






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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






def 				A	(			a_				,a_=0.999				,a_="cosine"				,)	->		int:

					if alpha_transform_type == "cosine":

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

					elif alpha_transform_type == "exp":

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

					else:
										raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'   )

					__UpperCamelCase :   str									=[]
					for i in range(a_   ):
										__UpperCamelCase :   Any									=i / num_diffusion_timesteps
										__UpperCamelCase :   List[str]									=(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(a_   ) / alpha_bar_fn(a_   )				,a_   )   )
					return torch.tensor(a_				,dtype=torch.floataa   )




class 							__A	(     _SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE					):





				"""simple docstring"""







				UpperCamelCase__					: Optional[int]     					=[e.name for e in KarrasDiffusionSchedulers]
				UpperCamelCase__					: Union[str, Any]     					=2


				@register_to_config
				def __init__(					self				,    lowerCamelCase__ = 1000				,    lowerCamelCase__ = 0.00_085				,    lowerCamelCase__ = 0.012				,    lowerCamelCase__ = "linear"				,    lowerCamelCase__ = None				,    lowerCamelCase__ = "epsilon"				,    lowerCamelCase__ = "linspace"				,    lowerCamelCase__ = 0				,    ):




									"""simple docstring"""

									if trained_betas is not None:
														__UpperCamelCase :   List[str]									=torch.tensor(A_				,    dtype=torch.floataa       )
									elif beta_schedule == "linear":
														__UpperCamelCase :   List[str]									=torch.linspace(A_				,    A_				,    A_				,    dtype=torch.floataa       )
									elif beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														__UpperCamelCase :   str									=(
														    torch.linspace(beta_start**0.5				,    beta_end**0.5				,    A_				,    dtype=torch.floataa       ) ** 2
														)
									elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														__UpperCamelCase :   Dict									=betas_for_alpha_bar(A_       )
									else:
														raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'       )

									__UpperCamelCase :   Tuple									=1.0 - self.betas
									__UpperCamelCase :   int									=torch.cumprod(self.alphas				,    dim=0       )

									#  set all values
									self.set_timesteps(A_				,    A_				,    A_       )


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__=None       ):




									"""simple docstring"""

									if schedule_timesteps is None:
														__UpperCamelCase :   Optional[Any]									=self.timesteps

									__UpperCamelCase :   Tuple									=(schedule_timesteps == timestep).nonzero()

									# The sigma index that is taken for the **very** first `step`
									# is always the second index (or the last index if there is only 1)
									# This way we can ensure we don't accidentally skip a sigma in
									# case we start in the middle of the denoising schedule (e.g. for image-to-image)
									if len(self._index_counter       ) == 0:
														__UpperCamelCase :   Optional[Any]									=1 if len(A_       ) > 1 else 0
									else:
														__UpperCamelCase :   Any									=timestep.cpu().item() if torch.is_tensor(A_       ) else timestep
														__UpperCamelCase :   Optional[Any]									=self._index_counter[timestep_int]

									return indices[pos].item()


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									if self.config.timestep_spacing in ["linspace", "trailing"]:
														return self.sigmas.max()

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


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    ):




									"""simple docstring"""

									__UpperCamelCase :   List[str]									=self.index_for_timestep(A_       )

									if self.state_in_first_order:
														__UpperCamelCase :   Dict									=self.sigmas[step_index]
									else:
														__UpperCamelCase :   Optional[Any]									=self.sigmas_interpol[step_index - 1]

									__UpperCamelCase :   List[Any]									=sample / ((sigma**2 + 1) ** 0.5)
									return sample


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__ = None				,    lowerCamelCase__ = None				,    ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[int]									=num_inference_steps

									__UpperCamelCase :   Union[str, Any]									=num_train_timesteps or self.config.num_train_timesteps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
														__UpperCamelCase :   Optional[Any]									=np.linspace(0				,    num_train_timesteps - 1				,    A_				,    dtype=A_       )[::-1].copy()
									elif self.config.timestep_spacing == "leading":
														__UpperCamelCase :   List[str]									=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
														__UpperCamelCase :   List[str]									=(np.arange(0				,    A_       ) * step_ratio).round()[::-1].copy().astype(A_       )
														timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
														__UpperCamelCase :   List[Any]									=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
														__UpperCamelCase :   List[str]									=(np.arange(A_				,    0				,    -step_ratio       )).round().copy().astype(A_       )
														timesteps -= 1
									else:
														raise ValueError(
														    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'       )

