"""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     _SCREAMING_SNAKE_CASE       :



   '''simple docstring'''



   def __init__(  self   ,							_SCREAMING_SNAKE_CASE   ,							):


         '''simple docstring'''

         __lowerCamelCase								=				parent
         __lowerCamelCase								=				13
         __lowerCamelCase								=				7
         __lowerCamelCase								=				True
         __lowerCamelCase								=				True
         __lowerCamelCase								=				True
         __lowerCamelCase								=				True
         __lowerCamelCase								=				True
         __lowerCamelCase								=				99
         __lowerCamelCase								=				32
         __lowerCamelCase								=				2
         __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
         __lowerCamelCase								=				self.vocab_size - 1







   def       _A							(  self  ):


         '''simple docstring'''

         __lowerCamelCase								=				ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size  )

         __lowerCamelCase								=				None
         if self.use_input_mask:
               __lowerCamelCase								=				random_attention_mask([self.batch_size, self.seq_length]  )

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

         __lowerCamelCase								=				None
         if self.use_mc_token_ids:
               __lowerCamelCase								=				ids_tensor([self.batch_size, self.num_choices]   ,							self.seq_length  )

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

         __lowerCamelCase								=				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								=				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   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							*_SCREAMING_SNAKE_CASE  ):


         '''simple docstring'''

         __lowerCamelCase								=				TFOpenAIGPTModel(config=lowercase_  )
         __lowerCamelCase								=				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         __lowerCamelCase								=				model(lowercase_  )

         __lowerCamelCase								=				[input_ids, input_mask]
         __lowerCamelCase								=				model(lowercase_  )

         __lowerCamelCase								=				model(lowercase_  )

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







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							*_SCREAMING_SNAKE_CASE  ):


         '''simple docstring'''

         __lowerCamelCase								=				TFOpenAIGPTLMHeadModel(config=lowercase_  )
         __lowerCamelCase								=				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         __lowerCamelCase								=				model(lowercase_  )
         self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.seq_length, self.vocab_size)  )







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							*_SCREAMING_SNAKE_CASE  ):


         '''simple docstring'''

         __lowerCamelCase								=				TFOpenAIGPTDoubleHeadsModel(config=lowercase_  )

         __lowerCamelCase								=				tf.tile(tf.expand_dims(lowercase_   ,							1  )   ,							(1, self.num_choices, 1)  )
         __lowerCamelCase								=				tf.tile(tf.expand_dims(lowercase_   ,							1  )   ,							(1, self.num_choices, 1)  )
         __lowerCamelCase								=				tf.tile(tf.expand_dims(lowercase_   ,							1  )   ,							(1, self.num_choices, 1)  )

         __lowerCamelCase								=				{
             '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,
         }
         __lowerCamelCase								=				model(lowercase_  )
         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   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							*_SCREAMING_SNAKE_CASE  ):


         '''simple docstring'''

         __lowerCamelCase								=				self.num_labels
         __lowerCamelCase								=				ids_tensor([self.batch_size]   ,							self.type_sequence_label_size  )
         __lowerCamelCase								=				{
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'token_type_ids': token_type_ids,
             'labels': sequence_labels,
         }
         __lowerCamelCase								=				TFOpenAIGPTForSequenceClassification(lowercase_  )
         __lowerCamelCase								=				model(lowercase_  )
         self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)  )







   def       _A							(  self  ):


         '''simple docstring'''

         __lowerCamelCase								=				self.prepare_config_and_inputs()

         (
             (
             __lowerCamelCase   
         )		,(
             __lowerCamelCase   
         )		,(
             __lowerCamelCase   
         )		,(
             __lowerCamelCase   
         )		,(
             __lowerCamelCase   
         )		,(
             __lowerCamelCase   
         )		,(
             __lowerCamelCase   
         )		,(
             __lowerCamelCase   
         )		,(
             __lowerCamelCase   
         )		,   
         )								=				config_and_inputs

         __lowerCamelCase								=				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
         return config, inputs_dict






@require_tf
class     _SCREAMING_SNAKE_CASE       ( lowerCamelCase__						,							lowerCamelCase__						,							unittest.TestCase							):



   '''simple docstring'''



   __UpperCAmelCase  :       Optional[int]						=						(
       (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
       if is_tf_available()
       else ()
   )
   __UpperCAmelCase  :       str						=						(
       (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
   )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
   __UpperCAmelCase  :       str						=						(
       {
           'feature-extraction': TFOpenAIGPTModel,
           'text-classification': TFOpenAIGPTForSequenceClassification,
           'text-generation': TFOpenAIGPTLMHeadModel,
           'zero-shot': TFOpenAIGPTForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   __UpperCAmelCase  :       Union[str, Any]						=						False
   __UpperCAmelCase  :       Optional[Any]						=						False







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):


         '''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'''

