


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 					UpperCamelCase   (      __magic_name__	:				Union[dict, list, tuple, torch.Tensor]	)  ->    List[Tuple[int, ...]]:
		"""simple docstring"""




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

		return shapes






@torch.jit.ignore
def 					UpperCamelCase   (      __magic_name__	:				int       ,       __magic_name__	:				Tuple[int, ...]	)  ->    Tuple[int, ...]:
		"""simple docstring"""




		lowercase__	     =						[]
		for d in reversed(__magic_name__	):
				idx.append(flat_idx % d	)
				lowercase__	     =						flat_idx // d

		return tuple(reversed(__magic_name__	)	)






@torch.jit.ignore
def 					UpperCamelCase   (      __magic_name__	:				Sequence[int]       ,       __magic_name__	:				Sequence[int]       ,       __magic_name__	:				Sequence[int]       ,       __magic_name__	:				Optional[Sequence[bool]] = None       ,       __magic_name__	:				Optional[Sequence[bool]] = None       ,       )  ->    List[Tuple[slice, ...]]:
		"""simple docstring"""




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

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

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

		lowercase__	     =						[]
		lowercase__	     =						[]

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

		lowercase__	     =						tuple(__magic_name__	)
		lowercase__	     =						len(__magic_name__	)

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

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

				lowercase__	     =						start[divergence_idx]
				return tuple(
				    path + (slice(__magic_name__       ,       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

				lowercase__	     =						end[divergence_idx]
				return tuple(
				    path + (slice(__magic_name__       ,       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()	)
				lowercase__	     =						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 					UpperCamelCase   (      __magic_name__	:				torch.Tensor       ,       __magic_name__	:				int       ,       __magic_name__	:				int       ,       __magic_name__	:				int	)  ->    torch.Tensor:
		"""simple docstring"""




		lowercase__	     =						t.shape[:no_batch_dims]
		lowercase__	     =						list(_flat_idx_to_idx(__magic_name__       ,       __magic_name__	)	)
		# _get_minimal_slice_set is inclusive
		lowercase__	     =						list(_flat_idx_to_idx(flat_end - 1       ,       __magic_name__	)	)

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

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

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






def 					UpperCamelCase   (      __magic_name__	:				Callable       ,       __magic_name__	:				Dict[str, Any]       ,       __magic_name__	:				int       ,       __magic_name__	:				int       ,       __magic_name__	:				bool = False       ,       __magic_name__	:				Any = None       ,       __magic_name__	:				bool = False       ,       )  ->    Any:
		"""simple docstring"""




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

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

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

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

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

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

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

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

				lowercase__	     =						tensor_tree_map(__magic_name__       ,       __magic_name__	)

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

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

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

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

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

				i += chunk_size

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

		return out







class 				A       :
					'''simple docstring'''






					def __init__(self       :		List[str] ,   _UpperCAmelCase       :		int = 512 ,   )		->  str:




							"""simple docstring"""


							lowercase__	     =						max_chunk_size
							lowercase__	     =						None
							lowercase__	     =						None

					def     lowerCamelCase__	(self       :		List[Any] ,   _UpperCAmelCase       :		Callable ,   _UpperCAmelCase       :		tuple ,   _UpperCAmelCase       :		int						)		->  int:




							"""simple docstring"""


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

							if min_chunk_size >= self.max_chunk_size:
									return min_chunk_size

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

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

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

							return candidates[min_viable_chunk_size_index]

					def     lowerCamelCase__	(self       :		Dict ,   _UpperCAmelCase       :		Iterable ,   _UpperCAmelCase       :		Iterable						)		->  bool:




							"""simple docstring"""


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

							return consistent


					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		Callable ,   _UpperCAmelCase       :		tuple ,   _UpperCAmelCase       :		int ,   )		->  int:




							"""simple docstring"""


							lowercase__	     =						True
							lowercase__	     =						tree_map(lambda _UpperCAmelCase						: a.shape if isinstance(_UpperCAmelCase ,   torch.Tensor						) else a ,   _UpperCAmelCase ,   _UpperCAmelCase						)
							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(_UpperCAmelCase						)
									lowercase__	     =						self._compare_arg_caches(self.cached_arg_data ,   _UpperCAmelCase						)
							else:
									# Otherwise, we can reuse the precomputed value
									lowercase__	     =						False

							if not consistent:
									lowercase__	     =						self._determine_favorable_chunk_size(
									    _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   )
									lowercase__	     =						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


_UpperCAmelCase						:  Union[str, Any]									=			logging.get_logger(__name__)

_UpperCAmelCase						:  Dict									=			{
    """microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}







class 					lowercase							(			lowercase_      ,    lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     '''resnet'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     ['''basic''', '''bottleneck''']




				def __init__(   self ,  snake_case=3 ,  snake_case=64 ,  snake_case=[256, 512, 1024, 2048] ,  snake_case=[3, 4, 6, 3] ,  snake_case="bottleneck" ,  snake_case="relu" ,  snake_case=False ,  snake_case=None ,  snake_case=None ,  **snake_case ,  ):
											super().__init__(**snake_case			)
											if layer_type not in self.layer_types:
																		raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types			)}'''			)
											snake_case_     							=       num_channels
											snake_case_     							=       embedding_size
											snake_case_     							=       hidden_sizes
											snake_case_     							=       depths
											snake_case_     							=       layer_type
											snake_case_     							=       hidden_act
											snake_case_     							=       downsample_in_first_stage
											snake_case_     							=       ['stem'] + [F'''stage{idx}''' for idx in range(1 ,  len(snake_case			) + 1			)]
											snake_case_							,       snake_case_     							=       get_aligned_output_features_output_indices(
											    out_features=snake_case ,  out_indices=snake_case ,  stage_names=self.stage_names			)







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     version.parse('''1.11'''     )




				@property
				def        a				(   self			):
											return OrderedDict(
											    [
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ]			)




				@property
				def        a				(   self			):
											return 1e-3




