




import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD







def 			lowerCamelCase_		(       )     ->       Tuple:





				"""simple docstring"""


				__lowerCamelCase		       =    'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
				__lowerCamelCase		       =    Image.open(requests.get(UpperCamelCase__ ,     stream=UpperCamelCase__      ).raw      ).convert('RGB'      )

				return image







def 			lowerCamelCase_		(       UpperCamelCase__				:      Dict      )     ->       Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				# fmt: off

				# vision encoder
				rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')      )
				rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')      )
				rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')      )
				rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')      )
				rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')      )
				rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')      )

				for i in range(config.vision_config.num_hidden_layers      ):
								rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.weight""", F"""vision_model.encoder.layers.{i}.layer_norm1.weight""")      )
								rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.bias""", F"""vision_model.encoder.layers.{i}.layer_norm1.bias""")      )
								rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.weight""", F"""vision_model.encoder.layers.{i}.layer_norm2.weight""")      )
								rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.bias""", F"""vision_model.encoder.layers.{i}.layer_norm2.bias""")      )
								rename_keys.append((F"""visual_encoder.blocks.{i}.attn.qkv.weight""", F"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""")      )
								rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.weight""", F"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",)      )
								rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.bias""", F"""vision_model.encoder.layers.{i}.self_attn.projection.bias""")      )
								rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc1.weight""")      )
								rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc1.bias""")      )
								rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc2.weight""")      )
								rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc2.bias""")      )

				# QFormer
				rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight')      )
				rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias')      )

				# fmt: on
				return rename_keys







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      Optional[Any]      )     ->       Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    dct.pop(UpperCamelCase__      )
				__lowerCamelCase		       =    val







def 			lowerCamelCase_		(       UpperCamelCase__				:      Any ,     UpperCamelCase__				:      Optional[Any]      )     ->       Optional[int]:





				"""simple docstring"""


				for i in range(config.vision_config.num_hidden_layers      ):
								# read in original q and v biases
								__lowerCamelCase		       =    state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""      )
								__lowerCamelCase		       =    state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""      )

								# next, set bias in the state dict
								__lowerCamelCase		       =    torch.cat((q_bias, torch.zeros_like(UpperCamelCase__ ,     requires_grad=UpperCamelCase__      ), v_bias)      )
								__lowerCamelCase		       =    qkv_bias







def 			lowerCamelCase_		(       UpperCamelCase__				:      Dict      )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    364 if 'coco' in model_name else 224
				__lowerCamelCase		       =    InstructBlipVisionConfig(image_size=UpperCamelCase__      ).to_dict()

				# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
				# seems like flan-T5 models don't have bos_token_id properly set?
				if "t5-xl" in model_name:
								__lowerCamelCase		       =    TaConfig.from_pretrained('google/flan-t5-xl' ,     dense_act_fn='gelu' ,     bos_token_id=1      ).to_dict()
				elif "t5-xxl" in model_name:
								__lowerCamelCase		       =    TaConfig.from_pretrained('google/flan-t5-xxl' ,     dense_act_fn='gelu' ,     bos_token_id=1      ).to_dict()
				elif "vicuna-7b" in model_name:
								__lowerCamelCase		       =    LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf' ,     vocab_size=3_2001      ).to_dict()
				elif "vicuna-13b" in model_name:
								__lowerCamelCase		       =    LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf' ,     vocab_size=3_2001      ).to_dict()
				else:
								raise ValueError('Model name not supported'      )

				# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
				__lowerCamelCase		       =    InstructBlipQFormerConfig(vocab_size=3_0523      ).to_dict()
				__lowerCamelCase		       =    InstructBlipConfig(vision_config=UpperCamelCase__ ,     text_config=UpperCamelCase__ ,     qformer_config=UpperCamelCase__      )

				return config, image_size







@torch.no_grad()
def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Any=None ,     UpperCamelCase__				:      Dict=False      )     ->       Any:





				"""simple docstring"""


