

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      snake_case_				(__A					:						Optional[Any] ,       __A					:						Dict					)      ->     int:
   __lowerCAmelCase  :      List[str]    =						checkpoint

   __lowerCAmelCase  :      List[str]    =						{}

   __lowerCAmelCase  :      List[str]    =						vae_state_dict["""encoder.conv_in.weight"""]
   __lowerCAmelCase  :      Tuple    =						vae_state_dict["""encoder.conv_in.bias"""]
   __lowerCAmelCase  :      Tuple    =						vae_state_dict["""encoder.conv_out.weight"""]
   __lowerCAmelCase  :      Dict    =						vae_state_dict["""encoder.conv_out.bias"""]
   __lowerCAmelCase  :      List[str]    =						vae_state_dict["""encoder.norm_out.weight"""]
   __lowerCAmelCase  :      List[str]    =						vae_state_dict["""encoder.norm_out.bias"""]

   __lowerCAmelCase  :      Tuple    =						vae_state_dict["""decoder.conv_in.weight"""]
   __lowerCAmelCase  :      Tuple    =						vae_state_dict["""decoder.conv_in.bias"""]
   __lowerCAmelCase  :      List[str]    =						vae_state_dict["""decoder.conv_out.weight"""]
   __lowerCAmelCase  :      int    =						vae_state_dict["""decoder.conv_out.bias"""]
   __lowerCAmelCase  :      Union[str, Any]    =						vae_state_dict["""decoder.norm_out.weight"""]
   __lowerCAmelCase  :      int    =						vae_state_dict["""decoder.norm_out.bias"""]

   __lowerCAmelCase  :      Dict    =						vae_state_dict["""quant_conv.weight"""]
   __lowerCAmelCase  :      List[str]    =						vae_state_dict["""quant_conv.bias"""]
   __lowerCAmelCase  :      Optional[Any]    =						vae_state_dict["""post_quant_conv.weight"""]
   __lowerCAmelCase  :      str    =						vae_state_dict["""post_quant_conv.bias"""]

   # Retrieves the keys for the encoder down blocks only
   __lowerCAmelCase  :      Tuple    =						len({""".""".join(layer.split("""."""					)[:3]					) for layer in vae_state_dict if """encoder.down""" in layer}					)
   __lowerCAmelCase  :      List[str]    =						{
       layer_id: [key for key in vae_state_dict if f'''down.{layer_id}''' in key] for layer_id in range(__A					)
   }

   # Retrieves the keys for the decoder up blocks only
   __lowerCAmelCase  :      int    =						len({""".""".join(layer.split("""."""					)[:3]					) for layer in vae_state_dict if """decoder.up""" in layer}					)
   __lowerCAmelCase  :      Optional[Any]    =						{
       layer_id: [key for key in vae_state_dict if f'''up.{layer_id}''' in key] for layer_id in range(__A					)
   }

   for i in range(__A					):
      __lowerCAmelCase  :      Tuple    =						[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:
         __lowerCAmelCase  :      int    =						vae_state_dict.pop(
             f'''encoder.down.{i}.downsample.conv.weight'''					)
         __lowerCAmelCase  :      Union[str, Any]    =						vae_state_dict.pop(
             f'''encoder.down.{i}.downsample.conv.bias'''					)

      __lowerCAmelCase  :      str    =						renew_vae_resnet_paths(__A					)
      __lowerCAmelCase  :      Dict    =						{"""old""": f'''down.{i}.block''', """new""": f'''down_blocks.{i}.resnets'''}
      assign_to_checkpoint(__A ,       __A ,       __A ,       additional_replacements=[meta_path] ,       config=__A					)

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

      __lowerCAmelCase  :      Optional[Any]    =						renew_vae_resnet_paths(__A					)
      __lowerCAmelCase  :      int    =						{"""old""": f'''mid.block_{i}''', """new""": f'''mid_block.resnets.{i - 1}'''}
      assign_to_checkpoint(__A ,       __A ,       __A ,       additional_replacements=[meta_path] ,       config=__A					)

   __lowerCAmelCase  :      Optional[Any]    =						[key for key in vae_state_dict if """encoder.mid.attn""" in key]
   __lowerCAmelCase  :      List[str]    =						renew_vae_attention_paths(__A					)
   __lowerCAmelCase  :      Optional[int]    =						{"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
   assign_to_checkpoint(__A ,       __A ,       __A ,       additional_replacements=[meta_path] ,       config=__A					)
   conv_attn_to_linear(__A					)

   for i in range(__A					):
      __lowerCAmelCase  :      List[Any]    =						num_up_blocks - 1 - i
      __lowerCAmelCase  :      Union[str, Any]    =						[
          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:
         __lowerCAmelCase  :      str    =						vae_state_dict[
             f'''decoder.up.{block_id}.upsample.conv.weight'''
         ]
         __lowerCAmelCase  :      Any    =						vae_state_dict[
             f'''decoder.up.{block_id}.upsample.conv.bias'''
         ]

      __lowerCAmelCase  :      Any    =						renew_vae_resnet_paths(__A					)
      __lowerCAmelCase  :      List[str]    =						{"""old""": f'''up.{block_id}.block''', """new""": f'''up_blocks.{i}.resnets'''}
      assign_to_checkpoint(__A ,       __A ,       __A ,       additional_replacements=[meta_path] ,       config=__A					)

