




import numpy as np
import qiskit


def 		lowerCamelCase__       (						_A = 8	,      _A = None				):







	'''simple docstring'''





	snake_case_						       =	np.random.default_rng(seed=_A				)

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

	# Quantum Circuit to simulate BB84
	snake_case_						       =	qiskit.QuantumCircuit(_A	,      name="BB84"				)

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

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

	bbaa_circ.barrier()
	bbaa_circ.measure_all()

	# Simulate the quantum circuit.
	snake_case_						       =	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_						       =	qiskit.execute(_A	,      _A	,      shots=1	,      seed_simulator=_A				)
	# Returns the result of measurement.
	snake_case_						       =	job.result().get_counts(_A				).most_frequent()

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

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


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

		testmod()






import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	old_name

	if "patch_embed" in old_name:
		snake_case_       , snake_case_       , snake_case_						       =	old_name.split("."				)

		if layer == "0":
			snake_case_						       =	old_name.replace("0"	,      "convolution1"				)
		elif layer == "1":
			snake_case_						       =	old_name.replace("1"	,      "batchnorm_before"				)
		elif layer == "3":
			snake_case_						       =	old_name.replace("3"	,      "convolution2"				)
		else:
			snake_case_						       =	old_name.replace("4"	,      "batchnorm_after"				)

	if "network" in old_name and re.search(R"\d\.\d"	,      _A				):
		snake_case_						       =	R"\b\d{2}\b"
		if bool(re.search(_A	,      _A				)				):
			snake_case_						       =	re.search(R"\d\.\d\d."	,      _A				).group()
		else:
			snake_case_						       =	re.search(R"\d\.\d."	,      _A				).group()
		if int(match[0]				) < 6:
			snake_case_						       =	old_name.replace(_A	,      ""				)
			snake_case_						       =	trimmed_name.replace("network"	,      match[0] + ".meta4D_layers.blocks." + match[2:-1]				)
			snake_case_						       =	"intermediate_stages." + trimmed_name
		else:
			snake_case_						       =	old_name.replace(_A	,      ""				)
			if int(match[2]				) < num_meta4D_last_stage:
				snake_case_						       =	trimmed_name.replace("network"	,      "meta4D_layers.blocks." + match[2]				)
			else:
				snake_case_						       =	str(int(match[2]				) - num_meta4D_last_stage				)
				snake_case_						       =	trimmed_name.replace("network"	,      "meta3D_layers.blocks." + layer_index				)
				if "norm1" in old_name:
					snake_case_						       =	trimmed_name.replace("norm1"	,      "layernorm1"				)
				elif "norm2" in old_name:
					snake_case_						       =	trimmed_name.replace("norm2"	,      "layernorm2"				)
				elif "fc1" in old_name:
					snake_case_						       =	trimmed_name.replace("fc1"	,      "linear_in"				)
				elif "fc2" in old_name:
					snake_case_						       =	trimmed_name.replace("fc2"	,      "linear_out"				)

			snake_case_						       =	"last_stage." + trimmed_name

	elif "network" in old_name and re.search(R".\d."	,      _A				):
		snake_case_						       =	old_name.replace("network"	,      "intermediate_stages"				)

	if "fc" in new_name:
		snake_case_						       =	new_name.replace("fc"	,      "convolution"				)
	elif ("norm1" in new_name) and ("layernorm1" not in new_name):
		snake_case_						       =	new_name.replace("norm1"	,      "batchnorm_before"				)
	elif ("norm2" in new_name) and ("layernorm2" not in new_name):
		snake_case_						       =	new_name.replace("norm2"	,      "batchnorm_after"				)
	if "proj" in new_name:
		snake_case_						       =	new_name.replace("proj"	,      "projection"				)
	if "dist_head" in new_name:
		snake_case_						       =	new_name.replace("dist_head"	,      "distillation_classifier"				)
	elif "head" in new_name:
		snake_case_						       =	new_name.replace("head"	,      "classifier"				)
	elif "patch_embed" in new_name:
		snake_case_						       =	"efficientformer." + new_name
	elif new_name == "norm.weight" or new_name == "norm.bias":
		snake_case_						       =	new_name.replace("norm"	,      "layernorm"				)
		snake_case_						       =	"efficientformer." + new_name
	else:
		snake_case_						       =	"efficientformer.encoder." + new_name

	return new_name


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	for key in checkpoint.copy().keys():
		snake_case_						       =	checkpoint.pop(_A				)
		snake_case_						       =	val

	return checkpoint


def 		lowerCamelCase__       (						):







