"""simple docstring"""



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__	:


 def __init__(      self  ,						lowerCamelCase  ,						lowerCamelCase=13  ,						lowerCamelCase=7  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=99  ,						lowerCamelCase=32  ,						lowerCamelCase=5  ,						lowerCamelCase=4  ,						lowerCamelCase=37  ,						lowerCamelCase="gelu"  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=512  ,						lowerCamelCase=16  ,						lowerCamelCase=2  ,						lowerCamelCase=0.02  ,						lowerCamelCase=3  ,						lowerCamelCase=4  ,						lowerCamelCase=None  ,						):
        __a	      =  parent
        __a	      =  batch_size
        __a	      =  seq_length
        __a	      =  is_training
        __a	      =  use_token_type_ids
        __a	      =  use_labels
        __a	      =  vocab_size
        __a	      =  hidden_size
        __a	      =  num_hidden_layers
        __a	      =  num_attention_heads
        __a	      =  intermediate_size
        __a	      =  hidden_act
        __a	      =  hidden_dropout_prob
        __a	      =  attention_probs_dropout_prob
        __a	      =  max_position_embeddings
        __a	      =  type_vocab_size
        __a	      =  type_sequence_label_size
        __a	      =  initializer_range
        __a	      =  num_labels
        __a	      =  num_choices
        __a	      =  scope
        __a	      =  self.vocab_size - 1


 def 		a__					(      self		):
        __a	      =  ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size		)

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

        __a	      =  None
        __a	      =  None
        __a	      =  None
        if self.use_labels:
               __a	      =  ids_tensor([self.batch_size]  ,						self.type_sequence_label_size		)
               __a	      =  ids_tensor([self.batch_size, self.seq_length]  ,						self.num_labels		)
               __a	      =  ids_tensor([self.batch_size]  ,						self.num_choices		)

        __a	      =  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  ,						)

        __a	      =  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 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						*lowerCamelCase		):
        __a	      =  OpenAIGPTModel(config=SCREAMING_SNAKE_CASE__		)
        model.to(SCREAMING_SNAKE_CASE__		)
        model.eval()

        __a	      =  model(SCREAMING_SNAKE_CASE__  ,						token_type_ids=SCREAMING_SNAKE_CASE__  ,						head_mask=SCREAMING_SNAKE_CASE__		)
        __a	      =  model(SCREAMING_SNAKE_CASE__  ,						token_type_ids=SCREAMING_SNAKE_CASE__		)
        __a	      =  model(SCREAMING_SNAKE_CASE__		)

        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		):
        __a	      =  OpenAIGPTLMHeadModel(SCREAMING_SNAKE_CASE__		)
        model.to(SCREAMING_SNAKE_CASE__		)
        model.eval()

        __a	      =  model(SCREAMING_SNAKE_CASE__  ,						token_type_ids=SCREAMING_SNAKE_CASE__  ,						labels=SCREAMING_SNAKE_CASE__		)
        self.parent.assertEqual(result.loss.shape  ,						()		)
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.vocab_size)		)


 def 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						*lowerCamelCase		):
        __a	      =  OpenAIGPTDoubleHeadsModel(SCREAMING_SNAKE_CASE__		)
        model.to(SCREAMING_SNAKE_CASE__		)
        model.eval()

        __a	      =  model(SCREAMING_SNAKE_CASE__  ,						token_type_ids=SCREAMING_SNAKE_CASE__  ,						labels=SCREAMING_SNAKE_CASE__		)
        self.parent.assertEqual(result.loss.shape  ,						()		)
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.vocab_size)		)


 def 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						*lowerCamelCase		):
        __a	      =  self.num_labels
        __a	      =  OpenAIGPTForSequenceClassification(SCREAMING_SNAKE_CASE__		)
        model.to(SCREAMING_SNAKE_CASE__		)
        model.eval()

        __a	      =  ids_tensor([self.batch_size]  ,						self.type_sequence_label_size		)
        __a	      =  model(SCREAMING_SNAKE_CASE__  ,						token_type_ids=SCREAMING_SNAKE_CASE__  ,						labels=SCREAMING_SNAKE_CASE__		)
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels)		)






 def 		a__					(      self		):
        __a	      =  self.prepare_config_and_inputs()
        (
            (
            __a       
        )      ,					(
            __a       
        )      ,					(
            __a       
        )      ,					(
            __a       
        )      ,					(
            __a       
        )      ,					(
            __a       
        )      ,					(
            __a       
        )      ,					       
        )	      =  config_and_inputs
        __a	      =  {
            "input_ids": input_ids,
            "token_type_ids": token_type_ids,
            "head_mask": head_mask,
        }

