




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      lowerCAmelCase_					(  _lowercase    :       Union[dict, list, tuple, torch.Tensor])							->	List[Tuple[int, ...]]:







			"""simple docstring"""
			a__       :						Tuple							=  []
			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      lowerCAmelCase_					(  _lowercase    :       int						,       _lowercase    :       Tuple[int, ...])							->	Tuple[int, ...]:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  []
			for d in reversed(_lowercase):
						idx.append(flat_idx % d)
						a__       :						List[Any]							=  flat_idx // d

			return tuple(reversed(_lowercase))

@torch.jit.ignore
def      lowerCAmelCase_					(  _lowercase    :       Sequence[int]						,       _lowercase    :       Sequence[int]						,       _lowercase    :       Sequence[int]						,       _lowercase    :       Optional[Sequence[bool]] = None						,       _lowercase    :       Optional[Sequence[bool]] = None						,       )							->	List[Tuple[slice, ...]]:







			"""simple docstring"""

			# 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:
						a__       :						int							=  True
						for i in range(len(_lowercase)):
									a__       :						Tuple							=  -1 * (i + 1)
									l[reversed_idx] &= tally
									a__       :						str							=  l[reversed_idx]

			if start_edges is None:
						a__       :						Tuple							=  [s == 0 for s in start]
						reduce_edge_list(_lowercase)
			if end_edges is None:
						a__       :						List[str]							=  [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__       :						Union[str, 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__       :						Optional[Any]							=  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__       :						Dict							=  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      lowerCAmelCase_					(  _lowercase    :       torch.Tensor						,       _lowercase    :       int						,       _lowercase    :       int						,       _lowercase    :       int)							->	torch.Tensor:







			"""simple docstring"""
			a__       :						str							=  t.shape[:no_batch_dims]
			a__       :						List[Any]							=  list(_flat_idx_to_idx(_lowercase						,       _lowercase))
			# _get_minimal_slice_set is inclusive
			a__       :						List[str]							=  list(_flat_idx_to_idx(flat_end - 1						,       _lowercase))

			# Get an ordered list of slices to perform
			a__       :						Dict							=  _get_minimal_slice_set(
			    _lowercase						,       _lowercase						,       _lowercase						,       )

			a__       :						Tuple							=  [t[s] for s in slices]

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

def      lowerCAmelCase_					(  _lowercase    :       Callable						,       _lowercase    :       Dict[str, Any]						,       _lowercase    :       int						,       _lowercase    :       int						,       _lowercase    :       bool = False						,       _lowercase    :       Any = None						,       _lowercase    :       bool = False						,       )							->	Any:







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

			a__       :						Optional[Any]							=  [shape[:no_batch_dims] for shape in _fetch_dims(_lowercase)]
			a__       :						Union[str, Any]							=  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__       :						Optional[Any]							=  t.expand(orig_batch_dims + t.shape[no_batch_dims:])
									a__       :						int							=  t.reshape(-1						,       *t.shape[no_batch_dims:])
						else:
									a__       :						Optional[Any]							=  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__       :						List[str]							=  tensor_tree_map(lambda _lowercase: t.view([-1] + list(t.shape[no_batch_dims:]))						,       _out)

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

			a__       :						str							=  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__       :						List[Any]							=  0
			a__       :						int							=  prepped_outputs
			for _ in range(_lowercase):
						# Chunk the input
						if not low_mem:
									a__       :						Union[str, Any]							=  _select_chunk
						else:
									a__       :						Any							=  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__       :						int							=  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__       :						Any							=  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__       :						int							=  output_chunk
						else:
									raise ValueError("""Not supported""")

						i += chunk_size

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

			return out





class        snake_case__    :







						"""simple docstring"""




						def __init__(       self ,       __lowercase = 5_1_2 ,       ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						Optional[Any]							=  max_chunk_size
									a__       :						Optional[int]							=  None
									a__       :						Optional[tuple]							=  None

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				int:
									"""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__       :						Optional[int]							=  [c for c in candidates if c > min_chunk_size]
									a__       :						Union[str, 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__       :						Optional[Any]							=  0
									a__       :						Union[str, Any]							=  len(__lowercase  ) - 1
									while i > min_viable_chunk_size_index:
												a__       :						Optional[int]							=  test_chunk_size(candidates[i]  )
												if not viable:
															a__       :						int							=  (min_viable_chunk_size_index + i) // 2
												else:
															a__       :						Dict							=  i
															a__       :						List[str]							=  (i + len(__lowercase  ) - 1) // 2

									return candidates[min_viable_chunk_size_index]

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase  ) ->				bool:
									"""simple docstring"""




									a__       :						str							=  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__       :						List[str]							=  [v for _, v in sorted(aa.items() ,       key=lambda __lowercase  : x[0]  )]
															a__       :						Optional[Any]							=  [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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       ) ->				int:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  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__       :						Tuple							=  self._compare_arg_caches(self.cached_arg_data ,       __lowercase  )
									else:
												# Otherwise, we can reuse the precomputed value
												a__       :						Optional[int]							=  False

									if not consistent:
												a__       :						Optional[Any]							=  self._determine_favorable_chunk_size(
												    __lowercase ,       __lowercase ,       __lowercase ,       )
												a__       :						Optional[Any]							=  arg_data

									assert self.cached_chunk_size is not None

									return self.cached_chunk_size









import torch

from diffusers import StableDiffusionPipeline


_lowercase		:				Optional[int]      		="path-to-your-trained-model"
_lowercase		:				Union[str, Any]      		=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

_lowercase		:				Any      		="A photo of sks dog in a bucket"
_lowercase		:				Any      		=pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")




