





'''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   UpperCAmelCase      :
 def __init__(							self :Tuple		,			lowercase_ :Optional[Any]		,			lowercase_ :int=13		,			lowercase_ :Any=7		,			lowercase_ :str=True		,			lowercase_ :Tuple=True		,			lowercase_ :Dict=True		,			lowercase_ :List[Any]=99		,			lowercase_ :Dict=32		,			lowercase_ :int=5		,			lowercase_ :Union[str, Any]=4		,			lowercase_ :List[str]=37		,			lowercase_ :Union[str, Any]="gelu"		,			lowercase_ :List[Any]=0.1		,			lowercase_ :List[str]=0.1		,			lowercase_ :Any=5_12		,			lowercase_ :Optional[Any]=16		,			lowercase_ :Optional[Any]=2		,			lowercase_ :Optional[int]=0.0_2		,			lowercase_ :str=3		,			lowercase_ :Tuple=4		,			lowercase_ :str=None		,			)->				List[Any]:
     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 	UpperCAmelCase_				(							self :Dict				)->				Dict:
     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 	UpperCAmelCase_				(							self :str		,			lowercase_ :Dict		,			lowercase_ :int		,			lowercase_ :Optional[Any]		,			lowercase_ :Union[str, Any]		,			*lowercase_ :Optional[Any]				)->				Union[str, Any]:
     A__							=     OpenAIGPTModel(config=lowercase_				)
     model.to(lowercase_				)
     model.eval()

     A__							=     model(lowercase_		,			token_type_ids=lowercase_		,			head_mask=lowercase_				)
     A__							=     model(lowercase_		,			token_type_ids=lowercase_				)
     A__							=     model(lowercase_				)

     self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)				)
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :Dict		,			lowercase_ :Any		,			lowercase_ :Tuple		,			lowercase_ :Optional[int]		,			*lowercase_ :int				)->				Tuple:
     A__							=     OpenAIGPTLMHeadModel(lowercase_				)
     model.to(lowercase_				)
     model.eval()

     A__							=     model(lowercase_		,			token_type_ids=lowercase_		,			labels=lowercase_				)
     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]		,			lowercase_ :str		,			lowercase_ :Union[str, Any]		,			lowercase_ :Union[str, Any]		,			lowercase_ :Dict		,			*lowercase_ :Union[str, Any]				)->				Tuple:
     A__							=     OpenAIGPTDoubleHeadsModel(lowercase_				)
     model.to(lowercase_				)
     model.eval()

     A__							=     model(lowercase_		,			token_type_ids=lowercase_		,			labels=lowercase_				)
     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		,			lowercase_ :Dict		,			lowercase_ :List[Any]		,			lowercase_ :str		,			lowercase_ :Tuple		,			*lowercase_ :int				)->				List[Any]:
     A__							=     self.num_labels
     A__							=     OpenAIGPTForSequenceClassification(lowercase_				)
     model.to(lowercase_				)
     model.eval()

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



 def 	UpperCAmelCase_				(							self :Optional[int]				)->				List[str]:
     A__							=     self.prepare_config_and_inputs()
     (
         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   UpperCAmelCase      (			__snake_case     ,			__snake_case     ,			__snake_case     ,			unittest.TestCase							):
 __lowercase  						=							(
     (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
     if is_torch_available()
     else ()
 )
 __lowercase  						=							(
     (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
 )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
 __lowercase  						=							(
     {
         """feature-extraction""": OpenAIGPTModel,
         """text-classification""": OpenAIGPTForSequenceClassification,
         """text-generation""": OpenAIGPTLMHeadModel,
         """zero-shot""": OpenAIGPTForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :Tuple		,			lowercase_ :List[str]		,			lowercase_ :List[str]		,			lowercase_ :Optional[int]		,			lowercase_ :Tuple				)->				Any:






     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 :str		,			lowercase_ :int		,			lowercase_ :Union[str, Any]		,			lowercase_ :int=False				)->				Optional[int]:
     A__							=     super()._prepare_for_class(lowercase_		,			lowercase_		,			return_labels=lowercase_				)

     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=lowercase_		,			)
             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=lowercase_		,			)
             A__							=     torch.zeros(
                 self.model_tester.batch_size		,			dtype=torch.long		,			device=lowercase_				)
     return inputs_dict
 def 	UpperCAmelCase_				(							self :List[Any]				)->				List[Any]:
     A__							=     OpenAIGPTModelTester(self				)
     A__							=     ConfigTester(self		,			config_class=lowercase_		,			n_embd=37				)
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				Dict:
     self.config_tester.run_common_tests()
 def 	UpperCAmelCase_				(							self :Dict				)->				int:
     A__							=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_openai_gpt_model(*lowercase_				)
 def 	UpperCAmelCase_				(							self :Dict				)->				Optional[Any]:
     A__							=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_lm_head_model(*lowercase_				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				Dict:
     A__							=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_double_lm_head_model(*lowercase_				)
 def 	UpperCAmelCase_				(							self :Any				)->				int:
     A__							=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase_				)



 @slow
 def 	UpperCAmelCase_				(							self :int				)->				Any:






     for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         A__							=     OpenAIGPTModel.from_pretrained(lowercase_				)
         self.assertIsNotNone(lowercase_				)






@require_torch
class   UpperCAmelCase      (			unittest.TestCase							):
 @slow
 def 	UpperCAmelCase_				(							self :List[str]				)->				str:
     A__							=     OpenAIGPTLMHeadModel.from_pretrained("openai-gpt"				)
     model.to(lowercase_				)
     A__							=     torch.tensor([[4_81, 47_35, 5_44]]		,			dtype=torch.long		,			device=lowercase_				)  # the president is
     A__							=     [
         4_81,
         47_35,
         5_44,
         2_46,
         9_63,
         8_70,
         7_62,
         2_39,
         2_44,
         4_04_77,
         2_44,
         2_49,
         7_19,
         8_81,
         4_87,
         5_44,
         2_40,
         2_44,
         6_03,
         4_81,
     ]  # the president is a very good man. " \n " i\'m sure he is, " said the

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





'''simple docstring'''




import random







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    A,    A,    A			:  Any   =		[], [], []
    for element in data:
        if element < pivot:
            less.append(snake_case__		)
        elif element > pivot:
            greater.append(snake_case__		)
        else:
            equal.append(snake_case__		)
    return less, equal, greater







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    if index >= len(snake_case__		) or index < 0:
        return None

    A			:  List[Any]   =		items[random.randint(0						,							len(snake_case__		) - 1		)]
    A			:  Optional[int]   =		0
    A,    A,    A			:  Dict   =		_partition(snake_case__						,							snake_case__		)
    A			:  int   =		len(snake_case__		)
    A			:  List[Any]   =		len(snake_case__		)

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(snake_case__						,							snake_case__		)
    # must be in larger
    else:
        return quick_select(snake_case__						,							index - (m + count)		)




