



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					(   lowerCAmelCase_       : Union[str, Any]=None      ,lowerCAmelCase_       : str=None    )				->			Any:






						'''simple docstring'''



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






@dataclass
class 		lowercase						:






	"""simple docstring"""


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



def 	__a					(   lowerCAmelCase_       : Tuple    )				->			Union[str, Any]:






						'''simple docstring'''



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



def 	__a					(   lowerCAmelCase_       : List[Any]    )				->			List[str]:






						'''simple docstring'''



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






class 		lowercase						:






	"""simple docstring"""


	def __init__(     self     :						Any						,  __UpperCAmelCase     :						List[str]					)      ->			Any:
							UpperCAmelCase_=       args
							UpperCAmelCase_=       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			int:
							UpperCAmelCase_,       UpperCAmelCase_=       plt.subplots()
							UpperCAmelCase_=       """Time usage""" if self.args.is_time else """Memory usage"""
							UpperCAmelCase_=       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()					):
													UpperCAmelCase_=       sorted(set(self.result_dict[model_name]["""bsz"""]					)					)
													UpperCAmelCase_=       sorted(set(self.result_dict[model_name]["""seq_len"""]					)					)
													UpperCAmelCase_=       self.result_dict[model_name]["""result"""]

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

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

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

																			UpperCAmelCase_=       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."""

							UpperCAmelCase_=       title_str[:-4]
							UpperCAmelCase_=       """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 	__a					(   )				->			Union[str, Any]:






						'''simple docstring'''



						UpperCAmelCase_=       HfArgumentParser(lowerCAmelCase_    )
						UpperCAmelCase_=       parser.parse_args_into_dataclasses()[0]
						UpperCAmelCase_=       Plot(args=lowerCAmelCase_    )
						plot.plot()


if __name__ == "__main__":
	main()









from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						Dict						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						List[Any]					)      ->			List[str]:
							super().__init__()

							# make sure scheduler can always be converted to DDIM
							UpperCAmelCase_=       DDIMScheduler.from_config(scheduler.config					)

							self.register_modules(unet=__UpperCAmelCase						,  scheduler=__UpperCAmelCase					)




	@torch.no_grad()
	def __call__(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						Optional[Union[torch.Generator, List[torch.Generator]]] = None						,  __UpperCAmelCase     :						float = 0.0						,  __UpperCAmelCase     :						int = 50						,  __UpperCAmelCase     :						Optional[bool] = None						,  __UpperCAmelCase     :						Optional[str] = "pil"						,  __UpperCAmelCase     :						bool = True						,  )      ->			Union[ImagePipelineOutput, Tuple]:

							# Sample gaussian noise to begin loop
							if isinstance(self.unet.config.sample_size						,  __UpperCAmelCase					):
													UpperCAmelCase_=       (
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
							else:
													UpperCAmelCase_=       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

							if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					) and len(__UpperCAmelCase					) != batch_size:
													raise ValueError(
													    F"""You have passed a list of generators of length {len(__UpperCAmelCase					)}, but requested an effective batch"""
													    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""					)

							UpperCAmelCase_=       randn_tensor(__UpperCAmelCase						,  generator=__UpperCAmelCase						,  device=self.device						,  dtype=self.unet.dtype					)

							# set step values
							self.scheduler.set_timesteps(__UpperCAmelCase					)

							for t in self.progress_bar(self.scheduler.timesteps					):
													# 1. predict noise model_output
													UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					).sample

													# 2. predict previous mean of image x_t-1 and add variance depending on eta
													# eta corresponds to η in paper and should be between [0, 1]
													# do x_t -> x_t-1
													UpperCAmelCase_=       self.scheduler.step(
													    __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  eta=__UpperCAmelCase						,  use_clipped_model_output=__UpperCAmelCase						,  generator=__UpperCAmelCase					).prev_sample

							UpperCAmelCase_=       (image / 2 + 0.5).clamp(0						,  1					)
							UpperCAmelCase_=       image.cpu().permute(0						,  2						,  3						,  1					).numpy()
							if output_type == "pil":
													UpperCAmelCase_=       self.numpy_to_pil(__UpperCAmelCase					)

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=__UpperCAmelCase					)




