"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest

import black


SCREAMING_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.
SCREAMING_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   lowerCAmelCase_    (			unittest.TestCase					):



    """simple docstring"""

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			tempfile.mkdtemp()
          os.makedirs(os.path.join(self.transformer_dir		,    'models/bert/'    )    )
          snake_case								=			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 		snake_case (				self    ):






          """simple docstring"""

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

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase=None    ):






          """simple docstring"""

          snake_case								=			comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
          if overwrite_result is not None:
                snake_case								=			comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
          snake_case								=			black.Mode(target_versions={black.TargetVersion.PYaa}		,    line_length=1_19    )
          snake_case								=			black.format_str(_lowerCamelCase		,    mode=_lowerCamelCase    )
          snake_case								=			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 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'    )
          self.assertEqual(_lowerCamelCase		,    _lowerCamelCase    )

    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'		,    _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
          snake_case								=			'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 		snake_case (				self    ):






          """simple docstring"""

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

          snake_case								=			(
              '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.'
          )
          snake_case								=			(
              '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'
          )
          snake_case								=			(
              '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'
          )

          snake_case					,snake_case								=			check_copies.convert_to_localized_md(
              _lowerCamelCase		,    _lowerCamelCase		,    localized_readme['format_model_list']    )

          self.assertFalse(_lowerCamelCase    )
          self.assertEqual(_lowerCamelCase		,    _lowerCamelCase    )

          snake_case					,snake_case								=			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    )

          snake_case								=			(
              '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.'
          )
          snake_case								=			(
              '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'
          )
          snake_case								=			(
              '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'
          )

          snake_case					,snake_case								=			check_copies.convert_to_localized_md(
              _lowerCamelCase		,    _lowerCamelCase		,    localized_readme['format_model_list']    )

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



"""simple docstring"""
import math





def    lowerCAmelCase__    (			_UpperCamelCase       :				float ,			_UpperCamelCase       :				float					)					->				float:





						"""simple docstring"""






						if initial_intensity < 0:
												raise ValueError('The value of intensity cannot be negative'					)
												# handling of negative values of initial intensity
						if angle < 0 or angle > 3_6_0:
												raise ValueError('In Malus Law, the angle is in the range 0-360 degrees'					)
												# handling of values out of allowed range
						return initial_intensity * (math.cos(math.radians(_UpperCamelCase					)					) ** 2)


if __name__ == "__main__":
					import doctest

					doctest.testmod(name="malus_law")