				__lowerCamelCase		       =    AutoTokenizer.from_pretrained('bert-base-uncased' ,     truncation_side='left'      )
				qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}      )

				if "t5" in model_name:
								__lowerCamelCase		       =    TaTokenizerFast.from_pretrained('google/flan-t5-xl' ,     truncation_side='left'      )
				elif "vicuna" in model_name:
								# the following was used in the original implementation:
								# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
								# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
								# tokenizer.add_special_tokens({"bos_token": "</s>"})
								# tokenizer.add_special_tokens({"eos_token": "</s>"})
								# tokenizer.add_special_tokens({"unk_token": "</s>"})
								__lowerCamelCase		       =    LlamaTokenizerFast.from_pretrained(
								    'huggyllama/llama-7b' ,     truncation_side='left' ,     bos_token='</s>' ,     unk_token='</s>'      )
								tokenizer.add_special_tokens({'pad_token': '[PAD]'}      )

				__lowerCamelCase       ,						__lowerCamelCase		       =    get_blipa_config(UpperCamelCase__      )
				__lowerCamelCase		       =    InstructBlipForConditionalGeneration(UpperCamelCase__      ).eval()

				__lowerCamelCase		       =    {
				    'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
				    'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
				    'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
				    'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
				}

				__lowerCamelCase       ,						__lowerCamelCase		       =    model_name_to_original[model_name]

				# load original model
				print('Loading original model...'      )
				__lowerCamelCase		       =    'cuda:1' if torch.cuda.is_available() else 'cpu'
				__lowerCamelCase		       =    'cuda:2' if torch.cuda.is_available() else 'cpu'
				__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    load_model_and_preprocess(
				    name=UpperCamelCase__ ,     model_type=UpperCamelCase__ ,     is_eval=UpperCamelCase__ ,     device=UpperCamelCase__      )
				original_model.eval()
				print('Done!'      )

				# update state dict keys
				__lowerCamelCase		       =    original_model.state_dict()
				__lowerCamelCase		       =    create_rename_keys(UpperCamelCase__      )
				for src, dest in rename_keys:
								rename_key(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )

				# some keys can be renamed efficiently
				for key, val in state_dict.copy().items():
								__lowerCamelCase		       =    state_dict.pop(UpperCamelCase__      )
								if key.startswith('Qformer.bert'      ):
												__lowerCamelCase		       =    key.replace('Qformer.bert' ,     'qformer'      )
								if "attention.self" in key:
												__lowerCamelCase		       =    key.replace('self' ,     'attention'      )
								if "llm_proj" in key:
												__lowerCamelCase		       =    key.replace('llm_proj' ,     'language_projection'      )
								if "t5_proj" in key:
												__lowerCamelCase		       =    key.replace('t5_proj' ,     'language_projection'      )
								if key.startswith('llm_model'      ):
												__lowerCamelCase		       =    key.replace('llm_model' ,     'language_model'      )
								if key.startswith('t5'      ):
												__lowerCamelCase		       =    key.replace('t5' ,     'language'      )
								__lowerCamelCase		       =    val

				# read in qv biases
				read_in_q_v_bias(UpperCamelCase__ ,     UpperCamelCase__      )

				# note: weights get loaded in torch.float32 by default
				hf_model.load_state_dict(UpperCamelCase__ ,     strict=UpperCamelCase__      )

				__lowerCamelCase		       =    load_demo_image()
				__lowerCamelCase		       =    'What is unusual about this image?'

				# create processor
				__lowerCamelCase		       =    BlipImageProcessor(
				    size={'height': image_size, 'width': image_size} ,     image_mean=UpperCamelCase__ ,     image_std=UpperCamelCase__      )
				__lowerCamelCase		       =    InstructBlipProcessor(
				    image_processor=UpperCamelCase__ ,     tokenizer=UpperCamelCase__ ,     qformer_tokenizer=UpperCamelCase__ ,     )
				__lowerCamelCase		       =    processor(images=UpperCamelCase__ ,     text=UpperCamelCase__ ,     return_tensors='pt'      ).to(UpperCamelCase__      )