         __lowerCamelCase								=				TFOpenAIGPTModelTester(self  )
         __lowerCamelCase								=				ConfigTester(self   ,							config_class=lowercase_   ,							n_embd=37  )







   def       _A							(  self  ):


         '''simple docstring'''

         self.config_tester.run_common_tests()







   def       _A							(  self  ):


         '''simple docstring'''

         __lowerCamelCase								=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_openai_gpt_model(*lowercase_  )







   def       _A							(  self  ):


         '''simple docstring'''

         __lowerCamelCase								=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_openai_gpt_lm_head(*lowercase_  )







   def       _A							(  self  ):


         '''simple docstring'''

         __lowerCamelCase								=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_openai_gpt_double_head(*lowercase_  )







   def       _A							(  self  ):


         '''simple docstring'''

         __lowerCamelCase								=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase_  )







   @slow
   def       _A							(  self  ):


         '''simple docstring'''

         for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __lowerCamelCase								=				TFOpenAIGPTModel.from_pretrained(lowercase_  )
               self.assertIsNotNone(lowercase_  )






@require_tf
class     _SCREAMING_SNAKE_CASE       ( unittest.TestCase							):



   '''simple docstring'''



   @slow
   def       _A							(  self  ):


         '''simple docstring'''

         __lowerCamelCase								=				TFOpenAIGPTLMHeadModel.from_pretrained('openai-gpt'  )
         __lowerCamelCase								=				tf.convert_to_tensor([[481, 4735, 544]]   ,							dtype=tf.intaa  )  # the president is
         __lowerCamelCase								=				[
             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

         __lowerCamelCase								=				model.generate(lowercase_   ,							do_sample=lowercase_  )
         self.assertListEqual(output_ids[0].numpy().tolist()   ,							lowercase_  )







"""simple docstring"""







from __future__ import annotations

from collections.abc import Iterable






class     _SCREAMING_SNAKE_CASE       :



   '''simple docstring'''



   def __init__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase								=				float(_SCREAMING_SNAKE_CASE  ), float(_SCREAMING_SNAKE_CASE  )







   def __eq__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      return self.x == other.x and self.y == other.y







   def __ne__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      return not self == other







   def __gt__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      if self.x > other.x:
         return True
      elif self.x == other.x:
         return self.y > other.y
      return False







   def __lt__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      return not self > other







   def __ge__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      if self.x > other.x:
         return True
      elif self.x == other.x:
         return self.y >= other.y
      return False







   def __le__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      if self.x < other.x:
         return True
      elif self.x == other.x:
         return self.y <= other.y
      return False







   def __repr__(  self  ):


      '''simple docstring'''

      return F'''({self.x}, {self.y})'''







   def __hash__(  self  ):


      '''simple docstring'''

      return hash(self.x  )



def 			A__						(  lowercase_    :  list[Point] | list[list[float]] | Iterable[list[float]]			,   )     ->       list[Point]:
   __lowerCamelCase								=				[]
   if list_of_tuples:
      for p in list_of_tuples:
         if isinstance(lowercase_			,   lowercase_	):
            points.append(lowercase_	)
         else:
            try:
               points.append(Point(p[0]			,   p[1]	)	)
            except (IndexError, TypeError):
               print(
                   F'''Ignoring deformed point {p}. All points'''
                   ' must have at least 2 coordinates.'	)
   return points



def 			A__						(  lowercase_    :  list[Point] | list[list[float]]	)     ->       list[Point]:

   if not hasattr(lowercase_			,   '__iter__'	):
      __lowerCamelCase								=				F'''Expecting an iterable object but got an non-iterable type {points}'''
      raise ValueError(lowercase_	)

   if not points:
      __lowerCamelCase								=				F'''Expecting a list of points but got {points}'''
      raise ValueError(lowercase_	)

   return _construct_points(lowercase_	)



def 			A__						(  lowercase_    :  Point			,   lowercase_    :  Point			,   lowercase_    :  Point	)     ->       float:
   __lowerCamelCase								=				(a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
   return det



def 			A__						(  lowercase_    :  list[Point]	)     ->       list[Point]:
   __lowerCamelCase								=				sorted(_validate_input(lowercase_	)	)
   __lowerCamelCase								=				len(lowercase_	)
   __lowerCamelCase								=				set()

   for i in range(n - 1	):
      for j in range(i + 1			,   lowercase_	):
         __lowerCamelCase								=				__lowerCamelCase								=				False
         __lowerCamelCase								=				True
         for k in range(lowercase_	):
            if k != i and k != j:
               __lowerCamelCase								=				_det(points[i]			,   points[j]			,   points[k]	)

               if det_k > 0:
                  __lowerCamelCase								=				True
               elif det_k < 0:
                  __lowerCamelCase								=				True
               else:
                  # point[i], point[j], point[k] all lie on a straight line
                  # if point[k] is to the left of point[i] or it's to the
                  # right of point[j], then point[i], point[j] cannot be
                  # part of the convex hull of A
                  if points[k] < points[i] or points[k] > points[j]:
                     __lowerCamelCase								=				False
                     break

            if points_left_of_ij and points_right_of_ij:
               __lowerCamelCase								=				False
               break

         if ij_part_of_convex_hull:
            convex_set.update([points[i], points[j]]	)

   return sorted(lowercase_	)



def 			A__						(  lowercase_    :  list[Point]	)     ->       list[Point]:
   __lowerCamelCase								=				sorted(_validate_input(lowercase_	)	)
   __lowerCamelCase								=				len(lowercase_	)

