Initial import: grid-bot — grid trading bot for BTC-USDT on Cifra Markets
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Kolp
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# mypy: allow-untyped-defs
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import os
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import torch
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from torch.jit._serialization import validate_map_location
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def _load_for_lite_interpreter(f, map_location=None):
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r"""
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Load a :class:`LiteScriptModule` saved with :func:`torch.jit._save_for_lite_interpreter`.
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Args:
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f: a file-like object (has to implement read, readline, tell, and seek),
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or a string containing a file name
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map_location: a string or torch.device used to dynamically remap
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storages to an alternative set of devices.
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Returns:
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A :class:`LiteScriptModule` object.
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Example:
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.. testcode::
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import torch
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import io
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# Load LiteScriptModule from saved file path
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torch.jit._load_for_lite_interpreter('lite_script_module.pt')
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# Load LiteScriptModule from io.BytesIO object
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with open('lite_script_module.pt', 'rb') as f:
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buffer = io.BytesIO(f.read())
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# Load all tensors to the original device
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torch.jit.mobile._load_for_lite_interpreter(buffer)
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"""
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if isinstance(f, (str, os.PathLike)):
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if not os.path.exists(f):
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raise ValueError(f"The provided filename {f} does not exist")
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if os.path.isdir(f):
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raise ValueError(f"The provided filename {f} is a directory")
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map_location = validate_map_location(map_location)
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if isinstance(f, (str, os.PathLike)):
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cpp_module = torch._C._load_for_lite_interpreter(os.fspath(f), map_location)
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else:
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cpp_module = torch._C._load_for_lite_interpreter_from_buffer(
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f.read(),
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map_location,
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)
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return LiteScriptModule(cpp_module)
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class LiteScriptModule:
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def __init__(self, cpp_module) -> None:
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self._c = cpp_module
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super().__init__()
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def __call__(self, *input):
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return self._c.forward(input)
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def find_method(self, method_name):
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return self._c.find_method(method_name)
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def forward(self, *input):
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return self._c.forward(input)
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def run_method(self, method_name, *input):
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return self._c.run_method(method_name, input)
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def _export_operator_list(module: LiteScriptModule):
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r"""Return a set of root operator names (with overload name) that are used by any method in this mobile module."""
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return torch._C._export_operator_list(module._c)
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def _get_model_bytecode_version(f_input) -> int:
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r"""Take a file-like object to return an integer.
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Args:
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f_input: a file-like object (has to implement read, readline, tell, and seek),
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or a string containing a file name
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Returns:
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version: An integer. If the integer is -1, the version is invalid. A warning
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will show in the log.
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Example:
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.. testcode::
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from torch.jit.mobile import _get_model_bytecode_version
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# Get bytecode version from a saved file path
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version = _get_model_bytecode_version("path/to/model.ptl")
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"""
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if isinstance(f_input, (str, os.PathLike)):
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if not os.path.exists(f_input):
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raise ValueError(f"The provided filename {f_input} does not exist")
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if os.path.isdir(f_input):
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raise ValueError(f"The provided filename {f_input} is a directory")
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if isinstance(f_input, (str, os.PathLike)):
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return torch._C._get_model_bytecode_version(os.fspath(f_input))
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else:
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return torch._C._get_model_bytecode_version_from_buffer(f_input.read())
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def _get_mobile_model_contained_types(f_input) -> int:
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r"""Take a file-like object and return a set of string, like ("int", "Optional").
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Args:
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f_input: a file-like object (has to implement read, readline, tell, and seek),
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or a string containing a file name
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Returns:
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type_list: A set of string, like ("int", "Optional"). These are types used in bytecode.
