



"""simple docstring"""





import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
lowerCamelCase       	=						(720, 1_280)  # Height, Width
lowerCamelCase       	=						(0.4, 0.6)  # if height or width lower than this scale, drop it.
lowerCamelCase       	=						1 / 100
lowerCamelCase       	=						""""""
lowerCamelCase       	=						""""""
lowerCamelCase       	=						""""""
lowerCamelCase       	=						250
def        a__     (				):
      UpperCAmelCase_			,   UpperCAmelCase_      			=							get_dataset(lowerCAmelCase__     ,      lowerCAmelCase__				)
      for index in range(lowerCAmelCase__				):
            UpperCAmelCase_      			=							random.sample(range(len(lowerCAmelCase__				)				)     ,      4				)
            UpperCAmelCase_			,   UpperCAmelCase_			,   UpperCAmelCase_      			=							update_image_and_anno(
                lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      filter_scale=lowerCAmelCase__     ,      )

            # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
            UpperCAmelCase_      			=							random_chars(32				)
            UpperCAmelCase_      			=							path.split(os.sep				)[-1].rsplit("."     ,      1				)[0]
            UpperCAmelCase_      			=							f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
            cva.imwrite(f"""{file_root}.jpg"""     ,      lowerCAmelCase__     ,      [cva.IMWRITE_JPEG_QUALITY, 85]				)
            print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""				)
            UpperCAmelCase_      			=							[]
            for anno in new_annos:
                  UpperCAmelCase_      			=							anno[3] - anno[1]
                  UpperCAmelCase_      			=							anno[4] - anno[2]
                  UpperCAmelCase_      			=							anno[1] + width / 2
                  UpperCAmelCase_      			=							anno[2] + height / 2
                  UpperCAmelCase_      			=							f"""{anno[0]} {x_center} {y_center} {width} {height}"""
                  annos_list.append(lowerCAmelCase__				)
            with open(f"""{file_root}.txt"""     ,      "w"				) as outfile:
                  outfile.write("\n".join(line for line in annos_list				)				)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      UpperCAmelCase_      			=							[]
      UpperCAmelCase_      			=							[]
      for label_file in glob.glob(os.path.join(lowerCAmelCase__     ,      "*.txt"				)				):
            UpperCAmelCase_      			=							label_file.split(os.sep				)[-1].rsplit("."     ,      1				)[0]
            with open(lowerCAmelCase__				) as in_file:
                  UpperCAmelCase_      			=							in_file.readlines()
            UpperCAmelCase_      			=							os.path.join(lowerCAmelCase__     ,      f"""{label_name}.jpg"""				)

            UpperCAmelCase_      			=							[]
            for obj_list in obj_lists:
                  UpperCAmelCase_      			=							obj_list.rstrip("\n"				).split(" "				)
                  UpperCAmelCase_      			=							float(obj[1]				) - float(obj[3]				) / 2
                  UpperCAmelCase_      			=							float(obj[2]				) - float(obj[4]				) / 2
                  UpperCAmelCase_      			=							float(obj[1]				) + float(obj[3]				) / 2
                  UpperCAmelCase_      			=							float(obj[2]				) + float(obj[4]				) / 2

                  boxes.append([int(obj[0]				), xmin, ymin, xmax, ymax]				)
            if not boxes:
                  continue
            img_paths.append(lowerCAmelCase__				)
            labels.append(lowerCAmelCase__				)
      return img_paths, labels
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__ = 0.0     ,      ):
      UpperCAmelCase_      			=							np.zeros([output_size[0], output_size[1], 3]     ,      dtype=np.uinta				)
      UpperCAmelCase_      			=							scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
      UpperCAmelCase_      			=							scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
      UpperCAmelCase_      			=							int(scale_x * output_size[1]				)
      UpperCAmelCase_      			=							int(scale_y * output_size[0]				)

