


'''simple docstring'''
import unittest

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

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 (
         ErnieMForInformationExtraction,
         ErnieMForMultipleChoice,
         ErnieMForQuestionAnswering,
         ErnieMForSequenceClassification,
         ErnieMForTokenClassification,
         ErnieMModel,
     )
     from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST







class 					_A						:



   '''simple docstring'''






   def __init__(	self	:					Union[str, Any]					, lowerCamelCase	:					Union[str, Any]					, lowerCamelCase	:					Optional[Any]=13					, lowerCamelCase	:					List[Any]=7					, lowerCamelCase	:					Tuple=True					, lowerCamelCase	:					Dict=True					, lowerCamelCase	:					Union[str, Any]=True					, lowerCamelCase	:					str=99					, lowerCamelCase	:					int=32					, lowerCamelCase	:					Dict=5					, lowerCamelCase	:					List[Any]=4					, lowerCamelCase	:					str=37					, lowerCamelCase	:					Any="gelu"					, lowerCamelCase	:					str=0.1					, lowerCamelCase	:					List[Any]=0.1					, lowerCamelCase	:					Any=512					, lowerCamelCase	:					Union[str, Any]=16					, lowerCamelCase	:					Any=2					, lowerCamelCase	:					Union[str, Any]=0.02					, lowerCamelCase	:					List[Any]=3					, lowerCamelCase	:					str=4					, lowerCamelCase	:					Optional[int]=None					, )->	List[Any]:
          snake_case__					:	Any          =					parent
          snake_case__					:	List[Any]          =					batch_size
          snake_case__					:	Any          =					seq_length
          snake_case__					:	Optional[int]          =					is_training
          snake_case__					:	List[str]          =					use_input_mask
          snake_case__					:	Tuple          =					use_labels
          snake_case__					:	str          =					vocab_size
          snake_case__					:	Union[str, Any]          =					hidden_size
          snake_case__					:	Optional[Any]          =					num_hidden_layers
          snake_case__					:	Any          =					num_attention_heads
          snake_case__					:	Tuple          =					intermediate_size
          snake_case__					:	Union[str, Any]          =					hidden_act
          snake_case__					:	Tuple          =					hidden_dropout_prob
          snake_case__					:	Optional[Any]          =					attention_probs_dropout_prob
          snake_case__					:	List[str]          =					max_position_embeddings
          snake_case__					:	Tuple          =					type_vocab_size
          snake_case__					:	Dict          =					type_sequence_label_size
          snake_case__					:	Optional[int]          =					initializer_range
          snake_case__					:	Tuple          =					num_labels
          snake_case__					:	Optional[Any]          =					num_choices
          snake_case__					:	int          =					scope




   def   __lowerCAmelCase						(	self	:					str						)->	List[str]:
          snake_case__					:	Union[str, Any]          =					ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size						)

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

          snake_case__					:	List[str]          =					None
          snake_case__					:	List[Any]          =					None
          snake_case__					:	Optional[Any]          =					None
          if self.use_labels:
                 snake_case__					:	str          =					ids_tensor([self.batch_size]					, self.type_sequence_label_size						)
                 snake_case__					:	Any          =					ids_tensor([self.batch_size, self.seq_length]					, self.num_labels						)
                 snake_case__					:	int          =					ids_tensor([self.batch_size]					, self.num_choices						)

          snake_case__					:	List[Any]          =					self.get_config()

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




   def   __lowerCAmelCase						(	self	:					Optional[Any]						)->	Dict:
          snake_case__					:	Any          =					ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size						)

          snake_case__					:	Union[str, Any]          =					None
          if self.use_input_mask:
                 snake_case__					:	List[Any]          =					random_attention_mask([self.batch_size, self.seq_length]						)
          snake_case__					:	List[str]          =					self.get_config()

          return config, input_ids, input_mask




   def   __lowerCAmelCase						(	self	:					List[str]						)->	List[Any]:
          return ErnieMConfig(
              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					, initializer_range=self.initializer_range					, )




   def   __lowerCAmelCase						(	self	:					Optional[int]					, lowerCamelCase	:					Tuple					, lowerCamelCase	:					Union[str, Any]					, lowerCamelCase	:					Optional[Any]					, lowerCamelCase	:					Dict					, lowerCamelCase	:					str					, lowerCamelCase	:					str						)->	List[Any]:
          snake_case__					:	Optional[Any]          =					ErnieMModel(config=lowercase__						)
          model.to(lowercase__						)
          model.eval()
          snake_case__					:	int          =					model(lowercase__					, return_dict=lowercase__						)
          self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size)						)




