
"""simple docstring"""




import numpy as np
import qiskit

def lowercase_  (   _snake_case = 8   ,_snake_case = None						):
							SCREAMING_SNAKE_CASE__						:       Any           =   np.random.default_rng(seed=_UpperCAmelCase						)

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

							# Quantum Circuit to simulate BB84
							SCREAMING_SNAKE_CASE__						:       Any           =   qiskit.QuantumCircuit(_UpperCAmelCase   ,name="""BB84"""						)

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

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

							bbaa_circ.barrier()
							bbaa_circ.measure_all()

							# Simulate the quantum circuit.
							SCREAMING_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.
							SCREAMING_SNAKE_CASE__						:       int           =   qiskit.execute(_UpperCAmelCase   ,_UpperCAmelCase   ,shots=1   ,seed_simulator=_UpperCAmelCase						)
							# Returns the result of measurement.
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   job.result().get_counts(_UpperCAmelCase						).most_frequent()

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

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


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

					testmod()









import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case						:Any      		=     logging.get_logger(__name__)

__snake_case						:Optional[Any]      		=     {
    '''b0''': efficientnet.EfficientNetBa,
    '''b1''': efficientnet.EfficientNetBa,
    '''b2''': efficientnet.EfficientNetBa,
    '''b3''': efficientnet.EfficientNetBa,
    '''b4''': efficientnet.EfficientNetBa,
    '''b5''': efficientnet.EfficientNetBa,
    '''b6''': efficientnet.EfficientNetBa,
    '''b7''': efficientnet.EfficientNetBa,
}

__snake_case						:List[Any]      		=     {
    '''b0''': {
        '''hidden_dim''': 1280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 224,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 240,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 1408,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 260,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 1536,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 300,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 1792,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 380,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2048,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 456,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 2304,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 528,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 2560,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 600,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				EfficientNetConfig()
    __a						       =				CONFIG_MAP[model_name]['''hidden_dim''']
    __a						       =				CONFIG_MAP[model_name]['''width_coef''']
    __a						       =				CONFIG_MAP[model_name]['''depth_coef''']
    __a						       =				CONFIG_MAP[model_name]['''image_size''']
    __a						       =				CONFIG_MAP[model_name]['''dropout_rate''']
    __a						       =				CONFIG_MAP[model_name]['''dw_padding''']

    __a						       =				'''huggingface/label-files'''
    __a						       =				'''imagenet-1k-id2label.json'''
    __a						       =				1000
    __a						       =				json.load(open(hf_hub_download(_UpperCAmelCase   , _UpperCAmelCase   , repo_type='''dataset'''				)   , '''r'''				)				)
    __a						       =				{int(_UpperCAmelCase				): v for k, v in idalabel.items()}

    __a						       =				idalabel
    __a						       =				{v: k for k, v in idalabel.items()}
    return config





def      __snake_case	(			):
    __a						       =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __a						       =				Image.open(requests.get(_UpperCAmelCase   , stream=_UpperCAmelCase				).raw				)
    return im





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				CONFIG_MAP[model_name]['''image_size''']
    __a						       =				EfficientNetImageProcessor(
        size={'''height''': size, '''width''': size}   , image_mean=[0.4_85, 0.4_56, 0.4_06]   , image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63]   , do_center_crop=_UpperCAmelCase   , )
    return preprocessor





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				[v.split('''_'''				)[0].split('''block'''				)[1] for v in original_param_names if v.startswith('''block'''				)]
    __a						       =				sorted(set(_UpperCAmelCase				)				)
    __a						       =				len(_UpperCAmelCase				)
    __a						       =				{b: str(_UpperCAmelCase				) for b, i in zip(_UpperCAmelCase   , range(_UpperCAmelCase				)				)}

    __a						       =				[]
    rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''')				)
    rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''')				)
    rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''')				)
    rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''')				)
    rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''')				)

    for b in block_names:
        __a						       =				block_name_mapping[b]
        rename_keys.append((f'block{b}_expand_conv/kernel:0', f'encoder.blocks.{hf_b}.expansion.expand_conv.weight')				)
        rename_keys.append((f'block{b}_expand_bn/gamma:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.weight')				)
        rename_keys.append((f'block{b}_expand_bn/beta:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.bias')				)
        rename_keys.append(
            (f'block{b}_expand_bn/moving_mean:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean')				)
        rename_keys.append(
            (f'block{b}_expand_bn/moving_variance:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.running_var')				)
        rename_keys.append(
            (f'block{b}_dwconv/depthwise_kernel:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight')				)
        rename_keys.append((f'block{b}_bn/gamma:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight')				)
        rename_keys.append((f'block{b}_bn/beta:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias')				)
        rename_keys.append(
            (f'block{b}_bn/moving_mean:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean')				)
        rename_keys.append(
            (f'block{b}_bn/moving_variance:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var')				)