				# make sure processor creates exact same pixel values
				__lowerCamelCase		       =    vis_processors['eval'](UpperCamelCase__      ).unsqueeze(0      ).to(UpperCamelCase__      )
				__lowerCamelCase		       =    inputs.pixel_values
				assert torch.allclose(original_pixel_values.to(pixel_values.device      ) ,     UpperCamelCase__      )

				original_model.to(UpperCamelCase__      )
				hf_model.to(UpperCamelCase__      )
				with torch.no_grad():
								if "vicuna" in model_name:
												__lowerCamelCase		       =    original_model({'image': original_pixel_values, 'text_input': [prompt]}      ).logits
												__lowerCamelCase		       =    hf_model(**UpperCamelCase__      ).logits
								else:
												__lowerCamelCase		       =    original_model(
												    {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}      ).logits
												__lowerCamelCase		       =    tokenizer('\n' ,     return_tensors='pt'      ).input_ids.to(UpperCamelCase__      )
												__lowerCamelCase		       =    label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id ,     -100      )
												__lowerCamelCase		       =    hf_model(**UpperCamelCase__ ,     labels=UpperCamelCase__      ).logits

				print('First values of original logits:' ,     original_logits[0, :3, :3]      )
				print('First values of HF logits:' ,     logits[0, :3, :3]      )

				# assert values
				assert original_logits.shape == logits.shape
				__lowerCamelCase		       =    1E-4 if 'vicuna' in model_name else 1E-5
				assert torch.allclose(original_logits.to(logits.device      ) ,     UpperCamelCase__ ,     atol=UpperCamelCase__      )
				print('Looks ok!'      )

				print('Generating with original model...'      )
				__lowerCamelCase		       =    original_model.generate({'image': original_pixel_values, 'prompt': prompt} ,     num_beams=5      )

				# important: we need to cast the weights of the HF model to the appropriate type
				print('Generating with HF model...'      )
				__lowerCamelCase		       =    hf_model.generate(
				    **UpperCamelCase__ ,     do_sample=UpperCamelCase__ ,     num_beams=5 ,     max_length=256 ,     min_length=1 ,     top_p=0.9 ,     repetition_penalty=1.5 ,     length_penalty=1.0 ,     temperature=1 ,     )
				if "vicuna" in model_name:
								# convert output id 0 to 2 (eos_token_id)
								# TODO add this in the generate method?
								__lowerCamelCase		       =    2
				print('Original generation:' ,     UpperCamelCase__      )
				__lowerCamelCase		       =    processor.batch_decode(UpperCamelCase__ ,     skip_special_tokens=UpperCamelCase__      )
				__lowerCamelCase		       =    [text.strip() for text in output_text]
				print('HF generation:' ,     UpperCamelCase__      )

				if pytorch_dump_folder_path is not None:
								processor.save_pretrained(UpperCamelCase__      )
								hf_model.save_pretrained(UpperCamelCase__      )

				if push_to_hub:
								processor.push_to_hub(F"""Salesforce/{model_name}"""      )
								hf_model.push_to_hub(F"""Salesforce/{model_name}"""      )


if __name__ == "__main__":
					__A        =    argparse.ArgumentParser()
					__A        =    [
					    "instructblip-vicuna-7b",
					    "instructblip-vicuna-13b",
					    "instructblip-flan-t5-xl",
					    "instructblip-flan-t5-xxl",
					]
					parser.add_argument(
					    "--model_name",
					    default="instructblip-flan-t5-xl",
					    choices=choices,
					    type=str,
					    help="Path to hf config.json of model to convert",
					)
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					parser.add_argument(
					    "--push_to_hub",
					    action="store_true",
					    help="Whether to push the model and processor to the hub after converting",
					)

					__A        =    parser.parse_args()

					convert_blipa_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        _a							(			UpperCamelCase__			):
							_lowercase	:			torch.FloatTensor


class        _a							(			nn.Module			):

							def __init__(							self:	int    ,						UpperCamelCase_:	int=3    ,						UpperCamelCase_:	Optional[Any]=3    ,						UpperCamelCase_:	Union[str, Any]=("DownEncoderBlock2D",)    ,						UpperCamelCase_:	Optional[int]=(64,)    ,						UpperCamelCase_:	Tuple=2    ,						UpperCamelCase_:	Dict=32    ,						UpperCamelCase_:	Any="silu"    ,						UpperCamelCase_:	Optional[int]=True    ,						)			->   Union[str, Any]:





