
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase :			Union[str, Any]              =	logging.get_logger(__name__)


def 				A	( lowercase			)       ->  List[Any]:
		'''simple docstring'''

		UpperCamelCase          =					SwinConfig(
		    embed_dim=192   ,	depths=(2, 2, 18, 2)   ,	num_heads=(6, 12, 24, 48)   ,	window_size=12   ,	out_features=['stage2', 'stage3', 'stage4']   ,	)

		UpperCamelCase          =					DetaConfig(
		    backbone_config=lowercase   ,	num_queries=900   ,	encoder_ffn_dim=2_048   ,	decoder_ffn_dim=2_048   ,	num_feature_levels=5   ,	assign_first_stage=lowercase   ,	with_box_refine=lowercase   ,	two_stage=lowercase   ,	)

		# set labels
		UpperCamelCase          =					'huggingface/label-files'
		if "o365" in model_name:
				UpperCamelCase          =					366
				UpperCamelCase          =					'object365-id2label.json'
		else:
				UpperCamelCase          =					91
				UpperCamelCase          =					'coco-detection-id2label.json'

		UpperCamelCase          =					num_labels
		UpperCamelCase          =					json.load(open(cached_download(hf_hub_url(lowercase   ,	lowercase   ,	repo_type='dataset'			)			)   ,	'r'			)			)
		UpperCamelCase          =					{int(lowercase			): v for k, v in idalabel.items()}
		UpperCamelCase          =					idalabel
		UpperCamelCase          =					{v: k for k, v in idalabel.items()}

		return config


def 				A	( lowercase			)       ->  int:
		'''simple docstring'''

		UpperCamelCase          =					[]

		# stem
		# fmt: off
		rename_keys.append(('backbone.0.body.patch_embed.proj.weight', 'model.backbone.model.embeddings.patch_embeddings.projection.weight')			)
		rename_keys.append(('backbone.0.body.patch_embed.proj.bias', 'model.backbone.model.embeddings.patch_embeddings.projection.bias')			)
		rename_keys.append(('backbone.0.body.patch_embed.norm.weight', 'model.backbone.model.embeddings.norm.weight')			)
		rename_keys.append(('backbone.0.body.patch_embed.norm.bias', 'model.backbone.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.0.body.layers.{i}.blocks.{j}.norm1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')			)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')			)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')			)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')			)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')			)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')			)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')			)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')			)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')			)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')			)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')			)
						rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')			)

				if i < 3:
						rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.reduction.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''')			)
						rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''')			)
						rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.bias''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''')			)

		rename_keys.append(('backbone.0.body.norm1.weight', 'model.backbone.model.hidden_states_norms.stage2.weight')			)
		rename_keys.append(('backbone.0.body.norm1.bias', 'model.backbone.model.hidden_states_norms.stage2.bias')			)
		rename_keys.append(('backbone.0.body.norm2.weight', 'model.backbone.model.hidden_states_norms.stage3.weight')			)
		rename_keys.append(('backbone.0.body.norm2.bias', 'model.backbone.model.hidden_states_norms.stage3.bias')			)
		rename_keys.append(('backbone.0.body.norm3.weight', 'model.backbone.model.hidden_states_norms.stage4.weight')			)
		rename_keys.append(('backbone.0.body.norm3.bias', 'model.backbone.model.hidden_states_norms.stage4.bias')			)

		# transformer encoder
		for i in range(config.encoder_layers			):
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')			)
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')			)
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')			)
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')			)
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''')			)
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''')			)
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''')			)
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''')			)
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')			)
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')			)
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''')			)
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''')			)
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''')			)
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''')			)
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''')			)
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''')			)

		# transformer decoder
		for i in range(config.decoder_layers			):
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''')			)
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''')			)

		# fmt: on

		return rename_keys


def 				A	( lowercase   ,	lowercase   ,	lowercase			)       ->  Any:
		'''simple docstring'''

