



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				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[int]    =    []
							if isinstance(_lowerCamelCase				, _lowerCamelCase  ):
														for v in tree.values():
																					shapes.extend(_fetch_dims(_lowerCamelCase  )  )
							elif isinstance(_lowerCamelCase				, (list, tuple)  ):
														for t in tree:
																					shapes.extend(_fetch_dims(_lowerCamelCase  )  )
							elif isinstance(_lowerCamelCase				, torch.Tensor  ):
														shapes.append(tree.shape  )
							else:
														raise ValueError("Not supported"  )

							return shapes






@torch.jit.ignore
def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							str    =    []
							for d in reversed(_lowerCamelCase  ):
														idx.append(flat_idx % d  )
														_lowerCAmelCase     :							Dict    =    flat_idx // d

							return tuple(reversed(_lowerCamelCase  )  )






@torch.jit.ignore
def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = None				, _lowerCamelCase = None				, ):





							'''simple docstring'''




							def reduce_edge_list(_lowerCamelCase  ) -> None:
														_lowerCAmelCase     :							List[str]    =    True
														for i in range(len(_lowerCamelCase  )  ):
																					_lowerCAmelCase     :							Any    =    -1 * (i + 1)
																					l[reversed_idx] &= tally
																					_lowerCAmelCase     :							str    =    l[reversed_idx]

							if start_edges is None:
														_lowerCAmelCase     :							Optional[int]    =    [s == 0 for s in start]
														reduce_edge_list(_lowerCamelCase  )
							if end_edges is None:
														_lowerCAmelCase     :							Dict    =    [e == (d - 1) for e, d in zip(_lowerCamelCase				, _lowerCamelCase  )]
														reduce_edge_list(_lowerCamelCase  )

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

							_lowerCAmelCase     :							List[Tuple[slice, ...]]    =    []
							_lowerCAmelCase     :							List[slice]    =    []

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

							_lowerCAmelCase     :							Tuple[slice, ...]    =    tuple(_lowerCamelCase  )
							_lowerCAmelCase     :							Tuple    =    len(_lowerCamelCase  )

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

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

														_lowerCAmelCase     :							Optional[int]    =    start[divergence_idx]
														return tuple(
														    path + (slice(_lowerCamelCase				, 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     :							str    =    end[divergence_idx]
														return tuple(
														    path + (slice(_lowerCamelCase				, 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     :							Tuple    =    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				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Any    =    t.shape[:no_batch_dims]
							_lowerCAmelCase     :							List[str]    =    list(_flat_idx_to_idx(_lowerCamelCase				, _lowerCamelCase  )  )
							# _get_minimal_slice_set is inclusive
							_lowerCAmelCase     :							List[str]    =    list(_flat_idx_to_idx(flat_end - 1				, _lowerCamelCase  )  )

							# Get an ordered list of slices to perform
							_lowerCAmelCase     :							str    =    _get_minimal_slice_set(
							    _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, )

							_lowerCAmelCase     :							Optional[int]    =    [t[s] for s in slices]

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






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = False				, _lowerCamelCase = None				, _lowerCamelCase = False				, ):





							'''simple docstring'''




							if not (len(_lowerCamelCase  ) > 0):
														raise ValueError("Must provide at least one input"  )

							_lowerCAmelCase     :							Tuple    =    [shape[:no_batch_dims] for shape in _fetch_dims(_lowerCamelCase  )]
							_lowerCAmelCase     :							Tuple    =    tuple([max(_lowerCamelCase  ) for s in zip(*_lowerCamelCase  )]  )

							def _prep_inputs(_lowerCamelCase  ) -> torch.Tensor:
														if not low_mem:
																					if not sum(t.shape[:no_batch_dims]  ) == no_batch_dims:
																												_lowerCAmelCase     :							Dict    =    t.expand(orig_batch_dims + t.shape[no_batch_dims:]  )
																					_lowerCAmelCase     :							Optional[Any]    =    t.reshape(-1				, *t.shape[no_batch_dims:]  )
														else:
																					_lowerCAmelCase     :							Union[str, Any]    =    t.expand(orig_batch_dims + t.shape[no_batch_dims:]  )
														return t

							_lowerCAmelCase     :							Dict[str, Any]    =    tensor_tree_map(_prep_inputs				, _lowerCamelCase  )
							_lowerCAmelCase     :							Tuple    =    None
							if _out is not None:
														_lowerCAmelCase     :							List[Any]    =    tensor_tree_map(lambda _lowerCamelCase  : t.view([-1] + list(t.shape[no_batch_dims:]  )  )				, _out  )

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

							_lowerCAmelCase     :							Union[str, Any]    =    flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

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

							_lowerCAmelCase     :							int    =    0
							_lowerCAmelCase     :							Any    =    prepped_outputs
							for _ in range(_lowerCamelCase  ):
														# Chunk the input
														if not low_mem:
																					_lowerCAmelCase     :							List[str]    =    _select_chunk
														else:
																					_lowerCAmelCase     :							Dict    =    partial(
																					    _chunk_slice				, flat_start=_lowerCamelCase				, flat_end=min(_lowerCamelCase				, i + chunk_size  )				, no_batch_dims=len(_lowerCamelCase  )				, )

