






import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch


def 							_a	(  UpperCamelCase_       :					Union[str, Any] )			-> Dict:  # picklable for multiprocessing
						"""simple docstring"""







						return x.sum()


def 							_a	(  UpperCamelCase_       :					Dict )			-> int:  # picklable for multiprocessing
						"""simple docstring"""







						return i + 1


@dataclass
class   lowercase__  :
	a_       =42
	a_       =42





class   lowercase__  (      _UpperCAmelCase						):

	def 		UpperCAmelCase	(				self							)->       Any:







							'''simple docstring'''





							lowerCAmelCase__			   =    {}
							lowerCAmelCase__			   =    []
							lowerCAmelCase__			   =    1
							lowerCAmelCase__			   =    [1, 2]
							lowerCAmelCase__			   =    {"a": 1, "b": 2}
							lowerCAmelCase__			   =    {"a": [1, 2], "b": [3, 4]}
							lowerCAmelCase__			   =    {"a": {"1": 1}, "b": 2}
							lowerCAmelCase__			   =    {"a": 1, "b": 2, "c": 3, "d": 4}
							lowerCAmelCase__			   =    {}
							lowerCAmelCase__			   =    []
							lowerCAmelCase__			   =    2
							lowerCAmelCase__			   =    [2, 3]
							lowerCAmelCase__			   =    {"a": 2, "b": 3}
							lowerCAmelCase__			   =    {"a": [2, 3], "b": [4, 5]}
							lowerCAmelCase__			   =    {"a": {"1": 2}, "b": 3}
							lowerCAmelCase__			   =    {"a": 2, "b": 3, "c": 4, "d": 5}
							self.assertEqual(map_nested(A_		,     A_							)		,     A_							)
							self.assertEqual(map_nested(A_		,     A_							)		,     A_							)
							self.assertEqual(map_nested(A_		,     A_							)		,     A_							)
							self.assertEqual(map_nested(A_		,     A_							)		,     A_							)
							self.assertEqual(map_nested(A_		,     A_							)		,     A_							)
							self.assertEqual(map_nested(A_		,     A_							)		,     A_							)
							self.assertEqual(map_nested(A_		,     A_							)		,     A_							)
							self.assertEqual(map_nested(A_		,     A_							)		,     A_							)

							lowerCAmelCase__			   =    2
							self.assertEqual(map_nested(A_		,     A_		,     num_proc=A_							)		,     A_							)
							self.assertEqual(map_nested(A_		,     A_		,     num_proc=A_							)		,     A_							)
							self.assertEqual(map_nested(A_		,     A_		,     num_proc=A_							)		,     A_							)
							self.assertEqual(map_nested(A_		,     A_		,     num_proc=A_							)		,     A_							)
							self.assertEqual(map_nested(A_		,     A_		,     num_proc=A_							)		,     A_							)
							self.assertEqual(map_nested(A_		,     A_		,     num_proc=A_							)		,     A_							)
							self.assertEqual(map_nested(A_		,     A_		,     num_proc=A_							)		,     A_							)
							self.assertEqual(map_nested(A_		,     A_		,     num_proc=A_							)		,     A_							)

							lowerCAmelCase__			   =    {"a": np.eye(2							), "b": np.zeros(3							), "c": np.ones(2							)}
							lowerCAmelCase__			   =    {"a": 2, "b": 0, "c": 2}
							lowerCAmelCase__			   =    {
							    "a": np.eye(2							).astype(A_							),
							    "b": np.zeros(3							).astype(A_							),
							    "c": np.ones(2							).astype(A_							),
							}
							self.assertEqual(map_nested(A_		,     A_		,     map_numpy=A_							)		,     A_							)
							self.assertEqual(
							    {k: v.tolist() for k, v in map_nested(A_		,     A_		,     map_numpy=A_							).items()}		,     {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}		,     )
							self.assertEqual(map_nested(A_		,     A_		,     map_numpy=A_		,     num_proc=A_							)		,     A_							)
							self.assertEqual(
							    {k: v.tolist() for k, v in map_nested(A_		,     A_		,     map_numpy=A_		,     num_proc=A_							).items()}		,     {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}		,     )
							with self.assertRaises(A_							):  # can't pickle a local lambda
													map_nested(lambda __UpperCAmelCase							: x + 1		,     A_		,     num_proc=A_							)

