






'''simple docstring'''




import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__          =						logging.get_logger(__name__)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[int]:
		UpperCamelCase__				:					str					=					SwinConfig.from_pretrained(
		    'microsoft/swin-tiny-patch4-window7-224'     ,      out_features=['stage1', 'stage2', 'stage3', 'stage4'])
		UpperCamelCase__				:					List[Any]					=					MaskFormerConfig(backbone_config=lowerCamelCase_)

		UpperCamelCase__				:					List[str]					=					'huggingface/label-files'
		if "ade20k-full" in model_name:
				# this should be ok
				UpperCamelCase__				:					Tuple					=					847
				UpperCamelCase__				:					List[str]					=					'maskformer-ade20k-full-id2label.json'
		elif "ade" in model_name:
				# this should be ok
				UpperCamelCase__				:					Tuple					=					150
				UpperCamelCase__				:					List[str]					=					'ade20k-id2label.json'
		elif "coco-stuff" in model_name:
				# this should be ok
				UpperCamelCase__				:					Dict					=					171
				UpperCamelCase__				:					List[Any]					=					'maskformer-coco-stuff-id2label.json'
		elif "coco" in model_name:
				# TODO
				UpperCamelCase__				:					Tuple					=					133
				UpperCamelCase__				:					List[Any]					=					'coco-panoptic-id2label.json'
		elif "cityscapes" in model_name:
				# this should be ok
				UpperCamelCase__				:					str					=					19
				UpperCamelCase__				:					Tuple					=					'cityscapes-id2label.json'
		elif "vistas" in model_name:
				# this should be ok
				UpperCamelCase__				:					Optional[int]					=					65
				UpperCamelCase__				:					Dict					=					'mapillary-vistas-id2label.json'

		UpperCamelCase__				:					Tuple					=					json.load(open(hf_hub_download(lowerCamelCase_     ,      lowerCamelCase_     ,      repo_type='dataset')     ,      'r'))
		UpperCamelCase__				:					Dict					=					{int(lowerCamelCase_): v for k, v in idalabel.items()}

		return config







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Tuple:
		UpperCamelCase__				:					Optional[Any]					=					[]
		# stem
		# fmt: off
		rename_keys.append(('backbone.patch_embed.proj.weight', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight'))
		rename_keys.append(('backbone.patch_embed.proj.bias', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias'))
		rename_keys.append(('backbone.patch_embed.norm.weight', 'model.pixel_level_module.encoder.model.embeddings.norm.weight'))
		rename_keys.append(('backbone.patch_embed.norm.bias', 'model.pixel_level_module.encoder.model.embeddings.norm.bias'))
		# stages
		for i in range(len(config.backbone_config.depths)):
				for j in range(config.backbone_config.depths[i]):
						rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm1.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight'))
						rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm1.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias'))
						rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table'))
						rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index'))
						rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.proj.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight'))
						rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.proj.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias'))
						rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm2.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight'))
						rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm2.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias'))
						rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight'))
						rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias'))
						rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight'))
						rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias'))

				if i < 3:
						rename_keys.append((f'backbone.layers.{i}.downsample.reduction.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight'))
						rename_keys.append((f'backbone.layers.{i}.downsample.norm.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight'))
						rename_keys.append((f'backbone.layers.{i}.downsample.norm.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias'))
				rename_keys.append((f'backbone.norm{i}.weight', f'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight'))
				rename_keys.append((f'backbone.norm{i}.bias', f'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias'))

		# FPN
		rename_keys.append(('sem_seg_head.layer_4.weight', 'model.pixel_level_module.decoder.fpn.stem.0.weight'))
		rename_keys.append(('sem_seg_head.layer_4.norm.weight', 'model.pixel_level_module.decoder.fpn.stem.1.weight'))
		rename_keys.append(('sem_seg_head.layer_4.norm.bias', 'model.pixel_level_module.decoder.fpn.stem.1.bias'))
		for source_index, target_index in zip(range(3     ,      0     ,      -1)     ,      range(0     ,      3)):
				rename_keys.append((f'sem_seg_head.adapter_{source_index}.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight'))
				rename_keys.append((f'sem_seg_head.adapter_{source_index}.norm.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight'))
				rename_keys.append((f'sem_seg_head.adapter_{source_index}.norm.bias', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias'))
				rename_keys.append((f'sem_seg_head.layer_{source_index}.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight'))
				rename_keys.append((f'sem_seg_head.layer_{source_index}.norm.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight'))
				rename_keys.append((f'sem_seg_head.layer_{source_index}.norm.bias', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias'))
		rename_keys.append(('sem_seg_head.mask_features.weight', 'model.pixel_level_module.decoder.mask_projection.weight'))
		rename_keys.append(('sem_seg_head.mask_features.bias', 'model.pixel_level_module.decoder.mask_projection.bias'))

