
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


SCREAMING_SNAKE_CASE_			      =					logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						42
							_snake_case		     =						None

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase=0.999				,  _lowerCAmelCase="cosine"				,  ):
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_lowerCAmelCase		):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(_lowerCAmelCase		):
																		return math.exp(t * -12.0		)

						else:
												raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""		)

						__lowerCAmelCase        =       []
						for i in range(_lowerCAmelCase		):
												__lowerCAmelCase        =       i / num_diffusion_timesteps
												__lowerCAmelCase        =       (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(_lowerCAmelCase		) / alpha_bar_fn(_lowerCAmelCase		)				,  _lowerCAmelCase		)		)
						return torch.tensor(_lowerCAmelCase				,  dtype=torch.floataa		)



class      lowerCAmelCase_						(							A__							,							A__				):



							'''simple docstring'''





							_snake_case		     =						[e.name for e in KarrasDiffusionSchedulers]
							_snake_case		     =						1




							@register_to_config
							def __init__(				self  ,							snake_case_ = 1_000  ,							snake_case_ = 0.0_001  ,							snake_case_ = 0.02  ,							snake_case_ = "linear"  ,							snake_case_ = None  ,							snake_case_ = "epsilon"  ,							snake_case_ = "linear"  ,							snake_case_ = False  ,							snake_case_ = "linspace"  ,							snake_case_ = 0  ,							)   ->       Any:
													if trained_betas is not None:
																			__lowerCAmelCase        =       torch.tensor(snake_case_  ,							dtype=torch.floataa							)
													elif beta_schedule == "linear":
																			__lowerCAmelCase        =       torch.linspace(snake_case_  ,							snake_case_  ,							snake_case_  ,							dtype=torch.floataa							)
													elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			__lowerCAmelCase        =       (
																			    torch.linspace(beta_start**0.5  ,							beta_end**0.5  ,							snake_case_  ,							dtype=torch.floataa							) ** 2
																			)
													elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			__lowerCAmelCase        =       betas_for_alpha_bar(snake_case_							)
													else:
																			raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""							)

													__lowerCAmelCase        =       1.0 - self.betas
													__lowerCAmelCase        =       torch.cumprod(self.alphas  ,							dim=0							)

													__lowerCAmelCase        =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
													__lowerCAmelCase        =       np.concatenate([sigmas[::-1], [0.0]]							).astype(np.floataa							)
													__lowerCAmelCase        =       torch.from_numpy(snake_case_							)

													# setable values
													__lowerCAmelCase        =       None
													__lowerCAmelCase        =       np.linspace(0  ,							num_train_timesteps - 1  ,							snake_case_  ,							dtype=snake_case_							)[::-1].copy()
													__lowerCAmelCase        =       torch.from_numpy(snake_case_							)
													__lowerCAmelCase        =       False
													__lowerCAmelCase        =       use_karras_sigmas




							@property
							def  A__		(				self							)   ->       Union[str, Any]:
													# standard deviation of the initial noise distribution
													if self.config.timestep_spacing in ["linspace", "trailing"]:
																			return self.sigmas.max()

													return (self.sigmas.max() ** 2 + 1) ** 0.5




							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       torch.FloatTensor:
													if isinstance(snake_case_  ,							torch.Tensor							):
																			__lowerCAmelCase        =       timestep.to(self.timesteps.device							)
													__lowerCAmelCase        =       (self.timesteps == timestep).nonzero().item()
													__lowerCAmelCase        =       self.sigmas[step_index]

													__lowerCAmelCase        =       sample / ((sigma**2 + 1) ** 0.5)

													__lowerCAmelCase        =       True
													return sample




							def  A__		(				self  ,							snake_case_  ,							snake_case_ = None							)   ->       Tuple:
													__lowerCAmelCase        =       num_inference_steps

													# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
													if self.config.timestep_spacing == "linspace":
																			__lowerCAmelCase        =       np.linspace(0  ,							self.config.num_train_timesteps - 1  ,							snake_case_  ,							dtype=snake_case_							)[
																			    ::-1
																			].copy()
													elif self.config.timestep_spacing == "leading":
																			__lowerCAmelCase        =       self.config.num_train_timesteps // self.num_inference_steps
																			# creates integer timesteps by multiplying by ratio
																			# casting to int to avoid issues when num_inference_step is power of 3
																			__lowerCAmelCase        =       (np.arange(0  ,							snake_case_							) * step_ratio).round()[::-1].copy().astype(snake_case_							)
																			timesteps += self.config.steps_offset
													elif self.config.timestep_spacing == "trailing":
																			__lowerCAmelCase        =       self.config.num_train_timesteps / self.num_inference_steps
																			# creates integer timesteps by multiplying by ratio
																			# casting to int to avoid issues when num_inference_step is power of 3
																			__lowerCAmelCase        =       (np.arange(self.config.num_train_timesteps  ,							0  ,							-step_ratio							)).round().copy().astype(snake_case_							)
																			timesteps -= 1
													else:
																			raise ValueError(
																			    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""							)