		UpperCamelCase          =					dct.pop(lowercase			)
		UpperCamelCase          =					val


def 				A	( lowercase   ,	lowercase			)       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					[int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
		for i in range(len(backbone_config.depths			)			):
				UpperCamelCase          =					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          =					state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''			)
						UpperCamelCase          =					state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'''			)
						# next, add query, keys and values (in that order) to the state dict
						UpperCamelCase          =					in_proj_weight[:dim, :]
						UpperCamelCase          =					in_proj_bias[: dim]
						UpperCamelCase          =					in_proj_weight[
						    dim : dim * 2, :
						]
						UpperCamelCase          =					in_proj_bias[
						    dim : dim * 2
						]
						UpperCamelCase          =					in_proj_weight[
						    -dim :, :
						]
						UpperCamelCase          =					in_proj_bias[-dim :]
						# fmt: on


def 				A	( lowercase   ,	lowercase			)       ->  Optional[Any]:
		'''simple docstring'''

		UpperCamelCase          =					config.d_model
		for i in range(config.decoder_layers			):
				# read in weights + bias of input projection layer of self-attention
				UpperCamelCase          =					state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''			)
				UpperCamelCase          =					state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''			)
				# next, add query, keys and values (in that order) to the state dict
				UpperCamelCase          =					in_proj_weight[:hidden_size, :]
				UpperCamelCase          =					in_proj_bias[:hidden_size]
				UpperCamelCase          =					in_proj_weight[
				    hidden_size : hidden_size * 2, :
				]
				UpperCamelCase          =					in_proj_bias[hidden_size : hidden_size * 2]
				UpperCamelCase          =					in_proj_weight[-hidden_size:, :]
				UpperCamelCase          =					in_proj_bias[-hidden_size:]


def 				A	( )       ->  Dict:
		'''simple docstring'''

		UpperCamelCase          =					'http://images.cocodataset.org/val2017/000000039769.jpg'
		UpperCamelCase          =					Image.open(requests.get(lowercase   ,	stream=lowercase			).raw			)

		return im


@torch.no_grad()
def 				A	( lowercase   ,	lowercase   ,	lowercase			)       ->  Tuple:
		'''simple docstring'''

		UpperCamelCase          =					get_deta_config(lowercase			)

		# load original state dict
		if model_name == "deta-swin-large":
				UpperCamelCase          =					hf_hub_download(repo_id='nielsr/deta-checkpoints'   ,	filename='adet_swin_ft.pth'			)
		elif model_name == "deta-swin-large-o365":
				UpperCamelCase          =					hf_hub_download(repo_id='jozhang97/deta-swin-l-o365'   ,	filename='deta_swin_pt_o365.pth'			)
		else:
				raise ValueError(f'''Model name {model_name} not supported'''			)

		UpperCamelCase          =					torch.load(lowercase   ,	map_location='cpu'			)['model']

		# original state dict
		for name, param in state_dict.items():
				print(lowercase   ,	param.shape			)

		# rename keys
		UpperCamelCase          =					create_rename_keys(lowercase			)
		for src, dest in rename_keys:
				rename_key(lowercase   ,	lowercase   ,	lowercase			)
		read_in_swin_q_k_v(lowercase   ,	config.backbone_config			)
		read_in_decoder_q_k_v(lowercase   ,	lowercase			)

		# fix some prefixes
		for key in state_dict.copy().keys():
				if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
						UpperCamelCase          =					state_dict.pop(lowercase			)
						UpperCamelCase          =					val
				if "input_proj" in key:
						UpperCamelCase          =					state_dict.pop(lowercase			)
						UpperCamelCase          =					val
				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
						UpperCamelCase          =					state_dict.pop(lowercase			)
						UpperCamelCase          =					val

    # finally, create HuggingFace model and load state dict
		UpperCamelCase          =					DetaForObjectDetection(lowercase			)
		model.load_state_dict(lowercase			)
		model.eval()

		UpperCamelCase          =					'cuda' if torch.cuda.is_available() else 'cpu'
		model.to(lowercase			)

		# load image processor
		UpperCamelCase          =					DetaImageProcessor(format='coco_detection'			)

		# verify our conversion on image
		UpperCamelCase          =					prepare_img()
		UpperCamelCase          =					processor(images=lowercase   ,	return_tensors='pt'			)
		UpperCamelCase          =					encoding['pixel_values']
		UpperCamelCase          =					model(pixel_values.to(lowercase			)			)

