





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        __snake_case			:





       '''simple docstring'''







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




       def 				UpperCAmelCase__					(    self					:       Any						):
         __snake_case:		List[Any]					    =		ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size						)

         __snake_case:		Optional[int]					    =		None
         if self.use_input_mask:
           __snake_case:		Dict					    =		random_attention_mask([self.batch_size, self.seq_length]						)

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

         __snake_case:		Tuple					    =		self.get_config()

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




       def 				UpperCAmelCase__					(    self					:       Dict						):
         __snake_case:		Union[str, Any]					    =		ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size						)

         __snake_case:		Any					    =		None
         if self.use_input_mask:
           __snake_case:		Any					    =		random_attention_mask([self.batch_size, self.seq_length]						)
         __snake_case:		Optional[int]					    =		self.get_config()

         return config, input_ids, input_mask




       def 				UpperCAmelCase__					(    self					:       Optional[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 				UpperCAmelCase__					(    self					:       Optional[Any]				,							A					:       Union[str, Any]				,							A					:       List[str]				,							A					:       int				,							A					:       Any				,							A					:       str				,							A					:       str						):
         __snake_case:		Optional[Any]					    =		ErnieMModel(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		Tuple					    =		model(A				,							return_dict=A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size)						)




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




       def 				UpperCAmelCase__					(    self					:       List[Any]				,							A					:       Dict				,							A					:       str				,							A					:       str				,							A					:       List[Any]				,							A					:       List[str]				,							A					:       Union[str, Any]						):
         __snake_case:		List[str]					    =		self.num_labels
         __snake_case:		List[str]					    =		ErnieMForTokenClassification(config=A						)
         model.to(A						)
         model.eval()
         input_ids.to(A						)
         input_mask.to(A						)
         token_labels.to(A						)

         __snake_case:		Optional[int]					    =		model(A				,							attention_mask=A				,							labels=A						)

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




       def 				UpperCAmelCase__					(    self					:       Union[str, Any]				,							A					:       Union[str, Any]				,							A					:       List[str]				,							A					:       int				,							A					:       Any				,							A					:       List[str]				,							A					:       Tuple						):
         __snake_case:		Tuple					    =		self.num_choices
         __snake_case:		int					    =		ErnieMForMultipleChoice(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		List[str]					    =		input_ids.unsqueeze(1						).expand(-1				,							self.num_choices				,							-1						).contiguous()
         __snake_case:		Any					    =		input_mask.unsqueeze(1						).expand(-1				,							self.num_choices				,							-1						).contiguous()
         __snake_case:		Tuple					    =		model(
             A				,							attention_mask=A				,							labels=A				,							)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_choices)						)






       def 				UpperCAmelCase__					(    self					:       List[Any]						):
         __snake_case:		Optional[Any]					    =		self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  
         ):		Tuple					    =		config_and_inputs
         __snake_case:		Optional[Any]					    =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
         return config, inputs_dict







@require_torch
class        __snake_case			(				__lowerCamelCase  ,     __lowerCamelCase  ,     unittest.TestCase     ):





       '''simple docstring'''







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




       def 				UpperCAmelCase__					(    self					:       Optional[Any]				,							A					:       List[Any]				,							A					:       Optional[Any]				,							A					:       Union[str, Any]				,							A					:       int				,							A					:       Optional[Any]						):
         if pipeline_test_casse_name == "QAPipelineTests":
           return True

         return False




       def 				UpperCAmelCase__					(    self					:       Dict						):
         __snake_case:		Optional[int]					    =		ErnieMModelTester(self						)
         __snake_case:		str					    =		ConfigTester(self				,							config_class=A				,							hidden_size=37						)




       def 				UpperCAmelCase__					(    self					:       Union[str, Any]						):
         self.config_tester.run_common_tests()




       def 				UpperCAmelCase__					(    self					:       List[Any]						):
         __snake_case:		List[str]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A						)




       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		List[str]					    =		self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
           __snake_case:		List[str]					    =		type
           self.model_tester.create_and_check_model(*A						)




       def 				UpperCAmelCase__					(    self					:       int						):
         __snake_case:		Optional[int]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*A						)




       def 				UpperCAmelCase__					(    self					:       Tuple						):
         __snake_case:		Optional[int]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*A						)




       def 				UpperCAmelCase__					(    self					:       Union[str, Any]						):
         __snake_case:		int					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*A						)




       def 				UpperCAmelCase__					(    self					:       Union[str, Any]						):
         __snake_case:		Dict					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_information_extraction(*A						)




       def 				UpperCAmelCase__					(    self					:       int						):
         __snake_case:		List[str]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*A						)






       @slow
       def 				UpperCAmelCase__					(    self					:       Any						):
         for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __snake_case:		int					    =		ErnieMModel.from_pretrained(A						)
           self.assertIsNotNone(A						)







@require_torch
class        __snake_case			(				unittest.TestCase     ):





       '''simple docstring'''







       @slow
       def 				UpperCAmelCase__					(    self					:       List[Any]						):
         __snake_case:		Optional[Any]					    =		ErnieMModel.from_pretrained("""susnato/ernie-m-base_pytorch"""						)
         model.eval()
         __snake_case:		Tuple					    =		torch.tensor([[0, 1, 2, 3, 4, 5]]						)
         __snake_case:		str					    =		model(A						)[0]

         # TODO Replace vocab size
         __snake_case:		List[Any]					    =		768

         __snake_case:		str					    =		torch.Size((1, 6, hidden_size)						)
         self.assertEqual(output.shape				,							A						)

         __snake_case:		Union[str, Any]					    =		torch.tensor(
             [[[-0.0012, 0.1245, -0.0214], [-0.0742, 0.0244, -0.0771], [-0.0333, 0.1164, -0.1554]]]						)

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













from .balanced_parentheses import balanced_parentheses
from .stack import Stack







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       int:
  return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(SCREAMING_SNAKE_CASE__   ,   -1)







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       str:
  if not balanced_parentheses(SCREAMING_SNAKE_CASE__):
    raise ValueError("""Mismatched parentheses""")
  __snake_case:		Stack[str]					    =		Stack()
  __snake_case:		Any					    =		[]
  for char in expression_str:
    if char.isalpha() or char.isdigit():
      postfix.append(SCREAMING_SNAKE_CASE__)
    elif char == "(":
      stack.push(SCREAMING_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(SCREAMING_SNAKE_CASE__) <= precedence(stack.peek()):
        postfix.append(stack.pop())
      stack.push(SCREAMING_SNAKE_CASE__)
  while not stack.is_empty():
    postfix.append(stack.pop())
  return " ".join(SCREAMING_SNAKE_CASE__)


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

      testmod()
      __UpperCAmelCase			:  Optional[int]										=				"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))






