



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

import black


_snake_case	=						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.
_snake_case	=						"    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 						UpperCAmelCase_					( unittest.TestCase):







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					tempfile.mkdtemp()
														os.makedirs(os.path.join(self.transformer_dir,    "models/bert/"))
														_lowerCAmelCase  :							Tuple				     =					self.transformer_dir
														shutil.copy(
														    os.path.join(__a,    "src/transformers/models/bert/modeling_bert.py"),    os.path.join(self.transformer_dir,    "models/bert/modeling_bert.py"),    )







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					"src/transformers"
														shutil.rmtree(self.transformer_dir)







							def      snake_case__   (     self,    __a,    __a,    __a,    __a=None):

														'''simple docstring'''

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead")
														self.assertEqual(__a,    __a)







							def      snake_case__   (     self):

														'''simple docstring'''

														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",    __a,    )

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

														# Copy consistency with a really long name
														_lowerCAmelCase  :							int				     =					"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",    __a,    __a),    )

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							str				     =					check_copies.LOCALIZED_READMES["README_zh-hans.md"]

														_lowerCAmelCase  :							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.\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."
														)
														_lowerCAmelCase  :							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"
														)
														_lowerCAmelCase  :							Tuple				     =					(
														    "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"
														)

														_lowerCAmelCase     ,					_lowerCAmelCase  :							List[Any]				     =					check_copies.convert_to_localized_md(
														    __a,    __a,    localized_readme["format_model_list"])

														self.assertFalse(__a)
														self.assertEqual(__a,    __a)

														_lowerCAmelCase     ,					_lowerCAmelCase  :							str				     =					check_copies.convert_to_localized_md(
														    __a,    __a,    localized_readme["format_model_list"])

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

														_lowerCAmelCase  :							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."
														)
														_lowerCAmelCase  :							Optional[Any]				     =					(
														    "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"
														)
														_lowerCAmelCase  :							Dict				     =					(
														    "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"
														)

														_lowerCAmelCase     ,					_lowerCAmelCase  :							Tuple				     =					check_copies.convert_to_localized_md(
														    __a,    __a,    localized_readme["format_model_list"])

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





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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							if (ksize % 2) == 0:
														_lowerCAmelCase  :							str				     =					ksize + 1
							_lowerCAmelCase  :							List[str]				     =					np.zeros((ksize, ksize)       ,				dtype=np.floataa						)

							# each value
							for y in range(_lowerCamelCase						):
														for x in range(_lowerCamelCase						):
																					# distance from center
																					_lowerCAmelCase  :							int				     =					x - ksize // 2
																					_lowerCAmelCase  :							Dict				     =					y - ksize // 2

																					# degree to radiant
																					_lowerCAmelCase  :							List[Any]				     =					theta / 180 * np.pi
																					_lowerCAmelCase  :							int				     =					np.cos(_theta						)
																					_lowerCAmelCase  :							Optional[int]				     =					np.sin(_theta						)

																					# get kernel x
																					_lowerCAmelCase  :							int				     =					cos_theta * px + sin_theta * py

																					# get kernel y
																					_lowerCAmelCase  :							str				     =					-sin_theta * px + cos_theta * py

																					# fill kernel
																					_lowerCAmelCase  :							Union[str, Any]				     =					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
						_snake_case	=						imread("../image_data/lena.jpg")
						# turn image in gray scale value
						_snake_case	=						cvtColor(img, COLOR_BGR2GRAY)

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

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

						waitKey(0)