   # divide all the points into an upper hull and a lower hull
   # the left most point and the right most point are definitely
   # members of the convex hull by definition.
   # use these two anchors to divide all the points into two hulls,
   # an upper hull and a lower hull.

   # all points to the left (above) the line joining the extreme points belong to the
   # upper hull
   # all points to the right (below) the line joining the extreme points below to the
   # lower hull
   # ignore all points on the line joining the extreme points since they cannot be
   # part of the convex hull

   __lowerCamelCase								=				points[0]
   __lowerCamelCase								=				points[n - 1]

   __lowerCamelCase								=				{left_most_point, right_most_point}
   __lowerCamelCase								=				[]
   __lowerCamelCase								=				[]

   for i in range(1			,   n - 1	):
      __lowerCamelCase								=				_det(lowercase_			,   lowercase_			,   points[i]	)

      if det > 0:
         upper_hull.append(points[i]	)
      elif det < 0:
         lower_hull.append(points[i]	)

   _construct_hull(lowercase_			,   lowercase_			,   lowercase_			,   lowercase_	)
   _construct_hull(lowercase_			,   lowercase_			,   lowercase_			,   lowercase_	)

   return sorted(lowercase_	)



def 			A__						(  lowercase_    :  list[Point]			,   lowercase_    :  Point			,   lowercase_    :  Point			,   lowercase_    :  set[Point]	)     ->       None:
   if points:
      __lowerCamelCase								=				None
      __lowerCamelCase								=				float('-inf'	)
      __lowerCamelCase								=				[]

      for p in points:
         __lowerCamelCase								=				_det(lowercase_			,   lowercase_			,   lowercase_	)

         if det > 0:
            candidate_points.append(lowercase_	)

            if det > extreme_point_distance:
               __lowerCamelCase								=				det
               __lowerCamelCase								=				p

      if extreme_point:
         _construct_hull(lowercase_			,   lowercase_			,   lowercase_			,   lowercase_	)
         convex_set.add(lowercase_	)
         _construct_hull(lowercase_			,   lowercase_			,   lowercase_			,   lowercase_	)



def 			A__						(  lowercase_    :  list[Point]	)     ->       list[Point]:
   __lowerCamelCase								=				sorted(_validate_input(lowercase_	)	)
   __lowerCamelCase								=				len(lowercase_	)

   __lowerCamelCase								=				points[:2]
   for i in range(2			,   lowercase_	):
      __lowerCamelCase								=				_det(convex_hull[1]			,   convex_hull[0]			,   points[i]	)
      if det > 0:
         convex_hull.insert(0			,   points[i]	)
         break
      elif det < 0:
         convex_hull.append(points[i]	)
         break
      else:
         __lowerCamelCase								=				points[i]
   i += 1

   for j in range(lowercase_			,   lowercase_	):
      if (
          _det(convex_hull[0]			,   convex_hull[-1]			,   points[j]	) > 0
          and _det(convex_hull[-1]			,   convex_hull[0]			,   points[1]	) < 0
      ):
         # The point lies within the convex hull
         continue

      convex_hull.insert(0			,   points[j]	)
      convex_hull.append(points[j]	)
      while _det(convex_hull[0]			,   convex_hull[1]			,   convex_hull[2]	) >= 0:
         del convex_hull[1]
      while _det(convex_hull[-1]			,   convex_hull[-2]			,   convex_hull[-3]	) <= 0:
         del convex_hull[-2]

    # `convex_hull` is contains the convex hull in circular order
   return sorted(convex_hull[1:] if len(lowercase_	) > 3 else convex_hull	)



def 			A__						(  )     ->       List[Any]:
   __lowerCamelCase								=				[
       (0, 3),
       (2, 2),
       (1, 1),
       (2, 1),
       (3, 0),
       (0, 0),
       (3, 3),
       (2, -1),
       (2, -4),
       (1, -3),
   ]
   # the convex set of points is
   # [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
   __lowerCamelCase								=				convex_hull_bf(lowercase_	)

   __lowerCamelCase								=				convex_hull_recursive(lowercase_	)
   assert results_bf == results_recursive

   __lowerCamelCase								=				convex_hull_melkman(lowercase_	)
   assert results_bf == results_melkman

   print(lowercase_	)


if __name__ == "__main__":
     main()