	'''simple docstring'''





	snake_case_						       =	"http://images.cocodataset.org/val2017/000000039769.jpg"
	snake_case_						       =	Image.open(requests.get(_A	,      stream=_A				).raw				)

	return image


def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	torch.load(_A	,      map_location="cpu"				)["model"]
	snake_case_						       =	EfficientFormerConfig.from_json_file(_A				)
	snake_case_						       =	EfficientFormerForImageClassificationWithTeacher(_A				)
	snake_case_						       =	"_".join(checkpoint_path.split("/"				)[-1].split("."				)[0].split("_"				)[:-1]				)

	snake_case_						       =	config.depths[-1] - config.num_metaad_blocks + 1
	snake_case_						       =	convert_torch_checkpoint(_A	,      _A				)

	model.load_state_dict(_A				)
	model.eval()

	snake_case_						       =	{
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

	# prepare image
	snake_case_						       =	prepare_img()
	snake_case_						       =	256
	snake_case_						       =	224
	snake_case_						       =	EfficientFormerImageProcessor(
	    size={"shortest_edge": image_size}	,      crop_size={"height": crop_size, "width": crop_size}	,      resample=pillow_resamplings["bicubic"]	,      )
	snake_case_						       =	processor(images=_A	,      return_tensors="pt"				).pixel_values

	# original processing pipeline
	snake_case_						       =	Compose(
	    [
	        Resize(_A	,      interpolation=pillow_resamplings["bicubic"]				),
	        CenterCrop(_A				),
	        ToTensor(),
	        Normalize(_A	,      _A				),
	    ]				)
	snake_case_						       =	image_transforms(_A				).unsqueeze(0				)

	assert torch.allclose(_A	,      _A				)

	snake_case_						       =	model(_A				)
	snake_case_						       =	outputs.logits

	snake_case_						       =	(1, 1000)

	if "l1" in model_name:
		snake_case_						       =	torch.Tensor(
		    [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]				)
		assert torch.allclose(logits[0, :10]	,      _A	,      atol=1E-3				)
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		snake_case_						       =	torch.Tensor(
		    [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]				)
		assert torch.allclose(logits[0, :10]	,      _A	,      atol=1E-3				)
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		snake_case_						       =	torch.Tensor(
		    [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]				)
		assert logits.shape == expected_shape
	else:
		raise ValueError(
		    f"Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"				)

	# Save Checkpoints
	Path(_A				).mkdir(exist_ok=_A				)
	model.save_pretrained(_A				)
	print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"				)
	processor.save_pretrained(_A				)
	print(f"Processor successfuly saved at {pytorch_dump_path}"				)

	if push_to_hub:
		print("Pushing model to the hub..."				)

		model.push_to_hub(
		    repo_id=f"Bearnardd/{pytorch_dump_path}"	,      commit_message="Add model"	,      use_temp_dir=_A	,      )
		processor.push_to_hub(
		    repo_id=f"Bearnardd/{pytorch_dump_path}"	,      commit_message="Add image processor"	,      use_temp_dir=_A	,      )


if __name__ == "__main__":
		lowercase__							:	Tuple											=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--pytorch_model_path",
		    default=None,
		    type=str,
		    required=True,
		    help="Path to EfficientFormer pytorch checkpoint.",
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help="The json file for EfficientFormer model config.",
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)

		parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
		parser.add_argument(
		    "--no-push_to_hub",
		    dest="push_to_hub",
		    action="store_false",
		    help="Do not push model and image processor to the hub",
		)
		parser.set_defaults(push_to_hub=True)

		lowercase__							:	Optional[Any]											=		parser.parse_args()
		convert_efficientformer_checkpoint(
		    checkpoint_path=args.pytorch_model_path,
		    efficientformer_config_file=args.config_file,
		    pytorch_dump_path=args.pytorch_dump_path,
		    push_to_hub=args.push_to_hub,
		)
