"""simple docstring"""







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__(       __SCREAMING_SNAKE_CASE							:     int=None     ,			__SCREAMING_SNAKE_CASE							:     int=None      ):
     return field(default_factory=lambda: default     ,			metadata=__SCREAMING_SNAKE_CASE      )







@dataclass
class        UpperCamelCase		:
   lowercase							=  field(
       metadata={'help': 'The csv file to plot.'}						,      )
   lowercase							=  field(
       default=lowercase_						,      metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}						,      )
   lowercase							=  field(
       default=lowercase_						,      metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}						,      )
   lowercase							=  field(
       default=lowercase_						,      metadata={'help': 'Disable logarithmic scale when plotting'}						,      )
   lowercase							=  field(
       default=lowercase_						,      metadata={
           'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
       }						,      )
   lowercase							=  field(
       default=lowercase_						,      metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}						,      )
   lowercase							=  list_field(
       default=lowercase_						,      metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}  )






def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Dict      ):
     try:
          int(__SCREAMING_SNAKE_CASE      )
          return True
     except ValueError:
          return False



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Tuple      ):
     try:
          float(__SCREAMING_SNAKE_CASE      )
          return True
     except ValueError:
          return False







class        UpperCamelCase		:
   def __init__(			self     ,__UpperCamelCase			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Any													=	args
        lowercase_	:				List[str]													=	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)

        with open(self.args.csv_file     ,newline=''			) as csv_file:
             lowercase_	:				Any													=	csv.DictReader(__UpperCamelCase			)
             for row in reader:
                  lowercase_	:				Tuple													=	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
                       lowercase_	:				str													=	int(row['result']			)
                  elif can_convert_to_float(row['result']			):
                       # value is not None
                       lowercase_	:				str													=	float(row['result']			)







   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				int													=	plt.subplots()
        lowercase_	:				Any													=	'Time usage' if self.args.is_time else 'Memory usage'
        lowercase_	:				Any													=	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()			):
             lowercase_	:				Tuple													=	sorted(set(self.result_dict[model_name]['bsz']			)			)
             lowercase_	:				str													=	sorted(set(self.result_dict[model_name]['seq_len']			)			)
             lowercase_	:				Optional[int]													=	self.result_dict[model_name]['result']

             ((lowercase_)							,  (lowercase_))	:				Tuple													=	(
                 (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
             )

             lowercase_	:				List[str]													=	(
                 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:
                       lowercase_	:				Optional[int]													=	np.asarray(
                           [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]     ,dtype=__UpperCamelCase     ,)
                  else:
                       lowercase_	:				List[Any]													=	np.asarray(
                           [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]     ,dtype=np.floataa     ,)

                  ((lowercase_)							,  (lowercase_))	:				Dict													=	(
                      ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
                  )

                  lowercase_	:				Dict													=	np.asarray(__UpperCamelCase     ,__UpperCamelCase			)[: len(__UpperCamelCase			)]
                  plt.scatter(
                      __UpperCamelCase     ,__UpperCamelCase     ,label=f'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''			)
                  plt.plot(__UpperCamelCase     ,__UpperCamelCase     ,'--'			)

             title_str += f''' {label_model_name} vs.'''

        lowercase_	:				List[Any]													=	title_str[:-4]
        lowercase_	:				int													=	'Time in s' if self.args.is_time else 'Memory in MB'

        # plot
        plt.title(__UpperCamelCase			)
        plt.xlabel(__UpperCamelCase			)
        plt.ylabel(__UpperCamelCase			)
        plt.legend()

        if self.args.figure_png_file is not None:
             plt.savefig(self.args.figure_png_file			)
        else:
             plt.show()






def 			lowercase__(       ):
     lowercase_	:				int													=	HfArgumentParser(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				List[str]													=	parser.parse_args_into_dataclasses()[0]
     lowercase_	:				Optional[int]													=	Plot(args=__SCREAMING_SNAKE_CASE      )
     plot.plot()


if __name__ == "__main__":
       main()