		# Transformer decoder
		for idx in range(config.decoder_config.decoder_layers):
				# self-attention out projection
				rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', f'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight'))
				rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', f'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias'))
				# cross-attention out projection
				rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', f'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight'))
				rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', f'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias'))
				# MLP 1
				rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', f'model.transformer_module.decoder.layers.{idx}.fc1.weight'))
				rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', f'model.transformer_module.decoder.layers.{idx}.fc1.bias'))
				# MLP 2
				rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', f'model.transformer_module.decoder.layers.{idx}.fc2.weight'))
				rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', f'model.transformer_module.decoder.layers.{idx}.fc2.bias'))
				# layernorm 1 (self-attention layernorm)
				rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', f'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight'))
				rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', f'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias'))
				# layernorm 2 (cross-attention layernorm)
				rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', f'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight'))
				rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', f'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias'))
				# layernorm 3 (final layernorm)
				rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', f'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight'))
				rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', f'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias'))

		rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.weight', 'model.transformer_module.decoder.layernorm.weight'))
		rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.bias', 'model.transformer_module.decoder.layernorm.bias'))

		# heads on top
		rename_keys.append(('sem_seg_head.predictor.query_embed.weight', 'model.transformer_module.queries_embedder.weight'))

		rename_keys.append(('sem_seg_head.predictor.input_proj.weight', 'model.transformer_module.input_projection.weight'))
		rename_keys.append(('sem_seg_head.predictor.input_proj.bias', 'model.transformer_module.input_projection.bias'))

		rename_keys.append(('sem_seg_head.predictor.class_embed.weight', 'class_predictor.weight'))
		rename_keys.append(('sem_seg_head.predictor.class_embed.bias', 'class_predictor.bias'))

		for i in range(3):
				rename_keys.append((f'sem_seg_head.predictor.mask_embed.layers.{i}.weight', f'mask_embedder.{i}.0.weight'))
				rename_keys.append((f'sem_seg_head.predictor.mask_embed.layers.{i}.bias', f'mask_embedder.{i}.0.bias'))
		# fmt: on

		return rename_keys







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Optional[Any]:
		UpperCamelCase__				:					Optional[Any]					=					dct.pop(lowerCamelCase_)
		UpperCamelCase__				:					Optional[Any]					=					val







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     Dict:
		UpperCamelCase__				:					Optional[Any]					=					[int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))]
		for i in range(len(backbone_config.depths)):
				UpperCamelCase__				:					str					=					num_features[i]
				for j in range(backbone_config.depths[i]):
						# fmt: off
						# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
						UpperCamelCase__				:					Dict					=					state_dict.pop(f'backbone.layers.{i}.blocks.{j}.attn.qkv.weight')
						UpperCamelCase__				:					List[str]					=					state_dict.pop(f'backbone.layers.{i}.blocks.{j}.attn.qkv.bias')
						# next, add query, keys and values (in that order) to the state dict
						UpperCamelCase__				:					str					=					in_proj_weight[:dim, :]
						UpperCamelCase__				:					int					=					in_proj_bias[: dim]
						UpperCamelCase__				:					Any					=					in_proj_weight[
						    dim : dim * 2, :
						]
						UpperCamelCase__				:					List[str]					=					in_proj_bias[
						    dim : dim * 2
						]
						UpperCamelCase__				:					Tuple					=					in_proj_weight[
						    -dim :, :
						]
						UpperCamelCase__				:					List[str]					=					in_proj_bias[-dim :]
						# fmt: on







