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# Copyright 2021 AlQuraishi Laboratory
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import math
from functools import partial
from typing import Any, Callable, Optional, Sequence, Tuple

import optree
import torch
from protenix.openfold_local.utils.tensor_utils import tensor_tree_map, tree_map


def _fetch_dims(tree):
    return optree.tree_flatten(optree.tree_map(lambda x: x.shape, tree))[0]


@torch.jit.ignore
def _flat_idx_to_idx(
    flat_idx: int,
    dims: Tuple[int],
) -> Tuple[int]:
    idx = []
    for d in reversed(dims):
        idx.append(flat_idx % d)
        flat_idx = flat_idx // d

    return tuple(reversed(idx))


@torch.jit.ignore
def _get_minimal_slice_set(
    start: Sequence[int],
    end: Sequence[int],
    dims: int,
    start_edges: Optional[Sequence[bool]] = None,
    end_edges: Optional[Sequence[bool]] = None,
) -> Sequence[Tuple[int]]:
    """
    Produces an ordered sequence of tensor slices that, when used in
    sequence on a tensor with shape dims, yields tensors that contain every
    leaf in the contiguous range [start, end]. Care is taken to yield a
    short sequence of slices, and perhaps even the shortest possible (I'm
    pretty sure it's the latter).

    end is INCLUSIVE.
    """

    # start_edges and end_edges both indicate whether, starting from any given
    # dimension, the start/end index is at the top/bottom edge of the
    # corresponding tensor, modeled as a tree
    def reduce_edge_list(l):
        tally = 1
        for i in range(len(l)):
            reversed_idx = -1 * (i + 1)
            l[reversed_idx] *= tally
            tally = l[reversed_idx]

    if start_edges is None:
        start_edges = [s == 0 for s in start]
        reduce_edge_list(start_edges)
    if end_edges is None:
        end_edges = [e == (d - 1) for e, d in zip(end, dims)]
        reduce_edge_list(end_edges)

    # Base cases. Either start/end are empty and we're done, or the final,
    # one-dimensional tensor can be simply sliced
    if len(start) == 0:
        return [tuple()]
    elif len(start) == 1:
        return [(slice(start[0], end[0] + 1),)]

    slices = []
    path = []

    # Dimensions common to start and end can be selected directly
    for s, e in zip(start, end):
        if s == e:
            path.append(slice(s, s + 1))
        else:
            break

    path = tuple(path)
    divergence_idx = len(path)

    # start == end, and we're done
    if divergence_idx == len(dims):
        return [tuple(path)]

    def upper():
        sdi = start[divergence_idx]
        return [
            path + (slice(sdi, sdi + 1),) + s
            for s in _get_minimal_slice_set(
                start[divergence_idx + 1 :],
                [d - 1 for d in dims[divergence_idx + 1 :]],
                dims[divergence_idx + 1 :],
                start_edges=start_edges[divergence_idx + 1 :],
                end_edges=[1 for _ in end_edges[divergence_idx + 1 :]],
            )
        ]

    def lower():
        edi = end[divergence_idx]
        return [
            path + (slice(edi, edi + 1),) + s
            for s in _get_minimal_slice_set(
                [0 for _ in start[divergence_idx + 1 :]],
                end[divergence_idx + 1 :],
                dims[divergence_idx + 1 :],
                start_edges=[1 for _ in start_edges[divergence_idx + 1 :]],
                end_edges=end_edges[divergence_idx + 1 :],
            )
        ]

    # If both start and end are at the edges of the subtree rooted at
    # divergence_idx, we can just select the whole subtree at once
    if start_edges[divergence_idx] and end_edges[divergence_idx]:
        slices.append(path + (slice(start[divergence_idx], end[divergence_idx] + 1),))
    # If just start is at the edge, we can grab almost all of the subtree,
    # treating only the ragged bottom edge as an edge case
    elif start_edges[divergence_idx]:
        slices.append(path + (slice(start[divergence_idx], end[divergence_idx]),))
        slices.extend(lower())
    # Analogous to the previous case, but the top is ragged this time
    elif end_edges[divergence_idx]:
        slices.extend(upper())
        slices.append(
            path + (slice(start[divergence_idx] + 1, end[divergence_idx] + 1),)
        )
    # If both sides of the range are ragged, we need to handle both sides
    # separately. If there's contiguous meat in between them, we can index it
    # in one big chunk
    else:
        slices.extend(upper())
        middle_ground = end[divergence_idx] - start[divergence_idx]
        if middle_ground > 1:
            slices.append(
                path + (slice(start[divergence_idx] + 1, end[divergence_idx]),)
            )
        slices.extend(lower())

    return [tuple(s) for s in slices]