"""simple docstring"""







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__SCREAMING_SNAKE_CASE    	={
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1000,
    "block_out_channels": [32, 64],
    "attention_head_dim": 8,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__SCREAMING_SNAKE_CASE    	={
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1000,
    "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__SCREAMING_SNAKE_CASE    	={
    "sample_size": 256,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "default",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__SCREAMING_SNAKE_CASE    	={
    "num_train_timesteps": 40,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

__SCREAMING_SNAKE_CASE    	={
    "num_train_timesteps": 201,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

__SCREAMING_SNAKE_CASE    	={
    "num_train_timesteps": 151,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[int]      ):
     if isinstance(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      ):
          return v
     if v.lower() in ("yes", "true", "t", "y", "1"):
          return True
     elif v.lower() in ("no", "false", "f", "n", "0"):
          return False
     else:
          raise argparse.ArgumentTypeError('boolean value expected'      )



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Tuple     ,			__SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     str=False      ):
     lowercase_	:				str													=	checkpoint[F'''{old_prefix}.in_layers.0.weight''']
     lowercase_	:				Any													=	checkpoint[F'''{old_prefix}.in_layers.0.bias''']
     lowercase_	:				str													=	checkpoint[F'''{old_prefix}.in_layers.2.weight''']
     lowercase_	:				str													=	checkpoint[F'''{old_prefix}.in_layers.2.bias''']
     lowercase_	:				Optional[Any]													=	checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
     lowercase_	:				Any													=	checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
     lowercase_	:				Tuple													=	checkpoint[F'''{old_prefix}.out_layers.0.weight''']
     lowercase_	:				Dict													=	checkpoint[F'''{old_prefix}.out_layers.0.bias''']
     lowercase_	:				Any													=	checkpoint[F'''{old_prefix}.out_layers.3.weight''']
     lowercase_	:				Dict													=	checkpoint[F'''{old_prefix}.out_layers.3.bias''']

     if has_skip:
          lowercase_	:				Optional[Any]													=	checkpoint[F'''{old_prefix}.skip_connection.weight''']
          lowercase_	:				Tuple													=	checkpoint[F'''{old_prefix}.skip_connection.bias''']

     return new_checkpoint



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     str=None      ):
     lowercase_							,  lowercase_							,  lowercase_	:				Optional[int]													=	checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3     ,			dim=0      )
     lowercase_							,  lowercase_							,  lowercase_	:				Dict													=	checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3     ,			dim=0      )

     lowercase_	:				Any													=	checkpoint[F'''{old_prefix}.norm.weight''']
     lowercase_	:				Tuple													=	checkpoint[F'''{old_prefix}.norm.bias''']

     lowercase_	:				List[str]													=	weight_q.squeeze(-1      ).squeeze(-1      )
     lowercase_	:				Tuple													=	bias_q.squeeze(-1      ).squeeze(-1      )
     lowercase_	:				Optional[Any]													=	weight_k.squeeze(-1      ).squeeze(-1      )
     lowercase_	:				int													=	bias_k.squeeze(-1      ).squeeze(-1      )
     lowercase_	:				Optional[int]													=	weight_v.squeeze(-1      ).squeeze(-1      )
     lowercase_	:				Tuple													=	bias_v.squeeze(-1      ).squeeze(-1      )

     lowercase_	:				List[str]													=	(
         checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1      ).squeeze(-1      )
     )
     lowercase_	:				int													=	checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1      ).squeeze(-1      )

     return new_checkpoint



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Tuple      ):
     lowercase_	:				Any													=	torch.load(__SCREAMING_SNAKE_CASE     ,			map_location='cpu'      )
     lowercase_	:				Any													=	{}

