code: import csv
  from collections import defaultdict
  from dataclasses import dataclass, field
  from typing import List, Optional
  
  import matplotlib.pyplot as plt
  import numpy as np
  from matplotlib.ticker import ScalarFormatter
  
  from transformers import HfArgumentParser
  def 				__lowercase	(						lowerCamelCase__=None ,				lowerCamelCase__=None					)				->Any:
  
    '''simple docstring'''
  
  
  
  
    return field(default_factory=lambda: default ,				metadata=lowerCamelCase__					)
  
  
  
  
  
  
  
  @dataclass
  class 					UpperCAmelCase_				:
      UpperCAmelCase_			:       str						 =							field(
          metadata={'help': 'The csv file to plot.'}     ,     )
      UpperCAmelCase_			:       bool						 =							field(
          default=_a     ,     metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}     ,     )
      UpperCAmelCase_			:       bool						 =							field(
          default=_a     ,     metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}     ,     )
      UpperCAmelCase_			:       bool						 =							field(
          default=_a     ,     metadata={'help': 'Disable logarithmic scale when plotting'}     ,     )
      UpperCAmelCase_			:       bool						 =							field(
          default=_a     ,     metadata={
              'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
          }     ,     )
      UpperCAmelCase_			:       Optional[str]						 =							field(
          default=_a     ,     metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}     ,     )
      UpperCAmelCase_			:       Optional[List[str]]						 =							list_field(
          default=_a     ,     metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}    )
  
  def 				__lowercase	(						lowerCamelCase__					)				->Optional[int]:
  
    '''simple docstring'''
  
  
  
  
    try:
      int(lowerCamelCase__					)
      return True
    except ValueError:
      return False
  def 				__lowercase	(						lowerCamelCase__					)				->Dict:
  
    '''simple docstring'''
  
  
  
  
    try:
      float(lowerCamelCase__					)
      return True
    except ValueError:
      return False
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				:
  
  
  
  
      def __init__(					self  :   str      ,      lowerCAmelCase  :   str   )	-> Optional[Any]:
        a__     		=     args
        a__     		=     defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}   )
  
        with open(self.args.csv_file      ,      newline=""   ) as csv_file:
          a__     		=     csv.DictReader(lowerCAmelCase   )
          for row in reader:
            a__     		=     row["model"]
            self.result_dict[model_name]["bsz"].append(int(row["batch_size"]   )   )
            self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]   )   )
            if can_convert_to_int(row["result"]   ):
              # value is not None
              a__     		=     int(row["result"]   )
            elif can_convert_to_float(row["result"]   ):
              # value is not None
              a__     		=     float(row["result"]   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> str:
        a__		,a__     		=     plt.subplots()
        a__     		=     "Time usage" if self.args.is_time else "Memory usage"
        a__     		=     title_str + " for training" if self.args.is_train else title_str + " for inference"
  
        if not self.args.no_log_scale:
          # set logarithm scales
          ax.set_xscale("log"   )
          ax.set_yscale("log"   )
  
        for axis in [ax.xaxis, ax.yaxis]:
          axis.set_major_formatter(ScalarFormatter()   )
  
        for model_name_idx, model_name in enumerate(self.result_dict.keys()   ):
          a__     		=     sorted(set(self.result_dict[model_name]["bsz"]   )   )
          a__     		=     sorted(set(self.result_dict[model_name]["seq_len"]   )   )
          a__     		=     self.result_dict[model_name]["result"]
  
          ((a__)		,(a__))     		=     (
              (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
          )
  
          a__     		=     (
              model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
          )
  
          for inner_loop_value in inner_loop_array:
            if self.args.plot_along_batch:
              a__     		=     np.asarray(
                  [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,      dtype=lowerCAmelCase      ,      )
            else:
              a__     		=     np.asarray(
                  [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,      dtype=np.floataa      ,      )
  
            ((a__)		,(a__))     		=     (
                ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
            )
  
            a__     		=     np.asarray(lowerCAmelCase      ,      lowerCAmelCase   )[: len(lowerCAmelCase   )]
            plt.scatter(
                lowerCAmelCase      ,      lowerCAmelCase      ,      label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"   )
            plt.plot(lowerCAmelCase      ,      lowerCAmelCase      ,      "--"   )
  
          title_str += f" {label_model_name} vs."
  
        a__     		=     title_str[:-4]
        a__     		=     "Time in s" if self.args.is_time else "Memory in MB"
  