@torch.jit.ignore
def _chunk_slice(
    t: torch.Tensor,
    flat_start: int,
    flat_end: int,
    no_batch_dims: int,
) -> torch.Tensor:
    """
    Equivalent to

        t.reshape((-1,) + t.shape[no_batch_dims:])[flat_start:flat_end]

    but without the need for the initial reshape call, which can be
    memory-intensive in certain situations. The only reshape operations
    in this function are performed on sub-tensors that scale with
    (flat_end - flat_start), the chunk size.
    """

    batch_dims = t.shape[:no_batch_dims]
    start_idx = list(_flat_idx_to_idx(flat_start, batch_dims))
    # _get_minimal_slice_set is inclusive
    end_idx = list(_flat_idx_to_idx(flat_end - 1, batch_dims))

    # Get an ordered list of slices to perform
    slices = _get_minimal_slice_set(
        start_idx,
        end_idx,
        batch_dims,
    )

    sliced_tensors = [t[s] for s in slices]

    return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]) for s in sliced_tensors])


def chunk_layer(
    layer: Callable,
    inputs: dict[str, Any],
    chunk_size: int,
    no_batch_dims: int,
    low_mem: bool = False,
    _out: Any = None,
    _add_into_out: bool = False,
) -> Any:
    """
    Implements the "chunking" procedure described in section 1.11.8.

    Layer outputs and inputs are assumed to be simple "pytrees,"
    consisting only of (arbitrarily nested) lists, tuples, and dicts with
    torch.Tensor leaves.

    Args:
        layer:
            The layer to be applied chunk-wise
        inputs:
            A (non-nested) dictionary of keyworded inputs. All leaves must
            be tensors and must share the same batch dimensions.
        chunk_size:
            The number of sub-batches per chunk. If multiple batch
            dimensions are specified, a "sub-batch" is defined as a single
            indexing of all batch dimensions simultaneously (s.t. the
            number of sub-batches is the product of the batch dimensions).
        no_batch_dims:
            How many of the initial dimensions of each input tensor can
            be considered batch dimensions.
        low_mem:
            Avoids flattening potentially large input tensors. Unnecessary
            in most cases, and is ever so slightly slower than the default
            setting.
    Returns:
        The reassembled output of the layer on the inputs.
    """
    if not (len(inputs) > 0):
        raise ValueError("Must provide at least one input")

    initial_dims = [shape[:no_batch_dims] for shape in _fetch_dims(inputs)]
    orig_batch_dims = tuple([max(s) for s in zip(*initial_dims)])

    def _prep_inputs(t):
        if not low_mem:
            if not sum(t.shape[:no_batch_dims]) == no_batch_dims:
                t = t.expand(orig_batch_dims + t.shape[no_batch_dims:])
            t = t.reshape(-1, *t.shape[no_batch_dims:])
        else:
            t = t.expand(orig_batch_dims + t.shape[no_batch_dims:])
        return t

    prepped_inputs = tensor_tree_map(_prep_inputs, inputs)
    prepped_outputs = None
    if _out is not None:
        reshape_fn = lambda t: t.view([-1] + list(t.shape[no_batch_dims:]))
        prepped_outputs = tensor_tree_map(reshape_fn, _out)

    flat_batch_dim = 1
    for d in orig_batch_dims:
        flat_batch_dim *= d

    no_chunks = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

    i = 0
    out = prepped_outputs
    for _ in range(no_chunks):
        # Chunk the input
        if not low_mem:
            select_chunk = lambda t: t[i : i + chunk_size] if t.shape[0] != 1 else t
        else:
            select_chunk = partial(
                _chunk_slice,
                flat_start=i,
                flat_end=min(flat_batch_dim, i + chunk_size),
                no_batch_dims=len(orig_batch_dims),
            )

        chunks = tensor_tree_map(select_chunk, prepped_inputs)

