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





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






def    A			(					_lowercase		):
						try:
												int(_lowercase		)
												return True
						except ValueError:
												return False
def    A			(					_lowercase		):
						try:
												float(_lowercase		)
												return True
						except ValueError:
												return False





class       lowercase__		:


						def __init__( self			:       Any					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





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

												with open(self.args.csv_file					,	newline=''''''		) as csv_file:
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							csv.DictReader(UpperCamelCase__		)
																		for row in reader:
																								SCREAMING_SNAKE_CASE							:					Dict           =							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
																														SCREAMING_SNAKE_CASE							:					Tuple           =							int(row['''result''']		)
																								elif can_convert_to_float(row['''result''']		):
																														# value is not None
																														SCREAMING_SNAKE_CASE							:					Dict           =							float(row['''result''']		)



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							plt.subplots()
												SCREAMING_SNAKE_CASE							:					List[str]           =							'''Time usage''' if self.args.is_time else '''Memory usage'''
												SCREAMING_SNAKE_CASE							:					Tuple           =							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()		):
																		SCREAMING_SNAKE_CASE							:					Any           =							sorted(set(self.result_dict[model_name]['''bsz''']		)		)
																		SCREAMING_SNAKE_CASE							:					int           =							sorted(set(self.result_dict[model_name]['''seq_len''']		)		)
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.result_dict[model_name]['''result''']

																		(SCREAMING_SNAKE_CASE)							:					List[str]           =							(
																		    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																		)

																		SCREAMING_SNAKE_CASE							:					str           =							(
																		    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:
																														SCREAMING_SNAKE_CASE							:					str           =							np.asarray(
																														    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]					,	dtype=UpperCamelCase__					,	)
																								else:
																														SCREAMING_SNAKE_CASE							:					Optional[Any]           =							np.asarray(
																														    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]					,	dtype=np.floataa					,	)

																								(SCREAMING_SNAKE_CASE)							:					Any           =							(
																								    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
																								)

																								SCREAMING_SNAKE_CASE							:					List[str]           =							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."""

												SCREAMING_SNAKE_CASE							:					List[Any]           =							title_str[:-4]
												SCREAMING_SNAKE_CASE							:					str           =							'''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			(					):
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							HfArgumentParser(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Dict           =							parser.parse_args_into_dataclasses()[0]
						SCREAMING_SNAKE_CASE							:					str           =							Plot(args=_lowercase		)
						plot.plot()


if __name__ == "__main__":
					main()

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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class       lowercase__		(     UpperCamelCase_			,							UpperCamelCase_):
						UpperCamelCase_  =	1


						@register_to_config
						def __init__( self			:       List[str]					,	UpperCamelCase__			:       int = 1000					,	UpperCamelCase__			:       Optional[Union[np.ndarray, List[float]]] = None		):





												'''simple docstring'''





												self.set_timesteps(UpperCamelCase__		)

												# standard deviation of the initial noise distribution
												SCREAMING_SNAKE_CASE							:					str           =							1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												SCREAMING_SNAKE_CASE							:					Tuple           =							4

												# running values
												SCREAMING_SNAKE_CASE							:					int           =							[]


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Union[str, torch.device] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							num_inference_steps
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.linspace(1					,	0					,	num_inference_steps + 1		)[:-1]
												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.cat([steps, torch.tensor([0.0]		)]		)

												if self.config.trained_betas is not None:
																		SCREAMING_SNAKE_CASE							:					int           =							torch.tensor(self.config.trained_betas					,	dtype=torch.floataa		)
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.sin(steps * math.pi / 2		) ** 2

												SCREAMING_SNAKE_CASE							:					Dict           =							(1.0 - self.betas**2) ** 0.5

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(torch.atana(self.betas					,	self.alphas		) / math.pi * 2)[:-1]
												SCREAMING_SNAKE_CASE							:					List[str]           =							timesteps.to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Any           =							[]


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       torch.FloatTensor					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       torch.FloatTensor					,	UpperCamelCase__			:       bool = True					,	):





												'''simple docstring'''





												if self.num_inference_steps is None:
																		raise ValueError(
																		    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							(self.timesteps == timestep).nonzero().item()
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							timestep_index + 1

												SCREAMING_SNAKE_CASE							:					int           =							sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(UpperCamelCase__		)

												if len(self.ets		) == 1:
																		SCREAMING_SNAKE_CASE							:					Dict           =							self.ets[-1]
												elif len(self.ets		) == 2:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							(3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets		) == 3:
																		SCREAMING_SNAKE_CASE							:					str           =							(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self._get_prev_sample(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												if not return_dict:
																		return (prev_sample,)

												return SchedulerOutput(prev_sample=UpperCamelCase__		)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       torch.FloatTensor					,	*UpperCamelCase__			:       List[str]					,	**UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												return sample


						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Any					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							self.alphas[timestep_index]
												SCREAMING_SNAKE_CASE							:					List[str]           =							self.betas[timestep_index]

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.alphas[prev_timestep_index]
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.betas[prev_timestep_index]

												SCREAMING_SNAKE_CASE							:					Dict           =							(sample - sigma * ets) / max(UpperCamelCase__					,	1E-8		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							next_alpha * pred + ets * next_sigma

												return prev_sample



						def __len__( self			:       int		):





												'''simple docstring'''





												return self.config.num_train_timesteps





