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						(	__lowercase						:			Union[dict, list, tuple, torch.Tensor]     ):
		'''simple docstring'''




		A_		:    Dict         =				[]
		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     UpperCamelCase						(	__lowercase						:			int						,__lowercase						:			Tuple[int, ...]     ):
		'''simple docstring'''




		A_		:    Optional[int]         =				[]
		for d in reversed(__lowercase     ):
				idx.append(flat_idx % d     )
				A_		:    List[Any]         =				flat_idx // d

		return tuple(reversed(__lowercase     )     )




@torch.jit.ignore
def     UpperCamelCase						(	__lowercase						:			Sequence[int]						,__lowercase						:			Sequence[int]						,__lowercase						:			Sequence[int]						,__lowercase						:			Optional[Sequence[bool]] = None						,__lowercase						:			Optional[Sequence[bool]] = None						,):
		'''simple docstring'''




		def reduce_edge_list(__lowercase						:			List[bool]     ) -> None:
				A_		:    Union[str, Any]         =				True
				for i in range(len(__lowercase     )     ):
						A_		:    List[Any]         =				-1 * (i + 1)
						l[reversed_idx] &= tally
						A_		:    Dict         =				l[reversed_idx]

		if start_edges is None:
				A_		:    str         =				[s == 0 for s in start]
				reduce_edge_list(__lowercase     )
		if end_edges is None:
				A_		:    Tuple         =				[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     ),)]

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

		# 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

		A_		:    Tuple[slice, ...]         =				tuple(__lowercase     )
		A_		:    List[Any]         =				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

				A_		:    List[str]         =				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

				A_		:    List[str]         =				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()     )
				A_		:    int         =				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						(	__lowercase						:			torch.Tensor						,__lowercase						:			int						,__lowercase						:			int						,__lowercase						:			int     ):
		'''simple docstring'''




		A_		:    Any         =				t.shape[:no_batch_dims]
		A_		:    Optional[Any]         =				list(_flat_idx_to_idx(__lowercase						,__lowercase     )     )
		# _get_minimal_slice_set is inclusive
		A_		:    Tuple         =				list(_flat_idx_to_idx(flat_end - 1						,__lowercase     )     )

		# Get an ordered list of slices to perform
		A_		:    List[Any]         =				_get_minimal_slice_set(
		    __lowercase						,__lowercase						,__lowercase						,)

		A_		:    int         =				[t[s] for s in slices]

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




def     UpperCamelCase						(	__lowercase						:			Callable						,__lowercase						:			Dict[str, Any]						,__lowercase						:			int						,__lowercase						:			int						,__lowercase						:			bool = False						,__lowercase						:			Any = None						,__lowercase						:			bool = False						,):
		'''simple docstring'''




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

		A_		:    List[str]         =				[shape[:no_batch_dims] for shape in _fetch_dims(__lowercase     )]
		A_		:    int         =				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:
								A_		:    List[str]         =				t.expand(orig_batch_dims + t.shape[no_batch_dims:]     )
						A_		:    Optional[int]         =				t.reshape(-1						,*t.shape[no_batch_dims:]     )
				else:
						A_		:    str         =				t.expand(orig_batch_dims + t.shape[no_batch_dims:]     )
				return t

		A_		:    Dict[str, Any]         =				tensor_tree_map(_prep_inputs						,__lowercase     )
		A_		:    Any         =				None
		if _out is not None:
				A_		:    int         =				tensor_tree_map(lambda __lowercase     : t.view([-1] + list(t.shape[no_batch_dims:]     )     )						,_out     )

		A_		:    List[Any]         =				1
		for d in orig_batch_dims:
				flat_batch_dim *= d

		A_		:    Optional[int]         =				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

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

				A_		:    Dict[str, Any]         =				tensor_tree_map(__lowercase						,__lowercase     )

				# Run the layer on the chunk
				A_		:    str         =				layer(**__lowercase     )

				# Allocate space for the output
				if out is None:
						A_		:    Dict         =				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:
														A_		:    int         =				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:
										A_		:    Dict         =				xa
				elif isinstance(__lowercase						,torch.Tensor     ):
						if _add_into_out:
								out[i : i + chunk_size] += output_chunk
						else:
								A_		:    str         =				output_chunk
				else:
						raise ValueError('Not supported'     )

				i += chunk_size

		A_		:    str         =				tensor_tree_map(lambda __lowercase     : t.view(orig_batch_dims + t.shape[1:]     )						,__lowercase     )

		return out





class UpperCAmelCase						:


				'''simple docstring'''






				def __init__(						self		,     lowercase = 5_1_2		,     ):



						"""simple docstring"""





						A_		:    str         =				max_chunk_size
						A_		:    Optional[int]         =				None
						A_		:    Optional[tuple]         =				None
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase ):



						"""simple docstring"""





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

						if min_chunk_size >= self.max_chunk_size:
								return min_chunk_size

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

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

						A_		:    int         =				0
						A_		:    Dict         =				len(lowercase ) - 1
						while i > min_viable_chunk_size_index:
								A_		:    Tuple         =				test_chunk_size(candidates[i] )
								if not viable:
										A_		:    Any         =				(min_viable_chunk_size_index + i) // 2
								else:
										A_		:    Optional[Any]         =				i
										A_		:    Tuple         =				(i + len(lowercase ) - 1) // 2

						return candidates[min_viable_chunk_size_index]
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase ):



						"""simple docstring"""





						A_		:    int         =				True
						for aa, aa in zip(lowercase		,     lowercase ):
								assert type(lowercase ) == type(lowercase )
								if isinstance(lowercase		,     (list, tuple) ):
										consistent &= self._compare_arg_caches(lowercase		,     lowercase )
								elif isinstance(lowercase		,     lowercase ):
										A_		:    Union[str, Any]         =				[v for _, v in sorted(aa.items()		,     key=lambda lowercase : x[0] )]
										A_		:    Tuple         =				[v for _, v in sorted(aa.items()		,     key=lambda lowercase : x[0] )]
										consistent &= self._compare_arg_caches(lowercase		,     lowercase )
								else:
										consistent &= aa == aa

						return consistent







				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     ):



						"""simple docstring"""





						A_		:    List[str]         =				True
						A_		:    tuple         =				tree_map(lambda lowercase : a.shape if isinstance(lowercase		,     torch.Tensor ) else a		,     lowercase		,     lowercase )
						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(lowercase )
								A_		:    Any         =				self._compare_arg_caches(self.cached_arg_data		,     lowercase )
						else:
								# Otherwise, we can reuse the precomputed value
								A_		:    List[str]         =				False

						if not consistent:
								A_		:    Dict         =				self._determine_favorable_chunk_size(
								    lowercase		,     lowercase		,     lowercase		,     )
								A_		:    Union[str, Any]         =				arg_data

						assert self.cached_chunk_size is not None

						return self.cached_chunk_size







from ..utils import DummyObject, requires_backends





class UpperCAmelCase						( metaclass=__A  ):


				'''simple docstring'''






				lowerCamelCase_    = ['''onnx''']
				def __init__(						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(self		,     ['onnx'] )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )







				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )






