





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

import black


_UpperCAmelCase				:       Optional[int]	   =				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.
_UpperCAmelCase				:       Union[str, Any]	   =				"""    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""

class    lowerCAmelCase							(					unittest.TestCase			):
 def      A_	(      self  :      str	)			->	Optional[Any]:
     lowerCamelCase__    :      Any					 =		tempfile.mkdtemp()
     os.makedirs(os.path.join(self.transformer_dir			,     'models/bert/'	)	)
     lowerCamelCase__    :      str					 =		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      A_	(      self  :      Optional[int]	)			->	Dict:
     lowerCamelCase__    :      Optional[Any]					 =		'src/transformers'
     shutil.rmtree(self.transformer_dir	)
 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      Dict			,     UpperCAmelCase  :      Tuple=None	)			->	List[Any]:
     lowerCamelCase__    :      List[str]					 =		comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
     if overwrite_result is not None:
         lowerCamelCase__    :      Tuple					 =		comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
     lowerCamelCase__    :      int					 =		black.Mode(target_versions={black.TargetVersion.PYaa}			,     line_length=119	)
     lowerCamelCase__    :      int					 =		black.format_str(UpperCAmelCase			,     mode=UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		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      A_	(      self  :      Optional[int]	)			->	str:
     lowerCamelCase__    :      Optional[Any]					 =		check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'	)
     self.assertEqual(UpperCAmelCase			,     UpperCAmelCase	)
 def      A_	(      self  :      Optional[Any]	)			->	Dict:
     # 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
     lowerCamelCase__    :      Tuple					 =		'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      A_	(      self  :      Any	)			->	Optional[Any]:
     lowerCamelCase__    :      Tuple					 =		check_copies.LOCALIZED_READMES['README_zh-hans.md']

     lowerCamelCase__    :      Optional[Any]					 =		(
         '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__    :      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'
     )

     lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		check_copies.convert_to_localized_md(
         UpperCAmelCase			,     UpperCAmelCase			,     localized_readme['format_model_list']	)

     self.assertFalse(UpperCAmelCase	)
     self.assertEqual(UpperCAmelCase			,     UpperCAmelCase	)

     lowerCamelCase__ ,							lowerCamelCase__    :      Tuple					 =		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	)

     lowerCamelCase__    :      Dict					 =		(
         '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__    :      List[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__    :      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。\n'
     )

     lowerCamelCase__ ,							lowerCamelCase__    :      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'''
import numpy as np
from PIL import Image


def        _lowerCAmelCase					(						_UpperCamelCase						:     np.ndarray							, _UpperCamelCase						:     int							, _UpperCamelCase						:     int				)      ->			np.ndarray:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =np.array(_UpperCamelCase				)
     if arr.shape[0] != arr.shape[1]:
          raise ValueError('The input array is not a square matrix'				)
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0

     # compute the shape of the output matrix
     _SCREAMING_SNAKE_CASE							    =(arr.shape[0] - size) // stride + 1
     # initialize the output matrix with zeros of shape maxpool_shape
     _SCREAMING_SNAKE_CASE							    =np.zeros((maxpool_shape, maxpool_shape)				)

     while i < arr.shape[0]:
          if i + size > arr.shape[0]:
               # if the end of the matrix is reached, break
               break
          while j < arr.shape[1]:
               # if the end of the matrix is reached, break
               if j + size > arr.shape[1]:
                    break
               # compute the maximum of the pooling matrix
               _SCREAMING_SNAKE_CASE							    =np.max(arr[i : i + size, j : j + size]				)
               # shift the pooling matrix by stride of column pixels
               j += stride
               mat_j += 1

          # shift the pooling matrix by stride of row pixels
          i += stride
          mat_i += 1

          # reset the column index to 0
          _SCREAMING_SNAKE_CASE							    =0
          _SCREAMING_SNAKE_CASE							    =0

     return updated_arr


def        _lowerCAmelCase					(						_UpperCamelCase						:     np.ndarray							, _UpperCamelCase						:     int							, _UpperCamelCase						:     int				)      ->			np.ndarray:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =np.array(_UpperCamelCase				)
     if arr.shape[0] != arr.shape[1]:
          raise ValueError('The input array is not a square matrix'				)
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0

     # compute the shape of the output matrix
     _SCREAMING_SNAKE_CASE							    =(arr.shape[0] - size) // stride + 1
     # initialize the output matrix with zeros of shape avgpool_shape
     _SCREAMING_SNAKE_CASE							    =np.zeros((avgpool_shape, avgpool_shape)				)

     while i < arr.shape[0]:
          # if the end of the matrix is reached, break
          if i + size > arr.shape[0]:
               break
          while j < arr.shape[1]:
               # if the end of the matrix is reached, break
               if j + size > arr.shape[1]:
                    break
               # compute the average of the pooling matrix
               _SCREAMING_SNAKE_CASE							    =int(np.average(arr[i : i + size, j : j + size]				)				)
               # shift the pooling matrix by stride of column pixels
               j += stride
               mat_j += 1

          # shift the pooling matrix by stride of row pixels
          i += stride
          mat_i += 1
          # reset the column index to 0
          _SCREAMING_SNAKE_CASE							    =0
          _SCREAMING_SNAKE_CASE							    =0

     return updated_arr


# Main Function
if __name__ == "__main__":
     from doctest import testmod

     testmod(name="avgpooling", verbose=True)

     # Loading the image
     lowerCamelCase :      Optional[Any]					 =     Image.open("path_to_image")

     # Converting the image to numpy array and maxpooling, displaying the result
     # Ensure that the image is a square matrix

     Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

     # Converting the image to numpy array and averagepooling, displaying the result
     # Ensure that the image is a square matrix

     Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
