


"""simple docstring"""



import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()







def 		__lowercase					(       snake_case_  :  List[Any]    ,snake_case_  :  Dict    ,snake_case_  :  Any=None						)       ->Dict:



    '''simple docstring'''







    assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
    __A   :						List[str]									=					nn.Parameter(snake_case_						)
    if bias is not None:
        assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
        __A   :						Union[str, Any]									=					nn.Parameter(snake_case_						)







def 		__lowercase					(       snake_case_  :  Optional[int]    ,snake_case_  :  Dict    ,snake_case_  :  List[str]						)       ->List[Any]:



    '''simple docstring'''







    __A   :						List[Any]									=					np.asarray(weights[0]						)
    __A   :						Tuple									=					np.asarray(weights[1]						)
    __A   :						Optional[int]									=					np.asarray(weights[2]						)

    set_param(
        torch_layer.self_attention.query_key    ,torch.tensor(snake_case_						).transpose(1    ,2						).contiguous().view(-1    ,snake_case_						)    ,)
    set_param(
        torch_layer.self_attention.value    ,torch.tensor(snake_case_						).transpose(1    ,2						).contiguous().view(-1    ,snake_case_						)    ,)
    set_param(
        torch_layer.output.dense    ,torch.tensor(snake_case_						).view(-1    ,snake_case_						).contiguous().transpose(0    ,1						)    ,)







def 		__lowercase					(       snake_case_  :  Optional[int]    ,snake_case_  :  List[Any]    ,snake_case_  :  Union[str, Any]						)       ->List[str]:



    '''simple docstring'''







    __A   :						List[str]									=					np.asarray(weights[0]						)
    __A   :						Optional[Any]									=					np.asarray(weights[1]						)
    __A   :						str									=					np.asarray(weights[2]						)
    __A   :						Any									=					np.asarray(weights[3]						)

    set_param(
        torch_layer.self_attention.query    ,torch.tensor(snake_case_						).transpose(1    ,2						).contiguous().view(-1    ,snake_case_						)    ,)
    set_param(
        torch_layer.self_attention.key    ,torch.tensor(snake_case_						).transpose(1    ,2						).contiguous().view(-1    ,snake_case_						)    ,)
    set_param(
        torch_layer.self_attention.value    ,torch.tensor(snake_case_						).transpose(1    ,2						).contiguous().view(-1    ,snake_case_						)    ,)
    set_param(
        torch_layer.output.dense    ,torch.tensor(snake_case_						).view(-1    ,snake_case_						).contiguous().transpose(0    ,1						)    ,)







def 		__lowercase					(       snake_case_  :  Any    ,snake_case_  :  Tuple    ,snake_case_  :  Union[str, Any]						)       ->int:



    '''simple docstring'''







    __A   :						Union[str, Any]									=					weights[0][0][0]
    __A   :						Optional[Any]									=					np.asarray(layer_norm_a[0]						)
    __A   :						Any									=					np.asarray(layer_norm_a[1]						)
    set_param(
        torch_block.attention.layer_norm    ,torch.tensor(snake_case_						)    ,torch.tensor(snake_case_						)    ,)

    # lsh weights + output
    __A   :						Optional[int]									=					weights[0][1]
    if len(snake_case_						) < 4:
        set_layer_weights_in_torch_lsh(snake_case_    ,torch_block.attention    ,snake_case_						)
    else:
        set_layer_weights_in_torch_local(snake_case_    ,torch_block.attention    ,snake_case_						)

    # intermediate weighs
    __A   :						str									=					weights[2][0][1][2]

    # Chunked Feed Forward
    if len(snake_case_						) == 4:
        __A   :						Tuple									=					intermediate_weights[2]

    # layernorm 2
    __A   :						Union[str, Any]									=					np.asarray(intermediate_weights[0][0]						)
    __A   :						Optional[int]									=					np.asarray(intermediate_weights[0][1]						)
    set_param(
        torch_block.feed_forward.layer_norm    ,torch.tensor(snake_case_						)    ,torch.tensor(snake_case_						)    ,)

    # intermediate dense
    __A   :						Any									=					np.asarray(intermediate_weights[1][0]						)
    __A   :						int									=					np.asarray(intermediate_weights[1][1]						)
    set_param(
        torch_block.feed_forward.dense.dense    ,torch.tensor(snake_case_						).transpose(0    ,1						).contiguous()    ,torch.tensor(snake_case_						)    ,)

