
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 							A__		:
      def __init__(      self    ,  __magic_name__    ,  __magic_name__=1_3    ,  __magic_name__=7    ,  __magic_name__=True    ,  __magic_name__=True    ,  __magic_name__=True    ,  __magic_name__=9_9    ,  __magic_name__=3_2    ,  __magic_name__=5    ,  __magic_name__=4    ,  __magic_name__=3_7    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_2    ,  __magic_name__=1_6    ,  __magic_name__=2    ,  __magic_name__=0.02    ,  __magic_name__=3    ,  __magic_name__=4    ,  __magic_name__=None    ,  ):
       lowerCamelCase		:	List[str]							=      parent
       lowerCamelCase		:	Tuple							=      batch_size
       lowerCamelCase		:	Tuple							=      seq_length
       lowerCamelCase		:	Union[str, Any]							=      is_training
       lowerCamelCase		:	List[Any]							=      use_token_type_ids
       lowerCamelCase		:	List[Any]							=      use_labels
       lowerCamelCase		:	int							=      vocab_size
       lowerCamelCase		:	Tuple							=      hidden_size
       lowerCamelCase		:	Tuple							=      num_hidden_layers
       lowerCamelCase		:	Tuple							=      num_attention_heads
       lowerCamelCase		:	str							=      intermediate_size
       lowerCamelCase		:	Union[str, Any]							=      hidden_act
       lowerCamelCase		:	List[Any]							=      hidden_dropout_prob
       lowerCamelCase		:	Dict							=      attention_probs_dropout_prob
       lowerCamelCase		:	Dict							=      max_position_embeddings
       lowerCamelCase		:	List[Any]							=      type_vocab_size
       lowerCamelCase		:	str							=      type_sequence_label_size
       lowerCamelCase		:	str							=      initializer_range
       lowerCamelCase		:	Tuple							=      num_labels
       lowerCamelCase		:	List[str]							=      num_choices
       lowerCamelCase		:	Optional[int]							=      scope
       lowerCamelCase		:	Any							=      self.vocab_size - 1
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size      )

       lowerCamelCase		:	int							=      None
       if self.use_token_type_ids:
        lowerCamelCase		:	Optional[Any]							=      ids_tensor([self.batch_size, self.seq_length]    ,  self.type_vocab_size      )

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

       lowerCamelCase		:	str							=      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    ,  )

       lowerCamelCase		:	List[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    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  *__magic_name__      ):
       lowerCamelCase		:	Optional[Any]							=      OpenAIGPTModel(config=__magic_name__      )
       model.to(__magic_name__      )
       model.eval()

       lowerCamelCase		:	List[Any]							=      model(__magic_name__    ,  token_type_ids=__magic_name__    ,  head_mask=__magic_name__      )
       lowerCamelCase		:	str							=      model(__magic_name__    ,  token_type_ids=__magic_name__      )
       lowerCamelCase		:	List[str]							=      model(__magic_name__      )

       self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length, self.hidden_size)      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  *__magic_name__      ):
       lowerCamelCase		:	Optional[int]							=      OpenAIGPTLMHeadModel(__magic_name__      )
       model.to(__magic_name__      )
       model.eval()

       lowerCamelCase		:	int							=      model(__magic_name__    ,  token_type_ids=__magic_name__    ,  labels=__magic_name__      )
       self.parent.assertEqual(result.loss.shape    ,  ()      )
       self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.vocab_size)      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  *__magic_name__      ):
       lowerCamelCase		:	Union[str, Any]							=      OpenAIGPTDoubleHeadsModel(__magic_name__      )
       model.to(__magic_name__      )
       model.eval()

       lowerCamelCase		:	Tuple							=      model(__magic_name__    ,  token_type_ids=__magic_name__    ,  labels=__magic_name__      )
       self.parent.assertEqual(result.loss.shape    ,  ()      )
       self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.vocab_size)      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  *__magic_name__      ):
       lowerCamelCase		:	Optional[int]							=      self.num_labels
       lowerCamelCase		:	Dict							=      OpenAIGPTForSequenceClassification(__magic_name__      )
       model.to(__magic_name__      )
       model.eval()

       lowerCamelCase		:	List[Any]							=      ids_tensor([self.batch_size]    ,  self.type_sequence_label_size      )
       lowerCamelCase		:	Optional[Any]							=      model(__magic_name__    ,  token_type_ids=__magic_name__    ,  labels=__magic_name__      )
       self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels)      )






