



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__:

   '''simple docstring'''




   def __init__(						self  :			Dict			,				lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			Tuple=13			,				lowerCAmelCase  :			List[str]=7			,				lowerCAmelCase  :			int=True			,				lowerCAmelCase  :			str=True			,				lowerCAmelCase  :			Union[str, Any]=True			,				lowerCAmelCase  :			str=99			,				lowerCAmelCase  :			Tuple=32			,				lowerCAmelCase  :			Optional[int]=5			,				lowerCAmelCase  :			Optional[Any]=4			,				lowerCAmelCase  :			Tuple=37			,				lowerCAmelCase  :			Tuple="gelu"			,				lowerCAmelCase  :			str=0.1			,				lowerCAmelCase  :			str=0.1			,				lowerCAmelCase  :			Any=5_12			,				lowerCAmelCase  :			Union[str, Any]=16			,				lowerCAmelCase  :			str=2			,				lowerCAmelCase  :			List[str]=0.02			,				lowerCAmelCase  :			Union[str, Any]=3			,				lowerCAmelCase  :			List[str]=4			,				lowerCAmelCase  :			List[str]=None			,				)	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  parent
         lowercase__     =  batch_size
         lowercase__     =  seq_length
         lowercase__     =  is_training
         lowercase__     =  use_token_type_ids
         lowercase__     =  use_labels
         lowercase__     =  vocab_size
         lowercase__     =  hidden_size
         lowercase__     =  num_hidden_layers
         lowercase__     =  num_attention_heads
         lowercase__     =  intermediate_size
         lowercase__     =  hidden_act
         lowercase__     =  hidden_dropout_prob
         lowercase__     =  attention_probs_dropout_prob
         lowercase__     =  max_position_embeddings
         lowercase__     =  type_vocab_size
         lowercase__     =  type_sequence_label_size
         lowercase__     =  initializer_range
         lowercase__     =  num_labels
         lowercase__     =  num_choices
         lowercase__     =  scope
         lowercase__     =  self.vocab_size - 1


   def 			UpperCAmelCase		(						self  :			Dict)	->				int:




         """simple docstring"""

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

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

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

         lowercase__     =  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			,				)

         lowercase__     =  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  :			Any			,				lowerCAmelCase  :			Union[str, Any]			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			Tuple			,				*lowerCAmelCase  :			Tuple)	->				List[str]:




         """simple docstring"""

         lowercase__     =  OpenAIGPTModel(config=lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()

         lowercase__     =  model(lowerCAmelCase			,				token_type_ids=lowerCAmelCase			,				head_mask=lowerCAmelCase)
         lowercase__     =  model(lowerCAmelCase			,				token_type_ids=lowerCAmelCase)
         lowercase__     =  model(lowerCAmelCase)

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


   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			Optional[Any]			,				*lowerCAmelCase  :			Union[str, Any])	->				List[str]:




         """simple docstring"""

         lowercase__     =  OpenAIGPTLMHeadModel(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()

         lowercase__     =  model(lowerCAmelCase			,				token_type_ids=lowerCAmelCase			,				labels=lowerCAmelCase)
         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[Any]			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			Tuple			,				*lowerCAmelCase  :			Any)	->				int:




         """simple docstring"""

         lowercase__     =  OpenAIGPTDoubleHeadsModel(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()

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


   def 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			int			,				lowerCAmelCase  :			Optional[Any]			,				*lowerCAmelCase  :			Optional[int])	->				Any:




         """simple docstring"""

         lowercase__     =  self.num_labels
         lowercase__     =  OpenAIGPTForSequenceClassification(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()

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



   def 			UpperCAmelCase		(						self  :			int)	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  self.prepare_config_and_inputs()
         (
             (
             lowercase__
         ),	(
             lowercase__
         ),	(
             lowercase__
         ),	(
             lowercase__
         ),	(
             lowercase__
         ),	(
             lowercase__
         ),	(
             lowercase__
         ),	
         )     =  config_and_inputs
         lowercase__     =  {
             'input_ids': input_ids,
             'token_type_ids': token_type_ids,
             'head_mask': head_mask,
         }

         return config, inputs_dict



@require_torch
class       UpperCAmelCase__(							lowerCamelCase     ,							lowerCamelCase     ,							lowerCamelCase     ,							unittest.TestCase	):

   '''simple docstring'''