        # Run the layer on the chunk
        output_chunk = layer(**chunks)

        # Allocate space for the output
        if out is None:
            allocate = lambda t: t.new_zeros((flat_batch_dim,) + t.shape[1:])
            out = tensor_tree_map(allocate, output_chunk)

        # Put the chunk in its pre-allocated space
        out_type = type(output_chunk)
        if out_type is dict:

            def assign(d1, d2):
                for k, v in d1.items():
                    if type(v) is dict:
                        assign(v, d2[k])
                    else:
                        if _add_into_out:
                            v[i : i + chunk_size] += d2[k]
                        else:
                            v[i : i + chunk_size] = d2[k]

            assign(out, output_chunk)
        elif out_type is tuple:
            for x1, x2 in zip(out, output_chunk):
                if _add_into_out:
                    x1[i : i + chunk_size] += x2
                else:
                    x1[i : i + chunk_size] = x2
        elif out_type is torch.Tensor:
            if _add_into_out:
                out[i : i + chunk_size] += output_chunk
            else:
                out[i : i + chunk_size] = output_chunk
        else:
            raise ValueError("Not supported")

        i += chunk_size

    reshape = lambda t: t.view(orig_batch_dims + t.shape[1:])
    out = tensor_tree_map(reshape, out)

    return out


class ChunkSizeTuner:
    def __init__(
        self,
        # Heuristically, runtimes for most of the modules in the network
        # plateau earlier than this on all GPUs I've run the model on.
        max_chunk_size=512,
    ):
        self.max_chunk_size = max_chunk_size
        self.cached_chunk_size = None
        self.cached_arg_data = None

    def _determine_favorable_chunk_size(self, fn, args, min_chunk_size):
        logging.info("Tuning chunk size...")

        if min_chunk_size >= self.max_chunk_size:
            return min_chunk_size

        candidates = [2**l for l in range(int(math.log(self.max_chunk_size, 2)) + 1)]
        candidates = [c for c in candidates if c > min_chunk_size]
        candidates = [min_chunk_size] + candidates
        candidates[-1] += 4

        def test_chunk_size(chunk_size):
            try:
                with torch.no_grad():
                    fn(*args, chunk_size=chunk_size)
                return True
            except RuntimeError:
                return False

        min_viable_chunk_size_index = 0
        i = len(candidates) - 1
        while i > min_viable_chunk_size_index:
            viable = test_chunk_size(candidates[i])
            if not viable:
                i = (min_viable_chunk_size_index + i) // 2
            else:
                min_viable_chunk_size_index = i
                i = (i + len(candidates) - 1) // 2

        return candidates[min_viable_chunk_size_index]

    def _compare_arg_caches(self, ac1, ac2):
        consistent = True
        for a1, a2 in zip(ac1, ac2):
            assert type(ac1) == type(ac2)
            if type(ac1) is list or type(ac1) is tuple:
                consistent &= self._compare_arg_caches(a1, a2)
            elif type(ac1) is dict:
                a1_items = [v for _, v in sorted(a1.items(), key=lambda x: x[0])]
                a2_items = [v for _, v in sorted(a2.items(), key=lambda x: x[0])]
                consistent &= self._compare_arg_caches(a1_items, a2_items)
            else:
                consistent &= a1 == a2

        return consistent

    def tune_chunk_size(
        self,
        representative_fn: Callable,
        args: Tuple[Any],
        min_chunk_size: int,
    ) -> int:
        consistent = True
        remove_tensors = lambda a: a.shape if type(a) is torch.Tensor else a
        arg_data = tree_map(remove_tensors, args, object)
        if self.cached_arg_data is not None:
            # If args have changed shape/value, we need to re-tune
            assert len(self.cached_arg_data) == len(arg_data)
            consistent = self._compare_arg_caches(self.cached_arg_data, arg_data)
        else:
            # Otherwise, we can reuse the precomputed value
            consistent = False

        if not consistent:
            self.cached_chunk_size = self._determine_favorable_chunk_size(
                representative_fn,
                args,
                min_chunk_size,
            )
            self.cached_arg_data = arg_data

        return self.cached_chunk_size

	# Copyright 2021 AlQuraishi Laboratory
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	import logging
	import math
	from functools import partial
	from typing import Any, Callable, Optional, Sequence, Tuple

	import optree
	import torch
	from protenix.openfold_local.utils.tensor_utils import tensor_tree_map, tree_map


	def _fetch_dims(tree):
	return optree.tree_flatten(optree.tree_map(lambda x: x.shape, tree))[0]