     lowercase_	:				Dict													=	checkpoint['time_embed.0.weight']
     lowercase_	:				Optional[Any]													=	checkpoint['time_embed.0.bias']
     lowercase_	:				List[str]													=	checkpoint['time_embed.2.weight']
     lowercase_	:				Any													=	checkpoint['time_embed.2.bias']

     if unet_config["num_class_embeds"] is not None:
          lowercase_	:				str													=	checkpoint['label_emb.weight']

     lowercase_	:				Optional[Any]													=	checkpoint['input_blocks.0.0.weight']
     lowercase_	:				Optional[int]													=	checkpoint['input_blocks.0.0.bias']

     lowercase_	:				Dict													=	unet_config['down_block_types']
     lowercase_	:				int													=	unet_config['layers_per_block']
     lowercase_	:				int													=	unet_config['attention_head_dim']
     lowercase_	:				Any													=	unet_config['block_out_channels']
     lowercase_	:				Optional[Any]													=	1
     lowercase_	:				Tuple													=	channels_list[0]

     for i, layer_type in enumerate(__SCREAMING_SNAKE_CASE      ):
          lowercase_	:				Tuple													=	channels_list[i]
          lowercase_	:				Any													=	current_channels != prev_channels
          if layer_type == "ResnetDownsampleBlock2D":
               for j in range(__SCREAMING_SNAKE_CASE      ):
                    lowercase_	:				List[str]													=	F'''down_blocks.{i}.resnets.{j}'''
                    lowercase_	:				int													=	F'''input_blocks.{current_layer}.0'''
                    lowercase_	:				List[str]													=	True if j == 0 and downsample_block_has_skip else False
                    lowercase_	:				str													=	convert_resnet(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			has_skip=__SCREAMING_SNAKE_CASE      )
                    current_layer += 1

          elif layer_type == "AttnDownBlock2D":
               for j in range(__SCREAMING_SNAKE_CASE      ):
                    lowercase_	:				Optional[int]													=	F'''down_blocks.{i}.resnets.{j}'''
                    lowercase_	:				Dict													=	F'''input_blocks.{current_layer}.0'''
                    lowercase_	:				Optional[Any]													=	True if j == 0 and downsample_block_has_skip else False
                    lowercase_	:				Union[str, Any]													=	convert_resnet(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			has_skip=__SCREAMING_SNAKE_CASE      )
                    lowercase_	:				str													=	F'''down_blocks.{i}.attentions.{j}'''
                    lowercase_	:				Union[str, Any]													=	F'''input_blocks.{current_layer}.1'''
                    lowercase_	:				int													=	convert_attention(
                        __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
                    current_layer += 1

          if i != len(__SCREAMING_SNAKE_CASE      ) - 1:
               lowercase_	:				Tuple													=	F'''down_blocks.{i}.downsamplers.0'''
               lowercase_	:				str													=	F'''input_blocks.{current_layer}.0'''
               lowercase_	:				Tuple													=	convert_resnet(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
               current_layer += 1

          lowercase_	:				int													=	current_channels

     # hardcoded the mid-block for now
     lowercase_	:				Any													=	'mid_block.resnets.0'
     lowercase_	:				Optional[Any]													=	'middle_block.0'
     lowercase_	:				Optional[int]													=	convert_resnet(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
     lowercase_	:				List[str]													=	'mid_block.attentions.0'
     lowercase_	:				Optional[Any]													=	'middle_block.1'
     lowercase_	:				Dict													=	convert_attention(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Any													=	'mid_block.resnets.1'
     lowercase_	:				str													=	'middle_block.2'
     lowercase_	:				Dict													=	convert_resnet(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     lowercase_	:				Union[str, Any]													=	0
     lowercase_	:				Union[str, Any]													=	unet_config['up_block_types']