	def 		UpperCAmelCase	(				self							)->       Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__			   =    {"a": 1, "b": 2}
							lowerCAmelCase__			   =    {"a": 3, "b": 4}
							lowerCAmelCase__			   =    {"a": 5, "b": 6}
							lowerCAmelCase__			   =    sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]							)
							self.assertEqual(sorted(zip_dict(A_		,     A_		,     A_							)							)		,     A_							)


	def 		UpperCAmelCase	(				self							)->       List[Any]:







							'''simple docstring'''





							class   lowercase__  :
								a_       ="""bar"""





							lowerCAmelCase__			   =    Foo()
							self.assertEqual(foo.my_attr		,     "bar"							)
							with temporary_assignment(A_		,     "my_attr"		,     "BAR"							):
													self.assertEqual(foo.my_attr		,     "BAR"							)
							self.assertEqual(foo.my_attr		,     "bar"							)





@pytest.mark.parametrize(
    "iterable_length, num_proc, expected_num_proc"     , [
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]     , )
def 							_a	(  UpperCamelCase_       :					int     , UpperCamelCase_       :					Optional[int]     , UpperCamelCase_       :					Optional[Any] )			-> Optional[Any]:
						"""simple docstring"""







						with patch("datasets.utils.py_utils._single_map_nested" ) as mock_single_map_nested, patch(
						    "datasets.parallel.parallel.Pool" ) as mock_multiprocessing_pool:
												lowerCAmelCase__			   =    {F"{i}": i for i in range(lowercase__ )}
												lowerCAmelCase__			   =    map_nested(lambda UpperCamelCase_ : x + 10     , lowercase__     , num_proc=lowercase__     , parallel_min_length=16 )
												if expected_num_proc == 1:
																		assert mock_single_map_nested.called
																		assert not mock_multiprocessing_pool.called
												else:
																		assert not mock_single_map_nested.called
																		assert mock_multiprocessing_pool.called
																		assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc


class   lowercase__  (      _UpperCAmelCase						):

	@require_tf
	def 		UpperCAmelCase	(				self							)->       Optional[Any]:







							'''simple docstring'''





							import tensorflow as tf
							from tensorflow.keras import layers

							lowerCAmelCase__			   =    layers.Dense(2							)

							def gen_random_output():
													lowerCAmelCase__			   =    tf.random.uniform((1, 3)							)
													return model(A_							).numpy()

							with temp_seed(42		,     set_tensorflow=A_							):
													lowerCAmelCase__			   =    gen_random_output()
							with temp_seed(42		,     set_tensorflow=A_							):
													lowerCAmelCase__			   =    gen_random_output()
							lowerCAmelCase__			   =    gen_random_output()

							np.testing.assert_equal(A_		,     A_							)
							self.assertGreater(np.abs(outa - outa							).sum()		,     0							)

	@require_torch
	def 		UpperCAmelCase	(				self							)->       Tuple:







							'''simple docstring'''





							import torch

							def gen_random_output():
													lowerCAmelCase__			   =    torch.nn.Linear(3		,     2							)
													lowerCAmelCase__			   =    torch.rand(1		,     3							)
													return model(A_							).detach().numpy()

							with temp_seed(42		,     set_pytorch=A_							):
													lowerCAmelCase__			   =    gen_random_output()
							with temp_seed(42		,     set_pytorch=A_							):
													lowerCAmelCase__			   =    gen_random_output()
							lowerCAmelCase__			   =    gen_random_output()

							np.testing.assert_equal(A_		,     A_							)
							self.assertGreater(np.abs(outa - outa							).sum()		,     0							)


	def 		UpperCAmelCase	(				self							)->       Tuple:







							'''simple docstring'''





							def gen_random_output():
													return np.random.rand(1		,     3							)

							with temp_seed(42							):
													lowerCAmelCase__			   =    gen_random_output()
							with temp_seed(42							):
													lowerCAmelCase__			   =    gen_random_output()
							lowerCAmelCase__			   =    gen_random_output()

							np.testing.assert_equal(A_		,     A_							)
							self.assertGreater(np.abs(outa - outa							).sum()		,     0							)





@pytest.mark.parametrize("input_data"     , [{}] )
def 							_a	(  UpperCamelCase_       :					Dict )			-> Tuple:
						"""simple docstring"""







						lowerCAmelCase__			   =    NestedDataStructure(lowercase__ ).data
						assert output_data == input_data


@pytest.mark.parametrize(
    "data, expected_output"     , [
        ({}, []),
        ([], []),
        ("foo", ["foo"]),
        (["foo", "bar"], ["foo", "bar"]),
        ([["foo", "bar"]], ["foo", "bar"]),
        ([[["foo"], ["bar"]]], ["foo", "bar"]),
        ([[["foo"], "bar"]], ["foo", "bar"]),
        ({"a": 1, "b": 2}, [1, 2]),
        ({"a": [1, 2], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, [4]]}, [1, 2, 3, 4]),
        ({"a": {"1": 1}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": [2]}, [1, 2]),
    ]     , )
def 							_a	(  UpperCamelCase_       :					Any     , UpperCamelCase_       :					List[str] )			-> Dict:
						"""simple docstring"""







						lowerCAmelCase__			   =    NestedDataStructure(lowercase__ ).flatten()
						assert output == expected_output


def 							_a	(  )			-> Union[str, Any]:
						"""simple docstring"""







						lowerCAmelCase__			   =    A(x=1     , y="foobar" )
						lowerCAmelCase__			   =    {"x": 1, "y": "foobar"}
						assert asdict(lowercase__ ) == expected_output

						lowerCAmelCase__			   =    {"a": {"b": A(x=10     , y="foo" )}, "c": [A(x=20     , y="bar" )]}
						lowerCAmelCase__			   =    {"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
						assert asdict(lowercase__ ) == expected_output

						with pytest.raises(lowercase__ ):
												asdict([1, A(x=10     , y="foo" )] )


def 							_a	(  UpperCamelCase_       :					str )			-> Optional[int]:
						"""simple docstring"""







						return text.split()


def 							_a	(  UpperCamelCase_       :					Optional[Any] )			-> Union[str, Any]:
						"""simple docstring"""







						yield (time.time(), content)
						time.sleep(2 )
						yield (time.time(), content)





def 							_a	(  )			-> List[str]:
						"""simple docstring"""







						with Pool(2 ) as pool:
												lowerCAmelCase__			   =    list(iflatmap_unordered(lowercase__     , _split_text     , kwargs_iterable=[{"text": "hello there"}] * 10 ) )
												assert out.count("hello" ) == 10
												assert out.count("there" ) == 10
												assert len(lowercase__ ) == 20

						# check multiprocess from pathos (uses dill for pickling)
						with multiprocess.Pool(2 ) as pool:
												lowerCAmelCase__			   =    list(iflatmap_unordered(lowercase__     , _split_text     , kwargs_iterable=[{"text": "hello there"}] * 10 ) )
												assert out.count("hello" ) == 10
												assert out.count("there" ) == 10
												assert len(lowercase__ ) == 20

						# check that we get items as fast as possible
						with Pool(2 ) as pool:
												lowerCAmelCase__			   =    []
												for yield_time, content in iflatmap_unordered(
												    lowercase__     , _aseconds_generator_of_aitems_with_timing     , kwargs_iterable=[{"content": "a"}, {"content": "b"}] ):
																		assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
																		out.append(lowercase__ )
												assert out.count("a" ) == 2
												assert out.count("b" ) == 2
												assert len(lowercase__ ) == 4