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     List[str]:
		# fmt: off
		UpperCamelCase__				:					str					=					config.decoder_config.hidden_size
		for idx in range(config.decoder_config.decoder_layers):
				# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
				UpperCamelCase__				:					int					=					state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight')
				UpperCamelCase__				:					List[str]					=					state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias')
				# next, add query, keys and values (in that order) to the state dict
				UpperCamelCase__				:					Union[str, Any]					=					in_proj_weight[: hidden_size, :]
				UpperCamelCase__				:					str					=					in_proj_bias[:config.hidden_size]
				UpperCamelCase__				:					List[Any]					=					in_proj_weight[hidden_size : hidden_size * 2, :]
				UpperCamelCase__				:					Optional[int]					=					in_proj_bias[hidden_size : hidden_size * 2]
				UpperCamelCase__				:					List[str]					=					in_proj_weight[-hidden_size :, :]
				UpperCamelCase__				:					Optional[Any]					=					in_proj_bias[-hidden_size :]
				# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
				UpperCamelCase__				:					Optional[int]					=					state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight')
				UpperCamelCase__				:					Any					=					state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias')
				# next, add query, keys and values (in that order) to the state dict
				UpperCamelCase__				:					Dict					=					in_proj_weight[: hidden_size, :]
				UpperCamelCase__				:					Tuple					=					in_proj_bias[:config.hidden_size]
				UpperCamelCase__				:					List[str]					=					in_proj_weight[hidden_size : hidden_size * 2, :]
				UpperCamelCase__				:					Tuple					=					in_proj_bias[hidden_size : hidden_size * 2]
				UpperCamelCase__				:					Any					=					in_proj_weight[-hidden_size :, :]
				UpperCamelCase__				:					Any					=					in_proj_bias[-hidden_size :]
		# fmt: on







def       __UpperCAmelCase						(  )							->     torch.Tensor:
		UpperCamelCase__				:					List[Any]					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
		UpperCamelCase__				:					Optional[Any]					=					Image.open(requests.get(lowerCamelCase_     ,      stream=lowerCamelCase_).raw)
		return im







@torch.no_grad()
def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_ = False)							->     Any:
		UpperCamelCase__				:					Any					=					get_maskformer_config(lowerCamelCase_)

		# load original state_dict
		with open(lowerCamelCase_     ,      'rb') as f:
				UpperCamelCase__				:					str					=					pickle.load(lowerCamelCase_)
		UpperCamelCase__				:					str					=					data['model']

		# for name, param in state_dict.items():
		#     print(name, param.shape)

		# rename keys
		UpperCamelCase__				:					Optional[int]					=					create_rename_keys(lowerCamelCase_)
		for src, dest in rename_keys:
				rename_key(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
		read_in_swin_q_k_v(lowerCamelCase_     ,      config.backbone_config)
		read_in_decoder_q_k_v(lowerCamelCase_     ,      lowerCamelCase_)

		# update to torch tensors
		for key, value in state_dict.items():
				UpperCamelCase__				:					Any					=					torch.from_numpy(lowerCamelCase_)

		# load 🤗 model
		UpperCamelCase__				:					List[Any]					=					MaskFormerForInstanceSegmentation(lowerCamelCase_)
		model.eval()

		for name, param in model.named_parameters():
				print(lowerCamelCase_     ,      param.shape)

		UpperCamelCase__,	UpperCamelCase__				:					int					=					model.load_state_dict(lowerCamelCase_     ,      strict=lowerCamelCase_)
		assert missing_keys == [
		    "model.pixel_level_module.encoder.model.layernorm.weight",
		    "model.pixel_level_module.encoder.model.layernorm.bias",
		]
		assert len(lowerCamelCase_) == 0, f'Unexpected keys: {unexpected_keys}'

		# verify results
		UpperCamelCase__				:					Union[str, Any]					=					prepare_img()
		if "vistas" in model_name:
				UpperCamelCase__				:					Tuple					=					65
		elif "cityscapes" in model_name:
				UpperCamelCase__				:					str					=					65_535
		else:
				UpperCamelCase__				:					Dict					=					255
		UpperCamelCase__				:					Tuple					=					True if 'ade' in model_name else False
		UpperCamelCase__				:					Dict					=					MaskFormerImageProcessor(ignore_index=lowerCamelCase_     ,      reduce_labels=lowerCamelCase_)

		UpperCamelCase__				:					List[str]					=					image_processor(lowerCamelCase_     ,      return_tensors='pt')

		UpperCamelCase__				:					Dict					=					model(**lowerCamelCase_)

		print('Logits:'     ,      outputs.class_queries_logits[0, :3, :3])

		if model_name == "maskformer-swin-tiny-ade":
				UpperCamelCase__				:					Dict					=					torch.tensor(
				    [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]])
		assert torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,      lowerCamelCase_     ,      atol=1e-4)
		print('Looks ok!')