      UpperCAmelCase_      			=							[]
      UpperCAmelCase_      			=							[]
      for i, index in enumerate(lowerCAmelCase__				):
            UpperCAmelCase_      			=							all_img_list[index]
            path_list.append(lowerCAmelCase__				)
            UpperCAmelCase_      			=							all_annos[index]
            UpperCAmelCase_      			=							cva.imread(lowerCAmelCase__				)
            if i == 0:  # top-left
                  UpperCAmelCase_      			=							cva.resize(lowerCAmelCase__     ,      (divid_point_x, divid_point_y)				)
                  UpperCAmelCase_      			=							img
                  for bbox in img_annos:
                        UpperCAmelCase_      			=							bbox[1] * scale_x
                        UpperCAmelCase_      			=							bbox[2] * scale_y
                        UpperCAmelCase_      			=							bbox[3] * scale_x
                        UpperCAmelCase_      			=							bbox[4] * scale_y
                        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
            elif i == 1:  # top-right
                  UpperCAmelCase_      			=							cva.resize(lowerCAmelCase__     ,      (output_size[1] - divid_point_x, divid_point_y)				)
                  UpperCAmelCase_      			=							img
                  for bbox in img_annos:
                        UpperCAmelCase_      			=							scale_x + bbox[1] * (1 - scale_x)
                        UpperCAmelCase_      			=							bbox[2] * scale_y
                        UpperCAmelCase_      			=							scale_x + bbox[3] * (1 - scale_x)
                        UpperCAmelCase_      			=							bbox[4] * scale_y
                        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
            elif i == 2:  # bottom-left
                  UpperCAmelCase_      			=							cva.resize(lowerCAmelCase__     ,      (divid_point_x, output_size[0] - divid_point_y)				)
                  UpperCAmelCase_      			=							img
                  for bbox in img_annos:
                        UpperCAmelCase_      			=							bbox[1] * scale_x
                        UpperCAmelCase_      			=							scale_y + bbox[2] * (1 - scale_y)
                        UpperCAmelCase_      			=							bbox[3] * scale_x
                        UpperCAmelCase_      			=							scale_y + bbox[4] * (1 - scale_y)
                        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
            else:  # bottom-right
                  UpperCAmelCase_      			=							cva.resize(
                      lowerCAmelCase__     ,      (output_size[1] - divid_point_x, output_size[0] - divid_point_y)				)
                  UpperCAmelCase_      			=							img
                  for bbox in img_annos:
                        UpperCAmelCase_      			=							scale_x + bbox[1] * (1 - scale_x)
                        UpperCAmelCase_      			=							scale_y + bbox[2] * (1 - scale_y)
                        UpperCAmelCase_      			=							scale_x + bbox[3] * (1 - scale_x)
                        UpperCAmelCase_      			=							scale_y + bbox[4] * (1 - scale_y)
                        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)

    # Remove bounding box small than scale of filter
      if filter_scale > 0:
            UpperCAmelCase_      			=							[
                anno
                for anno in new_anno
                if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
            ]

      return output_img, new_anno, path_list[0]
def        a__     (				lowerCAmelCase__				):
      assert number_char > 1, "The number of character should greater than 1"
      UpperCAmelCase_      			=							ascii_lowercase + digits
      return "".join(random.choice(lowerCAmelCase__				) for _ in range(lowerCAmelCase__				)				)


if __name__ == "__main__":
  main()
  print("""DONE ✅""")









"""simple docstring"""





import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      UpperCAmelCase_      			=							checkpoint

      UpperCAmelCase_      			=							{}

      UpperCAmelCase_      			=							vae_state_dict["encoder.conv_in.weight"]
      UpperCAmelCase_      			=							vae_state_dict["encoder.conv_in.bias"]
      UpperCAmelCase_      			=							vae_state_dict["encoder.conv_out.weight"]
      UpperCAmelCase_      			=							vae_state_dict["encoder.conv_out.bias"]
      UpperCAmelCase_      			=							vae_state_dict["encoder.norm_out.weight"]
      UpperCAmelCase_      			=							vae_state_dict["encoder.norm_out.bias"]

      UpperCAmelCase_      			=							vae_state_dict["decoder.conv_in.weight"]
      UpperCAmelCase_      			=							vae_state_dict["decoder.conv_in.bias"]
      UpperCAmelCase_      			=							vae_state_dict["decoder.conv_out.weight"]
      UpperCAmelCase_      			=							vae_state_dict["decoder.conv_out.bias"]
      UpperCAmelCase_      			=							vae_state_dict["decoder.norm_out.weight"]
      UpperCAmelCase_      			=							vae_state_dict["decoder.norm_out.bias"]

      UpperCAmelCase_      			=							vae_state_dict["quant_conv.weight"]
      UpperCAmelCase_      			=							vae_state_dict["quant_conv.bias"]
      UpperCAmelCase_      			=							vae_state_dict["post_quant_conv.weight"]
      UpperCAmelCase_      			=							vae_state_dict["post_quant_conv.bias"]

      # Retrieves the keys for the encoder down blocks only
      UpperCAmelCase_      			=							len({".".join(layer.split("."				)[:3]				) for layer in vae_state_dict if "encoder.down" in layer}				)
      UpperCAmelCase_      			=							{
          layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(lowerCAmelCase__				)
      }

      # Retrieves the keys for the decoder up blocks only
      UpperCAmelCase_      			=							len({".".join(layer.split("."				)[:3]				) for layer in vae_state_dict if "decoder.up" in layer}				)
      UpperCAmelCase_      			=							{
          layer_id: [key for key in vae_state_dict if f"""up.{layer_id}""" in key] for layer_id in range(lowerCAmelCase__				)
      }

      for i in range(lowerCAmelCase__				):
            UpperCAmelCase_      			=							[key for key in down_blocks[i] if f"""down.{i}""" in key and f"""down.{i}.downsample""" not in key]

            if f"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
                  UpperCAmelCase_      			=							vae_state_dict.pop(
                      f"""encoder.down.{i}.downsample.conv.weight"""				)
                  UpperCAmelCase_      			=							vae_state_dict.pop(
                      f"""encoder.down.{i}.downsample.conv.bias"""				)

            UpperCAmelCase_      			=							renew_vae_resnet_paths(lowerCAmelCase__				)
            UpperCAmelCase_      			=							{"old": f"""down.{i}.block""", "new": f"""down_blocks.{i}.resnets"""}
            assign_to_checkpoint(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      additional_replacements=[meta_path]     ,      config=lowerCAmelCase__				)

