




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):
        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  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    tempfile.mkdtemp()
											os.makedirs(os.path.join(self.transformer_dir		,					'''models/bert/'''		)		)
											SCREAMING_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     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    '''src/transformers'''
											shutil.rmtree(self.transformer_dir		)


				def     _A							(							self:   Dict		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   str=None		):
											SCREAMING_SNAKE_CASE_   	=    comment + f"\nclass {class_name}(nn.Module):\n" + class_code
											if overwrite_result is not None:
																		SCREAMING_SNAKE_CASE_   	=    comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
											SCREAMING_SNAKE_CASE_   	=    black.Mode(target_versions={black.TargetVersion.PYaa}		,					line_length=1_19		)
											SCREAMING_SNAKE_CASE_   	=    black.format_str(_lowerCamelCase		,					mode=_lowerCamelCase		)
											SCREAMING_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     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''		)
											self.assertEqual(_lowerCamelCase		,					_lowerCamelCase		)


				def     _A							(							self:   Union[str, Any]		):
											# 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'''		,					_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
											SCREAMING_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     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

											SCREAMING_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.'''
											)
											SCREAMING_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'''
											)
											SCREAMING_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'''
											)

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

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

											SCREAMING_SNAKE_CASE_	,	SCREAMING_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		)

											SCREAMING_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.'''
											)
											SCREAMING_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'''
											)
											SCREAMING_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'''
											)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_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		)








from __future__ import annotations

__SCREAMING_SNAKE_CASE 						={
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}






class  __magic_name__						:
				'''simple docstring'''







				def __init__(							self:   List[Any]		,					_lowerCamelCase:   dict[str, list[str]]		,					_lowerCamelCase:   str		):
											SCREAMING_SNAKE_CASE_   	=    graph
											# mapping node to its parent in resulting breadth first tree
											SCREAMING_SNAKE_CASE_   	=    {}
											SCREAMING_SNAKE_CASE_   	=    source_vertex


				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    {self.source_vertex}
											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    [self.source_vertex]  # first in first out queue

											while queue:
																		SCREAMING_SNAKE_CASE_   	=    queue.pop(0		)
																		for adjacent_vertex in self.graph[vertex]:
																									if adjacent_vertex not in visited:
																																visited.add(_lowerCamelCase		)
																																SCREAMING_SNAKE_CASE_   	=    vertex
																																queue.append(_lowerCamelCase		)





				def     _A							(							self:   List[str]		,					_lowerCamelCase:   str		):
											if target_vertex == self.source_vertex:
																		return self.source_vertex

											SCREAMING_SNAKE_CASE_   	=    self.parent.get(_lowerCamelCase		)
											if target_vertex_parent is None:
																		SCREAMING_SNAKE_CASE_   	=    (
																		    f"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
																		)
																		raise ValueError(_lowerCamelCase		)

											return self.shortest_path(_lowerCamelCase		) + f"->{target_vertex}"


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE 						=Graph(graph, """G""")
			g.breath_first_search()
			print(g.shortest_path("""D"""))
			print(g.shortest_path("""G"""))
			print(g.shortest_path("""Foo"""))


