





import unittest

from transformers import is_torch_available
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OpenAIGPTConfig,
          OpenAIGPTDoubleHeadsModel,
          OpenAIGPTForSequenceClassification,
          OpenAIGPTLMHeadModel,
          OpenAIGPTModel,
      )







class     __snake_case       :





       '''simple docstring'''







       def __init__(  self :    int							,				A :    Dict							,				A :    List[str]=13							,				A :    List[Any]=7							,				A :    List[str]=True							,				A :    Tuple=True							,				A :    str=True							,				A :    int=99							,				A :    str=32							,				A :    Optional[Any]=5							,				A :    Any=4							,				A :    Dict=37							,				A :    Optional[int]="gelu"							,				A :    int=0.1							,				A :    str=0.1							,				A :    Union[str, Any]=512							,				A :    List[str]=16							,				A :    str=2							,				A :    List[Any]=0.02							,				A :    Tuple=3							,				A :    Union[str, Any]=4							,				A :    Tuple=None							,				):
         __snake_case:						Tuple   =						parent
         __snake_case:						Optional[Any]   =						batch_size
         __snake_case:						Optional[Any]   =						seq_length
         __snake_case:						List[str]   =						is_training
         __snake_case:						List[Any]   =						use_token_type_ids
         __snake_case:						str   =						use_labels
         __snake_case:						Optional[int]   =						vocab_size
         __snake_case:						Union[str, Any]   =						hidden_size
         __snake_case:						List[Any]   =						num_hidden_layers
         __snake_case:						Union[str, Any]   =						num_attention_heads
         __snake_case:						Optional[int]   =						intermediate_size
         __snake_case:						Optional[Any]   =						hidden_act
         __snake_case:						List[Any]   =						hidden_dropout_prob
         __snake_case:						str   =						attention_probs_dropout_prob
         __snake_case:						Optional[Any]   =						max_position_embeddings
         __snake_case:						List[Any]   =						type_vocab_size
         __snake_case:						Any   =						type_sequence_label_size
         __snake_case:						Union[str, Any]   =						initializer_range
         __snake_case:						List[Any]   =						num_labels
         __snake_case:						Dict   =						num_choices
         __snake_case:						Optional[Any]   =						scope
         __snake_case:						str   =						self.vocab_size - 1




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

         __snake_case:						int   =						None
         if self.use_token_type_ids:
           __snake_case:						Tuple   =						ids_tensor([self.batch_size, self.seq_length]							,				self.type_vocab_size		)

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

         __snake_case:						int   =						OpenAIGPTConfig(
             vocab_size=self.vocab_size							,				n_embd=self.hidden_size							,				n_layer=self.num_hidden_layers							,				n_head=self.num_attention_heads							,				n_positions=self.max_position_embeddings							,				pad_token_id=self.pad_token_id							,				)

         __snake_case:						Union[str, Any]   =						ids_tensor([self.num_hidden_layers, self.num_attention_heads]							,				2		)

         return (
             config,
             input_ids,
             head_mask,
             token_type_ids,
             sequence_labels,
             token_labels,
             choice_labels,
         )




       def 							UpperCAmelCase__ (  self :    Any							,				A :    Dict							,				A :    List[str]							,				A :    Tuple							,				A :    Any							,				*A :    List[Any]		):
         __snake_case:						Union[str, Any]   =						OpenAIGPTModel(config=A		)
         model.to(A		)
         model.eval()

         __snake_case:						str   =						model(A							,				token_type_ids=A							,				head_mask=A		)
         __snake_case:						Optional[int]   =						model(A							,				token_type_ids=A		)
         __snake_case:						Any   =						model(A		)

         self.parent.assertEqual(result.last_hidden_state.shape							,				(self.batch_size, self.seq_length, self.hidden_size)		)




       def 							UpperCAmelCase__ (  self :    Dict							,				A :    Union[str, Any]							,				A :    Optional[Any]							,				A :    int							,				A :    Dict							,				*A :    Any		):
         __snake_case:						List[Any]   =						OpenAIGPTLMHeadModel(A		)
         model.to(A		)
         model.eval()