      UpperCAmelCase_      			=							[key for key in vae_state_dict if "encoder.mid.block" in key]
      UpperCAmelCase_      			=							2
      for i in range(1     ,      num_mid_res_blocks + 1				):
            UpperCAmelCase_      			=							[key for key in mid_resnets if f"""encoder.mid.block_{i}""" in key]

            UpperCAmelCase_      			=							renew_vae_resnet_paths(lowerCAmelCase__				)
            UpperCAmelCase_      			=							{"old": f"""mid.block_{i}""", "new": f"""mid_block.resnets.{i - 1}"""}
            assign_to_checkpoint(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      additional_replacements=[meta_path]     ,      config=lowerCAmelCase__				)

      UpperCAmelCase_      			=							[key for key in vae_state_dict if "encoder.mid.attn" in key]
      UpperCAmelCase_      			=							renew_vae_attention_paths(lowerCAmelCase__				)
      UpperCAmelCase_      			=							{"old": "mid.attn_1", "new": "mid_block.attentions.0"}
      assign_to_checkpoint(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      additional_replacements=[meta_path]     ,      config=lowerCAmelCase__				)
      conv_attn_to_linear(lowerCAmelCase__				)

      for i in range(lowerCAmelCase__				):
            UpperCAmelCase_      			=							num_up_blocks - 1 - i
            UpperCAmelCase_      			=							[
                key for key in up_blocks[block_id] if f"""up.{block_id}""" in key and f"""up.{block_id}.upsample""" not in key
            ]

            if f"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
                  UpperCAmelCase_      			=							vae_state_dict[
                      f"""decoder.up.{block_id}.upsample.conv.weight"""
                  ]
                  UpperCAmelCase_      			=							vae_state_dict[
                      f"""decoder.up.{block_id}.upsample.conv.bias"""
                  ]

            UpperCAmelCase_      			=							renew_vae_resnet_paths(lowerCAmelCase__				)
            UpperCAmelCase_      			=							{"old": f"""up.{block_id}.block""", "new": f"""up_blocks.{i}.resnets"""}
            assign_to_checkpoint(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      additional_replacements=[meta_path]     ,      config=lowerCAmelCase__				)

      UpperCAmelCase_      			=							[key for key in vae_state_dict if "decoder.mid.block" in key]
      UpperCAmelCase_      			=							2
      for i in range(1     ,      num_mid_res_blocks + 1				):
            UpperCAmelCase_      			=							[key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

            UpperCAmelCase_      			=							renew_vae_resnet_paths(lowerCAmelCase__				)
            UpperCAmelCase_      			=							{"old": f"""mid.block_{i}""", "new": f"""mid_block.resnets.{i - 1}"""}
            assign_to_checkpoint(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      additional_replacements=[meta_path]     ,      config=lowerCAmelCase__				)

      UpperCAmelCase_      			=							[key for key in vae_state_dict if "decoder.mid.attn" in key]
      UpperCAmelCase_      			=							renew_vae_attention_paths(lowerCAmelCase__				)
      UpperCAmelCase_      			=							{"old": "mid.attn_1", "new": "mid_block.attentions.0"}
      assign_to_checkpoint(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      additional_replacements=[meta_path]     ,      config=lowerCAmelCase__				)
      conv_attn_to_linear(lowerCAmelCase__				)
      return new_checkpoint
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      ):
      # Only support V1
      UpperCAmelCase_      			=							requests.get(
          " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"				)
      UpperCAmelCase_      			=							io.BytesIO(r.content				)

      UpperCAmelCase_      			=							OmegaConf.load(lowerCAmelCase__				)
      UpperCAmelCase_      			=							512
      UpperCAmelCase_      			=							"cuda" if torch.cuda.is_available() else "cpu"
      if checkpoint_path.endswith("safetensors"				):
            from safetensors import safe_open

            UpperCAmelCase_      			=							{}
            with safe_open(lowerCAmelCase__     ,      framework="pt"     ,      device="cpu"				) as f:
                  for key in f.keys():
                        UpperCAmelCase_      			=							f.get_tensor(lowerCAmelCase__				)
      else:
            UpperCAmelCase_      			=							torch.load(lowerCAmelCase__     ,      map_location=lowerCAmelCase__				)["state_dict"]

      # Convert the VAE model.
      UpperCAmelCase_      			=							create_vae_diffusers_config(lowerCAmelCase__     ,      image_size=lowerCAmelCase__				)
      UpperCAmelCase_      			=							custom_convert_ldm_vae_checkpoint(lowerCAmelCase__     ,      lowerCAmelCase__				)

      UpperCAmelCase_      			=							AutoencoderKL(**lowerCAmelCase__				)
      vae.load_state_dict(lowerCAmelCase__				)
      vae.save_pretrained(lowerCAmelCase__				)


if __name__ == "__main__":
  lowerCamelCase       	=						argparse.ArgumentParser()

  parser.add_argument("""--vae_pt_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")
  parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")

  lowerCamelCase       	=						parser.parse_args()

  vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)




