Initial import: grid-bot — grid trading bot for BTC-USDT on Cifra Markets

kronos-venv/lib/python3.12/site-packages/einops/layers/__init__.py

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+__author__ = "Alex Rogozhnikov"
+
+from typing import Any, Dict
+
+from einops import EinopsError
+from einops.einops import TransformRecipe, _apply_recipe, _prepare_recipes_for_all_dims, get_backend
+
+
+class RearrangeMixin:
+    """
+    Rearrange layer behaves identically to einops.rearrange operation.
+
+    :param pattern: str, rearrangement pattern
+    :param axes_lengths: any additional specification of dimensions
+
+    See einops.rearrange for source_examples.
+    """
+
+    def __init__(self, pattern: str, **axes_lengths: Any) -> None:
+        super().__init__()
+        self.pattern = pattern
+        self.axes_lengths = axes_lengths
+        # self._recipe = self.recipe()  # checking parameters
+        self._multirecipe = self.multirecipe()
+        self._axes_lengths = tuple(self.axes_lengths.items())
+
+    def __repr__(self) -> str:
+        params = repr(self.pattern)
+        for axis, length in self.axes_lengths.items():
+            params += f", {axis}={length}"
+        return f"{self.__class__.__name__}({params})"
+
+    def multirecipe(self) -> Dict[int, TransformRecipe]:
+        try:
+            return _prepare_recipes_for_all_dims(
+                self.pattern, operation="rearrange", axes_names=tuple(self.axes_lengths)
+            )
+        except EinopsError as e:
+            raise EinopsError(f" Error while preparing {self!r}\n {e}") from None
+
+    def _apply_recipe(self, x):
+        backend = get_backend(x)
+        return _apply_recipe(
+            backend=backend,
+            recipe=self._multirecipe[len(x.shape)],
+            tensor=x,
+            reduction_type="rearrange",
+            axes_lengths=self._axes_lengths,
+        )
+
+    def __getstate__(self):
+        return {"pattern": self.pattern, "axes_lengths": self.axes_lengths}
+
+    def __setstate__(self, state):
+        self.__init__(pattern=state["pattern"], **state["axes_lengths"])
+
+
+class ReduceMixin:
+    """
+    Reduce layer behaves identically to einops.reduce operation.
+
+    :param pattern: str, rearrangement pattern
+    :param reduction: one of available reductions ('min', 'max', 'sum', 'mean', 'prod'), case-sensitive
+    :param axes_lengths: any additional specification of dimensions
+
+    See einops.reduce for source_examples.
+    """
+
+    def __init__(self, pattern: str, reduction: str, **axes_lengths: Any):
+        super().__init__()
+        self.pattern = pattern
+        self.reduction = reduction
+        self.axes_lengths = axes_lengths
+        self._multirecipe = self.multirecipe()
+        self._axes_lengths = tuple(self.axes_lengths.items())
+
+    def __repr__(self):
+        params = f"{self.pattern!r}, {self.reduction!r}"
+        for axis, length in self.axes_lengths.items():
+            params += f", {axis}={length}"
+        return f"{self.__class__.__name__}({params})"
+
+    def multirecipe(self) -> Dict[int, TransformRecipe]:
+        try:
+            return _prepare_recipes_for_all_dims(
+                self.pattern, operation=self.reduction, axes_names=tuple(self.axes_lengths)
+            )
+        except EinopsError as e:
+            raise EinopsError(f" Error while preparing {self!r}\n {e}") from None
+
+    def _apply_recipe(self, x):
+        backend = get_backend(x)
+        return _apply_recipe(
+            backend=backend,
+            recipe=self._multirecipe[len(x.shape)],
+            tensor=x,
+            reduction_type=self.reduction,
+            axes_lengths=self._axes_lengths,
+        )
+
+    def __getstate__(self):
+        return {"pattern": self.pattern, "reduction": self.reduction, "axes_lengths": self.axes_lengths}
+
+    def __setstate__(self, state):
+        self.__init__(pattern=state["pattern"], reduction=state["reduction"], **state["axes_lengths"])