

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

import black


lowercase_									=   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.
lowercase_									=   '    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  __lowerCAmelCase     (					unittest.TestCase							):




  def   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =tempfile.mkdtemp()
        os.makedirs(os.path.join(self.transformer_dir							,   'models/bert/'					)					)
        _lowercase	       =self.transformer_dir
        shutil.copy(
            os.path.join(lowerCAmelCase							,   'src/transformers/models/bert/modeling_bert.py'					)							,   os.path.join(self.transformer_dir							,   'models/bert/modeling_bert.py'					)							,   )




  def   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






        _lowercase	       ='src/transformers'
        shutil.rmtree(self.transformer_dir					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase=None					)  -> int:
        '''simple docstring'''






        _lowercase	       =comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
        if overwrite_result is not None:
              _lowercase	       =comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
        _lowercase	       =black.Mode(target_versions={black.TargetVersion.PYaa}							,   line_length=119					)
        _lowercase	       =black.format_str(lowerCAmelCase							,   mode=lowerCAmelCase					)
        _lowercase	       =os.path.join(self.transformer_dir							,   'new_code.py'					)
        with open(lowerCAmelCase							,   'w'							,   newline='\n'					) as f:
              f.write(lowerCAmelCase					)
        if overwrite_result is None:
              self.assertTrue(len(check_copies.is_copy_consistent(lowerCAmelCase					)					) == 0					)
        else:
              check_copies.is_copy_consistent(f.name							,   overwrite=lowerCAmelCase					)
              with open(lowerCAmelCase							,   'r'					) as f:
                    self.assertTrue(f.read()							,   lowerCAmelCase					)




  def   A__     (				self					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'					)
        self.assertEqual(lowerCAmelCase							,   lowerCAmelCase					)




  def   A__     (				self					)  -> Any:
        '''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'							,   lowerCAmelCase							,   )

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

        # Copy consistency with a really long name
        _lowercase	       ='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'							,   lowerCAmelCase							,   lowerCAmelCase					)							,   )

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




  def   A__     (				self					)  -> Any:
        '''simple docstring'''






        _lowercase	       =check_copies.LOCALIZED_READMES['README_zh-hans.md']

        _lowercase	       =(
            '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.'
        )
        _lowercase	       =(
            '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'
        )
        _lowercase	       =(
            '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'
        )

        _lowercase       ,       _lowercase	       =check_copies.convert_to_localized_md(
            lowerCAmelCase							,   lowerCAmelCase							,   localized_readme['format_model_list']					)

        self.assertFalse(lowerCAmelCase					)
        self.assertEqual(lowerCAmelCase							,   lowerCAmelCase					)

        _lowercase       ,       _lowercase	       =check_copies.convert_to_localized_md(
            lowerCAmelCase							,   lowerCAmelCase							,   localized_readme['format_model_list']					)

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

        _lowercase	       =(
            '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.'
        )
        _lowercase	       =(
            '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'
        )
        _lowercase	       =(
            '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'
        )

        _lowercase       ,       _lowercase	       =check_copies.convert_to_localized_md(
            lowerCAmelCase							,   lowerCAmelCase							,   localized_readme['format_model_list']					)

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





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def      a      (			A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int     )    ->      np.ndarray:





      """simple docstring"""



      if (ksize % 2) == 0:
            _lowercase	       =ksize + 1
      _lowercase	       =np.zeros((ksize, ksize)      ,   dtype=np.floataa     )

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

                  # degree to radiant
                  _lowercase	       =theta / 180 * 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
  lowercase_									=   imread('../image_data/lena.jpg')
  # turn image in gray scale value
  lowercase_									=   cvtColor(img, COLOR_BGR2GRAY)

  # Apply multiple Kernel to detect edges
  lowercase_									=   np.zeros(gray.shape[:2])
  for theta in [0, 3_0, 6_0, 9_0, 1_2_0, 1_5_0]:
    lowercase_									=   gabor_filter_kernel(1_0, 8, theta, 1_0, 0, 0)
    out += filteraD(gray, CV_8UC3, kernel_aa)
  lowercase_									=   out / out.max() * 2_5_5
  lowercase_									=   out.astype(np.uinta)

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

  waitKey(0)


