



from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


a__			=		"\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n"

a__			=		"\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

a__			=		"\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		str ,				SCREAMING_SNAKE_CASE__				:		List[Any] )     ->   Optional[Any]:
 return float((preds == labels).mean() )



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Any ,				SCREAMING_SNAKE_CASE__				:		List[Any] )     ->   Optional[int]:
 _snake_case			:     Any 		=    simple_accuracy(__lowerCamelCase ,				__lowerCamelCase )
 _snake_case			:     Union[str, Any] 		=    float(fa_score(y_true=__lowerCamelCase ,				y_pred=__lowerCamelCase ) )
 return {
     "accuracy": acc,
     "f1": fa,
 }



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		List[Any] ,				SCREAMING_SNAKE_CASE__				:		Union[str, Any] )     ->   List[Any]:
 _snake_case			:     Dict 		=    float(pearsonr(__lowerCamelCase ,				__lowerCamelCase )[0] )
 _snake_case			:     int 		=    float(spearmanr(__lowerCamelCase ,				__lowerCamelCase )[0] )
 return {
     "pearson": pearson_corr,
     "spearmanr": spearman_corr,
 }







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,_KWARGS_DESCRIPTION     )
class 						snake_case  (		datasets.Metric     ):



   '''simple docstring'''






   def    UpperCamelCase_     (    self							:	Dict) -> Any:

    """simple docstring"""







    if self.config_name not in [
        "sst2",
        "mnli",
        "mnli_mismatched",
        "mnli_matched",
        "cola",
        "stsb",
        "mrpc",
        "qqp",
        "qnli",
        "rte",
        "wnli",
        "hans",
    ]:
     raise KeyError(
         """You should supply a configuration name selected in """
         """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
         """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]""")
    return datasets.MetricInfo(
        description=_DESCRIPTION ,						citation=_CITATION ,						inputs_description=_KWARGS_DESCRIPTION ,						features=datasets.Features(
            {
                """predictions""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""),
                """references""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""),
            }) ,						codebase_urls=[] ,						reference_urls=[] ,						format="""numpy""" ,						)


   def    UpperCamelCase_     (    self							:	List[str] ,						lowerCAmelCase							:	int ,						lowerCAmelCase							:	Union[str, Any]) -> int:

    """simple docstring"""







    if self.config_name == "cola":
     return {"matthews_correlation": matthews_corrcoef(__lowercase ,						__lowercase)}
    elif self.config_name == "stsb":
     return pearson_and_spearman(__lowercase ,						__lowercase)
    elif self.config_name in ["mrpc", "qqp"]:
     return acc_and_fa(__lowercase ,						__lowercase)
    elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
     return {"accuracy": simple_accuracy(__lowercase ,						__lowercase)}
    else:
     raise KeyError(
         """You should supply a configuration name selected in """
         """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
         """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]""")








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 				lowerCamelCase__      (    __lowerCamelCase			:     Dict  ,		__lowerCamelCase			:     int		):
  __UpperCAmelCase							:	List[Any]           =  checkpoint

  __UpperCAmelCase							:	Tuple           =  {}

  __UpperCAmelCase							:	Dict           =  vae_state_dict["""encoder.conv_in.weight"""]
  __UpperCAmelCase							:	Optional[Any]           =  vae_state_dict["""encoder.conv_in.bias"""]
  __UpperCAmelCase							:	Dict           =  vae_state_dict["""encoder.conv_out.weight"""]
  __UpperCAmelCase							:	Union[str, Any]           =  vae_state_dict["""encoder.conv_out.bias"""]
  __UpperCAmelCase							:	List[Any]           =  vae_state_dict["""encoder.norm_out.weight"""]
  __UpperCAmelCase							:	Tuple           =  vae_state_dict["""encoder.norm_out.bias"""]

  __UpperCAmelCase							:	Dict           =  vae_state_dict["""decoder.conv_in.weight"""]
  __UpperCAmelCase							:	Tuple           =  vae_state_dict["""decoder.conv_in.bias"""]
  __UpperCAmelCase							:	Optional[int]           =  vae_state_dict["""decoder.conv_out.weight"""]
  __UpperCAmelCase							:	Optional[int]           =  vae_state_dict["""decoder.conv_out.bias"""]
  __UpperCAmelCase							:	Optional[Any]           =  vae_state_dict["""decoder.norm_out.weight"""]
  __UpperCAmelCase							:	Union[str, Any]           =  vae_state_dict["""decoder.norm_out.bias"""]

  __UpperCAmelCase							:	Optional[int]           =  vae_state_dict["""quant_conv.weight"""]
  __UpperCAmelCase							:	int           =  vae_state_dict["""quant_conv.bias"""]
  __UpperCAmelCase							:	Union[str, Any]           =  vae_state_dict["""post_quant_conv.weight"""]
  __UpperCAmelCase							:	Any           =  vae_state_dict["""post_quant_conv.bias"""]