									"""simple docstring"""







									super().__init__()
									lowercase__            =      layers_per_block

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

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

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

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

									# mid
									lowercase__            =      UNetMidBlockaD(
									    in_channels=block_out_channels[-1]    ,						resnet_eps=1E-6    ,						resnet_act_fn=UpperCamelCase_    ,						output_scale_factor=1    ,						resnet_time_scale_shift='''default'''    ,						attention_head_dim=block_out_channels[-1]    ,						resnet_groups=UpperCamelCase_    ,						temb_channels=UpperCamelCase_    ,						)

									# out
									lowercase__            =      nn.GroupNorm(num_channels=block_out_channels[-1]    ,						num_groups=UpperCamelCase_    ,						eps=1E-6							)
									lowercase__            =      nn.SiLU()

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

									lowercase__            =      False


							def       lowerCamelCase_ (							self:	Optional[Any]    ,						UpperCamelCase_:	str							)			->   str:





									"""simple docstring"""







									lowercase__            =      x
									lowercase__            =      self.conv_in(UpperCamelCase_							)

									if self.training and self.gradient_checkpointing:

											def create_custom_forward(UpperCamelCase_:	Dict							):
													def custom_forward(*UpperCamelCase_:	List[str]							):
															return module(*UpperCamelCase_							)

													return custom_forward

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

									else:
											# down
											for down_block in self.down_blocks:
													lowercase__            =      down_block(UpperCamelCase_							)

											# middle
											lowercase__            =      self.mid_block(UpperCamelCase_							)

									# post-process
									lowercase__            =      self.conv_norm_out(UpperCamelCase_							)
									lowercase__            =      self.conv_act(UpperCamelCase_							)
									lowercase__            =      self.conv_out(UpperCamelCase_							)

									return sample


class        _a							(			nn.Module			):

							def __init__(							self:	Dict    ,						UpperCamelCase_:	int=3    ,						UpperCamelCase_:	Optional[Any]=3    ,						UpperCamelCase_:	int=("UpDecoderBlock2D",)    ,						UpperCamelCase_:	Any=(64,)    ,						UpperCamelCase_:	Optional[Any]=2    ,						UpperCamelCase_:	List[Any]=32    ,						UpperCamelCase_:	Optional[Any]="silu"    ,						UpperCamelCase_:	Dict="group"    ,						)			->   Any:





									"""simple docstring"""







									super().__init__()
									lowercase__            =      layers_per_block

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

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

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

									# mid
									lowercase__            =      UNetMidBlockaD(
									    in_channels=block_out_channels[-1]    ,						resnet_eps=1E-6    ,						resnet_act_fn=UpperCamelCase_    ,						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=UpperCamelCase_    ,						temb_channels=UpperCamelCase_    ,						)

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

											lowercase__            =      i == len(UpperCamelCase_							) - 1

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

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

									lowercase__            =      False


							def       lowerCamelCase_ (							self:	Optional[Any]    ,						UpperCamelCase_:	Union[str, Any]    ,						UpperCamelCase_:	List[str]=None							)			->   int:





									"""simple docstring"""







									lowercase__            =      z
									lowercase__            =      self.conv_in(UpperCamelCase_							)

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

											def create_custom_forward(UpperCamelCase_:	List[str]							):
													def custom_forward(*UpperCamelCase_:	int							):
															return module(*UpperCamelCase_							)

													return custom_forward

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

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

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

											# up
											for up_block in self.up_blocks:
													lowercase__            =      up_block(UpperCamelCase_    ,						UpperCamelCase_							)