"""simple docstring"""




import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map





def       a_ (	lowercase__ :Union[dict, list, tuple, torch.Tensor]     ):
			__lowerCamelCase          =      []
			if isinstance(lowercase__,			lowercase__     ):
						for v in tree.values():
									shapes.extend(_fetch_dims(lowercase__     )     )
			elif isinstance(lowercase__,			(list, tuple)     ):
						for t in tree:
									shapes.extend(_fetch_dims(lowercase__     )     )
			elif isinstance(lowercase__,			torch.Tensor     ):
						shapes.append(tree.shape     )
			else:
						raise ValueError("""Not supported"""     )

			return shapes





@torch.jit.ignore
def       a_ (	lowercase__ :int,			lowercase__ :Tuple[int, ...]     ):
			__lowerCamelCase          =      []
			for d in reversed(lowercase__     ):
						idx.append(flat_idx % d     )
						__lowerCamelCase          =      flat_idx // d

			return tuple(reversed(lowercase__     )     )





@torch.jit.ignore
def       a_ (	lowercase__ :Sequence[int],			lowercase__ :Sequence[int],			lowercase__ :Sequence[int],			lowercase__ :Optional[Sequence[bool]] = None,			lowercase__ :Optional[Sequence[bool]] = None,			):

			# 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(lowercase__ :List[bool]     ) -> None:
						__lowerCamelCase          =      True
						for i in range(len(lowercase__     )     ):
									__lowerCamelCase          =      -1 * (i + 1)
									l[reversed_idx] &= tally
									__lowerCamelCase          =      l[reversed_idx]

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

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

			__lowerCamelCase          =      []
			__lowerCamelCase          =      []

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

			__lowerCamelCase          =      tuple(lowercase__     )
			__lowerCamelCase          =      len(lowercase__     )

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

			def upper() -> Tuple[Tuple[slice, ...], ...]:
						assert start_edges is not None
						assert end_edges is not None

						__lowerCamelCase          =      start[divergence_idx]
						return tuple(
						    path + (slice(lowercase__,			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=[True for _ in end_edges[divergence_idx + 1 :]],			)     )

			def lower() -> Tuple[Tuple[slice, ...], ...]:
						assert start_edges is not None
						assert end_edges is not None

						__lowerCamelCase          =      end[divergence_idx]
						return tuple(
						    path + (slice(lowercase__,			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=[True 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()     )
						__lowerCamelCase          =      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 slices





@torch.jit.ignore
def       a_ (	lowercase__ :torch.Tensor,			lowercase__ :int,			lowercase__ :int,			lowercase__ :int     ):
			__lowerCamelCase          =      t.shape[:no_batch_dims]
			__lowerCamelCase          =      list(_flat_idx_to_idx(lowercase__,			lowercase__     )     )
			# _get_minimal_slice_set is inclusive
			__lowerCamelCase          =      list(_flat_idx_to_idx(flat_end - 1,			lowercase__     )     )

			# Get an ordered list of slices to perform
			__lowerCamelCase          =      _get_minimal_slice_set(
			    lowercase__,			lowercase__,			lowercase__,			)

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

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





def       a_ (	lowercase__ :Callable,			lowercase__ :Dict[str, Any],			lowercase__ :int,			lowercase__ :int,			lowercase__ :bool = False,			lowercase__ :Any = None,			lowercase__ :bool = False,			):
			if not (len(lowercase__     ) > 0):
						raise ValueError("""Must provide at least one input"""     )

			__lowerCamelCase          =      [shape[:no_batch_dims] for shape in _fetch_dims(lowercase__     )]
			__lowerCamelCase          =      tuple([max(lowercase__     ) for s in zip(*lowercase__     )]     )

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

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

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

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

			def _select_chunk(lowercase__ :torch.Tensor     ) -> torch.Tensor:
						return t[i : i + chunk_size] if t.shape[0] != 1 else t