		# verify logits
		print('Logits:'   ,	outputs.logits[0, :3, :3]			)
		print('Boxes:'   ,	outputs.pred_boxes[0, :3, :3]			)
		if model_name == "deta-swin-large":
				UpperCamelCase          =					torch.tensor(
				    [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]]			)
				UpperCamelCase          =					torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]]			)
		elif model_name == "deta-swin-large-o365":
				UpperCamelCase          =					torch.tensor(
				    [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]]			)
				UpperCamelCase          =					torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]]			)
		assert torch.allclose(outputs.logits[0, :3, :3]   ,	expected_logits.to(lowercase			)   ,	atol=1e-4			)
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,	expected_boxes.to(lowercase			)   ,	atol=1e-4			)
		print('Everything ok!'			)

		if pytorch_dump_folder_path:
				# Save model and processor
				logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''			)
				Path(lowercase			).mkdir(exist_ok=lowercase			)
				model.save_pretrained(lowercase			)
				processor.save_pretrained(lowercase			)

		# Push to hub
		if push_to_hub:
				print('Pushing model and processor to hub...'			)
				model.push_to_hub(f'''jozhang97/{model_name}'''			)
				processor.push_to_hub(f'''jozhang97/{model_name}'''			)


if __name__ == "__main__":
					_UpperCAmelCase :			int              =	argparse.ArgumentParser()

					parser.add_argument(
					    "--model_name",
					    type=str,
					    default="deta-swin-large",
					    choices=["deta-swin-large", "deta-swin-large-o365"],
					    help="Name of the model you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path",
					    default=None,
					    type=str,
					    help="Path to the folder to output PyTorch model.",
					)
					parser.add_argument(
					    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
					)
					_UpperCAmelCase :			List[Any]              =	parser.parse_args()
					convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block


@dataclass
class     lowercase (		_SCREAMING_SNAKE_CASE       ):
		__lowercase	:	torch.FloatTensor






class     lowercase (		nn.Module       ):



		def __init__(			self    ,				A_=3    ,				A_=3    ,				A_=("DownEncoderBlock2D",)    ,				A_=(64,)    ,				A_=2    ,				A_=32    ,				A_="silu"    ,				A_=True    ,				)  -> List[Any]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					layers_per_block

				UpperCamelCase          =					torch.nn.Convad(
				    A_    ,				block_out_channels[0]    ,				kernel_size=3    ,				stride=1    ,				padding=1    ,				)

				UpperCamelCase          =					None
				UpperCamelCase          =					nn.ModuleList([]	)

				# down
				UpperCamelCase          =					block_out_channels[0]
				for i, down_block_type in enumerate(A_	):
						UpperCamelCase          =					output_channel
						UpperCamelCase          =					block_out_channels[i]
						UpperCamelCase          =					i == len(A_	) - 1

						UpperCamelCase          =					get_down_block(
						    A_    ,				num_layers=self.layers_per_block    ,				in_channels=A_    ,				out_channels=A_    ,				add_downsample=not is_final_block    ,				resnet_eps=1e-6    ,				downsample_padding=0    ,				resnet_act_fn=A_    ,				resnet_groups=A_    ,				attention_head_dim=A_    ,				temb_channels=A_    ,				)
						self.down_blocks.append(A_	)

				# mid
				UpperCamelCase          =					UNetMidBlockaD(
				    in_channels=block_out_channels[-1]    ,				resnet_eps=1e-6    ,				resnet_act_fn=A_    ,				output_scale_factor=1    ,				resnet_time_scale_shift='default'    ,				attention_head_dim=block_out_channels[-1]    ,				resnet_groups=A_    ,				temb_channels=A_    ,				)

				# out
				UpperCamelCase          =					nn.GroupNorm(num_channels=block_out_channels[-1]    ,				num_groups=A_    ,				eps=1e-6	)
				UpperCamelCase          =					nn.SiLU()

				UpperCamelCase          =					2 * out_channels if double_z else out_channels
				UpperCamelCase          =					nn.Convad(block_out_channels[-1]    ,				A_    ,				3    ,				padding=1	)

