





"""simple docstring"""






import os
import re
import shutil
import sys
import tempfile
import unittest

import black


A	     =					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
A	     =					"    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"




class 			__lowercase     (     unittest.TestCase							):







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[Any]         =					tempfile.mkdtemp()
									os.makedirs(os.path.join(self.transformer_dir		,     '''models/bert/'''       )       )
									__a	:  Optional[Any]         =					self.transformer_dir
									shutil.copy(
									    os.path.join(_UpperCAmelCase		,     '''src/transformers/models/bert/modeling_bert.py'''       )		,     os.path.join(self.transformer_dir		,     '''models/bert/modeling_bert.py'''       )		,     )







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[Any]         =					'''src/transformers'''
									shutil.rmtree(self.transformer_dir       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase=None       ):
									__a	:  Optional[Any]         =					comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
									if overwrite_result is not None:
													__a	:  str         =					comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
									__a	:  Dict         =					black.Mode(target_versions={black.TargetVersion.PYaa}		,     line_length=119       )
									__a	:  Tuple         =					black.format_str(_UpperCAmelCase		,     mode=_UpperCAmelCase       )
									__a	:  List[Any]         =					os.path.join(self.transformer_dir		,     '''new_code.py'''       )
									with open(_UpperCAmelCase		,     '''w'''		,     newline='''\n'''       ) as f:
													f.write(_UpperCAmelCase       )
									if overwrite_result is None:
													self.assertTrue(len(check_copies.is_copy_consistent(_UpperCAmelCase       )       ) == 0       )
									else:
													check_copies.is_copy_consistent(f.name		,     overwrite=_UpperCAmelCase       )
													with open(_UpperCAmelCase		,     '''r'''       ) as f:
																	self.assertTrue(f.read()		,     _UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Any         =					check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''       )
									self.assertEqual(_UpperCAmelCase		,     _UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									# Base copy consistency
									self.check_copy_consistency(
									    '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'''		,     '''BertLMPredictionHead'''		,     REFERENCE_CODE + '''\n'''		,     )

									# With no empty line at the end
									self.check_copy_consistency(
									    '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'''		,     '''BertLMPredictionHead'''		,     _UpperCAmelCase		,     )

									# Copy consistency with rename
									self.check_copy_consistency(
									    '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'''		,     '''TestModelLMPredictionHead'''		,     re.sub('''Bert'''		,     '''TestModel'''		,     _UpperCAmelCase       )		,     )

									# Copy consistency with a really long name
									__a	:  Union[str, Any]         =					'''TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
									self.check_copy_consistency(
									    f"""# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"""		,     f"""{long_class_name}LMPredictionHead"""		,     re.sub('''Bert'''		,     _UpperCAmelCase		,     _UpperCAmelCase       )		,     )

									# Copy consistency with overwrite
									self.check_copy_consistency(
									    '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'''		,     '''TestModelLMPredictionHead'''		,     _UpperCAmelCase		,     overwrite_result=re.sub('''Bert'''		,     '''TestModel'''		,     _UpperCAmelCase       )		,     )







					def 	_lowerCamelCase						(  self       ):
									__a	:  str         =					check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

									__a	:  Optional[int]         =					(
									    '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'''
									    ''' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'''
									    ''' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'''
									    ''' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'''
									    ''' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'''
									    ''' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'''
									    ''' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'''
									    ''' method has been applied to compress GPT2 into'''
									    ''' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'''
									    ''' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'''
									    ''' Multilingual BERT into'''
									    ''' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'''
									    ''' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'''
									    ''' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'''
									    ''' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'''
									    ''' Luong, Quoc V. Le, Christopher D. Manning.'''
									)
									__a	:  Union[str, Any]         =					(
									    '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
									    ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
									    ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
									    ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
									)
									__a	:  Optional[int]         =					(
									    '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
									    ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
									    ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
									    ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'''
									    ''' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'''
									    ''' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'''
									    ''' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'''
									    ''' method has been applied to compress GPT2 into'''
									    ''' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'''
									    ''' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'''
									    ''' Multilingual BERT into'''
									    ''' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'''
									    ''' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'''
									    ''' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'''
									    ''' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'''
									    ''' Christopher D. Manning 发布。\n'''
									)

									__a    ,  __a	:  Union[str, Any]         =					check_copies.convert_to_localized_md(
									    _UpperCAmelCase		,     _UpperCAmelCase		,     localized_readme['''format_model_list''']       )

									self.assertFalse(_UpperCAmelCase       )
									self.assertEqual(_UpperCAmelCase		,     _UpperCAmelCase       )

									__a    ,  __a	:  Optional[int]         =					check_copies.convert_to_localized_md(
									    _UpperCAmelCase		,     _UpperCAmelCase		,     localized_readme['''format_model_list''']       )

									# Check whether the number of models is equal to README.md after conversion.
									self.assertTrue(_UpperCAmelCase       )

									__a	:  List[str]         =					(
									    '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'''
									    ''' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'''
									    ''' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'''
									    ''' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'''
									)
									__a	:  str         =					(
									    '''1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'''
									    ''' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
									    ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
									    ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
									)
									__a	:  Any         =					(
									    '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
									    ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
									    ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
									    ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
									)

									__a    ,  __a	:  List[Any]         =					check_copies.convert_to_localized_md(
									    _UpperCAmelCase		,     _UpperCAmelCase		,     localized_readme['''format_model_list''']       )

									# Check if the model link is synchronized.
									self.assertEqual(_UpperCAmelCase		,     _UpperCAmelCase       )






"""simple docstring"""






from pathlib import Path

import numpy as np
from PIL import Image

def       __A (     a_  :np.ndarray)						->						np.ndarray:
				__a    ,  __a    ,  __a	:  Union[str, Any]         =					rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
				return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b

def       __A (     a_  :np.ndarray)						->						np.ndarray:
				return (gray > 1_27) & (gray <= 2_55)

def       __A (     a_  :np.ndarray				,    a_  :np.ndarray)						->						np.ndarray:
				__a	:  Optional[int]         =					np.zeros_like(a_)
				__a	:  Dict         =					np.zeros(
				    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1))

				# Copy image to padded image
				__a	:  int         =					image

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


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