"""simple docstring"""




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

import black


__lowerCAmelCase     :				List[str]    				=    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.
__lowerCAmelCase     :				List[str]    				=    "    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    a_   (  unittest.TestCase      ):




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							lowerCAmelCase__   = tempfile.mkdtemp()
							os.makedirs(os.path.join(self.transformer_dir	,    """models/bert/"""  )  )
							lowerCAmelCase__   = self.transformer_dir
							shutil.copy(
							    os.path.join(snake_case__	,    """src/transformers/models/bert/modeling_bert.py"""  )	,    os.path.join(self.transformer_dir	,    """models/bert/modeling_bert.py"""  )	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							lowerCAmelCase__   = """src/transformers"""
							shutil.rmtree(self.transformer_dir  )




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




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""  )
							self.assertEqual(snake_case__	,    snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							# 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"""	,    snake_case__	,    )

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

							# Copy consistency with a really long name
							lowerCAmelCase__   = """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"""	,    snake_case__	,    snake_case__  )	,    )

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

							lowerCAmelCase__   = (
							    """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__   = (
							    """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__   = (
							    """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__   = check_copies.convert_to_localized_md(
							    snake_case__	,    snake_case__	,    localized_readme["""format_model_list"""]  )

							self.assertFalse(snake_case__  )
							self.assertEqual(snake_case__	,    snake_case__  )

							lowerCAmelCase__ ,       lowerCAmelCase__   = check_copies.convert_to_localized_md(
							    snake_case__	,    snake_case__	,    localized_readme["""format_model_list"""]  )

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

							lowerCAmelCase__   = (
							    """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__   = (
							    """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__   = (
							    """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__   = check_copies.convert_to_localized_md(
							    snake_case__	,    snake_case__	,    localized_readme["""format_model_list"""]  )

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





"""simple docstring"""




from itertools import permutations







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		if num[3] % 2 != 0:
				return False

		if (num[2] + num[3] + num[4]) % 3 != 0:
				return False

		if num[5] % 5 != 0:
				return False

		lowerCAmelCase__   = [7, 11, 13, 17]
		for i, test in enumerate(lowerCamelCase__  ):
				if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
						return False
		return True







def 				_UpperCAmelCase		(							lowerCamelCase__ = 10  ):





		"""simple docstring"""




		return sum(
		    int("""""".join(map(lowerCamelCase__      ,	lowerCamelCase__  )  )  )
		    for num in permutations(range(lowerCamelCase__  )  )
		    if is_substring_divisible(lowerCamelCase__  )  )


if __name__ == "__main__":
			print(F"{solution() = }")