         __snake_case:						str   =						model(A							,				token_type_ids=A							,				labels=A		)
         self.parent.assertEqual(result.loss.shape							,				()		)
         self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.seq_length, self.vocab_size)		)




       def 							UpperCAmelCase__ (  self :    Optional[int]							,				A :    int							,				A :    Tuple							,				A :    Optional[Any]							,				A :    Dict							,				*A :    int		):
         __snake_case:						Optional[int]   =						OpenAIGPTDoubleHeadsModel(A		)
         model.to(A		)
         model.eval()

         __snake_case:						List[str]   =						model(A							,				token_type_ids=A							,				labels=A		)
         self.parent.assertEqual(result.loss.shape							,				()		)
         self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.seq_length, self.vocab_size)		)




       def 							UpperCAmelCase__ (  self :    Dict							,				A :    List[Any]							,				A :    Tuple							,				A :    List[Any]							,				A :    Optional[int]							,				*A :    int		):
         __snake_case:						Any   =						self.num_labels
         __snake_case:						Optional[Any]   =						OpenAIGPTForSequenceClassification(A		)
         model.to(A		)
         model.eval()

         __snake_case:						str   =						ids_tensor([self.batch_size]							,				self.type_sequence_label_size		)
         __snake_case:						int   =						model(A							,				token_type_ids=A							,				labels=A		)
         self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_labels)		)






       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						Dict   =						self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )  ,   (
             __snake_case
         )  ,   (
             __snake_case
         )  ,   (
             __snake_case
         )  ,   (
             __snake_case
         )  ,   (
             __snake_case
         )  ,   (
             __snake_case
         )  ,   
         ):						Tuple   =						config_and_inputs
         __snake_case:						Optional[Any]   =						{
             """input_ids""": input_ids,
             """token_type_ids""": token_type_ids,
             """head_mask""": head_mask,
         }

         return config, inputs_dict







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





       '''simple docstring'''







       lowerCAmelCase__      		=     (
           (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
           if is_torch_available()
           else ()
       )
       lowerCAmelCase__      		=     (
           (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
       )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
       lowerCAmelCase__      		=     (
           {
               """feature-extraction""": OpenAIGPTModel,
               """text-classification""": OpenAIGPTForSequenceClassification,
               """text-generation""": OpenAIGPTLMHeadModel,
               """zero-shot""": OpenAIGPTForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )




       def 							UpperCAmelCase__ (  self :    Optional[int]							,				A :    str							,				A :    Any							,				A :    Dict							,				A :    Dict							,				A :    str		):
         if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
           # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
           # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
           # tiny config could not be created.
           return True

         return False




       def 							UpperCAmelCase__ (  self :    Dict							,				A :    List[Any]							,				A :    Optional[int]							,				A :    List[str]=False		):
         __snake_case:						Any   =						super()._prepare_for_class(A							,				A							,				return_labels=A		)

         if return_labels:
           if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
             __snake_case:						Optional[Any]   =						torch.zeros(
                 (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)							,				dtype=torch.long							,				device=A							,				)
             __snake_case:						List[Any]   =						inputs_dict["""labels"""]
             __snake_case:						int   =						inputs_dict["""labels"""]
             __snake_case:						Optional[int]   =						torch.zeros(
                 (self.model_tester.batch_size, self.model_tester.num_choices)							,				dtype=torch.long							,				device=A							,				)
             __snake_case:						Dict   =						torch.zeros(
                 self.model_tester.batch_size							,				dtype=torch.long							,				device=A		)
         return inputs_dict




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						str   =						OpenAIGPTModelTester(self		)
         __snake_case:						Dict   =						ConfigTester(self							,				config_class=A							,				n_embd=37		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         self.config_tester.run_common_tests()




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




       def 							UpperCAmelCase__ (  self :    str		):
         __snake_case:						str   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head_model(*A		)




       def 							UpperCAmelCase__ (  self :    Dict		):
         __snake_case:						str   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_double_lm_head_model(*A		)




       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						str   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*A		)






       @slow
       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __snake_case:						List[Any]   =						OpenAIGPTModel.from_pretrained(A		)
           self.assertIsNotNone(A		)