	@torch.jit.ignore
	def _flat_idx_to_idx(
	flat_idx: int,
	dims: Tuple[int],
	) -> Tuple[int]:
	idx = []
	for d in reversed(dims):
	idx.append(flat_idx % d)
	flat_idx = flat_idx // d

	return tuple(reversed(idx))


	@torch.jit.ignore
	def _get_minimal_slice_set(
	start: Sequence[int],
	end: Sequence[int],
	dims: int,
	start_edges: Optional[Sequence[bool]] = None,
	end_edges: Optional[Sequence[bool]] = None,
	) -> Sequence[Tuple[int]]:
	"""
	Produces an ordered sequence of tensor slices that, when used in
	sequence on a tensor with shape dims, yields tensors that contain every
	leaf in the contiguous range [start, end]. Care is taken to yield a
	short sequence of slices, and perhaps even the shortest possible (I'm
	pretty sure it's the latter).

	end is INCLUSIVE.
	"""

	# start_edges and end_edges both indicate whether, starting from any given
	# dimension, the start/end index is at the top/bottom edge of the
	# corresponding tensor, modeled as a tree
	def reduce_edge_list(l):
	tally = 1
	for i in range(len(l)):
	reversed_idx = -1 * (i + 1)
	l[reversed_idx] *= tally
	tally = l[reversed_idx]

	if start_edges is None:
	start_edges = [s == 0 for s in start]
	reduce_edge_list(start_edges)
	if end_edges is None:
	end_edges = [e == (d - 1) for e, d in zip(end, dims)]
	reduce_edge_list(end_edges)

	# Base cases. Either start/end are empty and we're done, or the final,
	# one-dimensional tensor can be simply sliced
	if len(start) == 0:
	return [tuple()]
	elif len(start) == 1:
	return [(slice(start[0], end[0] + 1),)]

	slices = []
	path = []

	# Dimensions common to start and end can be selected directly
	for s, e in zip(start, end):
	if s == e:
	path.append(slice(s, s + 1))
	else:
	break

	path = tuple(path)
	divergence_idx = len(path)

	# start == end, and we're done
	if divergence_idx == len(dims):
	return [tuple(path)]

	def upper():
	sdi = start[divergence_idx]
	return [
	path + (slice(sdi, sdi + 1),) + s
	for s in _get_minimal_slice_set(
	start[divergence_idx + 1 :],
	[d - 1 for d in dims[divergence_idx + 1 :]],
	dims[divergence_idx + 1 :],
	start_edges=start_edges[divergence_idx + 1 :],
	end_edges=[1 for _ in end_edges[divergence_idx + 1 :]],
	)
	]

	def lower():
	edi = end[divergence_idx]
	return [
	path + (slice(edi, edi + 1),) + s
	for s in _get_minimal_slice_set(
	[0 for _ in start[divergence_idx + 1 :]],
	end[divergence_idx + 1 :],
	dims[divergence_idx + 1 :],
	start_edges=[1 for _ in start_edges[divergence_idx + 1 :]],
	end_edges=end_edges[divergence_idx + 1 :],
	)
	]

	# If both start and end are at the edges of the subtree rooted at
	# divergence_idx, we can just select the whole subtree at once
	if start_edges[divergence_idx] and end_edges[divergence_idx]:
	slices.append(path + (slice(start[divergence_idx], end[divergence_idx] + 1),))
	# If just start is at the edge, we can grab almost all of the subtree,
	# treating only the ragged bottom edge as an edge case
	elif start_edges[divergence_idx]:
	slices.append(path + (slice(start[divergence_idx], end[divergence_idx]),))
	slices.extend(lower())
	# Analogous to the previous case, but the top is ragged this time
	elif end_edges[divergence_idx]:
	slices.extend(upper())
	slices.append(
	path + (slice(start[divergence_idx] + 1, end[divergence_idx] + 1),)
	)
	# If both sides of the range are ragged, we need to handle both sides
	# separately. If there's contiguous meat in between them, we can index it
	# in one big chunk
	else:
	slices.extend(upper())
	middle_ground = end[divergence_idx] - start[divergence_idx]
	if middle_ground > 1:
	slices.append(
	path + (slice(start[divergence_idx] + 1, end[divergence_idx]),)
	)
	slices.extend(lower())

	return [tuple(s) for s in slices]


