






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  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  checkpoint

	__lowercase             =  {}

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

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

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

	# Retrieves the keys for the encoder down blocks only
	__lowercase             =  len({""".""".join(layer.split("""."""					)[:3]					) for layer in vae_state_dict if """encoder.down""" in layer}					)
	__lowercase             =  {
	    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
	__lowercase             =  len({""".""".join(layer.split("""."""					)[:3]					) for layer in vae_state_dict if """decoder.up""" in layer}					)
	__lowercase             =  {
	    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					):
		__lowercase             =  [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:
			__lowercase             =  vae_state_dict.pop(
			    F'encoder.down.{i}.downsample.conv.weight'					)
			__lowercase             =  vae_state_dict.pop(
			    F'encoder.down.{i}.downsample.conv.bias'					)

		__lowercase             =  renew_vae_resnet_paths(lowerCamelCase					)
		__lowercase             =  {"""old""": F'down.{i}.block', """new""": F'down_blocks.{i}.resnets'}
		assign_to_checkpoint(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  additional_replacements=[meta_path]				,  config=lowerCamelCase					)

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

		__lowercase             =  renew_vae_resnet_paths(lowerCamelCase					)
		__lowercase             =  {"""old""": F'mid.block_{i}', """new""": F'mid_block.resnets.{i - 1}'}
		assign_to_checkpoint(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  additional_replacements=[meta_path]				,  config=lowerCamelCase					)

	__lowercase             =  [key for key in vae_state_dict if """encoder.mid.attn""" in key]
	__lowercase             =  renew_vae_attention_paths(lowerCamelCase					)
	__lowercase             =  {"""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					):
		__lowercase             =  num_up_blocks - 1 - i
		__lowercase             =  [
		    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:
			__lowercase             =  vae_state_dict[
			    F'decoder.up.{block_id}.upsample.conv.weight'
			]
			__lowercase             =  vae_state_dict[
			    F'decoder.up.{block_id}.upsample.conv.bias'
			]

		__lowercase             =  renew_vae_resnet_paths(lowerCamelCase					)
		__lowercase             =  {"""old""": F'up.{block_id}.block', """new""": F'up_blocks.{i}.resnets'}
		assign_to_checkpoint(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  additional_replacements=[meta_path]				,  config=lowerCamelCase					)

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

		__lowercase             =  renew_vae_resnet_paths(lowerCamelCase					)
		__lowercase             =  {"""old""": F'mid.block_{i}', """new""": F'mid_block.resnets.{i - 1}'}
		assign_to_checkpoint(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  additional_replacements=[meta_path]				,  config=lowerCamelCase					)

	__lowercase             =  [key for key in vae_state_dict if """decoder.mid.attn""" in key]
	__lowercase             =  renew_vae_attention_paths(lowerCamelCase					)
	__lowercase             =  {"""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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  ):




	'''simple docstring'''







	__lowercase             =  requests.get(
	    """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""					)
	__lowercase             =  io.BytesIO(r.content					)

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

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

	# Convert the VAE model.
	__lowercase             =  create_vae_diffusers_config(lowerCamelCase				,  image_size=lowerCamelCase					)
	__lowercase             =  custom_convert_ldm_vae_checkpoint(lowerCamelCase				,  lowerCamelCase					)

	__lowercase             =  AutoencoderKL(**lowerCamelCase					)
	vae.load_state_dict(lowerCamelCase					)
	vae.save_pretrained(lowerCamelCase					)


if __name__ == "__main__":
						__UpperCamelCase      :      Optional[Any]     						=						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      :      Tuple     						=						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      :      Optional[Any]     						=						logging.get_logger(__name__)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  set()
	__lowercase             =  []

	def parse_line(lowerCamelCase					):
		for line in fp:
			if isinstance(lowerCamelCase				,  lowerCamelCase					):
				__lowercase             =  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(lowerCamelCase					) > 0:
					__lowercase             =  """\n""".join(lowerCamelCase					)
					# Only keep the warnings specified in `targets`
					if any(F': {x}: ' in warning for x in targets					):
						selected_warnings.add(lowerCamelCase					)
					buffer.clear()
				continue
			else:
				__lowercase             =  line.strip()
				buffer.append(lowerCamelCase					)

	if from_gh:
		for filename in os.listdir(lowerCamelCase					):
			__lowercase             =  os.path.join(lowerCamelCase				,  lowerCamelCase					)
			if not os.path.isdir(lowerCamelCase					):
				# read the file
				if filename != "warnings.txt":
					continue
				with open(lowerCamelCase					) as fp:
					parse_line(lowerCamelCase					)
	else:
		try:
			with zipfile.ZipFile(lowerCamelCase					) as z:
				for filename in z.namelist():
					if not os.path.isdir(lowerCamelCase					):
						# read the file
						if filename != "warnings.txt":
							continue
						with z.open(lowerCamelCase					) as fp:
							parse_line(lowerCamelCase					)
		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  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  set()

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

	return selected_warnings


if __name__ == "__main__":





						def 				snake_case  (     lowerCamelCase					):




							'''simple docstring'''







							return values.split(""","""					)

						__UpperCamelCase      :      Union[str, Any]     						=						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      :      List[str]     						=						parser.parse_args()

						__UpperCamelCase      :      Union[str, Any]     						=						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      :      Any     						=						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      :      Union[str, Any]     						=						extract_warnings(args.output_dir, args.targets)
						__UpperCamelCase      :      Any     						=						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)