     for i, layer_type in enumerate(__SCREAMING_SNAKE_CASE      ):
          if layer_type == "ResnetUpsampleBlock2D":
               for j in range(layers_per_block + 1      ):
                    lowercase_	:				Any													=	F'''up_blocks.{i}.resnets.{j}'''
                    lowercase_	:				Any													=	F'''output_blocks.{current_layer}.0'''
                    lowercase_	:				Union[str, Any]													=	convert_resnet(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			has_skip=__SCREAMING_SNAKE_CASE      )
                    current_layer += 1

               if i != len(__SCREAMING_SNAKE_CASE      ) - 1:
                    lowercase_	:				Optional[Any]													=	F'''up_blocks.{i}.upsamplers.0'''
                    lowercase_	:				Optional[int]													=	F'''output_blocks.{current_layer-1}.1'''
                    lowercase_	:				Tuple													=	convert_resnet(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
          elif layer_type == "AttnUpBlock2D":
               for j in range(layers_per_block + 1      ):
                    lowercase_	:				Tuple													=	F'''up_blocks.{i}.resnets.{j}'''
                    lowercase_	:				List[Any]													=	F'''output_blocks.{current_layer}.0'''
                    lowercase_	:				Union[str, Any]													=	convert_resnet(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			has_skip=__SCREAMING_SNAKE_CASE      )
                    lowercase_	:				Tuple													=	F'''up_blocks.{i}.attentions.{j}'''
                    lowercase_	:				Dict													=	F'''output_blocks.{current_layer}.1'''
                    lowercase_	:				int													=	convert_attention(
                        __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
                    current_layer += 1

               if i != len(__SCREAMING_SNAKE_CASE      ) - 1:
                    lowercase_	:				Any													=	F'''up_blocks.{i}.upsamplers.0'''
                    lowercase_	:				str													=	F'''output_blocks.{current_layer-1}.2'''
                    lowercase_	:				int													=	convert_resnet(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     lowercase_	:				List[str]													=	checkpoint['out.0.weight']
     lowercase_	:				Dict													=	checkpoint['out.0.bias']
     lowercase_	:				Optional[int]													=	checkpoint['out.2.weight']
     lowercase_	:				Optional[int]													=	checkpoint['out.2.bias']

     return new_checkpoint


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE    	=argparse.ArgumentParser()

       parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
       parser.add_argument(
           "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
       )
       parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

       __SCREAMING_SNAKE_CASE    	=parser.parse_args()
       __SCREAMING_SNAKE_CASE    	=strabool(args.class_cond)

       __SCREAMING_SNAKE_CASE    	=os.path.basename(args.unet_path)
       print(F"Checkpoint: {ckpt_name}")

       # Get U-Net config
       if "imagenet64" in ckpt_name:
              __SCREAMING_SNAKE_CASE    	=IMAGENET_64_UNET_CONFIG
       elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              __SCREAMING_SNAKE_CASE    	=LSUN_256_UNET_CONFIG
       elif "test" in ckpt_name:
              __SCREAMING_SNAKE_CASE    	=TEST_UNET_CONFIG
       else:
              raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.")

       if not args.class_cond:
              __SCREAMING_SNAKE_CASE    	=None

       __SCREAMING_SNAKE_CASE    	=con_pt_to_diffuser(args.unet_path, unet_config)

       __SCREAMING_SNAKE_CASE    	=UNetaDModel(**unet_config)
       image_unet.load_state_dict(converted_unet_ckpt)

       # Get scheduler config
       if "cd" in ckpt_name or "test" in ckpt_name:
              __SCREAMING_SNAKE_CASE    	=CD_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
              __SCREAMING_SNAKE_CASE    	=CT_IMAGENET_64_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              __SCREAMING_SNAKE_CASE    	=CT_LSUN_256_SCHEDULER_CONFIG
       else:
              raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.")

       __SCREAMING_SNAKE_CASE    	=CMStochasticIterativeScheduler(**scheduler_config)

       __SCREAMING_SNAKE_CASE    	=ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
       consistency_model.save_pretrained(args.dump_path)

