


"""simple docstring"""




import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

__magic_name__						: Union[str, Any]    =				[
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

__magic_name__						: int    =				[
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

__magic_name__						: List[str]    =				[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
					# loop over downblocks/upblocks

					for j in range(2):
										# loop over resnets/attentions for downblocks
										__magic_name__						: Optional[int]    =				f"""down_blocks.{i}.resnets.{j}."""
										__magic_name__						: str    =				f"""input_blocks.{3*i + j + 1}.0."""
										unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

										if i < 3:
															# no attention layers in down_blocks.3
															__magic_name__						: Any    =				f"""down_blocks.{i}.attentions.{j}."""
															__magic_name__						: Tuple    =				f"""input_blocks.{3*i + j + 1}.1."""
															unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

					for j in range(3):
										# loop over resnets/attentions for upblocks
										__magic_name__						: List[Any]    =				f"""up_blocks.{i}.resnets.{j}."""
										__magic_name__						: Optional[Any]    =				f"""output_blocks.{3*i + j}.0."""
										unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

										if i > 0:
															# no attention layers in up_blocks.0
															__magic_name__						: Any    =				f"""up_blocks.{i}.attentions.{j}."""
															__magic_name__						: Optional[Any]    =				f"""output_blocks.{3*i + j}.1."""
															unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

					if i < 3:
										# no downsample in down_blocks.3
										__magic_name__						: List[str]    =				f"""down_blocks.{i}.downsamplers.0.conv."""
										__magic_name__						: Optional[Any]    =				f"""input_blocks.{3*(i+1)}.0.op."""
										unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

										# no upsample in up_blocks.3
										__magic_name__						: Tuple    =				f"""up_blocks.{i}.upsamplers.0."""
										__magic_name__						: List[Any]    =				f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
										unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

__magic_name__						: str    =				'mid_block.attentions.0.'
__magic_name__						: Any    =				'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
					__magic_name__						: Optional[int]    =				f"""mid_block.resnets.{j}."""
					__magic_name__						: Tuple    =				f"""middle_block.{2*j}."""
					unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





def       a_ (	lowercase__ :Dict     ):
			# buyer beware: this is a *brittle* function,
			# and correct output requires that all of these pieces interact in
			# the exact order in which I have arranged them.
			__lowerCamelCase          =      {k: k for k in unet_state_dict.keys()}
			for sd_name, hf_name in unet_conversion_map:
						__lowerCamelCase          =      sd_name
			for k, v in mapping.items():
						if "resnets" in k:
									for sd_part, hf_part in unet_conversion_map_resnet:
												__lowerCamelCase          =      v.replace(lowercase__,			lowercase__     )
									__lowerCamelCase          =      v
			for k, v in mapping.items():
						for sd_part, hf_part in unet_conversion_map_layer:
									__lowerCamelCase          =      v.replace(lowercase__,			lowercase__     )
						__lowerCamelCase          =      v
			__lowerCamelCase          =      {v: unet_state_dict[k] for k, v in mapping.items()}
			return new_state_dict


# ================#
# VAE Conversion #
# ================#

__magic_name__						: Any    =				[
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
					# down_blocks have two resnets
					for j in range(2):
										__magic_name__						: str    =				f"""encoder.down_blocks.{i}.resnets.{j}."""
										__magic_name__						: Optional[Any]    =				f"""encoder.down.{i}.block.{j}."""
										vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

					if i < 3:
										__magic_name__						: Dict    =				f"""down_blocks.{i}.downsamplers.0."""
										__magic_name__						: List[Any]    =				f"""down.{i}.downsample."""
										vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

										__magic_name__						: Any    =				f"""up_blocks.{i}.upsamplers.0."""
										__magic_name__						: Optional[Any]    =				f"""up.{3-i}.upsample."""
										vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

					# up_blocks have three resnets
					# also, up blocks in hf are numbered in reverse from sd
					for j in range(3):
										__magic_name__						: Union[str, Any]    =				f"""decoder.up_blocks.{i}.resnets.{j}."""
										__magic_name__						: Optional[Any]    =				f"""decoder.up.{3-i}.block.{j}."""
										vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
					__magic_name__						: List[Any]    =				f"""mid_block.resnets.{i}."""
					__magic_name__						: List[str]    =				f"""mid.block_{i+1}."""
					vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


__magic_name__						: str    =				[
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]





def       a_ (	lowercase__ :List[str]     ):
			# convert HF linear weights to SD conv2d weights
			return w.reshape(*w.shape,			1,			1     )





def       a_ (	lowercase__ :List[str]     ):
			__lowerCamelCase          =      {k: k for k in vae_state_dict.keys()}
			for k, v in mapping.items():
						for sd_part, hf_part in vae_conversion_map:
									__lowerCamelCase          =      v.replace(lowercase__,			lowercase__     )
						__lowerCamelCase          =      v
			for k, v in mapping.items():
						if "attentions" in k:
									for sd_part, hf_part in vae_conversion_map_attn:
												__lowerCamelCase          =      v.replace(lowercase__,			lowercase__     )
									__lowerCamelCase          =      v
			__lowerCamelCase          =      {v: vae_state_dict[k] for k, v in mapping.items()}
			__lowerCamelCase          =      ["""q""", """k""", """v""", """proj_out"""]
			for k, v in new_state_dict.items():
						for weight_name in weights_to_convert:
									if f'mid.attn_1.{weight_name}.weight' in k:
												print(f'Reshaping {k} for SD format'     )
												__lowerCamelCase          =      reshape_weight_for_sd(lowercase__     )
			return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


