
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				,			__UpperCamelCase				,			__UpperCamelCase=13				,			__UpperCamelCase=7				,			__UpperCamelCase=True				,			__UpperCamelCase=True				,			__UpperCamelCase=True				,			__UpperCamelCase=99				,			__UpperCamelCase=32				,			__UpperCamelCase=5				,			__UpperCamelCase=4				,			__UpperCamelCase=37				,			__UpperCamelCase="gelu"				,			__UpperCamelCase=0.1				,			__UpperCamelCase=0.1				,			__UpperCamelCase=512				,			__UpperCamelCase=16				,			__UpperCamelCase=2				,			__UpperCamelCase=0.02				,			__UpperCamelCase=3				,			__UpperCamelCase=4				,			__UpperCamelCase=None				,			):
									_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  ):
									_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				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			*__UpperCamelCase  ):
									_lowercase					       =					OpenAIGPTModel(config=__UpperCamelCase  )
									model.to(__UpperCamelCase  )
									model.eval()

									_lowercase					       =					model(__UpperCamelCase				,			token_type_ids=__UpperCamelCase				,			head_mask=__UpperCamelCase  )
									_lowercase					       =					model(__UpperCamelCase				,			token_type_ids=__UpperCamelCase  )
									_lowercase					       =					model(__UpperCamelCase  )

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






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			*__UpperCamelCase  ):
									_lowercase					       =					OpenAIGPTLMHeadModel(__UpperCamelCase  )
									model.to(__UpperCamelCase  )
									model.eval()

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






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			*__UpperCamelCase  ):
									_lowercase					       =					OpenAIGPTDoubleHeadsModel(__UpperCamelCase  )
									model.to(__UpperCamelCase  )
									model.eval()

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






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			*__UpperCamelCase  ):
									_lowercase					       =					self.num_labels
									_lowercase					       =					OpenAIGPTForSequenceClassification(__UpperCamelCase  )
									model.to(__UpperCamelCase  )
									model.eval()

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






			def    UpperCamelCase_				(      self  ):
									_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    a_ (			_a					,			_a					,			_a					,			unittest.TestCase    ):
			a	:			Union[str, Any]					   =							(
			    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
			    if is_torch_available()
			    else ()
			)
			a	:			Union[str, Any]					   =							(
			    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
			)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
			a	:			Any					   =							(
			    {
			        '''feature-extraction''': OpenAIGPTModel,
			        '''text-classification''': OpenAIGPTForSequenceClassification,
			        '''text-generation''': OpenAIGPTLMHeadModel,
			        '''zero-shot''': OpenAIGPTForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									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				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase=False  ):
									_lowercase					       =					super()._prepare_for_class(__UpperCamelCase				,			__UpperCamelCase				,			return_labels=__UpperCamelCase  )

									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=__UpperCamelCase				,			)
																					_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=__UpperCamelCase				,			)
																					_lowercase					       =					torch.zeros(
																					    self.model_tester.batch_size				,			dtype=torch.long				,			device=__UpperCamelCase  )
									return inputs_dict






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					OpenAIGPTModelTester(self  )
									_lowercase					       =					ConfigTester(self				,			config_class=__UpperCamelCase				,			n_embd=37  )






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






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_openai_gpt_model(*__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_lm_head_model(*__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_double_lm_head_model(*__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__UpperCamelCase  )






			@slow
			def    UpperCamelCase_				(      self  ):
									for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_lowercase					       =					OpenAIGPTModel.from_pretrained(__UpperCamelCase  )
															self.assertIsNotNone(__UpperCamelCase  )







@require_torch
class    a_ (			unittest.TestCase    ):






			@slow
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt"""  )
									model.to(__UpperCamelCase  )
									_lowercase					       =					torch.tensor([[481, 4_735, 544]]				,			dtype=torch.long				,			device=__UpperCamelCase  )  # the president is
									_lowercase					       =					[
									    481,
									    4_735,
									    544,
									    246,
									    963,
									    870,
									    762,
									    239,
									    244,
									    40_477,
									    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

									_lowercase					       =					model.generate(__UpperCamelCase				,			do_sample=__UpperCamelCase  )
									self.assertListEqual(output_ids[0].tolist()				,			__UpperCamelCase  )

import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     np.ndarray:

						# prepare kernel
						# the kernel size have to be odd
						if (ksize % 2) == 0:
												_lowercase					       =					ksize + 1
						_lowercase					       =					np.zeros((ksize, ksize)		,   dtype=np.floataa       )

						# each value
						for y in range(SCREAMING_SNAKE_CASE_       ):
												for x in range(SCREAMING_SNAKE_CASE_       ):
																		# distance from center
																		_lowercase					       =					x - ksize // 2
																		_lowercase					       =					y - ksize // 2

																		# degree to radiant
																		_lowercase					       =					theta / 1_80 * np.pi
																		_lowercase					       =					np.cos(_theta       )
																		_lowercase					       =					np.sin(_theta       )

																		# get kernel x
																		_lowercase					       =					cos_theta * px + sin_theta * py

																		# get kernel y
																		_lowercase					       =					-sin_theta * px + cos_theta * py

																		# fill kernel
																		_lowercase					       =					np.exp(
																		    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)       ) * np.cos(2 * np.pi * _x / lambd + psi       )

						return gabor


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# read original image
		A		: Optional[Any]								=     imread('''../image_data/lena.jpg''')
		# turn image in gray scale value
		A		: Optional[int]								=     cvtColor(img, COLOR_BGR2GRAY)

		# Apply multiple Kernel to detect edges
		A		: Optional[int]								=     np.zeros(gray.shape[:2])
		for theta in [0, 30, 60, 90, 120, 150]:
				A		: Optional[int]								=     gabor_filter_kernel(10, 8, theta, 10, 0, 0)
				out += filteraD(gray, CV_8UC3, kernel_aa)
		A		: int								=     out / out.max() * 255
		A		: str								=     out.astype(np.uinta)

		imshow('''Original''', gray)
		imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

		waitKey(0)