code: 
  
  """simple docstring"""
  from __future__ import annotations
  
  import unittest
  
  from transformers import OpenAIGPTConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
      import tensorflow as tf
  
      from transformers.models.openai.modeling_tf_openai import (
          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFOpenAIGPTDoubleHeadsModel,
          TFOpenAIGPTForSequenceClassification,
          TFOpenAIGPTLMHeadModel,
          TFOpenAIGPTModel,
      )
  
  class 			lowerCamelCase  :
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						parent
          UpperCAmelCase__ :       Dict						=						13
          UpperCAmelCase__ :       Dict						=						7
          UpperCAmelCase__ :       Dict						=						True
          UpperCAmelCase__ :       List[Any]						=						True
          UpperCAmelCase__ :       str						=						True
          UpperCAmelCase__ :       Union[str, Any]						=						True
          UpperCAmelCase__ :       Optional[Any]						=						True
          UpperCAmelCase__ :       Union[str, Any]						=						99
          UpperCAmelCase__ :       Any						=						32
          UpperCAmelCase__ :       Union[str, Any]						=						2
          UpperCAmelCase__ :       Union[str, Any]						=						4
          UpperCAmelCase__ :       str						=						37
          UpperCAmelCase__ :       int						=						"""gelu"""
          UpperCAmelCase__ :       Optional[Any]						=						0.1
          UpperCAmelCase__ :       List[Any]						=						0.1
          UpperCAmelCase__ :       List[str]						=						512
          UpperCAmelCase__ :       Optional[Any]						=						16
          UpperCAmelCase__ :       str						=						2
          UpperCAmelCase__ :       List[str]						=						0.02
          UpperCAmelCase__ :       Any						=						3
          UpperCAmelCase__ :       str						=						4
          UpperCAmelCase__ :       Any						=						None
          UpperCAmelCase__ :       Any						=						self.vocab_size - 1
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size  )
  
          UpperCAmelCase__ :       Tuple						=						None
          if self.use_input_mask:
                UpperCAmelCase__ :       List[str]						=						random_attention_mask([self.batch_size, self.seq_length]  )
  
          UpperCAmelCase__ :       Optional[int]						=						None
          if self.use_token_type_ids:
                UpperCAmelCase__ :       int						=						ids_tensor([self.batch_size, self.seq_length]       ,					self.type_vocab_size  )
  
          UpperCAmelCase__ :       int						=						None
          if self.use_mc_token_ids:
                UpperCAmelCase__ :       int						=						ids_tensor([self.batch_size, self.num_choices]       ,					self.seq_length  )
  
          UpperCAmelCase__ :       str						=						None
          UpperCAmelCase__ :       Dict						=						None
          UpperCAmelCase__ :       Optional[Any]						=						None
          if self.use_labels:
                UpperCAmelCase__ :       Union[str, Any]						=						ids_tensor([self.batch_size]       ,					self.type_sequence_label_size  )
                UpperCAmelCase__ :       Dict						=						ids_tensor([self.batch_size, self.seq_length]       ,					self.num_labels  )
                UpperCAmelCase__ :       str						=						ids_tensor([self.batch_size]       ,					self.num_choices  )
  
          UpperCAmelCase__ :       Any						=						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       ,					)
  
          UpperCAmelCase__ :       Tuple						=						ids_tensor([self.num_hidden_layers, self.num_attention_heads]       ,					2  )
  
          return (
              config,
              input_ids,
              input_mask,
              head_mask,
              token_type_ids,
              mc_token_ids,
              sequence_labels,
              token_labels,
              choice_labels,
          )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						TFOpenAIGPTModel(config=_lowerCamelCase  )
          UpperCAmelCase__ :       Any						=						{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
          UpperCAmelCase__ :       Optional[int]						=						model(_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[str]						=						[input_ids, input_mask]
          UpperCAmelCase__ :       Dict						=						model(_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[str]						=						model(_lowerCamelCase  )
  
          self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, self.seq_length, self.hidden_size)  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Union[str, Any]						=						TFOpenAIGPTLMHeadModel(config=_lowerCamelCase  )
          UpperCAmelCase__ :       Tuple						=						{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
          UpperCAmelCase__ :       str						=						model(_lowerCamelCase  )
          self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.vocab_size)  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						TFOpenAIGPTDoubleHeadsModel(config=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Optional[Any]						=						tf.tile(tf.expand_dims(_lowerCamelCase       ,					1  )       ,					(1, self.num_choices, 1)  )
          UpperCAmelCase__ :       Any						=						tf.tile(tf.expand_dims(_lowerCamelCase       ,					1  )       ,					(1, self.num_choices, 1)  )
          UpperCAmelCase__ :       Any						=						tf.tile(tf.expand_dims(_lowerCamelCase       ,					1  )       ,					(1, self.num_choices, 1)  )
  