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

									return sample


class        _a							(			nn.Module			):

							def __init__(							self:	Tuple    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	Optional[int]=None    ,						UpperCamelCase_:	List[str]="random"    ,						UpperCamelCase_:	Union[str, Any]=False    ,						UpperCamelCase_:	Dict=True							)			->   List[Any]:





									"""simple docstring"""







									super().__init__()
									lowercase__            =      n_e
									lowercase__            =      vq_embed_dim
									lowercase__            =      beta
									lowercase__            =      legacy

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

									lowercase__            =      remap
									if self.remap is not None:
											self.register_buffer('''used'''    ,						torch.tensor(np.load(self.remap							)							)							)
											lowercase__            =      self.used.shape[0]
											lowercase__            =      unknown_index  # "random" or "extra" or integer
											if self.unknown_index == "extra":
													lowercase__            =      self.re_embed
													lowercase__            =      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:
											lowercase__            =      n_e

									lowercase__            =      sane_index_shape

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	Any							)			->   Dict:





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	Tuple							)			->   str:





									"""simple docstring"""







									lowercase__            =      inds.shape
									assert len(UpperCamelCase_							) > 1
									lowercase__            =      inds.reshape(ishape[0]    ,						-1							)
									lowercase__            =      self.used.to(UpperCamelCase_							)
									if self.re_embed > self.used.shape[0]:  # extra token
											lowercase__            =      0  # simply set to zero
									lowercase__            =      torch.gather(used[None, :][inds.shape[0] * [0], :]    ,						1    ,						UpperCamelCase_							)
									return back.reshape(UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	Any							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      z.permute(0    ,						2    ,						3    ,						1							).contiguous()
									lowercase__            =      z.view(-1    ,						self.vq_embed_dim							)

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

									lowercase__            =      self.embedding(UpperCamelCase_							).view(z.shape							)
									lowercase__            =      None
									lowercase__            =      None

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

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

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

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

									if self.sane_index_shape:
											lowercase__            =      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       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	Tuple    ,						UpperCamelCase_:	List[Any]							)			->   List[str]:





									"""simple docstring"""







									if self.remap is not None:
											lowercase__            =      indices.reshape(shape[0]    ,						-1							)  # add batch axis
											lowercase__            =      self.unmap_to_all(UpperCamelCase_							)
											lowercase__            =      indices.reshape(-1							)  # flatten again

									# get quantized latent vectors
									lowercase__            =      self.embedding(UpperCamelCase_							)

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

									return z_q


class        _a							(			UpperCamelCase__			):

							def __init__(							self:	Union[str, Any]    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	List[Any]=False							)			->   str:





									"""simple docstring"""







									lowercase__            =      parameters
									lowercase__     ,		lowercase__            =      torch.chunk(UpperCamelCase_    ,						2    ,						dim=1							)
									lowercase__            =      torch.clamp(self.logvar    ,						-30.0    ,						20.0							)
									lowercase__            =      deterministic
									lowercase__            =      torch.exp(0.5 * self.logvar							)
									lowercase__            =      torch.exp(self.logvar							)
									if self.deterministic:
											lowercase__            =      lowercase__            =      torch.zeros_like(
											    self.mean    ,						device=self.parameters.device    ,						dtype=self.parameters.dtype							)

							def       lowerCamelCase_ (							self:	Any    ,						UpperCamelCase_:	Optional[torch.Generator] = None							)			->   torch.FloatTensor:





									"""simple docstring"""







									lowercase__            =      randn_tensor(
									    self.mean.shape    ,						generator=UpperCamelCase_    ,						device=self.parameters.device    ,						dtype=self.parameters.dtype							)
									lowercase__            =      self.mean + self.std * sample
									return x

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	str=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       lowerCamelCase_ (							self:	int    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	Any=[1, 2, 3]							)			->   Tuple:





									"""simple docstring"""







									if self.deterministic:
											return torch.Tensor([0.0]							)
									lowercase__            =      np.log(2.0 * np.pi							)
									return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean    ,						2							) / self.var    ,						dim=UpperCamelCase_							)


							def       lowerCamelCase_ (							self:	str							)			->   Any:





									"""simple docstring"""







									return self.mean