									__UpperCamelCase :   Union[str, Any]									=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
									__UpperCamelCase :   int									=torch.from_numpy(np.log(A_       )       ).to(A_       )

									__UpperCamelCase :   Any									=np.interp(A_				,    np.arange(0				,    len(A_       )       )				,    A_       )
									__UpperCamelCase :   Optional[int]									=np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
									__UpperCamelCase :   Any									=torch.from_numpy(A_       ).to(device=A_       )

									# compute up and down sigmas
									__UpperCamelCase :   Tuple									=sigmas.roll(-1       )
									__UpperCamelCase :   int									=0.0
									__UpperCamelCase :   List[str]									=(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
									__UpperCamelCase :   Dict									=(sigmas_next**2 - sigmas_up**2) ** 0.5
									__UpperCamelCase :   Union[str, Any]									=0.0

									# compute interpolated sigmas
									__UpperCamelCase :   Optional[int]									=sigmas.log().lerp(sigmas_down.log()				,    0.5       ).exp()
									__UpperCamelCase :   Optional[int]									=0.0

									# set sigmas
									__UpperCamelCase :   Tuple									=torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2       ), sigmas[-1:]]       )
									__UpperCamelCase :   Any									=torch.cat(
									    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2       ), sigmas_interpol[-1:]]       )
									__UpperCamelCase :   str									=torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2       ), sigmas_up[-1:]]       )
									__UpperCamelCase :   str									=torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2       ), sigmas_down[-1:]]       )

									if str(A_       ).startswith('mps'       ):
														# mps does not support float64
														__UpperCamelCase :   Any									=torch.from_numpy(A_       ).to(A_				,    dtype=torch.floataa       )
									else:
														__UpperCamelCase :   Any									=torch.from_numpy(A_       ).to(A_       )

									__UpperCamelCase :   str									=self.sigma_to_t(A_       ).to(A_				,    dtype=timesteps.dtype       )
									__UpperCamelCase :   Optional[int]									=torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])				,    dim=-1       ).flatten()

									__UpperCamelCase :   Any									=torch.cat([timesteps[:1], interleaved_timesteps]       )

									__UpperCamelCase :   Union[str, Any]									=None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									__UpperCamelCase :   Optional[Any]									=defaultdict(A_       )


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   int									=sigma.log()

									# get distribution
									__UpperCamelCase :   List[str]									=log_sigma - self.log_sigmas[:, None]

									# get sigmas range
									__UpperCamelCase :   Any									=dists.ge(0       ).cumsum(dim=0       ).argmax(dim=0       ).clamp(max=self.log_sigmas.shape[0] - 2       )
									__UpperCamelCase :   Dict									=low_idx + 1

									__UpperCamelCase :   Optional[int]									=self.log_sigmas[low_idx]
									__UpperCamelCase :   str									=self.log_sigmas[high_idx]

									# interpolate sigmas
									__UpperCamelCase :   List[str]									=(low - log_sigma) / (low - high)
									__UpperCamelCase :   Tuple									=w.clamp(0				,    1       )

									# transform interpolation to time range
									__UpperCamelCase :   Dict									=(1 - w) * low_idx + w * high_idx
									__UpperCamelCase :   Dict									=t.view(sigma.shape       )
									return t


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									return self.sample is None


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__ = None				,    lowerCamelCase__ = True				,    ):




									"""simple docstring"""

									__UpperCamelCase :   Union[str, Any]									=self.index_for_timestep(A_       )

									# advance index counter by 1
									__UpperCamelCase :   Any									=timestep.cpu().item() if torch.is_tensor(A_       ) else timestep
									self._index_counter[timestep_int] += 1

									if self.state_in_first_order:
														__UpperCamelCase :   Union[str, Any]									=self.sigmas[step_index]
														__UpperCamelCase :   int									=self.sigmas_interpol[step_index]
														__UpperCamelCase :   Optional[int]									=self.sigmas_up[step_index]
														__UpperCamelCase :   Dict									=self.sigmas_down[step_index - 1]
									else:
														# 2nd order / KPDM2's method
														__UpperCamelCase :   Union[str, Any]									=self.sigmas[step_index - 1]
														__UpperCamelCase :   Optional[Any]									=self.sigmas_interpol[step_index - 1]
														__UpperCamelCase :   Any									=self.sigmas_up[step_index - 1]
														__UpperCamelCase :   Optional[Any]									=self.sigmas_down[step_index - 1]