@require_torch
class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       @slow
       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						Optional[Any]   =						OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt"""		)
         model.to(A		)
         __snake_case:						int   =						torch.tensor([[481, 4_735, 544]]							,				dtype=torch.long							,				device=A		)  # the president is
         __snake_case:						Optional[Any]   =						[
             481,
             4_735,
             544,
             246,
             963,
             870,
             762,
             239,
             244,
             40_477,
             244,
             249,
             719,
             881,
             487,
             544,
             240,
             244,
             603,
             481,
         ]  # the president is a very good man. " \n " i\'m sure he is, " said the

         __snake_case:						List[Any]   =						model.generate(A							,				do_sample=A		)
         self.assertListEqual(output_ids[0].tolist()							,				A		)













def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Union[str, Any]:
  __snake_case:						List[Any]   =						[]
  __snake_case:						List[str]   =						[]
  __snake_case:						List[str]   =						{
      """^""": 3,
      """*""": 2,
      """/""": 2,
      """%""": 2,
      """+""": 1,
      """-""": 1,
  }  # Priority of each operator
  __snake_case:						Union[str, Any]   =						len(SCREAMING_SNAKE_CASE__) if (len(SCREAMING_SNAKE_CASE__) > 7) else 7

  # Print table header for output
  print(
      """Symbol""".center(8)					,					"""Stack""".center(SCREAMING_SNAKE_CASE__)					,					"""Postfix""".center(SCREAMING_SNAKE_CASE__)					,					sep=""" | """					,					)
  print("""-""" * (print_width * 3 + 7))

  for x in infix:
    if x.isalpha() or x.isdigit():
      post_fix.append(SCREAMING_SNAKE_CASE__)  # if x is Alphabet / Digit, add it to Postfix
    elif x == "(":
      stack.append(SCREAMING_SNAKE_CASE__)  # if x is "(" push to Stack
    elif x == ")":  # if x is ")" pop stack until "(" is encountered
      while stack[-1] != "(":
        post_fix.append(stack.pop())  # Pop stack & add the content to Postfix
      stack.pop()
    else:
      if len(SCREAMING_SNAKE_CASE__) == 0:
        stack.append(SCREAMING_SNAKE_CASE__)  # If stack is empty, push x to stack
      else:  # while priority of x is not > priority of element in the stack
        while len(SCREAMING_SNAKE_CASE__) > 0 and priority[x] <= priority[stack[-1]]:
          post_fix.append(stack.pop())  # pop stack & add to Postfix
        stack.append(SCREAMING_SNAKE_CASE__)  # push x to stack

    print(
        x.center(8)					,					("""""".join(SCREAMING_SNAKE_CASE__)).ljust(SCREAMING_SNAKE_CASE__)					,					("""""".join(SCREAMING_SNAKE_CASE__)).ljust(SCREAMING_SNAKE_CASE__)					,					sep=""" | """					,					)  # Output in tabular format

  while len(SCREAMING_SNAKE_CASE__) > 0:  # while stack is not empty
    post_fix.append(stack.pop())  # pop stack & add to Postfix
    print(
        """ """.center(8)					,					("""""".join(SCREAMING_SNAKE_CASE__)).ljust(SCREAMING_SNAKE_CASE__)					,					("""""".join(SCREAMING_SNAKE_CASE__)).ljust(SCREAMING_SNAKE_CASE__)					,					sep=""" | """					,					)  # Output in tabular format

  return "".join(SCREAMING_SNAKE_CASE__)  # return Postfix as str







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Tuple:
  __snake_case:						Optional[int]   =						list(infix[::-1])  # reverse the infix equation

  for i in range(len(SCREAMING_SNAKE_CASE__)):
    if infix[i] == "(":
      __snake_case:						int   =						""")"""  # change "(" to ")"
    elif infix[i] == ")":
      __snake_case:						Dict   =						"""("""  # change ")" to "("

  return (infix_2_postfix("""""".join(SCREAMING_SNAKE_CASE__)))[
      ::-1
  ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
      __UpperCAmelCase       :   Optional[int]				       =							input("\nEnter an Infix Equation = ")  # Input an Infix equation
      __UpperCAmelCase       :   int				       =							"".join(Infix.split())  # Remove spaces from the input
      print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")