	@torch.jit.ignore
	def _chunk_slice(
	t: torch.Tensor,
	flat_start: int,
	flat_end: int,
	no_batch_dims: int,
	) -> torch.Tensor:
	"""
	Equivalent to

	t.reshape((-1,) + t.shape[no_batch_dims:])[flat_start:flat_end]

	but without the need for the initial reshape call, which can be
	memory-intensive in certain situations. The only reshape operations
	in this function are performed on sub-tensors that scale with
	(flat_end - flat_start), the chunk size.
	"""

	batch_dims = t.shape[:no_batch_dims]
	start_idx = list(_flat_idx_to_idx(flat_start, batch_dims))
	# _get_minimal_slice_set is inclusive
	end_idx = list(_flat_idx_to_idx(flat_end - 1, batch_dims))

	# Get an ordered list of slices to perform
	slices = _get_minimal_slice_set(
	start_idx,
	end_idx,
	batch_dims,
	)

	sliced_tensors = [t[s] for s in slices]

	return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]) for s in sliced_tensors])


	def chunk_layer(
	layer: Callable,
	inputs: dict[str, Any],
	chunk_size: int,
	no_batch_dims: int,
	low_mem: bool = False,
	_out: Any = None,
	_add_into_out: bool = False,
	) -> Any:
	"""
	Implements the "chunking" procedure described in section 1.11.8.

	Layer outputs and inputs are assumed to be simple "pytrees,"
	consisting only of (arbitrarily nested) lists, tuples, and dicts with
	torch.Tensor leaves.

	Args:
	layer:
	The layer to be applied chunk-wise
	inputs:
	A (non-nested) dictionary of keyworded inputs. All leaves must
	be tensors and must share the same batch dimensions.
	chunk_size:
	The number of sub-batches per chunk. If multiple batch
	dimensions are specified, a "sub-batch" is defined as a single
	indexing of all batch dimensions simultaneously (s.t. the
	number of sub-batches is the product of the batch dimensions).
	no_batch_dims:
	How many of the initial dimensions of each input tensor can
	be considered batch dimensions.
	low_mem:
	Avoids flattening potentially large input tensors. Unnecessary
	in most cases, and is ever so slightly slower than the default
	setting.
	Returns:
	The reassembled output of the layer on the inputs.
	"""
	if not (len(inputs) > 0):
	raise ValueError("Must provide at least one input")

	initial_dims = [shape[:no_batch_dims] for shape in _fetch_dims(inputs)]
	orig_batch_dims = tuple([max(s) for s in zip(*initial_dims)])

	def _prep_inputs(t):
	if not low_mem:
	if not sum(t.shape[:no_batch_dims]) == no_batch_dims:
	t = t.expand(orig_batch_dims + t.shape[no_batch_dims:])
	t = t.reshape(-1, *t.shape[no_batch_dims:])
	else:
	t = t.expand(orig_batch_dims + t.shape[no_batch_dims:])
	return t

	prepped_inputs = tensor_tree_map(_prep_inputs, inputs)
	prepped_outputs = None
	if _out is not None:
	reshape_fn = lambda t: t.view([-1] + list(t.shape[no_batch_dims:]))
	prepped_outputs = tensor_tree_map(reshape_fn, _out)

	flat_batch_dim = 1
	for d in orig_batch_dims:
	flat_batch_dim *= d

	no_chunks = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

	i = 0
	out = prepped_outputs
	for _ in range(no_chunks):
	# Chunk the input
	if not low_mem:
	select_chunk = lambda t: t[i : i + chunk_size] if t.shape[0] != 1 else t
	else:
	select_chunk = partial(
	_chunk_slice,
	flat_start=i,
	flat_end=min(flat_batch_dim, i + chunk_size),
	no_batch_dims=len(orig_batch_dims),
	)

	chunks = tensor_tree_map(select_chunk, prepped_inputs)