				UpperCamelCase          =					False



		def __UpperCamelCase			(			self    ,				A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					x
				UpperCamelCase          =					self.conv_in(A_	)

				if self.training and self.gradient_checkpointing:

						def create_custom_forward(A_	):
								def custom_forward(*A_	):
										return module(*A_	)

								return custom_forward

						# down
						if is_torch_version('>='    ,				'1.11.0'	):
								for down_block in self.down_blocks:
										UpperCamelCase          =					torch.utils.checkpoint.checkpoint(
										    create_custom_forward(A_	)    ,				A_    ,				use_reentrant=A_	)
								# middle
								UpperCamelCase          =					torch.utils.checkpoint.checkpoint(
								    create_custom_forward(self.mid_block	)    ,				A_    ,				use_reentrant=A_	)
						else:
								for down_block in self.down_blocks:
										UpperCamelCase          =					torch.utils.checkpoint.checkpoint(create_custom_forward(A_	)    ,				A_	)
								# middle
								UpperCamelCase          =					torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block	)    ,				A_	)

				else:
						# down
						for down_block in self.down_blocks:
								UpperCamelCase          =					down_block(A_	)

						# middle
						UpperCamelCase          =					self.mid_block(A_	)

				# post-process
				UpperCamelCase          =					self.conv_norm_out(A_	)
				UpperCamelCase          =					self.conv_act(A_	)
				UpperCamelCase          =					self.conv_out(A_	)

				return sample






class     lowercase (		nn.Module       ):



		def __init__(			self    ,				A_=3    ,				A_=3    ,				A_=("UpDecoderBlock2D",)    ,				A_=(64,)    ,				A_=2    ,				A_=32    ,				A_="silu"    ,				A_="group"    ,				)  -> Tuple:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					layers_per_block

				UpperCamelCase          =					nn.Convad(
				    A_    ,				block_out_channels[-1]    ,				kernel_size=3    ,				stride=1    ,				padding=1    ,				)

				UpperCamelCase          =					None
				UpperCamelCase          =					nn.ModuleList([]	)

				UpperCamelCase          =					in_channels if norm_type == 'spatial' else None

				# mid
				UpperCamelCase          =					UNetMidBlockaD(
				    in_channels=block_out_channels[-1]    ,				resnet_eps=1e-6    ,				resnet_act_fn=A_    ,				output_scale_factor=1    ,				resnet_time_scale_shift='default' if norm_type == 'group' else norm_type    ,				attention_head_dim=block_out_channels[-1]    ,				resnet_groups=A_    ,				temb_channels=A_    ,				)

				# up
				UpperCamelCase          =					list(reversed(A_	)	)
				UpperCamelCase          =					reversed_block_out_channels[0]
				for i, up_block_type in enumerate(A_	):
						UpperCamelCase          =					output_channel
						UpperCamelCase          =					reversed_block_out_channels[i]

						UpperCamelCase          =					i == len(A_	) - 1

						UpperCamelCase          =					get_up_block(
						    A_    ,				num_layers=self.layers_per_block + 1    ,				in_channels=A_    ,				out_channels=A_    ,				prev_output_channel=A_    ,				add_upsample=not is_final_block    ,				resnet_eps=1e-6    ,				resnet_act_fn=A_    ,				resnet_groups=A_    ,				attention_head_dim=A_    ,				temb_channels=A_    ,				resnet_time_scale_shift=A_    ,				)
						self.up_blocks.append(A_	)
						UpperCamelCase          =					output_channel

				# out
				if norm_type == "spatial":
						UpperCamelCase          =					SpatialNorm(block_out_channels[0]    ,				A_	)
				else:
						UpperCamelCase          =					nn.GroupNorm(num_channels=block_out_channels[0]    ,				num_groups=A_    ,				eps=1e-6	)
				UpperCamelCase          =					nn.SiLU()
				UpperCamelCase          =					nn.Convad(block_out_channels[0]    ,				A_    ,				3    ,				padding=1	)

				UpperCamelCase          =					False



		def __UpperCamelCase			(			self    ,				A_    ,				A_=None	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					z
				UpperCamelCase          =					self.conv_in(A_	)

				UpperCamelCase          =					next(iter(self.up_blocks.parameters()	)	).dtype
				if self.training and self.gradient_checkpointing:

						def create_custom_forward(A_	):
								def custom_forward(*A_	):
										return module(*A_	)

								return custom_forward

						if is_torch_version('>='    ,				'1.11.0'	):
								# middle
								UpperCamelCase          =					torch.utils.checkpoint.checkpoint(
								    create_custom_forward(self.mid_block	)    ,				A_    ,				A_    ,				use_reentrant=A_	)
								UpperCamelCase          =					sample.to(A_	)

