





"""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 							a__     (					_UpperCAmelCase=None		,       _UpperCAmelCase=None		)      ->					Any:

 '''simple docstring'''






 return field(default_factory=lambda: default		,       metadata=_UpperCAmelCase		)



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





def 							a__     (					_UpperCAmelCase		)      ->					Any:

 '''simple docstring'''






 try:
  int(_UpperCAmelCase		)
  return True
 except ValueError:
  return False





def 							a__     (					_UpperCAmelCase		)      ->					Union[str, Any]:

 '''simple docstring'''






 try:
  float(_UpperCAmelCase		)
  return True
 except ValueError:
  return False



class        lowercase_			:

       def __init__(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			args
        snake_case__      							=			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )

        with open(self.args.csv_file				,   newline=""       ) as csv_file:
         snake_case__      							=			csv.DictReader(__SCREAMING_SNAKE_CASE       )
         for row in reader:
          snake_case__      							=			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
           snake_case__      							=			int(row["result"]       )
          elif can_convert_to_float(row["result"]       ):
           # value is not None
           snake_case__      							=			float(row["result"]       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__							,				snake_case__      							=			plt.subplots()
        snake_case__      							=			"Time usage" if self.args.is_time else "Memory usage"
        snake_case__      							=			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()       ):
         snake_case__      							=			sorted(set(self.result_dict[model_name]["bsz"]       )       )
         snake_case__      							=			sorted(set(self.result_dict[model_name]["seq_len"]       )       )
         snake_case__      							=			self.result_dict[model_name]["result"]

         ((snake_case__)							,				(snake_case__))      							=			(
             (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
         )

         snake_case__      							=			(
             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:
           snake_case__      							=			np.asarray(
               [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]				,   dtype=__SCREAMING_SNAKE_CASE				,   )
          else:
           snake_case__      							=			np.asarray(
               [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]				,   dtype=np.floataa				,   )

          ((snake_case__)							,				(snake_case__))      							=			(
              ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
          )

          snake_case__      							=			np.asarray(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )[: len(__SCREAMING_SNAKE_CASE       )]
          plt.scatter(
              __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"       )
          plt.plot(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   "--"       )

         title_str += F" {label_model_name} vs."

        snake_case__      							=			title_str[:-4]
        snake_case__      							=			"Time in s" if self.args.is_time else "Memory in MB"

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

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





def 							a__     (					)      ->					Optional[Any]:

 '''simple docstring'''






 snake_case__      							=			HfArgumentParser(_UpperCAmelCase		)
 snake_case__      							=			parser.parse_args_into_dataclasses()[0]
 snake_case__      							=			Plot(args=_UpperCAmelCase		)
 plot.plot()


if __name__ == "__main__":
   main()








"""simple docstring"""


from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)



class        lowercase_			(       __lowerCAmelCase							):

       def __init__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__()

        self.register_modules(vae=__SCREAMING_SNAKE_CASE				,   text_encoder=__SCREAMING_SNAKE_CASE				,   tokenizer=__SCREAMING_SNAKE_CASE				,   unet=__SCREAMING_SNAKE_CASE				,   scheduler=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        with torch.no_grad():
         snake_case__      							=			self.vae.encode(tfms.ToTensor()(__SCREAMING_SNAKE_CASE       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
         snake_case__      							=			0.18215 * latent.latent_dist.sample()
        return latent

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			(1 / 0.18215) * latent
        with torch.no_grad():
         snake_case__      							=			self.vae.decode(__SCREAMING_SNAKE_CASE       ).sample
        snake_case__      							=			(img / 2 + 0.5).clamp(0				,   1       )
        snake_case__      							=			img.detach().cpu().permute(0				,   2				,   3				,   1       ).numpy()
        snake_case__      							=			(img * 255).round().astype("uint8"       )
        return Image.fromarray(img[0]       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			self.tokenizer(
            __SCREAMING_SNAKE_CASE				,   padding="max_length"				,   max_length=self.tokenizer.model_max_length				,   truncation=__SCREAMING_SNAKE_CASE				,   return_tensors="pt"				,   )

        snake_case__      							=			self.text_encoder(text_input.input_ids.to(self.device       )       )[0]

        snake_case__      							=			self.tokenizer(
            ""				,   padding="max_length"				,   max_length=self.tokenizer.model_max_length				,   truncation=__SCREAMING_SNAKE_CASE				,   return_tensors="pt"				,   )

        snake_case__      							=			self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

        return torch.cat([uncond_embedding, text_embedding]       )

       def __call__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE = 0.3				,   __SCREAMING_SNAKE_CASE = 0.6				,   __SCREAMING_SNAKE_CASE = 0.5				,   __SCREAMING_SNAKE_CASE = 42				,   __SCREAMING_SNAKE_CASE = 50				,   __SCREAMING_SNAKE_CASE = 7.5				,   ):






        '''simple docstring'''

        snake_case__      							=			steps - int(kmin * steps       )
        snake_case__      							=			steps - int(kmax * steps       )

        snake_case__      							=			self.prep_text(__SCREAMING_SNAKE_CASE       )

        self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )

        snake_case__							,				snake_case__      							=			img.size
        snake_case__      							=			self.encode(__SCREAMING_SNAKE_CASE       )

        torch.manual_seed(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)				,   ).to(self.device       )

        snake_case__      							=			self.scheduler.add_noise(
            __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   timesteps=self.scheduler.timesteps[tmax]				,   )

        snake_case__      							=			torch.cat([latents] * 2       )

        snake_case__      							=			self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   self.scheduler.timesteps[tmax]       )

        with torch.no_grad():
         snake_case__      							=			self.unet(
             __SCREAMING_SNAKE_CASE				,   self.scheduler.timesteps[tmax]				,   encoder_hidden_states=__SCREAMING_SNAKE_CASE				,   ).sample

        snake_case__							,				snake_case__      							=			pred.chunk(2       )
        snake_case__      							=			pred_uncond + guidance_scale * (pred_text - pred_uncond)

        snake_case__      							=			self.scheduler.step(__SCREAMING_SNAKE_CASE				,   self.scheduler.timesteps[tmax]				,   __SCREAMING_SNAKE_CASE       ).prev_sample

        for i, t in enumerate(tqdm(self.scheduler.timesteps       )       ):
         if i > tmax:
          if i < tmin:  # layout generation phase
           snake_case__      							=			self.scheduler.add_noise(
               __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   timesteps=__SCREAMING_SNAKE_CASE				,   )

           snake_case__      							=			(mix_factor * latents) + (
               1 - mix_factor
           ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
           snake_case__      							=			torch.cat([input] * 2       )

          else:  # content generation phase
           snake_case__      							=			torch.cat([latents] * 2       )

          snake_case__      							=			self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

          with torch.no_grad():
           snake_case__      							=			self.unet(
               __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   encoder_hidden_states=__SCREAMING_SNAKE_CASE				,   ).sample

          snake_case__							,				snake_case__      							=			pred.chunk(2       )
          snake_case__      							=			pred_uncond + guidance_scale * (pred_text - pred_uncond)

          snake_case__      							=			self.scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ).prev_sample

        return self.decode(__SCREAMING_SNAKE_CASE       )