									# currently only gamma=0 is supported. This usually works best anyways.
									# We can support gamma in the future but then need to scale the timestep before
									# passing it to the model which requires a change in API
									__UpperCamelCase :   str									=0
									__UpperCamelCase :   Union[str, Any]									=sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									__UpperCamelCase :   List[str]									=model_output.device
									__UpperCamelCase :   List[Any]									=randn_tensor(model_output.shape				,    dtype=model_output.dtype				,    device=A_				,    generator=A_       )

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
														__UpperCamelCase :   List[Any]									=sigma_hat if self.state_in_first_order else sigma_interpol
														__UpperCamelCase :   Dict									=sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
														__UpperCamelCase :   int									=sigma_hat if self.state_in_first_order else sigma_interpol
														__UpperCamelCase :   Any									=model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
														    sample / (sigma_input**2 + 1)
														)
									elif self.config.prediction_type == "sample":
														raise NotImplementedError('prediction_type not implemented yet: sample'       )
									else:
														raise ValueError(
														    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'       )

									if self.state_in_first_order:
														# 2. Convert to an ODE derivative for 1st order
														__UpperCamelCase :   List[str]									=(sample - pred_original_sample) / sigma_hat
														# 3. delta timestep
														__UpperCamelCase :   Optional[int]									=sigma_interpol - sigma_hat

														# store for 2nd order step
														__UpperCamelCase :   List[Any]									=sample
														__UpperCamelCase :   Optional[Any]									=dt
														__UpperCamelCase :   str									=sample + derivative * dt
									else:
														# DPM-Solver-2
														# 2. Convert to an ODE derivative for 2nd order
														__UpperCamelCase :   Union[str, Any]									=(sample - pred_original_sample) / sigma_interpol
														# 3. delta timestep
														__UpperCamelCase :   Dict									=sigma_down - sigma_hat

														__UpperCamelCase :   Dict									=self.sample
														__UpperCamelCase :   str									=None

														__UpperCamelCase :   Union[str, Any]									=sample + derivative * dt
														__UpperCamelCase :   int									=prev_sample + noise * sigma_up

									if not return_dict:
														return (prev_sample,)

									return SchedulerOutput(prev_sample=A_       )


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    ):




									"""simple docstring"""

									__UpperCamelCase :   Any									=self.sigmas.to(device=original_samples.device				,    dtype=original_samples.dtype       )
									if original_samples.device.type == "mps" and torch.is_floating_point(A_       ):
														# mps does not support float64
														__UpperCamelCase :   Tuple									=self.timesteps.to(original_samples.device				,    dtype=torch.floataa       )
														__UpperCamelCase :   List[str]									=timesteps.to(original_samples.device				,    dtype=torch.floataa       )
									else:
														__UpperCamelCase :   Optional[Any]									=self.timesteps.to(original_samples.device       )
														__UpperCamelCase :   Optional[Any]									=timesteps.to(original_samples.device       )

									__UpperCamelCase :   Any									=[self.index_for_timestep(A_				,    A_       ) for t in timesteps]

									__UpperCamelCase :   Union[str, Any]									=sigmas[step_indices].flatten()
									while len(sigma.shape       ) < len(original_samples.shape       ):
														__UpperCamelCase :   Optional[int]									=sigma.unsqueeze(-1       )

									__UpperCamelCase :   Optional[int]									=original_samples + noise * sigma
									return noisy_samples







				def __len__(					self       ):




									"""simple docstring"""

									return self.config.num_train_timesteps



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			(   lowercase=None       ,			lowercase=None					)  ->    Tuple:
		'''simple docstring'''

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


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






def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

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


def  A			(   lowercase					)  ->    Optional[Any]:
		'''simple docstring'''

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


class 							lowercase   :



		def __init__(					self							, A_			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	args
				UpperCamelCase            =	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)

				with open(self.args.csv_file							, newline=''			) as csv_file:
						UpperCamelCase            =	csv.DictReader(A_			)
						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 __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				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=A_							, )
								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(A_							, A_			)[: len(A_			)]
								plt.scatter(
								    A_							, A_							, label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''			)
								plt.plot(A_							, A_							, '--'			)

						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(A_			)
				plt.xlabel(A_			)
				plt.ylabel(A_			)
				plt.legend()

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






def  A			(   )  ->    Dict:
		'''simple docstring'''

		UpperCamelCase            =	HfArgumentParser(lowercase					)
		UpperCamelCase            =	parser.parse_args_into_dataclasses()[0]
		UpperCamelCase            =	Plot(args=lowercase					)
		plot.plot()


if __name__ == "__main__":
					main()
