


'''simple docstring'''






import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class 						__UpperCamelCase	:





							def 		a__   (  self		:Optional[int]  ,_UpperCamelCase		:Dict  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:Tuple			):
									return None





class 						__UpperCamelCase	:





							def 		a__   (  self		:Optional[int]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:Tuple			):
									return None





class 						__UpperCamelCase	(						unittest.TestCase							):
							lowercase			:		str   		=  [
							    # (model_name, model_kwargs)
							    ('bert-base-cased', {}),
							    ('gpt2', {'use_cache': False}),  # We don't support exporting GPT2 past keys anymore
							]





							@require_tf
							@slow
							def 		a__   (  self		:int			):
									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(_UpperCamelCase  ,"""tf"""  ,1_2  ,**_UpperCamelCase			)





							@require_torch
							@slow
							def 		a__   (  self		:Union[str, Any]			):
									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(_UpperCamelCase  ,"""pt"""  ,1_2  ,**_UpperCamelCase			)





							@require_torch
							@slow
							def 		a__   (  self		:int			):
									from transformers import BertModel

									snake_case_		:							Optional[Any]			       =						["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
									with NamedTemporaryFile(mode="""w+t"""			) as vocab_file:
											vocab_file.write("""\n""".join(_UpperCamelCase			)			)
											vocab_file.flush()
											snake_case_		:							List[Any]			       =						BertTokenizerFast(vocab_file.name			)

									with TemporaryDirectory() as bert_save_dir:
											snake_case_		:							int			       =						BertModel(BertConfig(vocab_size=len(_UpperCamelCase			)			)			)
											model.save_pretrained(_UpperCamelCase			)
											self._test_export(_UpperCamelCase  ,"""pt"""  ,1_2  ,_UpperCamelCase			)





							@require_tf
							@slow
							def 		a__   (  self		:str			):
									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											snake_case_		:							Optional[Any]			       =						self._test_export(_UpperCamelCase  ,"""tf"""  ,1_2  ,**_UpperCamelCase			)
											snake_case_		:							Any			       =						quantize(Path(_UpperCamelCase			)			)

											# Ensure the actual quantized model is not bigger than the original one
											if quantized_path.stat().st_size >= Path(_UpperCamelCase			).stat().st_size:
													self.fail("""Quantized model is bigger than initial ONNX model"""			)





							@require_torch
							@slow
							def 		a__   (  self		:List[str]			):
									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											snake_case_		:							List[str]			       =						self._test_export(_UpperCamelCase  ,"""pt"""  ,1_2  ,**_UpperCamelCase			)
											snake_case_		:							Tuple			       =						quantize(_UpperCamelCase			)

											# Ensure the actual quantized model is not bigger than the original one
											if quantized_path.stat().st_size >= Path(_UpperCamelCase			).stat().st_size:
													self.fail("""Quantized model is bigger than initial ONNX model"""			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:Dict  ,_UpperCamelCase		:int  ,_UpperCamelCase		:Dict=None  ,**_UpperCamelCase		:str			):
									try:
											# Compute path
											with TemporaryDirectory() as tempdir:
													snake_case_		:							List[str]			       =						Path(_UpperCamelCase			).joinpath("""model.onnx"""			)

											# Remove folder if exists
											if path.parent.exists():
													path.parent.rmdir()

											# Export
											convert(_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase  ,**_UpperCamelCase			)

											return path
									except Exception as e:
											self.fail(_UpperCamelCase			)





							@require_torch
							@require_tokenizers
							@slow
							def 		a__   (  self		:str			):
									from transformers import BertModel

									snake_case_		:							List[Any]			       =						BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""			)			)
									snake_case_		:							Tuple			       =						BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""			)
									self._test_infer_dynamic_axis(_UpperCamelCase  ,_UpperCamelCase  ,"""pt"""			)





							@require_tf
							@require_tokenizers
							@slow
							def 		a__   (  self		:List[str]			):
									from transformers import TFBertModel

									snake_case_		:							int			       =						TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""			)			)
									snake_case_		:							Optional[int]			       =						BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""			)
									self._test_infer_dynamic_axis(_UpperCamelCase  ,_UpperCamelCase  ,"""tf"""			)





							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:Dict  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:Optional[int]			):
									snake_case_		:							int			       =						FeatureExtractionPipeline(_UpperCamelCase  ,_UpperCamelCase			)

									snake_case_		:							str			       =						["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
									snake_case_		:							Dict			       =						infer_shapes(_UpperCamelCase  ,_UpperCamelCase			)

									# Assert all variables are present
									self.assertEqual(len(_UpperCamelCase			)  ,len(_UpperCamelCase			)			)
									self.assertTrue(all(var_name in shapes for var_name in variable_names			)			)
									self.assertSequenceEqual(variable_names[:3]  ,_UpperCamelCase			)
									self.assertSequenceEqual(variable_names[3:]  ,_UpperCamelCase			)