													__lowerCAmelCase        =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
													__lowerCAmelCase        =       np.log(snake_case_							)

													if self.config.interpolation_type == "linear":
																			__lowerCAmelCase        =       np.interp(snake_case_  ,							np.arange(0  ,							len(snake_case_							)							)  ,							snake_case_							)
													elif self.config.interpolation_type == "log_linear":
																			__lowerCAmelCase        =       torch.linspace(np.log(sigmas[-1]							)  ,							np.log(sigmas[0]							)  ,							num_inference_steps + 1							).exp()
													else:
																			raise ValueError(
																			    f"""{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"""
																			    """ 'linear' or 'log_linear'"""							)

													if self.use_karras_sigmas:
																			__lowerCAmelCase        =       self._convert_to_karras(in_sigmas=snake_case_  ,							num_inference_steps=self.num_inference_steps							)
																			__lowerCAmelCase        =       np.array([self._sigma_to_t(snake_case_  ,							snake_case_							) for sigma in sigmas]							)

													__lowerCAmelCase        =       np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
													__lowerCAmelCase        =       torch.from_numpy(snake_case_							).to(device=snake_case_							)
													if str(snake_case_							).startswith("""mps"""							):
																			# mps does not support float64
																			__lowerCAmelCase        =       torch.from_numpy(snake_case_							).to(snake_case_  ,							dtype=torch.floataa							)
													else:
																			__lowerCAmelCase        =       torch.from_numpy(snake_case_							).to(device=snake_case_							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       Union[str, Any]:
													# get log sigma
													__lowerCAmelCase        =       np.log(snake_case_							)

													# get distribution
													__lowerCAmelCase        =       log_sigma - log_sigmas[:, np.newaxis]

													# get sigmas range
													__lowerCAmelCase        =       np.cumsum((dists >= 0)  ,							axis=0							).argmax(axis=0							).clip(max=log_sigmas.shape[0] - 2							)
													__lowerCAmelCase        =       low_idx + 1

													__lowerCAmelCase        =       log_sigmas[low_idx]
													__lowerCAmelCase        =       log_sigmas[high_idx]

													# interpolate sigmas
													__lowerCAmelCase        =       (low - log_sigma) / (low - high)
													__lowerCAmelCase        =       np.clip(snake_case_  ,							0  ,							1							)

													# transform interpolation to time range
													__lowerCAmelCase        =       (1 - w) * low_idx + w * high_idx
													__lowerCAmelCase        =       t.reshape(sigma.shape							)
													return t




							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       torch.FloatTensor:
													__lowerCAmelCase        =       in_sigmas[-1].item()
													__lowerCAmelCase        =       in_sigmas[0].item()

													__lowerCAmelCase        =       7.0  # 7.0 is the value used in the paper
													__lowerCAmelCase        =       np.linspace(0  ,							1  ,							snake_case_							)
													__lowerCAmelCase        =       sigma_min ** (1 / rho)
													__lowerCAmelCase        =       sigma_max ** (1 / rho)
													__lowerCAmelCase        =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
													return sigmas




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_ = 0.0  ,							snake_case_ = 0.0  ,							snake_case_ = float("""inf"""							)  ,							snake_case_ = 1.0  ,							snake_case_ = None  ,							snake_case_ = True  ,							)   ->       Union[EulerDiscreteSchedulerOutput, Tuple]:

													if (
													    isinstance(snake_case_  ,							snake_case_							)
													    or isinstance(snake_case_  ,							torch.IntTensor							)
													    or isinstance(snake_case_  ,							torch.LongTensor							)
													):
																			raise ValueError(
																			    (
																			        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
																			        """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
																			        """ one of the `scheduler.timesteps` as a timestep."""
																			    )  ,							)

													if not self.is_scale_input_called:
																			logger.warning(
																			    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
																			    """See `StableDiffusionPipeline` for a usage example."""							)

													if isinstance(snake_case_  ,							torch.Tensor							):
																			__lowerCAmelCase        =       timestep.to(self.timesteps.device							)

													__lowerCAmelCase        =       (self.timesteps == timestep).nonzero().item()
													__lowerCAmelCase        =       self.sigmas[step_index]

