





'''simple docstring'''


import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
				import tensorflow as tf

if is_tensorflow_text_available():
				from transformers.models.bert import TFBertTokenizer


_lowerCamelCase :		Optional[Any] 			=				['''bert-base-uncased''', '''bert-base-cased''']
_lowerCamelCase :		List[str] 			=				'''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():



				class 							__UpperCAmelCase     (				tf.keras.Model  ):







						'''simple docstring'''







						def __init__(self  :					List[str]					,					_lowerCAmelCase  :					Union[str, Any]					):
							super().__init__()
							A							     =					tokenizer
							A							     =					AutoConfig.from_pretrained(__lowerCamelCase					)
							A							     =					TFAutoModel.from_config(__lowerCamelCase					)







						def 				A   (self  :					Tuple					,					_lowerCAmelCase  :					str					):
							A							     =					self.tokenizer(__lowerCamelCase					)
							A							     =					self.bert(**__lowerCamelCase					)
							return out["pooler_output"]



@require_tf
@require_tensorflow_text
class 							__UpperCAmelCase     (				unittest.TestCase  ):







		'''simple docstring'''







		def 				A   (self  :					Dict					):
			super().setUp()

			A							     =					[
			    BertTokenizer.from_pretrained(__lowerCamelCase					) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
			]  # repeat for when fast_bert_tokenizer=false
			A							     =					[TFBertTokenizer.from_pretrained(__lowerCamelCase					) for checkpoint in TOKENIZER_CHECKPOINTS] + [
			    TFBertTokenizer.from_pretrained(__lowerCamelCase					,					use_fast_bert_tokenizer=__lowerCamelCase					)
			    for checkpoint in TOKENIZER_CHECKPOINTS
			]
			assert len(self.tokenizers					) == len(self.tf_tokenizers					)

			A							     =					[
			    """This is a straightforward English test sentence.""",
			    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
			    """Now we're going to add some Chinese: 一 二 三 一二三""",
			    """And some much more rare Chinese: 齉 堃 齉堃""",
			    """Je vais aussi écrire en français pour tester les accents""",
			    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
			]
			A							     =					list(zip(self.test_sentences					,					self.test_sentences[::-1]					)					)







		def 				A   (self  :					Optional[int]					):
			for tokenizer, tf_tokenizer in zip(self.tokenizers					,					self.tf_tokenizers					):
				for test_inputs in (self.test_sentences, self.paired_sentences):
					A							     =					tokenizer(__lowerCamelCase					,					return_tensors="""tf"""					,					padding="""longest"""					)
					A							     =					tf_tokenizer(__lowerCamelCase					)

					for key in python_outputs.keys():
						self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape					)					)
						self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]					,					tf.intaa					) == tf_outputs[key]					)					)







		@slow
		def 				A   (self  :					Dict					):
			for tf_tokenizer in self.tf_tokenizers:
				A							     =					tf_tokenizer(self.paired_sentences					)
				A							     =					tf_tokenizer(
				    text=[sentence[0] for sentence in self.paired_sentences]					,					text_pair=[sentence[1] for sentence in self.paired_sentences]					,					)
				for key in merged_outputs.keys():
					self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]					,					tf.intaa					) == separated_outputs[key]					)					)







		@slow
		def 				A   (self  :					List[str]					):
			for tf_tokenizer in self.tf_tokenizers:
				A							     =					tf.function(__lowerCamelCase					)
				for test_inputs in (self.test_sentences, self.paired_sentences):
					A							     =					tf.constant(__lowerCamelCase					)
					A							     =					compiled_tokenizer(__lowerCamelCase					)
					A							     =					tf_tokenizer(__lowerCamelCase					)

					for key in eager_outputs.keys():
						self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]					)					)







		@slow
		def 				A   (self  :					Tuple					):
			for tf_tokenizer in self.tf_tokenizers:
				A							     =					ModelToSave(tokenizer=__lowerCamelCase					)
				A							     =					tf.convert_to_tensor(self.test_sentences					)
				A							     =					model(__lowerCamelCase					)  # Build model with some sample inputs
				with TemporaryDirectory() as tempdir:
					A							     =					Path(__lowerCamelCase					) / """saved.model"""
					model.save(__lowerCamelCase					)
					A							     =					tf.keras.models.load_model(__lowerCamelCase					)
				A							     =					loaded_model(__lowerCamelCase					)
				# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
				self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output					)					)					,					1e-5					)









import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int = 3					)		->			qiskit.result.counts.Counts:






	"""simple docstring"""



	if isinstance(__magic_name__	,			__magic_name__					):
		raise TypeError("""number of qubits must be a integer."""					)
	if number_of_qubits <= 0:
		raise ValueError("""number of qubits must be > 0."""					)
	if math.floor(__magic_name__					) != number_of_qubits:
		raise ValueError("""number of qubits must be exact integer."""					)
	if number_of_qubits > 10:
		raise ValueError("""number of qubits too large to simulate(>10)."""					)

	UpperCamelCase     :int          =	QuantumRegister(__magic_name__	,			"""qr"""					)
	UpperCamelCase     :str          =	ClassicalRegister(__magic_name__	,			"""cr"""					)

	UpperCamelCase     :str          =	QuantumCircuit(__magic_name__	,			__magic_name__					)

	UpperCamelCase     :List[Any]          =	number_of_qubits

	for i in range(__magic_name__					):
		quantum_circuit.h(number_of_qubits - i - 1					)
		counter -= 1
		for j in range(__magic_name__					):
			quantum_circuit.cp(np.pi / 2 ** (counter - j)	,			__magic_name__	,			__magic_name__					)

	for k in range(number_of_qubits // 2					):
		quantum_circuit.swap(__magic_name__	,			number_of_qubits - k - 1					)

	# measure all the qubits
	quantum_circuit.measure(__magic_name__	,			__magic_name__					)
	# simulate with 10000 shots
	UpperCamelCase     :str          =	Aer.get_backend("""qasm_simulator"""					)
	UpperCamelCase     :Dict          =	execute(__magic_name__	,			__magic_name__	,			shots=1_0000					)

	return job.result().get_counts(__magic_name__					)


if __name__ == "__main__":
	print(
	    F'''Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'''
	)