	# Run the layer on the chunk
	output_chunk = layer(**chunks)

	# Allocate space for the output
	if out is None:
	allocate = lambda t: t.new_zeros((flat_batch_dim,) + t.shape[1:])
	out = tensor_tree_map(allocate, output_chunk)

	# Put the chunk in its pre-allocated space
	out_type = type(output_chunk)
	if out_type is dict:

	def assign(d1, d2):
	for k, v in d1.items():
	if type(v) is dict:
	assign(v, d2[k])
	else:
	if _add_into_out:
	v[i : i + chunk_size] += d2[k]
	else:
	v[i : i + chunk_size] = d2[k]

	assign(out, output_chunk)
	elif out_type is tuple:
	for x1, x2 in zip(out, output_chunk):
	if _add_into_out:
	x1[i : i + chunk_size] += x2
	else:
	x1[i : i + chunk_size] = x2
	elif out_type is torch.Tensor:
	if _add_into_out:
	out[i : i + chunk_size] += output_chunk
	else:
	out[i : i + chunk_size] = output_chunk
	else:
	raise ValueError("Not supported")

	i += chunk_size

	reshape = lambda t: t.view(orig_batch_dims + t.shape[1:])
	out = tensor_tree_map(reshape, out)

	return out


	class ChunkSizeTuner:
	def __init__(
	self,
	# Heuristically, runtimes for most of the modules in the network
	# plateau earlier than this on all GPUs I've run the model on.
	max_chunk_size=512,
	):
	self.max_chunk_size = max_chunk_size
	self.cached_chunk_size = None
	self.cached_arg_data = None

	def _determine_favorable_chunk_size(self, fn, args, min_chunk_size):
	logging.info("Tuning chunk size...")

	if min_chunk_size >= self.max_chunk_size:
	return min_chunk_size

	candidates = [2**l for l in range(int(math.log(self.max_chunk_size, 2)) + 1)]
	candidates = [c for c in candidates if c > min_chunk_size]
	candidates = [min_chunk_size] + candidates
	candidates[-1] += 4

	def test_chunk_size(chunk_size):
	try:
	with torch.no_grad():
	fn(*args, chunk_size=chunk_size)
	return True
	except RuntimeError:
	return False

	min_viable_chunk_size_index = 0
	i = len(candidates) - 1
	while i > min_viable_chunk_size_index:
	viable = test_chunk_size(candidates[i])
	if not viable:
	i = (min_viable_chunk_size_index + i) // 2
	else:
	min_viable_chunk_size_index = i
	i = (i + len(candidates) - 1) // 2

	return candidates[min_viable_chunk_size_index]

	def _compare_arg_caches(self, ac1, ac2):
	consistent = True
	for a1, a2 in zip(ac1, ac2):
	assert type(ac1) == type(ac2)
	if type(ac1) is list or type(ac1) is tuple:
	consistent &= self._compare_arg_caches(a1, a2)
	elif type(ac1) is dict:
	a1_items = [v for _, v in sorted(a1.items(), key=lambda x: x[0])]
	a2_items = [v for _, v in sorted(a2.items(), key=lambda x: x[0])]
	consistent &= self._compare_arg_caches(a1_items, a2_items)
	else:
	consistent &= a1 == a2

	return consistent

	def tune_chunk_size(
	self,
	representative_fn: Callable,
	args: Tuple[Any],
	min_chunk_size: int,
	) -> int:
	consistent = True
	remove_tensors = lambda a: a.shape if type(a) is torch.Tensor else a
	arg_data = tree_map(remove_tensors, args, object)
	if self.cached_arg_data is not None:
	# If args have changed shape/value, we need to re-tune
	assert len(self.cached_arg_data) == len(arg_data)
	consistent = self._compare_arg_caches(self.cached_arg_data, arg_data)
	else:
	# Otherwise, we can reuse the precomputed value
	consistent = False

	if not consistent:
	self.cached_chunk_size = self._determine_favorable_chunk_size(
	representative_fn,
	args,
	min_chunk_size,
	)
	self.cached_arg_data = arg_data

	return self.cached_chunk_size