__magic_name__						: Dict    =				[
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
__magic_name__						: str    =				{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__magic_name__						: str    =				re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
__magic_name__						: Optional[int]    =				{'q': 0, 'k': 1, 'v': 2}





def       a_ (	lowercase__ :Union[str, Any]     ):
			__lowerCamelCase          =      {}
			__lowerCamelCase          =      {}
			__lowerCamelCase          =      {}
			for k, v in text_enc_dict.items():
						if (
						    k.endswith(""".self_attn.q_proj.weight"""     )
						    or k.endswith(""".self_attn.k_proj.weight"""     )
						    or k.endswith(""".self_attn.v_proj.weight"""     )
						):
									__lowerCamelCase          =      k[: -len(""".q_proj.weight"""     )]
									__lowerCamelCase          =      k[-len("""q_proj.weight"""     )]
									if k_pre not in capture_qkv_weight:
												__lowerCamelCase          =      [None, None, None]
									__lowerCamelCase          =      v
									continue

						if (
						    k.endswith(""".self_attn.q_proj.bias"""     )
						    or k.endswith(""".self_attn.k_proj.bias"""     )
						    or k.endswith(""".self_attn.v_proj.bias"""     )
						):
									__lowerCamelCase          =      k[: -len(""".q_proj.bias"""     )]
									__lowerCamelCase          =      k[-len("""q_proj.bias"""     )]
									if k_pre not in capture_qkv_bias:
												__lowerCamelCase          =      [None, None, None]
									__lowerCamelCase          =      v
									continue

						__lowerCamelCase          =      textenc_pattern.sub(lambda lowercase__     : protected[re.escape(m.group(0     )     )],			lowercase__     )
						__lowerCamelCase          =      v

			for k_pre, tensors in capture_qkv_weight.items():
						if None in tensors:
									raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""     )
						__lowerCamelCase          =      textenc_pattern.sub(lambda lowercase__     : protected[re.escape(m.group(0     )     )],			lowercase__     )
						__lowerCamelCase          =      torch.cat(lowercase__     )

			for k_pre, tensors in capture_qkv_bias.items():
						if None in tensors:
									raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""     )
						__lowerCamelCase          =      textenc_pattern.sub(lambda lowercase__     : protected[re.escape(m.group(0     )     )],			lowercase__     )
						__lowerCamelCase          =      torch.cat(lowercase__     )

			return new_state_dict





def       a_ (	lowercase__ :str     ):
			return text_enc_dict


if __name__ == "__main__":
					__magic_name__						: Dict    =				argparse.ArgumentParser()

					parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
					parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
					parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
					parser.add_argument(
					    '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
					)

					__magic_name__						: List[str]    =				parser.parse_args()

					assert args.model_path is not None, "Must provide a model path!"

					assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

					# Path for safetensors
					__magic_name__						: List[Any]    =				osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
					__magic_name__						: Dict    =				osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
					__magic_name__						: Optional[Any]    =				osp.join(args.model_path, 'text_encoder', 'model.safetensors')

					# Load models from safetensors if it exists, if it doesn't pytorch
					if osp.exists(unet_path):
										__magic_name__						: Dict    =				load_file(unet_path, device='cpu')
					else:
										__magic_name__						: List[Any]    =				osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
										__magic_name__						: str    =				torch.load(unet_path, map_location='cpu')

					if osp.exists(vae_path):
										__magic_name__						: str    =				load_file(vae_path, device='cpu')
					else:
										__magic_name__						: Optional[Any]    =				osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
										__magic_name__						: List[Any]    =				torch.load(vae_path, map_location='cpu')

					if osp.exists(text_enc_path):
										__magic_name__						: str    =				load_file(text_enc_path, device='cpu')
					else:
										__magic_name__						: List[Any]    =				osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
										__magic_name__						: List[Any]    =				torch.load(text_enc_path, map_location='cpu')

					# Convert the UNet model
					__magic_name__						: Optional[int]    =				convert_unet_state_dict(unet_state_dict)
					__magic_name__						: Dict    =				{'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

					# Convert the VAE model
					__magic_name__						: int    =				convert_vae_state_dict(vae_state_dict)
					__magic_name__						: Optional[Any]    =				{'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

					# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
					__magic_name__						: List[str]    =				'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

					if is_vaa_model:
										# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
										__magic_name__						: Tuple    =				{'transformer.' + k: v for k, v in text_enc_dict.items()}
										__magic_name__						: Optional[Any]    =				convert_text_enc_state_dict_vaa(text_enc_dict)
										__magic_name__						: str    =				{'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
					else:
										__magic_name__						: List[Any]    =				convert_text_enc_state_dict(text_enc_dict)
										__magic_name__						: Union[str, Any]    =				{'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