        # plot
        plt.title(lowerCAmelCase   )
        plt.xlabel(lowerCAmelCase   )
        plt.ylabel(lowerCAmelCase   )
        plt.legend()
  
        if self.args.figure_png_file is not None:
          plt.savefig(self.args.figure_png_file   )
        else:
          plt.show()
  
  def 				__lowercase	(						)				->str:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     HfArgumentParser(lowerCamelCase__					)
    a__     		=     parser.parse_args_into_dataclasses()[0]
    a__     		=     Plot(args=lowerCamelCase__					)
    plot.plot()
  
  
  if __name__ == "__main__":
       main()
  
  
style_context: import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->List[str]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     UNetaDModel(**lowerCamelCase__					)
    a__     		=     checkpoint["all_modules.0.W"].data
    a__     		=     checkpoint["all_modules.0.W"].data
    a__     		=     checkpoint["all_modules.1.weight"].data
    a__     		=     checkpoint["all_modules.1.bias"].data
  
    a__     		=     checkpoint["all_modules.2.weight"].data
    a__     		=     checkpoint["all_modules.2.bias"].data
  
    a__     		=     checkpoint["all_modules.3.weight"].data
    a__     		=     checkpoint["all_modules.3.bias"].data
  
    a__     		=     checkpoint[list(checkpoint.keys()					)[-4]].data
    a__     		=     checkpoint[list(checkpoint.keys()					)[-3]].data
    a__     		=     checkpoint[list(checkpoint.keys()					)[-2]].data
    a__     		=     checkpoint[list(checkpoint.keys()					)[-1]].data
  
    a__     		=     4
  
    def set_attention_weights(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					):
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T
  
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_2.b"].data
  
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_3.b"].data
  
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
    def set_resnet_weights(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					):
      a__     		=     old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
      a__     		=     old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data
  
      a__     		=     old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data
  
      if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
        a__     		=     old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
        a__     		=     old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data
  
    for i, block in enumerate(new_model_architecture.downsample_blocks					):
      a__     		=     hasattr(lowerCamelCase__ ,				"attentions"					)
      for j in range(len(block.resnets					)					):
        set_resnet_weights(block.resnets[j] ,				lowerCamelCase__ ,				lowerCamelCase__					)
        module_index += 1
        if has_attentions:
          set_attention_weights(block.attentions[j] ,				lowerCamelCase__ ,				lowerCamelCase__					)
          module_index += 1
  
      if hasattr(lowerCamelCase__ ,				"downsamplers"					) and block.downsamplers is not None:
        set_resnet_weights(block.resnet_down ,				lowerCamelCase__ ,				lowerCamelCase__					)
        module_index += 1
        a__     		=     checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
        a__     		=     checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
        module_index += 1
  
    set_resnet_weights(new_model_architecture.mid_block.resnets[0] ,				lowerCamelCase__ ,				lowerCamelCase__					)
    module_index += 1
    set_attention_weights(new_model_architecture.mid_block.attentions[0] ,				lowerCamelCase__ ,				lowerCamelCase__					)
    module_index += 1
    set_resnet_weights(new_model_architecture.mid_block.resnets[1] ,				lowerCamelCase__ ,				lowerCamelCase__					)
    module_index += 1
  
    for i, block in enumerate(new_model_architecture.up_blocks					):
      a__     		=     hasattr(lowerCamelCase__ ,				"attentions"					)
      for j in range(len(block.resnets					)					):
        set_resnet_weights(block.resnets[j] ,				lowerCamelCase__ ,				lowerCamelCase__					)
        module_index += 1
      if has_attentions:
        set_attention_weights(
            block.attentions[0] ,				lowerCamelCase__ ,				lowerCamelCase__					)  # why can there only be a single attention layer for up?
        module_index += 1
  
      if hasattr(lowerCamelCase__ ,				"resnet_up"					) and block.resnet_up is not None:
        a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
        a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
        module_index += 1
        a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
        a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
        module_index += 1
        set_resnet_weights(block.resnet_up ,				lowerCamelCase__ ,				lowerCamelCase__					)
        module_index += 1
  
    a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
    a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
    module_index += 1
    a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
    a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
  
    return new_model_architecture.state_dict()
  
  
  if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE   						=    argparse.ArgumentParser()
  
       parser.add_argument(
           """--checkpoint_path""",
           default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin""",
           type=str,
           required=False,
           help="""Path to the checkpoint to convert.""",
       )
  
       parser.add_argument(
           """--config_file""",
           default="""/Users/arthurzucker/Work/diffusers/ArthurZ/config.json""",
           type=str,
           required=False,
           help="""The config json file corresponding to the architecture.""",
       )
  
       parser.add_argument(
           """--dump_path""",
           default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt""",
           type=str,
           required=False,
           help="""Path to the output model.""",
       )
  
       _SCREAMING_SNAKE_CASE   						=    parser.parse_args()
  
       _SCREAMING_SNAKE_CASE   						=    torch.load(args.checkpoint_path, map_location="""cpu""")
  
       with open(args.config_file) as f:
            _SCREAMING_SNAKE_CASE   						=    json.loads(f.read())
  
       _SCREAMING_SNAKE_CASE   						=    convert_ncsnpp_checkpoint(
           checkpoint,
           config,
       )
  
       if "sde" in config:
            del config["sde"]
  
       _SCREAMING_SNAKE_CASE   						=    UNetaDModel(**config)
       model.load_state_dict(converted_checkpoint)
  
       try:
            _SCREAMING_SNAKE_CASE   						=    ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
  
            _SCREAMING_SNAKE_CASE   						=    ScoreSdeVePipeline(unet=model, scheduler=scheduler)
            pipe.save_pretrained(args.dump_path)
       except:  # noqa: E722
            model.save_pretrained(args.dump_path)
  
  