          UpperCAmelCase__ :       List[Any]						=						{
              """input_ids""": multiple_choice_inputs_ids,
              """mc_token_ids""": mc_token_ids,
              """attention_mask""": multiple_choice_input_mask,
              """token_type_ids""": multiple_choice_token_type_ids,
          }
          UpperCAmelCase__ :       int						=						model(_lowerCamelCase  )
          self.parent.assertEqual(
              result.logits.shape       ,					(self.batch_size, self.num_choices, self.seq_length, self.vocab_size)  )
          self.parent.assertEqual(result.mc_logits.shape       ,					(self.batch_size, self.num_choices)  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						self.num_labels
          UpperCAmelCase__ :       int						=						ids_tensor([self.batch_size]       ,					self.type_sequence_label_size  )
          UpperCAmelCase__ :       Any						=						{
              """input_ids""": input_ids,
              """attention_mask""": input_mask,
              """token_type_ids""": token_type_ids,
              """labels""": sequence_labels,
          }
          UpperCAmelCase__ :       Tuple						=						TFOpenAIGPTForSequenceClassification(_lowerCamelCase  )
          UpperCAmelCase__ :       Union[str, Any]						=						model(_lowerCamelCase  )
          self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)  )
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						self.prepare_config_and_inputs()
  
          (
              (
              UpperCAmelCase__
          )				,		(
              UpperCAmelCase__
          )				,		(
              UpperCAmelCase__
          )				,		(
              UpperCAmelCase__
          )				,		(
              UpperCAmelCase__
          )				,		(
              UpperCAmelCase__
          )				,		(
              UpperCAmelCase__
          )				,		(
              UpperCAmelCase__
          )				,		(
              UpperCAmelCase__
          )				,		
          ) :       Optional[Any]						=						config_and_inputs
  
          UpperCAmelCase__ :       Tuple						=						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
          return config, inputs_dict
  
  
  
  
  @require_tf
  class 			lowerCamelCase  (					lowerCAmelCase__				,				lowerCAmelCase__				,				unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     (
        (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
        if is_tf_available()
        else ()
    )
    SCREAMING_SNAKE_CASE							=     (
        (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
    SCREAMING_SNAKE_CASE							=     (
        {
            'feature-extraction': TFOpenAIGPTModel,
            'text-classification': TFOpenAIGPTForSequenceClassification,
            'text-generation': TFOpenAIGPTLMHeadModel,
            'zero-shot': TFOpenAIGPTForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    SCREAMING_SNAKE_CASE							=     False
    SCREAMING_SNAKE_CASE							=     False
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          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    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						TFOpenAIGPTModelTester(self  )
          UpperCAmelCase__ :       Optional[int]						=						ConfigTester(self       ,					config_class=_lowerCamelCase       ,					n_embd=37  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          self.config_tester.run_common_tests()
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_openai_gpt_model(*_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Union[str, Any]						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_openai_gpt_lm_head(*_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_openai_gpt_double_head(*_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_lowerCamelCase  )
  
  
  
  
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase__ :       int						=						TFOpenAIGPTModel.from_pretrained(_lowerCamelCase  )
                self.assertIsNotNone(_lowerCamelCase  )
  
  
  
  
  @require_tf
  class 			lowerCamelCase  (					unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						TFOpenAIGPTLMHeadModel.from_pretrained("""openai-gpt"""  )
          UpperCAmelCase__ :       Optional[int]						=						tf.convert_to_tensor([[481, 4735, 544]]       ,					dtype=tf.intaa  )  # the president is
          UpperCAmelCase__ :       Union[str, Any]						=						[
              481,
              4735,
              544,
              246,
              963,
              870,
              762,
              239,
              244,
              40477,
              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
  
          UpperCAmelCase__ :       Any						=						model.generate(_lowerCamelCase       ,					do_sample=_lowerCamelCase  )
          self.assertListEqual(output_ids[0].numpy().tolist()       ,					_lowerCamelCase  )
  
  
style_context: 
  
  """simple docstring"""
  import requests
  
  try:
      from django.contrib.auth import authenticate, login
      from django.shortcuts import redirect, render
  except ImportError:
      _A						=     _A						=     _A						=     _A						=     print
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   str:
        # Enter your recaptcha secret key here
        UpperCAmelCase__ :       int						=						"""secretKey"""
        UpperCAmelCase__ :       Any						=						"""https://www.google.com/recaptcha/api/siteverify"""
  
        # when method is not POST, direct user to login page
        if request.method != "POST":
              return render(lowerCAmelCase ,  """login.html"""						)
  
        # from the frontend, get username, password, and client_key
        UpperCAmelCase__ :       List[Any]						=						request.POST.get("""username"""						)
        UpperCAmelCase__ :       Optional[Any]						=						request.POST.get("""password"""						)
        UpperCAmelCase__ :       List[str]						=						request.POST.get("""g-recaptcha-response"""						)
  
        # post recaptcha response to Google's recaptcha api
        UpperCAmelCase__ :       Any						=						requests.post(lowerCAmelCase ,  data={"""secret""": secret_key, """response""": client_key}						)
        # if the recaptcha api verified our keys
        if response.json().get("""success""" ,  lowerCAmelCase						):
              # authenticate the user
              UpperCAmelCase__ :       List[str]						=						authenticate(lowerCAmelCase ,  username=lowerCAmelCase ,  password=lowerCAmelCase						)
              if user_in_database:
                    login(lowerCAmelCase ,  lowerCAmelCase						)
                    return redirect("""/your-webpage"""						)
        return render(lowerCAmelCase ,  """login.html"""						)
  
  