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

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

   __lowerCAmelCase  :      Dict    =						[key for key in vae_state_dict if """decoder.mid.attn""" in key]
   __lowerCAmelCase  :      Optional[Any]    =						renew_vae_attention_paths(__A					)
   __lowerCAmelCase  :      Optional[Any]    =						{"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
   assign_to_checkpoint(__A ,       __A ,       __A ,       additional_replacements=[meta_path] ,       config=__A					)
   conv_attn_to_linear(__A					)
   return new_checkpoint





def      snake_case_				(__A					:						str ,       __A					:						str ,       )      ->     Optional[int]:
   # Only support V1
   __lowerCAmelCase  :      Optional[int]    =						requests.get(
       """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""					)
   __lowerCAmelCase  :      Union[str, Any]    =						io.BytesIO(r.content					)

   __lowerCAmelCase  :      Tuple    =						OmegaConf.load(__A					)
   __lowerCAmelCase  :      List[str]    =						5_1_2
   __lowerCAmelCase  :      Optional[int]    =						"""cuda""" if torch.cuda.is_available() else """cpu"""
   if checkpoint_path.endswith("""safetensors"""					):
      from safetensors import safe_open

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

   # Convert the VAE model.
   __lowerCAmelCase  :      str    =						create_vae_diffusers_config(__A ,       image_size=__A					)
   __lowerCAmelCase  :      int    =						custom_convert_ldm_vae_checkpoint(__A ,       __A					)

   __lowerCAmelCase  :      Any    =						AutoencoderKL(**__A					)
   vae.load_state_dict(__A					)
   vae.save_pretrained(__A					)


if __name__ == "__main__":
   __UpperCAmelCase								=			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.""")

   __UpperCAmelCase								=			parser.parse_args()

   vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)





import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__UpperCAmelCase								=			logging.get_logger(__name__)





def      snake_case_				(__A					:						List[str] ,       __A					:						str					)      ->     int:
   __lowerCAmelCase  :      str    =						set()
   __lowerCAmelCase  :      int    =						[]

   def parse_line(__A					:						List[Any]					):
      for line in fp:
         if isinstance(__A ,       __A					):
            __lowerCAmelCase  :      str    =						line.decode("""UTF-8"""					)
         if "warnings summary (final)" in line:
            continue
         # This means we are outside the body of a warning
         elif not line.startswith(""" """					):
            # process a single warning and move it to `selected_warnings`.
            if len(__A					) > 0:
               __lowerCAmelCase  :      Tuple    =						"""\n""".join(__A					)
               # Only keep the warnings specified in `targets`
               if any(f''': {x}: ''' in warning for x in targets					):
                  selected_warnings.add(__A					)
               buffer.clear()
            continue
         else:
            __lowerCAmelCase  :      Optional[int]    =						line.strip()
            buffer.append(__A					)

   if from_gh:
      for filename in os.listdir(__A					):
         __lowerCAmelCase  :      Optional[Any]    =						os.path.join(__A ,       __A					)
         if not os.path.isdir(__A					):
            # read the file
            if filename != "warnings.txt":
               continue
            with open(__A					) as fp:
               parse_line(__A					)
   else:
      try:
         with zipfile.ZipFile(__A					) as z:
            for filename in z.namelist():
               if not os.path.isdir(__A					):
                  # read the file
                  if filename != "warnings.txt":
                     continue
                  with z.open(__A					) as fp:
                     parse_line(__A					)
      except Exception:
         logger.warning(
             f'''{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.'''					)

   return selected_warnings





def      snake_case_				(__A					:						Dict ,       __A					:						Union[str, Any]					)      ->     Dict:
   __lowerCAmelCase  :      Any    =						set()

   __lowerCAmelCase  :      Optional[int]    =						[os.path.join(__A ,       __A					) for p in os.listdir(__A					) if (p.endswith(""".zip"""					) or from_gh)]
   for p in paths:
      selected_warnings.update(extract_warnings_from_single_artifact(__A ,       __A					)					)

   return selected_warnings


if __name__ == "__main__":





   def      snake_case_				(__A					:						int					)      ->     Tuple:
      return values.split(""","""					)

   __UpperCAmelCase								=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
   parser.add_argument(
       """--output_dir""",
       type=str,
       required=True,
       help="""Where to store the downloaded artifacts and other result files.""",
   )
   parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
   # optional parameters
   parser.add_argument(
       """--targets""",
       default="""DeprecationWarning,UserWarning,FutureWarning""",
       type=list_str,
       help="""Comma-separated list of target warning(s) which we want to extract.""",
   )
   parser.add_argument(
       """--from_gh""",
       action="""store_true""",
       help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
   )

   __UpperCAmelCase								=			parser.parse_args()

   __UpperCAmelCase								=			args.from_gh
   if from_gh:
      # The artifacts have to be downloaded using `actions/download-artifact@v3`
      pass
   else:
      os.makedirs(args.output_dir, exist_ok=True)

      # get download links
      __UpperCAmelCase								=			get_artifacts_links(args.workflow_run_id, token=args.token)
      with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
         json.dump(artifacts, fp, ensure_ascii=False, indent=4)

      # download artifacts
      for idx, (name, url) in enumerate(artifacts.items()):
         print(name)
         print(url)
         print("""=""" * 80)
         download_artifact(name, url, args.output_dir, args.token)
         # Be gentle to GitHub
         time.sleep(1)

    # extract warnings from artifacts
   __UpperCAmelCase								=			extract_warnings(args.output_dir, args.targets)
   __UpperCAmelCase								=			sorted(selected_warnings)
   with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
      json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)