								# up
								for up_block in self.up_blocks:
										UpperCamelCase          =					torch.utils.checkpoint.checkpoint(
										    create_custom_forward(A_	)    ,				A_    ,				A_    ,				use_reentrant=A_	)
						else:
								# middle
								UpperCamelCase          =					torch.utils.checkpoint.checkpoint(
								    create_custom_forward(self.mid_block	)    ,				A_    ,				A_	)
								UpperCamelCase          =					sample.to(A_	)

								# up
								for up_block in self.up_blocks:
										UpperCamelCase          =					torch.utils.checkpoint.checkpoint(create_custom_forward(A_	)    ,				A_    ,				A_	)
				else:
						# middle
						UpperCamelCase          =					self.mid_block(A_    ,				A_	)
						UpperCamelCase          =					sample.to(A_	)

						# up
						for up_block in self.up_blocks:
								UpperCamelCase          =					up_block(A_    ,				A_	)

        # post-process
				if latent_embeds is None:
						UpperCamelCase          =					self.conv_norm_out(A_	)
				else:
						UpperCamelCase          =					self.conv_norm_out(A_    ,				A_	)
				UpperCamelCase          =					self.conv_act(A_	)
				UpperCamelCase          =					self.conv_out(A_	)

				return sample






class     lowercase (		nn.Module       ):



		def __init__(			self    ,				A_    ,				A_    ,				A_    ,				A_=None    ,				A_="random"    ,				A_=False    ,				A_=True	)  -> Tuple:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase          =					n_e
				UpperCamelCase          =					vq_embed_dim
				UpperCamelCase          =					beta
				UpperCamelCase          =					legacy

				UpperCamelCase          =					nn.Embedding(self.n_e    ,				self.vq_embed_dim	)
				self.embedding.weight.data.uniform_(-1.0 / self.n_e    ,				1.0 / self.n_e	)

				UpperCamelCase          =					remap
				if self.remap is not None:
						self.register_buffer('used'    ,				torch.tensor(np.load(self.remap	)	)	)
						UpperCamelCase          =					self.used.shape[0]
						UpperCamelCase          =					unknown_index  # "random" or "extra" or integer
						if self.unknown_index == "extra":
								UpperCamelCase          =					self.re_embed
								UpperCamelCase          =					self.re_embed + 1
						print(
						    F'''Remapping {self.n_e} indices to {self.re_embed} indices. '''
						    F'''Using {self.unknown_index} for unknown indices.'''	)
				else:
						UpperCamelCase          =					n_e

				UpperCamelCase          =					sane_index_shape



		def __UpperCamelCase			(			self    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					inds.shape
				assert len(A_	) > 1
				UpperCamelCase          =					inds.reshape(ishape[0]    ,				-1	)
				UpperCamelCase          =					self.used.to(A_	)
				UpperCamelCase          =					(inds[:, :, None] == used[None, None, ...]).long()
				UpperCamelCase          =					match.argmax(-1	)
				UpperCamelCase          =					match.sum(2	) < 1
				if self.unknown_index == "random":
						UpperCamelCase          =					torch.randint(0    ,				self.re_embed    ,				size=new[unknown].shape	).to(device=new.device	)
				else:
						UpperCamelCase          =					self.unknown_index
				return new.reshape(A_	)



		def __UpperCamelCase			(			self    ,				A_	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					inds.shape
				assert len(A_	) > 1
				UpperCamelCase          =					inds.reshape(ishape[0]    ,				-1	)
				UpperCamelCase          =					self.used.to(A_	)
				if self.re_embed > self.used.shape[0]:  # extra token
						UpperCamelCase          =					0  # simply set to zero
				UpperCamelCase          =					torch.gather(used[None, :][inds.shape[0] * [0], :]    ,				1    ,				A_	)
				return back.reshape(A_	)



		def __UpperCamelCase			(			self    ,				A_	)  -> List[str]:
				"""simple docstring"""
				# reshape z -> (batch, height, width, channel) and flatten
				UpperCamelCase          =					z.permute(0    ,				2    ,				3    ,				1	).contiguous()
				UpperCamelCase          =					z.view(-1    ,				self.vq_embed_dim	)

				# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
				UpperCamelCase          =					torch.argmin(torch.cdist(A_    ,				self.embedding.weight	)    ,				dim=1	)

				UpperCamelCase          =					self.embedding(A_	).view(z.shape	)
				UpperCamelCase          =					None
				UpperCamelCase          =					None

				# compute loss for embedding
				if not self.legacy:
						UpperCamelCase          =					self.beta * torch.mean((z_q.detach() - z) ** 2	) + torch.mean((z_q - z.detach()) ** 2	)
				else:
						UpperCamelCase          =					torch.mean((z_q.detach() - z) ** 2	) + self.beta * torch.mean((z_q - z.detach()) ** 2	)

				# preserve gradients
				UpperCamelCase          =					z + (z_q - z).detach()

				# reshape back to match original input shape
				UpperCamelCase          =					z_q.permute(0    ,				3    ,				1    ,				2	).contiguous()

				if self.remap is not None:
						UpperCamelCase          =					min_encoding_indices.reshape(z.shape[0]    ,				-1	)  # add batch axis
						UpperCamelCase          =					self.remap_to_used(A_	)
						UpperCamelCase          =					min_encoding_indices.reshape(-1    ,				1	)  # flatten

				if self.sane_index_shape:
						UpperCamelCase          =					min_encoding_indices.reshape(z_q.shape[0]    ,				z_q.shape[2]    ,				z_q.shape[3]	)

				return z_q, loss, (perplexity, min_encodings, min_encoding_indices)



		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> Optional[int]:
				"""simple docstring"""
				# shape specifying (batch, height, width, channel)
				if self.remap is not None:
						UpperCamelCase          =					indices.reshape(shape[0]    ,				-1	)  # add batch axis
						UpperCamelCase          =					self.unmap_to_all(A_	)
						UpperCamelCase          =					indices.reshape(-1	)  # flatten again

				# get quantized latent vectors
				UpperCamelCase          =					self.embedding(A_	)

				if shape is not None:
						UpperCamelCase          =					z_q.view(A_	)
						# reshape back to match original input shape
						UpperCamelCase          =					z_q.permute(0    ,				3    ,				1    ,				2	).contiguous()

				return z_q






class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __init__(			self    ,				A_    ,				A_=False	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					parameters
				UpperCamelCase     ,  UpperCamelCase          =					torch.chunk(A_    ,				2    ,				dim=1	)
				UpperCamelCase          =					torch.clamp(self.logvar    ,				-30.0    ,				20.0	)
				UpperCamelCase          =					deterministic
				UpperCamelCase          =					torch.exp(0.5 * self.logvar	)
				UpperCamelCase          =					torch.exp(self.logvar	)
				if self.deterministic:
						UpperCamelCase          =					UpperCamelCase          =					torch.zeros_like(
						    self.mean    ,				device=self.parameters.device    ,				dtype=self.parameters.dtype	)



		def __UpperCamelCase			(			self    ,				A_ = None	)  -> torch.FloatTensor:
				"""simple docstring"""
				# make sure sample is on the same device as the parameters and has same dtype
				UpperCamelCase          =					randn_tensor(
				    self.mean.shape    ,				generator=A_    ,				device=self.parameters.device    ,				dtype=self.parameters.dtype	)
				UpperCamelCase          =					self.mean + self.std * sample
				return x



		def __UpperCamelCase			(			self    ,				A_=None	)  -> Tuple:
				"""simple docstring"""
				if self.deterministic:
						return torch.Tensor([0.0]	)
				else:
						if other is None:
								return 0.5 * torch.sum(torch.pow(self.mean    ,				2	) + self.var - 1.0 - self.logvar    ,				dim=[1, 2, 3]	)
						else:
								return 0.5 * torch.sum(
								    torch.pow(self.mean - other.mean    ,				2	) / other.var
								    + self.var / other.var
								    - 1.0
								    - self.logvar
								    + other.logvar    ,				dim=[1, 2, 3]    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				A_=[1, 2, 3]	)  -> Optional[Any]:
				"""simple docstring"""
				if self.deterministic:
						return torch.Tensor([0.0]	)
				UpperCamelCase          =					np.log(2.0 * np.pi	)
				return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean    ,				2	) / self.var    ,				dim=A_	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				return self.mean