									# Assert inputs are {0: batch, 1: sequence}
									for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
											self.assertDictEqual(shapes[var_name]  ,{0: """batch""", 1: """sequence"""}			)

									# Assert outputs are {0: batch, 1: sequence} and {0: batch}
									self.assertDictEqual(shapes["""output_0"""]  ,{0: """batch""", 1: """sequence"""}			)
									self.assertDictEqual(shapes["""output_1"""]  ,{0: """batch"""}			)





							def 		a__   (  self		:List[str]			):
									snake_case_		:							Union[str, Any]			       =						["""input_ids""", """attention_mask""", """token_type_ids"""]
									snake_case_		:							List[str]			       =						{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
									snake_case_		:							int			       =						ensure_valid_input(FuncContiguousArgs()  ,_UpperCamelCase  ,_UpperCamelCase			)

									# Should have exactly the same number of args (all are valid)
									self.assertEqual(len(_UpperCamelCase			)  ,3			)

									# Should have exactly the same input names
									self.assertEqual(set(_UpperCamelCase			)  ,set(_UpperCamelCase			)			)

									# Parameter should be reordered according to their respective place in the function:
									# (input_ids, token_type_ids, attention_mask)
									self.assertEqual(_UpperCamelCase  ,(tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""])			)

									# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
									snake_case_		:							Any			       =						ensure_valid_input(FuncNonContiguousArgs()  ,_UpperCamelCase  ,_UpperCamelCase			)

									# Should have exactly the one arg (all before the one not provided "some_other_args")
									self.assertEqual(len(_UpperCamelCase			)  ,1			)
									self.assertEqual(len(_UpperCamelCase			)  ,1			)

									# Should have only "input_ids"
									self.assertEqual(inputs_args[0]  ,tokens["""input_ids"""]			)
									self.assertEqual(ordered_input_names[0]  ,"""input_ids"""			)





							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							Optional[Any]			       =						generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""			)  ,"""-test"""			)
									self.assertEqual("""/home/something/my_fake_model-test.onnx"""  ,generated.as_posix()			)



'''simple docstring'''






import numpy as np
import qiskit





def      UpperCAmelCase      (		lowerCamelCase_  :int = 8				,      lowerCamelCase_  :int | None = None ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						np.random.default_rng(seed=lowerCamelCase_ )

		# Roughly 25% of the qubits will contribute to the key.
		# So we take more than we need.
		snake_case_		:							Union[str, Any]			       =						6 * key_len
		# Measurement basis for Alice's qubits.
		snake_case_		:							Tuple			       =						rng.integers(2				,      size=lowerCamelCase_ )
		# The set of states Alice will prepare.
		snake_case_		:							Tuple			       =						rng.integers(2				,      size=lowerCamelCase_ )
		# Measurement basis for Bob's qubits.
		snake_case_		:							List[Any]			       =						rng.integers(2				,      size=lowerCamelCase_ )

		# Quantum Circuit to simulate BB84
		snake_case_		:							Optional[int]			       =						qiskit.QuantumCircuit(lowerCamelCase_				,      name="""BB84""" )

		# Alice prepares her qubits according to rules above.
		for index, _ in enumerate(lowerCamelCase_ ):
				if alice_state[index] == 1:
						bbaa_circ.x(lowerCamelCase_ )
				if alice_basis[index] == 1:
						bbaa_circ.h(lowerCamelCase_ )
		bbaa_circ.barrier()

		# Bob measures the received qubits according to rules above.
		for index, _ in enumerate(lowerCamelCase_ ):
				if bob_basis[index] == 1:
						bbaa_circ.h(lowerCamelCase_ )

		bbaa_circ.barrier()
		bbaa_circ.measure_all()

		# Simulate the quantum circuit.
		snake_case_		:							Union[str, Any]			       =						qiskit.Aer.get_backend("""aer_simulator""" )
		# We only need to run one shot because the key is unique.
		# Multiple shots will produce the same key.
		snake_case_		:							Tuple			       =						qiskit.execute(lowerCamelCase_				,      lowerCamelCase_				,      shots=1				,      seed_simulator=lowerCamelCase_ )
		# Returns the result of measurement.
		snake_case_		:							Tuple			       =						job.result().get_counts(lowerCamelCase_ ).most_frequent()

		# Extracting the generated key from the simulation results.
		# Only keep measurement results where Alice and Bob chose the same basis.
		snake_case_		:							Optional[Any]			       =						"""""".join(
		    [
		        result_bit
		        for alice_basis_bit, bob_basis_bit, result_bit in zip(
		            lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_ )
		        if alice_basis_bit == bob_basis_bit
		    ] )

		# Get final key. Pad with 0 if too short, otherwise truncate.
		snake_case_		:							str			       =						gen_key[:key_len] if len(lowerCamelCase_ ) >= key_len else gen_key.ljust(lowerCamelCase_				,      """0""" )
		return key


if __name__ == "__main__":
							print(F'The generated key is : {bbaa(8, seed=0)}')
							from doctest import testmod

							testmod()