        rename_keys.append((f'block{b}_se_reduce/kernel:0', f'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight')				)
        rename_keys.append((f'block{b}_se_reduce/bias:0', f'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias')				)
        rename_keys.append((f'block{b}_se_expand/kernel:0', f'encoder.blocks.{hf_b}.squeeze_excite.expand.weight')				)
        rename_keys.append((f'block{b}_se_expand/bias:0', f'encoder.blocks.{hf_b}.squeeze_excite.expand.bias')				)
        rename_keys.append(
            (f'block{b}_project_conv/kernel:0', f'encoder.blocks.{hf_b}.projection.project_conv.weight')				)
        rename_keys.append((f'block{b}_project_bn/gamma:0', f'encoder.blocks.{hf_b}.projection.project_bn.weight')				)
        rename_keys.append((f'block{b}_project_bn/beta:0', f'encoder.blocks.{hf_b}.projection.project_bn.bias')				)
        rename_keys.append(
            (f'block{b}_project_bn/moving_mean:0', f'encoder.blocks.{hf_b}.projection.project_bn.running_mean')				)
        rename_keys.append(
            (f'block{b}_project_bn/moving_variance:0', f'encoder.blocks.{hf_b}.projection.project_bn.running_var')				)

    rename_keys.append(('''top_conv/kernel:0''', '''encoder.top_conv.weight''')				)
    rename_keys.append(('''top_bn/gamma:0''', '''encoder.top_bn.weight''')				)
    rename_keys.append(('''top_bn/beta:0''', '''encoder.top_bn.bias''')				)
    rename_keys.append(('''top_bn/moving_mean:0''', '''encoder.top_bn.running_mean''')				)
    rename_keys.append(('''top_bn/moving_variance:0''', '''encoder.top_bn.running_var''')				)

    __a						       =				{}
    for item in rename_keys:
        if item[0] in original_param_names:
            __a						       =				'''efficientnet.''' + item[1]

    __a						       =				'''classifier.weight'''
    __a						       =				'''classifier.bias'''
    return key_mapping





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    for key, value in tf_params.items():
        if "normalization" in key:
            continue

        __a						       =				key_mapping[key]
        if "_conv" in key and "kernel" in key:
            __a						       =				torch.from_numpy(_UpperCAmelCase				).permute(3   , 2   , 0   , 1				)
        elif "depthwise_kernel" in key:
            __a						       =				torch.from_numpy(_UpperCAmelCase				).permute(2   , 3   , 0   , 1				)
        elif "kernel" in key:
            __a						       =				torch.from_numpy(np.transpose(_UpperCAmelCase				)				)
        else:
            __a						       =				torch.from_numpy(_UpperCAmelCase				)

        # Replace HF parameters with original TF model parameters
        assert hf_params[hf_key].shape == new_hf_value.shape
        hf_params[hf_key].copy_(_UpperCAmelCase				)





@torch.no_grad()
def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				model_classes[model_name](
        include_top=_UpperCAmelCase   , weights='''imagenet'''   , input_tensor=_UpperCAmelCase   , input_shape=_UpperCAmelCase   , pooling=_UpperCAmelCase   , classes=1000   , classifier_activation='''softmax'''   , )

    __a						       =				original_model.trainable_variables
    __a						       =				original_model.non_trainable_variables
    __a						       =				{param.name: param.numpy() for param in tf_params}
    for param in tf_non_train_params:
        __a						       =				param.numpy()
    __a						       =				list(tf_params.keys()				)

    # Load HuggingFace model
    __a						       =				get_efficientnet_config(_UpperCAmelCase				)
    __a						       =				EfficientNetForImageClassification(_UpperCAmelCase				).eval()
    __a						       =				hf_model.state_dict()

    # Create src-to-dst parameter name mapping dictionary
    print('''Converting parameters...'''				)
    __a						       =				rename_keys(_UpperCAmelCase				)
    replace_params(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				)

    # Initialize preprocessor and preprocess input image
    __a						       =				convert_image_processor(_UpperCAmelCase				)
    __a						       =				preprocessor(images=prepare_img()   , return_tensors='''pt'''				)

    # HF model inference
    hf_model.eval()
    with torch.no_grad():
        __a						       =				hf_model(**_UpperCAmelCase				)
    __a						       =				outputs.logits.detach().numpy()

    # Original model inference
    __a						       =				False
    __a						       =				CONFIG_MAP[model_name]['''image_size''']
    __a						       =				prepare_img().resize((image_size, image_size)   , resample=PIL.Image.NEAREST				)
    __a						       =				image.img_to_array(_UpperCAmelCase				)
    __a						       =				np.expand_dims(_UpperCAmelCase   , axis=0				)
    __a						       =				original_model.predict(_UpperCAmelCase				)

    # Check whether original and HF model outputs match  -> np.allclose
    assert np.allclose(_UpperCAmelCase   , _UpperCAmelCase   , atol=1E-3				), "The predicted logits are not the same."
    print('''Model outputs match!'''				)

    if save_model:
        # Create folder to save model
        if not os.path.isdir(_UpperCAmelCase				):
            os.mkdir(_UpperCAmelCase				)
        # Save converted model and image processor
        hf_model.save_pretrained(_UpperCAmelCase				)
        preprocessor.save_pretrained(_UpperCAmelCase				)

    if push_to_hub:
        # Push model and image processor to hub
        print(f'Pushing converted {model_name} to the hub...'				)
        __a						       =				f'efficientnet-{model_name}'
        preprocessor.push_to_hub(_UpperCAmelCase				)
        hf_model.push_to_hub(_UpperCAmelCase				)


if __name__ == "__main__":
 __snake_case						:int      		=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--model_name''',
     default='''b0''',
     type=str,
     help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default='''hf_model''',
     type=str,
     help='''Path to the output PyTorch model directory.''',
 )
 parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
 parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')

 __snake_case						:Optional[int]      		=     parser.parse_args()
 convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