    # intermediate out
    __A   :						Union[str, Any]									=					np.asarray(intermediate_weights[4][0]						)
    __A   :						List[Any]									=					np.asarray(intermediate_weights[4][1]						)
    set_param(
        torch_block.feed_forward.output.dense    ,torch.tensor(snake_case_						).transpose(0    ,1						).contiguous()    ,torch.tensor(snake_case_						)    ,)







def 		__lowercase					(       snake_case_  :  List[str]    ,snake_case_  :  Union[str, Any]    ,snake_case_  :  List[Any]						)       ->Optional[Any]:



    '''simple docstring'''







    __A   :						Tuple									=					torch_model.reformer

    # word embeds
    __A   :						Union[str, Any]									=					np.asarray(weights[1]						)
    set_param(
        torch_model_reformer.embeddings.word_embeddings    ,torch.tensor(snake_case_						)    ,)

    if isinstance(weights[3]    ,snake_case_						):
        __A   :						List[Any]									=					torch_model_reformer.embeddings.position_embeddings
        for emb_idx in range(len(position_embeddings.weights						)						):
            __A   :						List[Any]									=					np.asarray(weights[3][emb_idx][0]						)
            assert (
                position_embeddings.weights[emb_idx].shape == emb_weights.shape
            ), F"""{position_embeddings[emb_idx]} emb does not match"""
            __A   :						Optional[Any]									=					nn.Parameter(torch.tensor(snake_case_						)						)

    __A   :						str									=					weights[5]
    assert len(torch_model_reformer.encoder.layers						) * 4 == len(
        snake_case_						), "HF and trax model do not have the same number of layers"
    for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers						):
        __A   :						Union[str, Any]									=					trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
        set_block_weights_in_torch(snake_case_    ,snake_case_    ,snake_case_						)

    # output layer norm
    __A   :						int									=					np.asarray(weights[7][0]						)
    __A   :						List[str]									=					np.asarray(weights[7][1]						)
    set_param(
        torch_model_reformer.encoder.layer_norm    ,torch.tensor(snake_case_						)    ,torch.tensor(snake_case_						)    ,)

    # output embeddings
    __A   :						Dict									=					np.asarray(weights[9][0]						)
    __A   :						int									=					np.asarray(weights[9][1]						)
    set_param(
        torch_model.lm_head.decoder    ,torch.tensor(snake_case_						).transpose(0    ,1						).contiguous()    ,torch.tensor(snake_case_						)    ,)







def 		__lowercase					(       snake_case_  :  Dict    ,snake_case_  :  List[str]    ,snake_case_  :  Any						)       ->Dict:



    '''simple docstring'''







    __A   :						Tuple									=					ReformerConfig.from_json_file(snake_case_						)
    print(F"""Building PyTorch model from configuration: {config}"""						)
    __A   :						str									=					ReformerModelWithLMHead(snake_case_						)

    with open(snake_case_    ,'''rb'''						) as f:
        __A   :						str									=					pickle.load(snake_case_						)['''weights''']

    set_model_weights_in_torch(snake_case_    ,snake_case_    ,config.hidden_size						)

    # Save pytorch-model
    print(F"""Save PyTorch model to {pytorch_dump_path}"""						)
    torch.save(model.state_dict()    ,snake_case_						)


if __name__ == "__main__":
       a_			=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           required=True,
           help=(
               """The config json file corresponding to the pre-trained Reformer model. \n"""
               """This specifies the model architecture."""
           ),
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       a_			=      parser.parse_args()
       convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)











"""simple docstring"""



import numpy as np
import qiskit







def 		__lowercase					(       snake_case_  :  int = 8    ,snake_case_  :  int | None = None						)       ->str:



    '''simple docstring'''







    __A   :						str									=					np.random.default_rng(seed=snake_case_						)

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

    # Quantum Circuit to simulate BB84
    __A   :						Dict									=					qiskit.QuantumCircuit(snake_case_    ,name='''BB84'''						)

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

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

    bbaa_circ.barrier()
    bbaa_circ.measure_all()

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

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

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


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

       testmod()




