




"""simple docstring"""







import argparse
import os
import re


__A   :  Any					=      'src/diffusers'

# Pattern that looks at the indentation in a line.
__A   :  Union[str, Any]					=      re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
__A   :  Optional[int]					=      re.compile(R'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A   :  List[str]					=      re.compile(R'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
__A   :  Union[str, Any]					=      re.compile(R'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A   :  Union[str, Any]					=      re.compile(R'\[([^\]]+)\]')

def 						snake_case__       (					_lowerCamelCase				)			->Optional[Any]:






		"""simple docstring"""


		__lowercase				:		List[Any]												=				_re_indent.search(a__				)
		return "" if search is None else search.groups()[0]

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase="",  _lowerCamelCase=None,  _lowerCamelCase=None				)			->Dict:






		"""simple docstring"""


		__lowercase				:		Union[str, Any]												=				0
		__lowercase				:		str												=				code.split("\n"				)
		if start_prompt is not None:
				while not lines[index].startswith(a__				):
						index += 1
				__lowercase				:		List[Any]												=				["\n".join(lines[:index]				)]
		else:
				__lowercase				:		str												=				[]

		# We split into blocks until we get to the `end_prompt` (or the end of the block).
		__lowercase				:		str												=				[lines[index]]
		index += 1
		while index < len(a__				) and (end_prompt is None or not lines[index].startswith(a__				)):
				if len(lines[index]				) > 0 and get_indent(lines[index]				) == indent_level:
						if len(a__				) > 0 and get_indent(current_block[-1]				).startswith(indent_level + " "				):
								current_block.append(lines[index]				)
								blocks.append("\n".join(a__				)				)
								if index < len(a__				) - 1:
										__lowercase				:		List[str]												=				[lines[index + 1]]
										index += 1
								else:
										__lowercase				:		Optional[int]												=				[]
						else:
								blocks.append("\n".join(a__				)				)
								__lowercase				:		Union[str, Any]												=				[lines[index]]
				else:
						current_block.append(lines[index]				)
				index += 1

		# Adds current block if it's nonempty.
		if len(a__				) > 0:
				blocks.append("\n".join(a__				)				)

		# Add final block after end_prompt if provided.
		if end_prompt is not None and index < len(a__				):
				blocks.append("\n".join(lines[index:]				)				)

		return blocks

def 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


		def _inner(_lowerCamelCase				):
				return key(a__				).lower().replace("_",  ""				)

		return _inner

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase=None				)			->Union[str, Any]:






		"""simple docstring"""


		def noop(_lowerCamelCase				):
				return x

		if key is None:
				__lowercase				:		Optional[int]												=				noop
		# Constants are all uppercase, they go first.
		__lowercase				:		int												=				[obj for obj in objects if key(a__				).isupper()]
		# Classes are not all uppercase but start with a capital, they go second.
		__lowercase				:		Union[str, Any]												=				[obj for obj in objects if key(a__				)[0].isupper() and not key(a__				).isupper()]
		# Functions begin with a lowercase, they go last.
		__lowercase				:		Dict												=				[obj for obj in objects if not key(a__				)[0].isupper()]

		__lowercase				:		Optional[int]												=				ignore_underscore(a__				)
		return sorted(a__,  key=a__				) + sorted(a__,  key=a__				) + sorted(a__,  key=a__				)

def 						snake_case__       (					_lowerCamelCase				)			->List[Any]:






		"""simple docstring"""


		def _replace(_lowerCamelCase				):
				__lowercase				:		List[Any]												=				match.groups()[0]
				if "," not in imports:
						return F'[{imports}]'
				__lowercase				:		List[Any]												=				[part.strip().replace("\"",  ""				) for part in imports.split(","				)]
				# We will have a final empty element if the line finished with a comma.
				if len(keys[-1]				) == 0:
						__lowercase				:		Dict												=				keys[:-1]
				return "[" + ", ".join([F'"{k}"' for k in sort_objects(a__				)]				) + "]"

		__lowercase				:		Tuple												=				import_statement.split("\n"				)
		if len(a__				) > 3:
				# Here we have to sort internal imports that are on several lines (one per name):
				# key: [
				#     "object1",
				#     "object2",
				#     ...
				# ]