   def   __lowerCAmelCase						(	self	:					Dict					, lowerCamelCase	:					Optional[int]					, lowerCamelCase	:					int					, lowerCamelCase	:					Optional[int]					, lowerCamelCase	:					List[Any]					, lowerCamelCase	:					Tuple					, lowerCamelCase	:					Dict						)->	List[Any]:
          snake_case__					:	Optional[int]          =					ErnieMForQuestionAnswering(config=lowercase__						)
          model.to(lowercase__						)
          model.eval()
          snake_case__					:	List[Any]          =					model(
              lowercase__					, attention_mask=lowercase__					, start_positions=lowercase__					, end_positions=lowercase__					, )
          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	:					List[str]					, lowerCamelCase	:					Dict					, lowerCamelCase	:					List[Any]					, lowerCamelCase	:					Dict					, lowerCamelCase	:					Optional[int]					, lowerCamelCase	:					Union[str, Any]					, lowerCamelCase	:					Optional[Any]						)->	int:
          snake_case__					:	Optional[int]          =					ErnieMForInformationExtraction(config=lowercase__						)
          model.to(lowercase__						)
          model.eval()
          snake_case__					:	Any          =					torch.ones_like(lowercase__					, dtype=torch.floataa						)
          snake_case__					:	Union[str, Any]          =					model(
              lowercase__					, attention_mask=lowercase__					, start_positions=lowercase__					, end_positions=lowercase__					, )
          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	:					List[str]					, lowerCamelCase	:					Optional[Any]					, lowerCamelCase	:					List[str]					, lowerCamelCase	:					Any					, lowerCamelCase	:					List[Any]					, lowerCamelCase	:					Optional[Any]					, lowerCamelCase	:					Any						)->	Tuple:
          snake_case__					:	str          =					self.num_labels
          snake_case__					:	Optional[int]          =					ErnieMForSequenceClassification(lowercase__						)
          model.to(lowercase__						)
          model.eval()
          snake_case__					:	str          =					model(lowercase__					, attention_mask=lowercase__					, labels=lowercase__						)
          self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_labels)						)




   def   __lowerCAmelCase						(	self	:					str					, lowerCamelCase	:					Optional[int]					, lowerCamelCase	:					int					, lowerCamelCase	:					Any					, lowerCamelCase	:					Optional[int]					, lowerCamelCase	:					int					, lowerCamelCase	:					int						)->	Optional[Any]:
          snake_case__					:	int          =					self.num_labels
          snake_case__					:	Tuple          =					ErnieMForTokenClassification(config=lowercase__						)
          model.to(lowercase__						)
          model.eval()
          input_ids.to(lowercase__						)
          input_mask.to(lowercase__						)
          token_labels.to(lowercase__						)

          snake_case__					:	Dict          =					model(lowercase__					, attention_mask=lowercase__					, labels=lowercase__						)

          self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.num_labels)						)




   def   __lowerCAmelCase						(	self	:					Dict					, lowerCamelCase	:					List[str]					, lowerCamelCase	:					Optional[Any]					, lowerCamelCase	:					Dict					, lowerCamelCase	:					List[str]					, lowerCamelCase	:					List[str]					, lowerCamelCase	:					str						)->	Dict:
          snake_case__					:	Tuple          =					self.num_choices
          snake_case__					:	str          =					ErnieMForMultipleChoice(config=lowercase__						)
          model.to(lowercase__						)
          model.eval()
          snake_case__					:	Union[str, Any]          =					input_ids.unsqueeze(1						).expand(-1					, self.num_choices					, -1						).contiguous()
          snake_case__					:	Dict          =					input_mask.unsqueeze(1						).expand(-1					, self.num_choices					, -1						).contiguous()
          snake_case__					:	Union[str, Any]          =					model(
              lowercase__					, attention_mask=lowercase__					, labels=lowercase__					, )
          self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_choices)						)




   def   __lowerCAmelCase						(	self	:					Dict						)->	Optional[int]:
          snake_case__					:	Optional[Any]          =					self.prepare_config_and_inputs()
          (
              (
              snake_case__		
          )       ,   (
              snake_case__		
          )       ,   (
              snake_case__		
          )       ,   (
              snake_case__		
          )       ,   (
              snake_case__		
          )       ,   (
              snake_case__		
          )       ,   		
          )					:	str          =					config_and_inputs
          snake_case__					:	Dict          =					{"""input_ids""": input_ids, """attention_mask""": input_mask}
          return config, inputs_dict







@require_torch
class 					_A						(						_snake_case    ,  _snake_case    ,  unittest.TestCase							):



   '''simple docstring'''