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Example:
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.. testcode::
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from torch.jit.mobile import _get_mobile_model_contained_types
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# Get type list from a saved file path
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type_list = _get_mobile_model_contained_types("path/to/model.ptl")
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"""
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if isinstance(f_input, (str, os.PathLike)):
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if not os.path.exists(f_input):
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raise ValueError(f"The provided filename {f_input} does not exist")
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if os.path.isdir(f_input):
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raise ValueError(f"The provided filename {f_input} is a directory")
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if isinstance(f_input, (str, os.PathLike)):
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return torch._C._get_mobile_model_contained_types(os.fspath(f_input))
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else:
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return torch._C._get_mobile_model_contained_types_from_buffer(f_input.read())
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def _backport_for_mobile(f_input, f_output, to_version):
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r"""Take a input string containing a file name (file-like object) and a new destination to return a boolean.
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Args:
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f_input: a file-like object (has to implement read, readline, tell, and seek),
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or a string containing a file name
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f_output: path to new model destination
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to_version: the expected output model bytecode version
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Returns:
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success: A boolean. If backport success, return true, otherwise false
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"""
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if isinstance(f_input, (str, os.PathLike)):
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if not os.path.exists(f_input):
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raise ValueError(f"The provided filename {f_input} does not exist")
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if os.path.isdir(f_input):
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raise ValueError(f"The provided filename {f_input} is a directory")
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if (isinstance(f_input, (str, os.PathLike))) and (
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isinstance(f_output, (str, os.PathLike))
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):
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return torch._C._backport_for_mobile(
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os.fspath(f_input),
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os.fspath(f_output),
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to_version,
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)
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else:
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return torch._C._backport_for_mobile_from_buffer(
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f_input.read(),
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str(f_output),
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to_version,
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)
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def _backport_for_mobile_to_buffer(f_input, to_version):
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r"""Take a string containing a file name (file-like object).
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Args:
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f_input: a file-like object (has to implement read, readline, tell, and seek),
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or a string containing a file name
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"""
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if isinstance(f_input, (str, os.PathLike)):
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if not os.path.exists(f_input):
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raise ValueError(f"The provided filename {f_input} does not exist")
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if os.path.isdir(f_input):
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raise ValueError(f"The provided filename {f_input} is a directory")
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if isinstance(f_input, (str, os.PathLike)):
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return torch._C._backport_for_mobile_to_buffer(os.fspath(f_input), to_version)
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else:
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return torch._C._backport_for_mobile_from_buffer_to_buffer(
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f_input.read(),
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to_version,
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)
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def _get_model_ops_and_info(f_input):
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r"""Retrieve the root (top level) operators of a model and their corresponding compatibility info.
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These root operators can call other operators within them (traced ops), and
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a root op can call many different traced ops depending on internal code paths in the root op.
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These traced ops are not returned by this function. Those operators are abstracted into the
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runtime as an implementation detail (and the traced ops themselves can also call other operators)
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making retrieving them difficult and their value from this api negligible since they will differ
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between which runtime version the model is run on. Because of this, there is a false positive this
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api can't prevent in a compatibility usecase. All the root ops of a model are present in a
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target runtime, but not all the traced ops are which prevents a model from being able to run.
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Args:
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f_input: a file-like object (has to implement read, readline, tell, and seek),
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or a string containing a file name
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Returns:
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Operators and info: A Dictionary mapping strings (the qualified names of the root operators)
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of the model to their OperatorInfo structs.
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Example:
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.. testcode::
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from torch.jit.mobile import _get_model_ops_and_info
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# Get bytecode version from a saved file path
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ops_and_info = _get_model_ops_and_info("path/to/model.ptl")
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"""
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if isinstance(f_input, (str, os.PathLike)):
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if not os.path.exists(f_input):
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raise ValueError(f"The provided filename {f_input} does not exist")
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if os.path.isdir(f_input):
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raise ValueError(f"The provided filename {f_input} is a directory")
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if isinstance(f_input, (str, os.PathLike)):
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return torch._C._get_model_ops_and_info(os.fspath(f_input))
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else:
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return torch._C._get_model_ops_and_info(f_input.read())
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