



'''simple docstring'''

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

import black


a							   =				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.
a							   =				'    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    UpperCamelCase     (				self   :						List[str]			)			->					int:
										snake_case:    Dict      							=tempfile.mkdtemp()
										os.makedirs(os.path.join(self.transformer_dir      ,						'models/bert/'			)			)
										snake_case:    Optional[Any]      							=self.transformer_dir
										shutil.copy(
										    os.path.join(a_      ,						'src/transformers/models/bert/modeling_bert.py'			)      ,						os.path.join(self.transformer_dir      ,						'models/bert/modeling_bert.py'			)      ,						)


							def    UpperCamelCase     (				self   :						List[str]			)			->					Any:
										snake_case:    List[str]      							='src/transformers'
										shutil.rmtree(self.transformer_dir			)


							def    UpperCamelCase     (				self   :						Union[str, Any]      ,						a_   :						Union[str, Any]      ,						a_   :						Optional[int]      ,						a_   :						Optional[int]      ,						a_   :						str=None			)			->					str:
										snake_case:    List[Any]      							=comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
										if overwrite_result is not None:
													snake_case:    Tuple      							=comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
										snake_case:    int      							=black.Mode(target_versions={black.TargetVersion.PYaa}      ,						line_length=1_1_9			)
										snake_case:    Dict      							=black.format_str(a_      ,						mode=a_			)
										snake_case:    Optional[int]      							=os.path.join(self.transformer_dir      ,						'new_code.py'			)
										with open(a_      ,						'w'      ,						newline='\n'			) as f:
													f.write(a_			)
										if overwrite_result is None:
													self.assertTrue(len(check_copies.is_copy_consistent(a_			)			) == 0			)
										else:
													check_copies.is_copy_consistent(f.name      ,						overwrite=a_			)
													with open(a_      ,						'r'			) as f:
																self.assertTrue(f.read()      ,						a_			)


							def    UpperCamelCase     (				self   :						int			)			->					int:
										snake_case:    Optional[Any]      							=check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'			)
										self.assertEqual(a_      ,						a_			)


							def    UpperCamelCase     (				self   :						str			)			->					List[str]:
										# 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'      ,						a_      ,						)

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

										# Copy consistency with a really long name
										snake_case:    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'      ,						a_      ,						a_			)      ,						)

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




							def    UpperCamelCase     (				self   :						Any			)			->					Tuple:
										snake_case:    Tuple      							=check_copies.LOCALIZED_READMES['README_zh-hans.md']

										snake_case:    Optional[int]      							=(
										    '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:    Optional[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'
										)
										snake_case:    List[str]      							=(
										    '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:    Optional[Any]      							=check_copies.convert_to_localized_md(
										    a_      ,						a_      ,						localized_readme['format_model_list']			)

										self.assertFalse(a_			)
										self.assertEqual(a_      ,						a_			)

										snake_case							,  snake_case:    Union[str, Any]      							=check_copies.convert_to_localized_md(
										    a_      ,						a_      ,						localized_readme['format_model_list']			)

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

										snake_case:    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.'
										)
										snake_case:    Union[str, 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'
										)
										snake_case:    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'
										)

										snake_case							,  snake_case:    Any      							=check_copies.convert_to_localized_md(
										    a_      ,						a_      ,						localized_readme['format_model_list']			)

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









'''simple docstring'''

import qiskit







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					qiskit.result.counts.Counts:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=qiskit.Aer.get_backend('aer_simulator'					)

			# Create a Quantum Circuit acting on the q register
			snake_case:    Dict      							=qiskit.QuantumCircuit(__UpperCAmelCase				,							__UpperCAmelCase					)

			# Map the quantum measurement to the classical bits
			circuit.measure([0]				,							[0]					)

			# Execute the circuit on the simulator
			snake_case:    Dict      							=qiskit.execute(__UpperCAmelCase				,							__UpperCAmelCase				,							shots=10_00					)

			# Return the histogram data of the results of the experiment.
			return job.result().get_counts(__UpperCAmelCase					)


if __name__ == "__main__":
	print(F"""Total count for various states are: {single_qubit_measure(1, 1)}""")