														_lowerCAmelCase     :							Dict[str, Any]    =    tensor_tree_map(_lowerCamelCase				, _lowerCamelCase  )

														# Run the layer on the chunk
														_lowerCAmelCase     :							int    =    layer(**_lowerCamelCase  )

														# Allocate space for the output
														if out is None:
																					_lowerCAmelCase     :							Union[str, Any]    =    tensor_tree_map(lambda _lowerCamelCase  : t.new_zeros((flat_batch_dim,) + t.shape[1:]  )				, _lowerCamelCase  )

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

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

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

														i += chunk_size

							_lowerCAmelCase     :							Any    =    tensor_tree_map(lambda _lowerCamelCase  : t.view(orig_batch_dims + t.shape[1:]  )				, _lowerCamelCase  )

							return out



class   UpperCAmelCase_    :







							def __init__(   self,			__a = 512,			):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    max_chunk_size
														_lowerCAmelCase     :							Optional[int]    =    None
														_lowerCAmelCase     :							Optional[tuple]    =    None







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														logging.info("Tuning chunk size...")

														if min_chunk_size >= self.max_chunk_size:
																					return min_chunk_size

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

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

														_lowerCAmelCase     :							List[Any]    =    0
														_lowerCAmelCase     :							List[str]    =    len(__a) - 1
														while i > min_viable_chunk_size_index:
																					_lowerCAmelCase     :							Tuple    =    test_chunk_size(candidates[i])
																					if not viable:
																												_lowerCAmelCase     :							int    =    (min_viable_chunk_size_index + i) // 2
																					else:
																												_lowerCAmelCase     :							Any    =    i
																												_lowerCAmelCase     :							Union[str, Any]    =    (i + len(__a) - 1) // 2

														return candidates[min_viable_chunk_size_index]







							def    snake_case__      (   self,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    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     :							Optional[Any]    =    [v for _, v in sorted(aa.items(),			key=lambda __a: x[0])]
																												_lowerCAmelCase     :							Dict    =    [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    snake_case__      (   self,			__a,			__a,			__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    True
														_lowerCAmelCase     :							tuple    =    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     :							Tuple    =    self._compare_arg_caches(self.cached_arg_data,			__a)
														else:
																					# Otherwise, we can reuse the precomputed value
																					_lowerCAmelCase     :							Union[str, Any]    =    False

														if not consistent:
																					_lowerCAmelCase     :							Any    =    self._determine_favorable_chunk_size(
																					    __a,			__a,			__a,			)
																					_lowerCAmelCase     :							Any    =    arg_data

														assert self.cached_chunk_size is not None

														return self.cached_chunk_size





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case		=  logging.get_logger(__name__)

_snake_case		=  {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}



class   UpperCAmelCase_    (       a   ,					a):
							lowerCamelCase__   			=		'swin'

							lowerCamelCase__   			=		{
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}







							def __init__(   self,			__a=224,			__a=4,			__a=3,			__a=96,			__a=[2, 2, 6, 2],			__a=[3, 6, 12, 24],			__a=7,			__a=4.0,			__a=True,			__a=0.0,			__a=0.0,			__a=0.1,			__a="gelu",			__a=False,			__a=0.02,			__a=1E-5,			__a=32,			__a=None,			__a=None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a)

														_lowerCAmelCase     :							Any    =    image_size
														_lowerCAmelCase     :							Union[str, Any]    =    patch_size
														_lowerCAmelCase     :							Tuple    =    num_channels
														_lowerCAmelCase     :							List[Any]    =    embed_dim
														_lowerCAmelCase     :							Tuple    =    depths
														_lowerCAmelCase     :							Optional[Any]    =    len(__a)
														_lowerCAmelCase     :							int    =    num_heads
														_lowerCAmelCase     :							int    =    window_size
														_lowerCAmelCase     :							int    =    mlp_ratio
														_lowerCAmelCase     :							List[Any]    =    qkv_bias
														_lowerCAmelCase     :							str    =    hidden_dropout_prob
														_lowerCAmelCase     :							Union[str, Any]    =    attention_probs_dropout_prob
														_lowerCAmelCase     :							Any    =    drop_path_rate
														_lowerCAmelCase     :							int    =    hidden_act
														_lowerCAmelCase     :							Tuple    =    use_absolute_embeddings
														_lowerCAmelCase     :							Optional[int]    =    layer_norm_eps
														_lowerCAmelCase     :							Tuple    =    initializer_range
														_lowerCAmelCase     :							Tuple    =    encoder_stride
														# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														_lowerCAmelCase     :							List[str]    =    int(embed_dim * 2 ** (len(__a) - 1))
														_lowerCAmelCase     :							List[Any]    =    ["stem"] + [f"stage{idx}" for idx in range(1,			len(__a) + 1)]
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[int]    =    get_aligned_output_features_output_indices(
														    out_features=__a,			out_indices=__a,			stage_names=self.stage_names)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		version.parse('1.11')







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														    ])







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 1E-4