				# We may have to ignore one or two lines on each side.
				__lowercase				:		Union[str, Any]												=				2 if lines[1].strip() == "[" else 1
				__lowercase				:		Tuple												=				[(i, _re_strip_line.search(a__				).groups()[0]) for i, line in enumerate(lines[idx:-idx]				)]
				__lowercase				:		Dict												=				sort_objects(a__,  key=lambda _lowerCamelCase				: x[1]				)
				__lowercase				:		Union[str, Any]												=				[lines[x[0] + idx] for x in sorted_indices]
				return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]				)
		elif len(a__				) == 3:
				# Here we have to sort internal imports that are on one separate line:
				# key: [
				#     "object1", "object2", ...
				# ]
				if _re_bracket_content.search(lines[1]				) is not None:
						__lowercase				:		Union[str, Any]												=				_re_bracket_content.sub(_replace,  lines[1]				)
				else:
						__lowercase				:		Union[str, Any]												=				[part.strip().replace("\"",  ""				) for part in lines[1].split(","				)]
						# We will have a final empty element if the line finished with a comma.
						if len(keys[-1]				) == 0:
								__lowercase				:		List[Any]												=				keys[:-1]
						__lowercase				:		Dict												=				get_indent(lines[1]				) + ", ".join([F'"{k}"' for k in sort_objects(a__				)]				)
				return "\n".join(a__				)
		else:
				# Finally we have to deal with imports fitting on one line
				__lowercase				:		int												=				_re_bracket_content.sub(_replace,  a__				)
				return import_statement

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase=True				)			->Dict:






		"""simple docstring"""


		with open(a__,  "r"				) as f:
				__lowercase				:		str												=				f.read()

		if "_import_structure" not in code:
				return

		# Blocks of indent level 0
		__lowercase				:		Any												=				split_code_in_indented_blocks(
		    a__,  start_prompt="_import_structure = {",  end_prompt="if TYPE_CHECKING:"				)

		# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
		for block_idx in range(1,  len(a__				) - 1				):
				# Check if the block contains some `_import_structure`s thingy to sort.
				__lowercase				:		Optional[int]												=				main_blocks[block_idx]
				__lowercase				:		List[str]												=				block.split("\n"				)

				# Get to the start of the imports.
				__lowercase				:		List[Any]												=				0
				while line_idx < len(a__				) and "_import_structure" not in block_lines[line_idx]:
						# Skip dummy import blocks
						if "import dummy" in block_lines[line_idx]:
								__lowercase				:		Optional[Any]												=				len(a__				)
						else:
								line_idx += 1
				if line_idx >= len(a__				):
						continue

				# Ignore beginning and last line: they don't contain anything.
				__lowercase				:		Optional[int]												=				"\n".join(block_lines[line_idx:-1]				)
				__lowercase				:		Optional[Any]												=				get_indent(block_lines[1]				)
				# Slit the internal block into blocks of indent level 1.
				__lowercase				:		Union[str, Any]												=				split_code_in_indented_blocks(a__,  indent_level=a__				)
				# We have two categories of import key: list or _import_structure[key].append/extend
				__lowercase				:		Any												=				_re_direct_key if "_import_structure" in block_lines[0] else _re_indirect_key
				# Grab the keys, but there is a trap: some lines are empty or just comments.
				__lowercase				:		Union[str, Any]												=				[(pattern.search(a__				).groups()[0] if pattern.search(a__				) is not None else None) for b in internal_blocks]
				# We only sort the lines with a key.
				__lowercase				:		Dict												=				[(i, key) for i, key in enumerate(a__				) if key is not None]
				__lowercase				:		Tuple												=				[x[0] for x in sorted(a__,  key=lambda _lowerCamelCase				: x[1]				)]

				# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
				__lowercase				:		int												=				0
				__lowercase				:		Optional[Any]												=				[]
				for i in range(len(a__				)				):
						if keys[i] is None:
								reordered_blocks.append(internal_blocks[i]				)
						else:
								__lowercase				:		int												=				sort_objects_in_import(internal_blocks[sorted_indices[count]]				)
								reordered_blocks.append(a__				)
								count += 1