			__lowerCamelCase          =      0
			__lowerCamelCase          =      prepped_outputs
			for _ in range(lowercase__     ):
						# Chunk the input
						if not low_mem:
									__lowerCamelCase          =      _select_chunk
						else:
									__lowerCamelCase          =      partial(
									    _chunk_slice,			flat_start=lowercase__,			flat_end=min(lowercase__,			i + chunk_size     ),			no_batch_dims=len(lowercase__     ),			)

						__lowerCamelCase          =      tensor_tree_map(lowercase__,			lowercase__     )

						# Run the layer on the chunk
						__lowerCamelCase          =      layer(**lowercase__     )

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

						# Put the chunk in its pre-allocated space
						if isinstance(lowercase__,			lowercase__     ):

									def assign(lowercase__ :dict,			lowercase__ :dict     ) -> None:
												for k, v in da.items():
															if isinstance(lowercase__,			lowercase__     ):
																		assign(lowercase__,			da[k]     )
															else:
																		if _add_into_out:
																					v[i : i + chunk_size] += da[k]
																		else:
																					__lowerCamelCase          =      da[k]

									assign(lowercase__,			lowercase__     )
						elif isinstance(lowercase__,			lowercase__     ):
									for xa, xa in zip(lowercase__,			lowercase__     ):
												if _add_into_out:
															xa[i : i + chunk_size] += xa
												else:
															__lowerCamelCase          =      xa
						elif isinstance(lowercase__,			torch.Tensor     ):
									if _add_into_out:
												out[i : i + chunk_size] += output_chunk
									else:
												__lowerCamelCase          =      output_chunk
						else:
									raise ValueError("""Not supported"""     )

						i += chunk_size

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

			return out




class 			__snake_case   :




							def __init__(     self:    Union[str, Any]     ,							A_:    int = 5_12     ,							):
										__lowerCamelCase          =      max_chunk_size
										__lowerCamelCase          =      None
										__lowerCamelCase          =      None




							def   __a   (     self:    Tuple     ,							A_:    Callable     ,							A_:    tuple     ,							A_:    int   ):
										logging.info("""Tuning chunk size..."""   )

										if min_chunk_size >= self.max_chunk_size:
													return min_chunk_size

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

										def test_chunk_size(A_:    int   ) -> bool:
													try:
																with torch.no_grad():
																			fn(*A_     ,							chunk_size=A_   )
																return True
													except RuntimeError:
																return False

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

										return candidates[min_viable_chunk_size_index]




							def   __a   (     self:    Tuple     ,							A_:    Iterable     ,							A_:    Iterable   ):
										__lowerCamelCase          =      True
										for aa, aa in zip(A_     ,							A_   ):
													assert type(A_   ) == type(A_   )
													if isinstance(A_     ,							(list, tuple)   ):
																consistent &= self._compare_arg_caches(A_     ,							A_   )
													elif isinstance(A_     ,							A_   ):
																__lowerCamelCase          =      [v for _, v in sorted(aa.items()     ,							key=lambda A_   : x[0]   )]
																__lowerCamelCase          =      [v for _, v in sorted(aa.items()     ,							key=lambda A_   : x[0]   )]
																consistent &= self._compare_arg_caches(A_     ,							A_   )
													else:
																consistent &= aa == aa

										return consistent




							def   __a   (     self:    str     ,							A_:    Callable     ,							A_:    tuple     ,							A_:    int     ,							):
										__lowerCamelCase          =      True
										__lowerCamelCase          =      tree_map(lambda A_   : a.shape if isinstance(A_     ,							torch.Tensor   ) else a     ,							A_     ,							A_   )
										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(A_   )
													__lowerCamelCase          =      self._compare_arg_caches(self.cached_arg_data     ,							A_   )
										else:
													# Otherwise, we can reuse the precomputed value
													__lowerCamelCase          =      False

										if not consistent:
													__lowerCamelCase          =      self._determine_favorable_chunk_size(
													    A_     ,							A_     ,							A_     ,							)
													__lowerCamelCase          =      arg_data

										assert self.cached_chunk_size is not None

										return self.cached_chunk_size