   _lowercase    						= (
       (
           ErnieMModel,
           ErnieMForMultipleChoice,
           ErnieMForQuestionAnswering,
           ErnieMForSequenceClassification,
           ErnieMForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   _lowercase    						= ()
   _lowercase    						= (
       {
           'feature-extraction': ErnieMModel,
           'question-answering': ErnieMForQuestionAnswering,
           'text-classification': ErnieMForSequenceClassification,
           'token-classification': ErnieMForTokenClassification,
           'zero-shot': ErnieMForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   _lowercase    						= False




   def   __lowerCAmelCase						(	self	:					int					, lowerCamelCase	:					Optional[Any]					, lowerCamelCase	:					Optional[Any]					, lowerCamelCase	:					Tuple					, lowerCamelCase	:					List[str]					, lowerCamelCase	:					int						)->	Any:



          if pipeline_test_casse_name == "QAPipelineTests":
                 return True

          return False




   def   __lowerCAmelCase						(	self	:					Any						)->	Any:
          snake_case__					:	List[str]          =					ErnieMModelTester(self						)
          snake_case__					:	Tuple          =					ConfigTester(self					, config_class=lowercase__					, hidden_size=37						)




   def   __lowerCAmelCase						(	self	:					Tuple						)->	Tuple:
          self.config_tester.run_common_tests()




   def   __lowerCAmelCase						(	self	:					Any						)->	str:
          snake_case__					:	List[str]          =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase__						)




   def   __lowerCAmelCase						(	self	:					int						)->	Tuple:
          snake_case__					:	Any          =					self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
                 snake_case__					:	Union[str, Any]          =					type
                 self.model_tester.create_and_check_model(*lowercase__						)




   def   __lowerCAmelCase						(	self	:					Dict						)->	List[str]:
          snake_case__					:	List[str]          =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*lowercase__						)




   def   __lowerCAmelCase						(	self	:					List[str]						)->	List[str]:
          snake_case__					:	Dict          =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*lowercase__						)




   def   __lowerCAmelCase						(	self	:					str						)->	Optional[int]:
          snake_case__					:	str          =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*lowercase__						)




   def   __lowerCAmelCase						(	self	:					Optional[Any]						)->	Optional[int]:
          snake_case__					:	List[Any]          =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_information_extraction(*lowercase__						)




   def   __lowerCAmelCase						(	self	:					Any						)->	Dict:
          snake_case__					:	Optional[Any]          =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*lowercase__						)




   @slow
   def   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	Union[str, Any]:



          for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case__					:	List[str]          =					ErnieMModel.from_pretrained(lowercase__						)
                 self.assertIsNotNone(lowercase__						)







@require_torch
class 					_A						(						unittest.TestCase							):



   '''simple docstring'''






   @slow
   def   __lowerCAmelCase						(	self	:					Dict						)->	int:
          snake_case__					:	Optional[Any]          =					ErnieMModel.from_pretrained("""susnato/ernie-m-base_pytorch"""						)
          model.eval()
          snake_case__					:	Union[str, Any]          =					torch.tensor([[0, 1, 2, 3, 4, 5]]						)
          snake_case__					:	Any          =					model(lowercase__						)[0]

          # TODO Replace vocab size
          snake_case__					:	Optional[int]          =					768

          snake_case__					:	Dict          =					torch.Size((1, 6, hidden_size)						)
          self.assertEqual(output.shape					, lowercase__						)

          snake_case__					:	Union[str, Any]          =					torch.tensor(
              [[[-0.0_012, 0.1_245, -0.0_214], [-0.0_742, 0.0_244, -0.0_771], [-0.0_333, 0.1_164, -0.1_554]]]						)

          self.assertTrue(torch.allclose(output[:, :3, :3]					, lowercase__					, atol=1e-3						)						)







'''simple docstring'''







from .balanced_parentheses import balanced_parentheses
from .stack import Stack






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(snake_case_							,       -1	)






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	if not balanced_parentheses(snake_case_	):
		raise ValueError("""Mismatched parentheses"""	)
	_lowercase							=     Stack()
	_lowercase							=     []
	for char in expression_str:
		if char.isalpha() or char.isdigit():
			postfix.append(snake_case_	)
		elif char == "(":
			stack.push(snake_case_	)
		elif char == ")":
			while not stack.is_empty() and stack.peek() != "(":
				postfix.append(stack.pop()	)
			stack.pop()
		else:
			while not stack.is_empty() and precedence(snake_case_	) <= precedence(stack.peek()	):
				postfix.append(stack.pop()	)
			stack.push(snake_case_	)
	while not stack.is_empty():
		postfix.append(stack.pop()	)
	return " ".join(snake_case_	)


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							_lowerCamelCase    =  'a+b*(c^d-e)^(f+g*h)-i'

							print('Infix to Postfix Notation demonstration:\n')
							print('Infix notation: ' + expression)
							print('Postfix notation: ' + infix_to_postfix(expression))




