





"""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"""


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Optional[int]										=       (LMSDiscreteScheduler,)
       snake_case__			:							int										=       10

       def 						_SCREAMING_SNAKE_CASE(     self				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			{
            "num_train_timesteps": 1100,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**__SCREAMING_SNAKE_CASE       )
        return config

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]				,   [0.0002, 0.002, 0.02]       ):
         self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE				,   beta_end=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for t in [0, 500, 800]:
         self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config()
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

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

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 1006.388       ) < 1e-2
        assert abs(result_mean.item() - 1.31       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config(prediction_type="v_prediction"       )
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

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

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 0.0017       ) < 1e-2
        assert abs(result_mean.item() - 2.2676e-06       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config()
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps				,   device=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        snake_case__      							=			sample.to(__SCREAMING_SNAKE_CASE       )

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

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

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 1006.388       ) < 1e-2
        assert abs(result_mean.item() - 1.31       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config()
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE				,   use_karras_sigmas=__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps				,   device=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter.to(__SCREAMING_SNAKE_CASE       ) * scheduler.init_noise_sigma
        snake_case__      							=			sample.to(__SCREAMING_SNAKE_CASE       )

        for t in scheduler.timesteps:
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

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

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 3812.9927       ) < 2e-2
        assert abs(result_mean.item() - 4.9648       ) < 1e-3