  # Retrieves the keys for the encoder down blocks only
  __UpperCAmelCase							:	int           =  len({""".""".join(layer.split("""."""		)[:3]		) for layer in vae_state_dict if """encoder.down""" in layer}		)
  __UpperCAmelCase							:	Optional[Any]           =  {
      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							:	Dict           =  len({""".""".join(layer.split("""."""		)[:3]		) for layer in vae_state_dict if """decoder.up""" in layer}		)
  __UpperCAmelCase							:	Optional[int]           =  {
      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							:	List[Any]           =  [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							:	Optional[Any]           =  vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.weight"""		)
      __UpperCAmelCase							:	int           =  vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.bias"""		)

    __UpperCAmelCase							:	Optional[int]           =  renew_vae_resnet_paths(__lowerCamelCase		)
    __UpperCAmelCase							:	Optional[Any]           =  {"""old""": f"""down.{i}.block""", """new""": f"""down_blocks.{i}.resnets"""}
    assign_to_checkpoint(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		additional_replacements=[meta_path]  ,		config=__lowerCamelCase		)

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

    __UpperCAmelCase							:	Tuple           =  renew_vae_resnet_paths(__lowerCamelCase		)
    __UpperCAmelCase							:	Tuple           =  {"""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							:	List[Any]           =  [key for key in vae_state_dict if """encoder.mid.attn""" in key]
  __UpperCAmelCase							:	str           =  renew_vae_attention_paths(__lowerCamelCase		)
  __UpperCAmelCase							:	List[str]           =  {"""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							:	Optional[Any]           =  num_up_blocks - 1 - i
    __UpperCAmelCase							:	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:
      __UpperCAmelCase							:	int           =  vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.weight"""
      ]
      __UpperCAmelCase							:	Dict           =  vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.bias"""
      ]

    __UpperCAmelCase							:	Dict           =  renew_vae_resnet_paths(__lowerCamelCase		)
    __UpperCAmelCase							:	Optional[Any]           =  {"""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							:	Tuple           =  [key for key in vae_state_dict if """decoder.mid.block""" in key]
  __UpperCAmelCase							:	Union[str, Any]           =  2
  for i in range(1  ,		num_mid_res_blocks + 1		):
    __UpperCAmelCase							:	Dict           =  [key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

    __UpperCAmelCase							:	List[Any]           =  renew_vae_resnet_paths(__lowerCamelCase		)
    __UpperCAmelCase							:	int           =  {"""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							:	Dict           =  [key for key in vae_state_dict if """decoder.mid.attn""" in key]
  __UpperCAmelCase							:	List[Any]           =  renew_vae_attention_paths(__lowerCamelCase		)
  __UpperCAmelCase							:	List[str]           =  {"""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 				lowerCamelCase__      (    __lowerCamelCase			:     str  ,		__lowerCamelCase			:     str  ,		):
  # Only support V1
  __UpperCAmelCase							:	Optional[int]           =  requests.get(
      """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""		)
  __UpperCAmelCase							:	Optional[int]           =  io.BytesIO(r.content		)

  __UpperCAmelCase							:	Dict           =  OmegaConf.load(__lowerCamelCase		)
  __UpperCAmelCase							:	str           =  512
  __UpperCAmelCase							:	Any           =  """cuda""" if torch.cuda.is_available() else """cpu"""
  if checkpoint_path.endswith("""safetensors"""		):
    from safetensors import safe_open

    __UpperCAmelCase							:	List[Any]           =  {}
    with safe_open(__lowerCamelCase  ,		framework="""pt"""  ,		device="""cpu"""		) as f:
      for key in f.keys():
        __UpperCAmelCase							:	str           =  f.get_tensor(__lowerCamelCase		)
  else:
    __UpperCAmelCase							:	Optional[int]           =  torch.load(__lowerCamelCase  ,		map_location=__lowerCamelCase		)["""state_dict"""]

  # Convert the VAE model.
  __UpperCAmelCase							:	Optional[int]           =  create_vae_diffusers_config(__lowerCamelCase  ,		image_size=__lowerCamelCase		)
  __UpperCAmelCase							:	Union[str, Any]           =  custom_convert_ldm_vae_checkpoint(__lowerCamelCase  ,		__lowerCamelCase		)

  __UpperCAmelCase							:	Optional[int]           =  AutoencoderKL(**__lowerCamelCase		)
  vae.load_state_dict(__lowerCamelCase		)
  vae.save_pretrained(__lowerCamelCase		)


if __name__ == "__main__":
       a		:       List[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.")

       a		:       Optional[int]			     = parser.parse_args()

       vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)