													__lowerCAmelCase        =       min(s_churn / (len(self.sigmas							) - 1)  ,							2**0.5 - 1							) if s_tmin <= sigma <= s_tmax else 0.0

													__lowerCAmelCase        =       randn_tensor(
													    model_output.shape  ,							dtype=model_output.dtype  ,							device=model_output.device  ,							generator=snake_case_							)

													__lowerCAmelCase        =       noise * s_noise
													__lowerCAmelCase        =       sigma * (gamma + 1)

													if gamma > 0:
																			__lowerCAmelCase        =       sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													# NOTE: "original_sample" should not be an expected prediction_type but is left in for
													# backwards compatibility
													if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
																			__lowerCAmelCase        =       model_output
													elif self.config.prediction_type == "epsilon":
																			__lowerCAmelCase        =       sample - sigma_hat * model_output
													elif self.config.prediction_type == "v_prediction":
																			# * c_out + input * c_skip
																			__lowerCAmelCase        =       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
													else:
																			raise ValueError(
																			    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""							)

													# 2. Convert to an ODE derivative
													__lowerCAmelCase        =       (sample - pred_original_sample) / sigma_hat

													__lowerCAmelCase        =       self.sigmas[step_index + 1] - sigma_hat

													__lowerCAmelCase        =       sample + derivative * dt

													if not return_dict:
																			return (prev_sample,)

													return EulerDiscreteSchedulerOutput(prev_sample=snake_case_  ,							pred_original_sample=snake_case_							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							)   ->       torch.FloatTensor:
													# Make sure sigmas and timesteps have the same device and dtype as original_samples
													__lowerCAmelCase        =       self.sigmas.to(device=original_samples.device  ,							dtype=original_samples.dtype							)
													if original_samples.device.type == "mps" and torch.is_floating_point(snake_case_							):
																			# mps does not support float64
																			__lowerCAmelCase        =       self.timesteps.to(original_samples.device  ,							dtype=torch.floataa							)
																			__lowerCAmelCase        =       timesteps.to(original_samples.device  ,							dtype=torch.floataa							)
													else:
																			__lowerCAmelCase        =       self.timesteps.to(original_samples.device							)
																			__lowerCAmelCase        =       timesteps.to(original_samples.device							)

													__lowerCAmelCase        =       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

													__lowerCAmelCase        =       sigmas[step_indices].flatten()
													while len(sigma.shape							) < len(original_samples.shape							):
																			__lowerCAmelCase        =       sigma.unsqueeze(-1							)

													__lowerCAmelCase        =       original_samples + noise * sigma
													return noisy_samples




							def __len__(				self							)   ->       str:
													return self.config.num_train_timesteps


"""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      (_lowerCAmelCase=None				,  _lowerCAmelCase=None		):
						return field(default_factory=lambda: default				,  metadata=_lowerCAmelCase		)
@dataclass
class      lowerCAmelCase_						:



							'''simple docstring'''





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

def 				lowercase      (_lowerCAmelCase		):
						try:
												int(_lowerCAmelCase		)
												return True
						except ValueError:
												return False

def 				lowercase      (_lowerCAmelCase		):
						try:
												float(_lowerCAmelCase		)
												return True
						except ValueError:
												return False
class      lowerCAmelCase_						:



							'''simple docstring'''





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

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




							def  A__		(				self							)   ->       List[Any]:
													__lowerCAmelCase ,      __lowerCAmelCase        =       plt.subplots()
													__lowerCAmelCase        =       """Time usage""" if self.args.is_time else """Memory usage"""
													__lowerCAmelCase        =       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()							):
																			__lowerCAmelCase        =       sorted(set(self.result_dict[model_name]["""bsz"""]							)							)
																			__lowerCAmelCase        =       sorted(set(self.result_dict[model_name]["""seq_len"""]							)							)
																			__lowerCAmelCase        =       self.result_dict[model_name]["""result"""]

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

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

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

																									__lowerCAmelCase        =       np.asarray(snake_case_  ,							snake_case_							)[: len(snake_case_							)]
																									plt.scatter(
																									    snake_case_  ,							snake_case_  ,							label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""							)
																									plt.plot(snake_case_  ,							snake_case_  ,							"""--"""							)

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

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

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

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

def 				lowercase      ():
						__lowerCAmelCase        =       HfArgumentParser(_lowerCAmelCase		)
						__lowerCAmelCase        =       parser.parse_args_into_dataclasses()[0]
						__lowerCAmelCase        =       Plot(args=_lowerCAmelCase		)
						plot.plot()


if __name__ == "__main__":
			main()