								# And we put our main block back together with its first and last line.
				__lowercase				:		List[Any]												=				"\n".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]				)

		if code != "\n".join(a__				):
				if check_only:
						return True
				else:
						print(F'Overwriting {file}.'				)
						with open(a__,  "w"				) as f:
								f.write("\n".join(a__				)				)

def 						snake_case__       (					_lowerCamelCase=True				)			->Optional[Any]:






		"""simple docstring"""


		__lowercase				:		Tuple												=				[]
		for root, _, files in os.walk(a__				):
				if "__init__.py" in files:
						__lowercase				:		Tuple												=				sort_imports(os.path.join(a__,  "__init__.py"				),  check_only=a__				)
						if result:
								__lowercase				:		List[Any]												=				[os.path.join(a__,  "__init__.py"				)]
		if len(a__				) > 0:
				raise ValueError(F'Would overwrite {len(a__				)} files, run `make style`.'				)


if __name__ == "__main__":
						__A   :  List[str]					=      argparse.ArgumentParser()
						parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
						__A   :  Optional[Any]					=      parser.parse_args()

						sort_imports_in_all_inits(check_only=args.check_only)






"""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 						snake_case__       (					_lowerCamelCase				)			->List[Tuple[int, ...]]:






		"""simple docstring"""


		__lowercase				:		Union[str, Any]												=				[]
		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 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Tuple[int, ...]:






		"""simple docstring"""


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

		return tuple(reversed(_lowerCamelCase				)				)

@torch.jit.ignore
def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase = None,  _lowerCamelCase = None,  )			->List[Tuple[slice, ...]]:






		"""simple docstring"""


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

		if start_edges is None:
				__lowercase				:		Optional[Any]												=				[s == 0 for s in start]
				reduce_edge_list(_lowerCamelCase				)
		if end_edges is None:
				__lowercase				:		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				),)]

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

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

				__lowercase				:		Any												=				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

				__lowercase				:		int												=				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()				)
				__lowercase				:		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 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->torch.Tensor:






		"""simple docstring"""


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

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

		__lowercase				:		Optional[Any]												=				[t[s] for s in slices]

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

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase = False,  _lowerCamelCase = None,  _lowerCamelCase = False,  )			->Any:






		"""simple docstring"""


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

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

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

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

		__lowercase				:		Dict												=				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

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

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

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

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

				i += chunk_size

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

		return out





class       lowerCAmelCase__       :


				"""simple docstring"""





				def __init__(     self					:	int    ,   lowercase__					:	int = 5_1_2    ,   ):
						__lowercase				:		str												=				max_chunk_size
						__lowercase				:		Optional[int]												=				None
						__lowercase				:		Optional[tuple]												=				None



				def       snake_case    (     self					:	List[str]    ,   lowercase__					:	Callable    ,   lowercase__					:	tuple    ,   lowercase__					:	int						):
						logging.info("Tuning chunk size..."						)

						if min_chunk_size >= self.max_chunk_size:
								return min_chunk_size

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

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

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

						return candidates[min_viable_chunk_size_index]



				def       snake_case    (     self					:	Dict    ,   lowercase__					:	Iterable    ,   lowercase__					:	Iterable						):
						__lowercase				:		List[Any]												=				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__						):
										__lowercase				:		Dict												=				[v for _, v in sorted(aa.items()    ,   key=lambda lowercase__						: x[0]						)]
										__lowercase				:		Optional[int]												=				[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       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	Callable    ,   lowercase__					:	tuple    ,   lowercase__					:	int    ,   ):
						__lowercase				:		Tuple												=				True
						__lowercase				:		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__						)
								__lowercase				:		Optional[int]												=				self._compare_arg_caches(self.cached_arg_data    ,   lowercase__						)
						else:
								# Otherwise, we can reuse the precomputed value
								__lowercase				:		int												=				False

						if not consistent:
								__lowercase				:		List[Any]												=				self._determine_favorable_chunk_size(
								    lowercase__    ,   lowercase__    ,   lowercase__    ,   )
								__lowercase				:		Union[str, Any]												=				arg_data

						assert self.cached_chunk_size is not None

						return self.cached_chunk_size