   A    :  Any       				=    (
       (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
       if is_torch_available()
       else ()
   )
   A    :  Optional[Any]       				=    (
       (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
   )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
   A    :  Union[str, Any]       				=    (
       {
           "feature-extraction": OpenAIGPTModel,
           "text-classification": OpenAIGPTForSequenceClassification,
           "text-generation": OpenAIGPTLMHeadModel,
           "zero-shot": OpenAIGPTForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )


   def 			UpperCAmelCase		(						self  :			List[str]			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			List[str]			,				lowerCAmelCase  :			List[str]			,				lowerCAmelCase  :			Tuple			,				lowerCAmelCase  :			Optional[int])	->				List[Any]:




         """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 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			Union[str, Any]			,				lowerCAmelCase  :			Tuple			,				lowerCAmelCase  :			str=False)	->				Dict:




         """simple docstring"""

         lowercase__     =  super()._prepare_for_class(lowerCAmelCase			,				lowerCAmelCase			,				return_labels=lowerCAmelCase)

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


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				str:




         """simple docstring"""

         lowercase__     =  OpenAIGPTModelTester(self)
         lowercase__     =  ConfigTester(self			,				config_class=lowerCAmelCase			,				n_embd=37)


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				str:




         """simple docstring"""

         self.config_tester.run_common_tests()


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				List[str]:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_openai_gpt_model(*lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			str)	->				List[str]:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Tuple)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_double_lm_head_model(*lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Any:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowerCAmelCase)



   @slow
   def 			UpperCAmelCase		(						self  :			Any)	->				List[Any]:




         """simple docstring"""

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



@require_torch
class       UpperCAmelCase__(							unittest.TestCase	):

   '''simple docstring'''




   @slow
   def 			UpperCAmelCase		(						self  :			Any)	->				str:




         """simple docstring"""

         lowercase__     =  OpenAIGPTLMHeadModel.from_pretrained('openai-gpt')
         model.to(lowerCAmelCase)
         lowercase__     =  torch.tensor([[4_81, 47_35, 5_44]]			,				dtype=torch.long			,				device=lowerCAmelCase)  # the president is
         lowercase__     =  [
             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

         lowercase__     =  model.generate(lowerCAmelCase			,				do_sample=lowerCAmelCase)
         self.assertListEqual(output_ids[0].tolist()			,				lowerCAmelCase)





import cva
import numpy as np



class       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			Union[str, Any]			,				lowerCAmelCase  :			float			,				lowerCAmelCase  :			int)	->				Dict:




         """simple docstring"""

         if k in (0.04, 0.06):
               lowercase__     =  k
               lowercase__     =  window_size
         else:
               raise ValueError('invalid k value')


   def __str__(						self  :			Tuple)	->				str:




         """simple docstring"""

         return str(self.k)



   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			str)	->				tuple[cva.Mat, list[list[int]]]:




         """simple docstring"""

         lowercase__     =  cva.imread(lowerCAmelCase			,				0)
         lowercase__,	lowercase__     =  img.shape
         lowercase__     =  []
         lowercase__     =  img.copy()
         lowercase__     =  cva.cvtColor(lowerCAmelCase			,				cva.COLOR_GRAY2RGB)
         lowercase__,	lowercase__     =  np.gradient(lowerCAmelCase)
         lowercase__     =  dx**2
         lowercase__     =  dy**2
         lowercase__     =  dx * dy
         lowercase__     =  0.04
         lowercase__     =  self.window_size // 2
         for y in range(lowerCAmelCase			,				h - offset):
               for x in range(lowerCAmelCase			,				w - offset):
                     lowercase__     =  ixx[
                         y - offset : y + offset + 1, x - offset : x + offset + 1
                     ].sum()
                     lowercase__     =  iyy[
                         y - offset : y + offset + 1, x - offset : x + offset + 1
                     ].sum()
                     lowercase__     =  ixy[
                         y - offset : y + offset + 1, x - offset : x + offset + 1
                     ].sum()

                     lowercase__     =  (wxx * wyy) - (wxy**2)
                     lowercase__     =  wxx + wyy
                     lowercase__     =  det - k * (trace**2)
                     # Can change the value
                     if r > 0.5:
                           corner_list.append([x, y, r])
                           color_img.itemset((y, x, 0)			,				0)
                           color_img.itemset((y, x, 1)			,				0)
                           color_img.itemset((y, x, 2)			,				2_55)
         return color_img, corner_list


if __name__ == "__main__":
       a__      :				Dict   	=			HarrisCorner(0.0_4, 3)
       a__	,		a__      :				Dict   	=			edge_detect.detect("path_to_image")
       cva.imwrite("detect.png", color_img)