		if pytorch_dump_folder_path is not None:
				print(f'Saving model and image processor to {pytorch_dump_folder_path}')
				Path(lowerCamelCase_).mkdir(exist_ok=lowerCamelCase_)
				model.save_pretrained(lowerCamelCase_)
				image_processor.save_pretrained(lowerCamelCase_)

		if push_to_hub:
				print('Pushing model and image processor to the hub...')
				model.push_to_hub(f'nielsr/{model_name}')
				image_processor.push_to_hub(f'nielsr/{model_name}')


if __name__ == "__main__":
		lowerCAmelCase__          =						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--model_name',
		    default='maskformer-swin-tiny-ade',
		    type=str,
		    help=('Name of the MaskFormer model you\'d like to convert',),
		)
		parser.add_argument(
		    '--checkpoint_path',
		    default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
		    type=str,
		    help='Path to the original state dict (.pth file).',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
		)
		parser.add_argument(
		    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
		)

		lowerCAmelCase__          =						parser.parse_args()
		convert_maskformer_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)












'''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       __UpperCAmelCase						(  lowerCamelCase_)							->     List[Tuple[int, ...]]:
		UpperCamelCase__				:					int					=					[]
		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       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     Tuple[int, ...]:
		UpperCamelCase__				:					int					=					[]
		for d in reversed(lowerCamelCase_):
				idx.append(flat_idx % d)
				UpperCamelCase__				:					Any					=					flat_idx // d

		return tuple(reversed(lowerCamelCase_))







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

		# 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(lowerCamelCase_) -> None:
				UpperCamelCase__				:					Tuple					=					True
				for i in range(len(lowerCamelCase_)):
						UpperCamelCase__				:					List[Any]					=					-1 * (i + 1)
						l[reversed_idx] &= tally
						UpperCamelCase__				:					Optional[Any]					=					l[reversed_idx]

		if start_edges is None:
				UpperCamelCase__				:					int					=					[s == 0 for s in start]
				reduce_edge_list(lowerCamelCase_)
		if end_edges is None:
				UpperCamelCase__				:					List[str]					=					[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),)]

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

		UpperCamelCase__				:					Tuple[slice, ...]					=					tuple(lowerCamelCase_)
		UpperCamelCase__				:					Dict					=					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

				UpperCamelCase__				:					str					=					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

				UpperCamelCase__				:					Optional[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())
				UpperCamelCase__				:					Dict					=					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						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     torch.Tensor:
		UpperCamelCase__				:					List[Any]					=					t.shape[:no_batch_dims]
		UpperCamelCase__				:					Optional[int]					=					list(_flat_idx_to_idx(lowerCamelCase_     ,      lowerCamelCase_))
		# _get_minimal_slice_set is inclusive
		UpperCamelCase__				:					Dict					=					list(_flat_idx_to_idx(flat_end - 1     ,      lowerCamelCase_))

		# Get an ordered list of slices to perform
		UpperCamelCase__				:					int					=					_get_minimal_slice_set(
		    lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      )

		UpperCamelCase__				:					List[Any]					=					[t[s] for s in slices]

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







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_ = False     ,      lowerCamelCase_ = None     ,      lowerCamelCase_ = False     ,      )							->     Any:
		if not (len(lowerCamelCase_) > 0):
				raise ValueError('Must provide at least one input')

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

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

		UpperCamelCase__				:					Dict					=					1
		for d in orig_batch_dims:
				flat_batch_dim *= d

		UpperCamelCase__				:					int					=					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

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

				UpperCamelCase__				:					Dict[str, Any]					=					tensor_tree_map(lowerCamelCase_     ,      lowerCamelCase_)

				# Run the layer on the chunk
				UpperCamelCase__				:					List[Any]					=					layer(**lowerCamelCase_)

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

				i += chunk_size

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

		return out







class       __lowercase					:

	def __init__(      self   :						List[str]  ,					UpperCAmelCase_   :						int = 512  ,					):
			UpperCamelCase__				:					str					=					max_chunk_size
			UpperCamelCase__				:					Optional[int]					=					None
			UpperCamelCase__				:					Optional[tuple]					=					None

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						Callable  ,					UpperCAmelCase_   :						tuple  ,					UpperCAmelCase_   :						int):
			logging.info('Tuning chunk size...')

			if min_chunk_size >= self.max_chunk_size:
					return min_chunk_size

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

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

			return candidates[min_viable_chunk_size_index]

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

			if not consistent:
					UpperCamelCase__				:					Tuple					=					self._determine_favorable_chunk_size(
					    UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_  ,					)
					UpperCamelCase__				:					Optional[Any]					=					arg_data

			assert self.cached_chunk_size is not None

			return self.cached_chunk_size




