def deploy_tokens_and_fund_accounts(
    token_amount: int,
    number_of_tokens: int,
    deploy_service: BlockChainService,
    participants: typing.List[typing.Address],
    contract_manager: ContractManager,
) -> typing.List[typing.TokenAddress]:
    """ Deploy `number_of_tokens` ERC20 token instances with `token_amount` minted and
    distributed among `blockchain_services`. Optionally the instances will be registered with
    the raiden registry.

    Args:
        token_amount (int): number of units that will be created per token
        number_of_tokens (int): number of token instances that will be created
        deploy_service (BlockChainService): the blockchain connection that will deploy
        participants (list(address)): participant addresses that will receive tokens
    """
    result = list()
    for _ in range(number_of_tokens):
        token_address = deploy_contract_web3(
            CONTRACT_HUMAN_STANDARD_TOKEN,
            deploy_service.client,
            contract_manager=contract_manager,
            constructor_arguments=(token_amount, 2, "raiden", "Rd"),
        )

        result.append(token_address)

        # only the creator of the token starts with a balance (deploy_service),
        # transfer from the creator to the other nodes
        for transfer_to in participants:
            deploy_service.token(token_address).transfer(
                to_address=transfer_to, amount=token_amount // len(participants)
            )

    return result
def __init__(self, root, split="train", transforms=None):
        super().__init__(root=root, transforms=transforms)

        verify_str_arg(split, "split", valid_values=("train", "val"))

        root = Path(root) / "FlyingChairs"
        images = sorted(glob(str(root / "data" / "*.ppm")))
        flows = sorted(glob(str(root / "data" / "*.flo")))

        split_file_name = "FlyingChairs_train_val.txt"

        if not os.path.exists(root / split_file_name):
            raise FileNotFoundError(
                "The FlyingChairs_train_val.txt file was not found - please download it from the dataset page (see docstring)."
            )

        split_list = np.loadtxt(str(root / split_file_name), dtype=np.int32)
        for i in range(len(flows)):
            split_id = split_list[i]
            if (split == "train" and split_id == 1) or (split == "val" and split_id == 2):
                self._flow_list += [flows[i]]
                self._image_list += [[images[2 * i], images[2 * i + 1]]]