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()


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)

