
  	    or right >= len(lowerCAmelCase	)
  	    or right < -len(lowerCAmelCase	)
  	):
  		raise IndexError('list index out of range'	)
  	if left == right:
  		return nums[left]
  	__magic_name__ :					Tuple  			=  (left + right) >> 1  # the middle
  	__magic_name__ :					str  			=  find_min(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)  # find min in range[left, mid]
  	__magic_name__ :					Optional[Any]  			=  find_min(lowerCAmelCase	,	mid + 1	,	lowerCAmelCase	)  # find min in range[mid + 1, right]
  
  	return left_min if left_min <= right_min else right_min
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod(verbose=True)