
'''simple docstring'''





import gc
import unittest

from transformers import CTRLConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
							    CTRLForSequenceClassification,
							    CTRLLMHeadModel,
							    CTRLModel,
							)







class        lowercase	:




							def __init__(       self     ,A__     ,A__=1_4     ,A__=7     ,A__=True     ,A__=True     ,A__=True     ,A__=True     ,A__=True     ,A__=9_9     ,A__=3_2     ,A__=5     ,A__=4     ,A__=3_7     ,A__="gelu"     ,A__=0.1     ,A__=0.1     ,A__=5_1_2     ,A__=1_6     ,A__=2     ,A__=0.02     ,A__=3     ,A__=4     ,A__=None     ,):
												lowercase					=		parent
												lowercase					=		batch_size
												lowercase					=		seq_length
												lowercase					=		is_training
												lowercase					=		use_token_type_ids
												lowercase					=		use_input_mask
												lowercase					=		use_labels
												lowercase					=		use_mc_token_ids
												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 				A__   (       self):
												lowercase					=		ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size)

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

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

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

												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					=		self.get_config()

												lowercase					=		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):
												return CTRLConfig(
												    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     ,)




							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,*A__):
												lowercase					=		CTRLModel(config=A__)
												model.to(A__)
												model.eval()

												model(A__     ,token_type_ids=A__     ,head_mask=A__)
												model(A__     ,token_type_ids=A__)
												lowercase					=		model(A__)
												self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size))
												self.parent.assertEqual(len(result.past_key_values)     ,config.n_layer)




							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,*A__):
												lowercase					=		CTRLLMHeadModel(A__)
												model.to(A__)
												model.eval()

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




							def 				A__   (       self):
												lowercase					=		self.prepare_config_and_inputs()

												(
												    (
												    lowercase		
												)			,		(
												    lowercase		
												)			,		(
												    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





							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,*A__):
												lowercase					=		self.num_labels
												lowercase					=		CTRLForSequenceClassification(A__)
												model.to(A__)
												model.eval()
												lowercase					=		ids_tensor([self.batch_size]     ,self.type_sequence_label_size)
												lowercase					=		model(A__     ,token_type_ids=A__     ,labels=A__)
												self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels))







@require_torch
class        lowercase	(      SCREAMING_SNAKE_CASE__ ,						SCREAMING_SNAKE_CASE__ ,						SCREAMING_SNAKE_CASE__ ,						unittest.TestCase	):
							lowercase_ : Optional[int]       =(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
							lowercase_ : List[Any]       =(CTRLLMHeadModel,) if is_torch_available() else ()
							lowercase_ : Union[str, Any]       =(
							    {
							        '''feature-extraction''': CTRLModel,
							        '''text-classification''': CTRLForSequenceClassification,
							        '''text-generation''': CTRLLMHeadModel,
							        '''zero-shot''': CTRLForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowercase_ : Optional[Any]       =True
							lowercase_ : List[str]       =False
							lowercase_ : Optional[int]       =False




							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__):
												if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																	# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																	# `CTRLConfig` 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):
												lowercase					=		CTRLModelTester(self)
												lowercase					=		ConfigTester(self     ,config_class=A__     ,n_embd=3_7)




							def 				A__   (       self):
												super().tearDown()
												# clean-up as much as possible GPU memory occupied by PyTorch
												gc.collect()
												torch.cuda.empty_cache()




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




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_ctrl_model(*A__)




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_lm_head_model(*A__)




							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
							def 				A__   (       self):
												pass




							@slow
							def 				A__   (       self):
												for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase					=		CTRLModel.from_pretrained(A__)
																	self.assertIsNotNone(A__)





							@unittest.skip('''The model doesn\'t support left padding''')  # and it's not used enough to be worth fixing :)
							def 				A__   (       self):
												pass







@require_torch
class        lowercase	(      unittest.TestCase	):




							def 				A__   (       self):
												super().tearDown()
												# clean-up as much as possible GPU memory occupied by PyTorch
												gc.collect()
												torch.cuda.empty_cache()





							@slow
							def 				A__   (       self):
												lowercase					=		CTRLLMHeadModel.from_pretrained('''ctrl''')
												model.to(A__)
												lowercase					=		torch.tensor(
												    [[1_1_8_5_9, 0, 1_6_1_1, 8]]     ,dtype=torch.long     ,device=A__)  # Legal the president is
												lowercase					=		[
												    1_1_8_5_9,
												    0,
												    1_6_1_1,
												    8,
												    5,
												    1_5_0,
												    2_6_4_4_9,
												    2,
												    1_9,
												    3_4_8,
												    4_6_9,
												    3,
												    2_5_9_5,
												    4_8,
												    2_0_7_4_0,
												    2_4_6_5_3_3,
												    2_4_6_5_3_3,
												    1_9,
												    3_0,
												    5,
												]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

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






from pathlib import Path

import numpy as np
from PIL import Image



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase			,		lowercase					=		rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
					return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return (gray > 127) & (gray <= 255)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		np.zeros_like(lowerCAmelCase__	)
					lowercase					=		np.zeros(
					    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

					# Copy image to padded image
					lowercase					=		image

					# Iterate over image & apply kernel
					for x in range(image.shape[1]	):
										for y in range(image.shape[0]	):
															lowercase					=		(
															    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
															).sum()
															lowercase					=		int(summation > 0	)
					return output


if __name__ == "__main__":
							# read original image
							lowercase__	:str										=					Path(__file__).resolve().parent / "image_data" / "lena.jpg"
							lowercase__	:List[str]										=					np.array(Image.open(lena_path))
							# kernel to be applied
							lowercase__	:Union[str, Any]										=					np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
							lowercase__	:Optional[int]										=					dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
							# Save the output image
							lowercase__	:str										=					Image.fromarray(output).convert("RGB")
							pil_img.save("result_dilation.png")