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Dict							=      self.prepare_config_and_inputs()
       (
           (
           lowerCamelCase
       )			,    (
           lowerCamelCase
       )			,    (
           lowerCamelCase
       )			,    (
           lowerCamelCase
       )			,    (
           lowerCamelCase
       )			,    (
           lowerCamelCase
       )			,    (
           lowerCamelCase
       )			,    
       )		:	Optional[Any]							=      config_and_inputs
       lowerCamelCase		:	Optional[int]							=      {
           """input_ids""": input_ids,
           """token_type_ids""": token_type_ids,
           """head_mask""": head_mask,
       }

       return config, inputs_dict





@require_torch
class 							A__		(	__SCREAMING_SNAKE_CASE  ,   __SCREAMING_SNAKE_CASE  ,   __SCREAMING_SNAKE_CASE  ,   unittest.TestCase):
      _UpperCAmelCase				:    Union[str, Any]           =			(
          (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
          if is_torch_available()
          else ()
      )
      _UpperCAmelCase				:    List[Any]           =			(
          (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
      )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
      _UpperCAmelCase				:    List[Any]           =			(
          {
              """feature-extraction""": OpenAIGPTModel,
              """text-classification""": OpenAIGPTForSequenceClassification,
              """text-generation""": OpenAIGPTLMHeadModel,
              """zero-shot""": OpenAIGPTForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       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    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__=False      ):
       lowerCamelCase		:	Optional[int]							=      super()._prepare_for_class(__magic_name__    ,  __magic_name__    ,  return_labels=__magic_name__      )

       if return_labels:
        if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
         lowerCamelCase		:	int							=      torch.zeros(
             (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)    ,  dtype=torch.long    ,  device=__magic_name__    ,  )
         lowerCamelCase		:	Dict							=      inputs_dict["""labels"""]
         lowerCamelCase		:	int							=      inputs_dict["""labels"""]
         lowerCamelCase		:	Any							=      torch.zeros(
             (self.model_tester.batch_size, self.model_tester.num_choices)    ,  dtype=torch.long    ,  device=__magic_name__    ,  )
         lowerCamelCase		:	Any							=      torch.zeros(
             self.model_tester.batch_size    ,  dtype=torch.long    ,  device=__magic_name__      )
       return inputs_dict
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[Any]							=      OpenAIGPTModelTester(self      )
       lowerCamelCase		:	int							=      ConfigTester(self    ,  config_class=__magic_name__    ,  n_embd=3_7      )
      def 						UpperCamelCase__							(      self      ):
       self.config_tester.run_common_tests()
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_model(*__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	int							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head_model(*__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_double_lm_head_model(*__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	int							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__magic_name__      )






      @slow
      def 						UpperCamelCase__							(      self      ):
       for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCamelCase		:	Dict							=      OpenAIGPTModel.from_pretrained(__magic_name__      )
        self.assertIsNotNone(__magic_name__      )





@require_torch
class 							A__		(	unittest.TestCase):
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Tuple							=      OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt"""      )
       model.to(__magic_name__      )
       lowerCamelCase		:	Any							=      torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]]    ,  dtype=torch.long    ,  device=__magic_name__      )  # the president is
       lowerCamelCase		:	int							=      [
           4_8_1,
           4_7_3_5,
           5_4_4,
           2_4_6,
           9_6_3,
           8_7_0,
           7_6_2,
           2_3_9,
           2_4_4,
           4_0_4_7_7,
           2_4_4,
           2_4_9,
           7_1_9,
           8_8_1,
           4_8_7,
           5_4_4,
           2_4_0,
           2_4_4,
           6_0_3,
           4_8_1,
       ]  # the president is a very good man. " \n " i\'m sure he is, " said the

       lowerCamelCase		:	Union[str, Any]							=      model.generate(__magic_name__    ,  do_sample=__magic_name__      )
       self.assertListEqual(output_ids[0].tolist()    ,  __magic_name__      )






def     _a					(						lowerCamelCase							):
 return " ".join(
     """""".join(word[::-1]							) if len(lowerCamelCase							) > 4 else word for word in sentence.split()							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(reverse_long_words("""Hey wollef sroirraw"""))




