


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

import black


_lowercase       :      Any	=	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.
_lowercase       :      Any	=	"    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       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					A								=				tempfile.mkdtemp()
					os.makedirs(os.path.join(self.transformer_dir				,   """models/bert/"""   )   )
					A								=				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   )	->				Optional[Any]:
					A								=				"""src/transformers"""
					shutil.rmtree(self.transformer_dir   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__				,   a__=None   )	->				Optional[int]:
					A								=				comment + f'\nclass {class_name}(nn.Module):\n' + class_code
					if overwrite_result is not None:
						A								=				comment + f'\nclass {class_name}(nn.Module):\n' + overwrite_result
					A								=				black.Mode(target_versions={black.TargetVersion.PYaa}				,   line_length=119   )
					A								=				black.format_str(a__				,   mode=a__   )
					A								=				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   )	->				Optional[Any]:
					A								=				check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""   )
					self.assertEqual(a__				,   a__   )

				def      _UpperCAmelCase					(						self   )	->				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
					A								=				"""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   )	->				List[Any]:
					A								=				check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

					A								=				(
					    """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."""
					)
					A								=				(
					    """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"""
					)
					A								=				(
					    """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"""
					)

					A					,			A								=				check_copies.convert_to_localized_md(
					    a__				,   a__				,   localized_readme["""format_model_list"""]   )

					self.assertFalse(a__   )
					self.assertEqual(a__				,   a__   )

					A					,			A								=				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__   )

					A								=				(
					    """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."""
					)
					A								=				(
					    """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"""
					)
					A								=				(
					    """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"""
					)

					A					,			A								=				check_copies.convert_to_localized_md(
					    a__				,   a__				,   localized_readme["""format_model_list"""]   )

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







import qiskit





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 2						) ->   qiskit.result.counts.Counts:





	"""simple docstring"""







	A								=				qubits

	# Using Aer's simulator
	A								=				qiskit.Aer.get_backend("""aer_simulator"""						)

	# Creating a Quantum Circuit acting on the q register
	A								=				qiskit.QuantumCircuit(UpperCamelCase__							,					UpperCamelCase__						)

	# Adding a H gate on qubit 0 (now q0 in superposition)
	circuit.h(0						)

	for i in range(1							,					UpperCamelCase__						):
		# Adding CX (CNOT) gate
		circuit.cx(i - 1							,					UpperCamelCase__						)

	# Mapping the quantum measurement to the classical bits
	circuit.measure(list(range(UpperCamelCase__						)						)							,					list(range(UpperCamelCase__						)						)						)

	# Now measuring any one qubit would affect other qubits to collapse
	# their super position and have same state as the measured one.

	# Executing the circuit on the simulator
	A								=				qiskit.execute(UpperCamelCase__							,					UpperCamelCase__							,					shots=10_00						)

	return job.result().get_counts(UpperCamelCase__						)


if __name__ == "__main__":
				print(f'''Total count for various states are: {quantum_entanglement(3)}''')