					# Put together new checkpoint
					__magic_name__						: Tuple    =				{**unet_state_dict, **vae_state_dict, **text_enc_dict}
					if args.half:
										__magic_name__						: str    =				{k: v.half() for k, v in state_dict.items()}

					if args.use_safetensors:
										save_file(state_dict, args.checkpoint_path)
					else:
										__magic_name__						: str    =				{'state_dict': state_dict}
										torch.save(state_dict, args.checkpoint_path)








"""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       a_ (	lowercase__ :Union[dict, list, tuple, torch.Tensor]     ):
			__lowerCamelCase          =      []
			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       a_ (	lowercase__ :int,			lowercase__ :Tuple[int, ...]     ):
			__lowerCamelCase          =      []
			for d in reversed(lowercase__     ):
						idx.append(flat_idx % d     )
						__lowerCamelCase          =      flat_idx // d

			return tuple(reversed(lowercase__     )     )





@torch.jit.ignore
def       a_ (	lowercase__ :Sequence[int],			lowercase__ :Sequence[int],			lowercase__ :Sequence[int],			lowercase__ :Optional[Sequence[bool]] = None,			lowercase__ :Optional[Sequence[bool]] = None,			):

			# 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:
						__lowerCamelCase          =      True
						for i in range(len(lowercase__     )     ):
									__lowerCamelCase          =      -1 * (i + 1)
									l[reversed_idx] &= tally
									__lowerCamelCase          =      l[reversed_idx]

			if start_edges is None:
						__lowerCamelCase          =      [s == 0 for s in start]
						reduce_edge_list(lowercase__     )
			if end_edges is None:
						__lowerCamelCase          =      [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     ),)]

			__lowerCamelCase          =      []
			__lowerCamelCase          =      []

			# 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

			__lowerCamelCase          =      tuple(lowercase__     )
			__lowerCamelCase          =      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

						__lowerCamelCase          =      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

						__lowerCamelCase          =      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()     )
						__lowerCamelCase          =      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_ (	lowercase__ :torch.Tensor,			lowercase__ :int,			lowercase__ :int,			lowercase__ :int     ):
			__lowerCamelCase          =      t.shape[:no_batch_dims]
			__lowerCamelCase          =      list(_flat_idx_to_idx(lowercase__,			lowercase__     )     )
			# _get_minimal_slice_set is inclusive
			__lowerCamelCase          =      list(_flat_idx_to_idx(flat_end - 1,			lowercase__     )     )

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

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

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





def       a_ (	lowercase__ :Callable,			lowercase__ :Dict[str, Any],			lowercase__ :int,			lowercase__ :int,			lowercase__ :bool = False,			lowercase__ :Any = None,			lowercase__ :bool = False,			):
			if not (len(lowercase__     ) > 0):
						raise ValueError("""Must provide at least one input"""     )

			__lowerCamelCase          =      [shape[:no_batch_dims] for shape in _fetch_dims(lowercase__     )]
			__lowerCamelCase          =      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:
												__lowerCamelCase          =      t.expand(orig_batch_dims + t.shape[no_batch_dims:]     )
									__lowerCamelCase          =      t.reshape(-1,			*t.shape[no_batch_dims:]     )
						else:
									__lowerCamelCase          =      t.expand(orig_batch_dims + t.shape[no_batch_dims:]     )
						return t

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

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

			__lowerCamelCase          =      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

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

						__lowerCamelCase          =      tensor_tree_map(lowercase__,			lowercase__     )

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

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

						i += chunk_size

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

			return out




class 			__snake_case   :




							def __init__(     self:    Union[str, Any]     ,							A_:    int = 5_12     ,							):
										__lowerCamelCase          =      max_chunk_size
										__lowerCamelCase          =      None
										__lowerCamelCase          =      None




							def   __a   (     self:    Tuple     ,							A_:    Callable     ,							A_:    tuple     ,							A_:    int   ):
										logging.info("""Tuning chunk size..."""   )

										if min_chunk_size >= self.max_chunk_size:
													return min_chunk_size

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

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

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

										return candidates[min_viable_chunk_size_index]




							def   __a   (     self:    Tuple     ,							A_:    Iterable     ,							A_:    Iterable   ):
										__lowerCamelCase          =      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          =      [v for _, v in sorted(aa.items()     ,							key=lambda A_   : x[0]   )]
																__lowerCamelCase          =      [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   __a   (     self:    str     ,							A_:    Callable     ,							A_:    tuple     ,							A_:    int     ,							):
										__lowerCamelCase          =      True
										__lowerCamelCase          =      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          =      self._compare_arg_caches(self.cached_arg_data     ,							A_   )
										else:
													# Otherwise, we can reuse the precomputed value
													__lowerCamelCase          =      False

										if not consistent:
													__lowerCamelCase          =      self._determine_favorable_chunk_size(
													    A_     ,							A_     ,							A_     ,							)
													__lowerCamelCase          =      arg_data

										assert self.cached_chunk_size is not None

										return self.cached_chunk_size