        return config, inputs_dict






@require_torch
class       snake_case__	(    UpperCAmelCase__,		UpperCAmelCase__,		UpperCAmelCase__,		unittest.TestCase    ):
 _snake_case    : List[str]				   =		(
     (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
     if is_torch_available()
     else ()
 )
 _snake_case    : Any				   =		(
     (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
 )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
 _snake_case    : str				   =		(
     {
         """feature-extraction""": OpenAIGPTModel,
         """text-classification""": OpenAIGPTForSequenceClassification,
         """text-generation""": OpenAIGPTLMHeadModel,
         """zero-shot""": OpenAIGPTForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )


 def 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        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  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase=False		):
        __a	      =  super()._prepare_for_class(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						return_labels=SCREAMING_SNAKE_CASE__		)

        if return_labels:
               if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                      __a	      =  torch.zeros(
                          (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)  ,						dtype=torch.long  ,						device=SCREAMING_SNAKE_CASE__  ,						)
                      __a	      =  inputs_dict["labels"]
                      __a	      =  inputs_dict["labels"]
                      __a	      =  torch.zeros(
                          (self.model_tester.batch_size, self.model_tester.num_choices)  ,						dtype=torch.long  ,						device=SCREAMING_SNAKE_CASE__  ,						)
                      __a	      =  torch.zeros(
                          self.model_tester.batch_size  ,						dtype=torch.long  ,						device=SCREAMING_SNAKE_CASE__		)
        return inputs_dict


 def 		a__					(      self		):
        __a	      =  OpenAIGPTModelTester(self		)
        __a	      =  ConfigTester(self  ,						config_class=SCREAMING_SNAKE_CASE__  ,						n_embd=37		)


 def 		a__					(      self		):
        self.config_tester.run_common_tests()


 def 		a__					(      self		):
        __a	      =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_model(*SCREAMING_SNAKE_CASE__		)


 def 		a__					(      self		):
        __a	      =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(*SCREAMING_SNAKE_CASE__		)


 def 		a__					(      self		):
        __a	      =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_double_lm_head_model(*SCREAMING_SNAKE_CASE__		)


 def 		a__					(      self		):
        __a	      =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*SCREAMING_SNAKE_CASE__		)






 @slow
 def 		a__					(      self		):
        for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a	      =  OpenAIGPTModel.from_pretrained(SCREAMING_SNAKE_CASE__		)
               self.assertIsNotNone(SCREAMING_SNAKE_CASE__		)






@require_torch
class       snake_case__	(    unittest.TestCase    ):


 @slow
 def 		a__					(      self		):
        __a	      =  OpenAIGPTLMHeadModel.from_pretrained("openai-gpt"		)
        model.to(SCREAMING_SNAKE_CASE__		)
        __a	      =  torch.tensor([[481, 4735, 544]]  ,						dtype=torch.long  ,						device=SCREAMING_SNAKE_CASE__		)  # the president is
        __a	      =  [
            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

        __a	      =  model.generate(SCREAMING_SNAKE_CASE__  ,						do_sample=SCREAMING_SNAKE_CASE__		)
        self.assertListEqual(output_ids[0].tolist()  ,						SCREAMING_SNAKE_CASE__		)

'''simple docstring'''

from __future__ import annotations

from dataclasses import dataclass







@dataclass
class 				_lowercase     :


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       float
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None







def      UpperCamelCase_						(					snake_case_       :       TreeNode | None      )					->     bool:






 '''simple docstring'''

 def is_valid_tree(snake_case_       :       TreeNode | None      ) -> bool:
  if node is None:
   return True

  if not isinstance(snake_case_						,		snake_case_      ):
   return False

  try:
   float(node.data      )
  except (TypeError, ValueError):
   return False

  return is_valid_tree(node.left      ) and is_valid_tree(node.right      )

 if not is_valid_tree(snake_case_      ):
  raise ValueError(
      """Each node should be type of TreeNode and data should be float."""      )

 def is_binary_search_tree_recursive_check(
     snake_case_       :       TreeNode | None						,		snake_case_       :       float						,		snake_case_       :       float      ) -> bool:

  if node is None:
   return True

  return (
      left_bound < node.data < right_bound
      and is_binary_search_tree_recursive_check(node.left						,		snake_case_						,		node.data      )
      and is_binary_search_tree_recursive_check(
          node.right						,		node.data						,		snake_case_      )
  )

 return is_binary_search_tree_recursive_check(snake_case_						,		-float("""inf"""      )						,		float("""inf"""      )      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






