



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest






class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):
							__lowerCamelCase		:		List[Any]      =(DDIMScheduler,)
							__lowerCamelCase		:		str      =(('eta', 0.0), ('num_inference_steps', 50))





							def   UpperCamelCase_   (				self						:						Optional[int] ,			**__lowercase						:						Optional[int]		):

											'''simple docstring'''
											__a					       =						{
											    """num_train_timesteps""": 1000,
											    """beta_start""": 0.0001,
											    """beta_end""": 0.02,
											    """beta_schedule""": """linear""",
											    """clip_sample""": True,
											}

											config.update(**__lowercase		)
											return config





							def   UpperCamelCase_   (				self						:						Tuple ,			**__lowercase						:						Tuple		):

											'''simple docstring'''
											__a					       =						self.scheduler_classes[0]
											__a					       =						self.get_scheduler_config(**__lowercase		)
											__a					       =						scheduler_class(**__lowercase		)

											__a       ,   __a					       =						10, 0.0

											__a					       =						self.dummy_model()
											__a					       =						self.dummy_sample_deter

											scheduler.set_timesteps(__lowercase		)

											for t in scheduler.timesteps:
															__a					       =						model(__lowercase ,			__lowercase		)
															__a					       =						scheduler.step(__lowercase ,			__lowercase ,			__lowercase ,			__lowercase		).prev_sample

											return sample





							def   UpperCamelCase_   (				self						:						Tuple		):

											'''simple docstring'''
											for timesteps in [100, 500, 1000]:
															self.check_over_configs(num_train_timesteps=__lowercase		)





							def   UpperCamelCase_   (				self						:						List[str]		):

											'''simple docstring'''
											for steps_offset in [0, 1]:
															self.check_over_configs(steps_offset=__lowercase		)

											__a					       =						self.scheduler_classes[0]
											__a					       =						self.get_scheduler_config(steps_offset=1		)
											__a					       =						scheduler_class(**__lowercase		)
											scheduler.set_timesteps(5		)
											assert torch.equal(scheduler.timesteps ,			torch.LongTensor([801, 601, 401, 201, 1]		)		)





							def   UpperCamelCase_   (				self						:						Tuple		):

											'''simple docstring'''
											for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,			[0.002, 0.02, 0.2, 2]		):
															self.check_over_configs(beta_start=__lowercase ,			beta_end=__lowercase		)





							def   UpperCamelCase_   (				self						:						Union[str, Any]		):

											'''simple docstring'''
											for schedule in ["linear", "squaredcos_cap_v2"]:
															self.check_over_configs(beta_schedule=__lowercase		)





							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=__lowercase		)





							def   UpperCamelCase_   (				self						:						List[str]		):

											'''simple docstring'''
											for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=__lowercase		)





							def   UpperCamelCase_   (				self						:						Optional[int]		):

											'''simple docstring'''
											for timestep_spacing in ["trailing", "leading"]:
															self.check_over_configs(timestep_spacing=__lowercase		)





							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											for rescale_betas_zero_snr in [True, False]:
															self.check_over_configs(rescale_betas_zero_snr=__lowercase		)





							def   UpperCamelCase_   (				self						:						Tuple		):

											'''simple docstring'''
											self.check_over_configs(thresholding=__lowercase		)
											for threshold in [0.5, 1.0, 2.0]:
															for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(
																			    thresholding=__lowercase ,			prediction_type=__lowercase ,			sample_max_value=__lowercase ,			)





							def   UpperCamelCase_   (				self						:						Union[str, Any]		):

											'''simple docstring'''
											for t in [1, 10, 49]:
															self.check_over_forward(time_step=__lowercase		)





							def   UpperCamelCase_   (				self						:						Dict		):

											'''simple docstring'''
											for t, num_inference_steps in zip([1, 10, 50] ,			[10, 50, 500]		):
															self.check_over_forward(time_step=__lowercase ,			num_inference_steps=__lowercase		)





							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											for t, eta in zip([1, 10, 49] ,			[0.0, 0.5, 1.0]		):
															self.check_over_forward(time_step=__lowercase ,			eta=__lowercase		)





							def   UpperCamelCase_   (				self						:						List[str]		):

											'''simple docstring'''
											__a					       =						self.scheduler_classes[0]
											__a					       =						self.get_scheduler_config()
											__a					       =						scheduler_class(**__lowercase		)

											assert torch.sum(torch.abs(scheduler._get_variance(0 ,			0		) - 0.0		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(420 ,			400		) - 0.14771		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(980 ,			960		) - 0.32460		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(0 ,			0		) - 0.0		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(487 ,			486		) - 0.00979		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(999 ,			998		) - 0.02		)		) < 1E-5





							def   UpperCamelCase_   (				self						:						Any		):

											'''simple docstring'''
											__a					       =						self.full_loop()

											__a					       =						torch.sum(torch.abs(__lowercase		)		)
											__a					       =						torch.mean(torch.abs(__lowercase		)		)

											assert abs(result_sum.item() - 172.0067		) < 1E-2
											assert abs(result_mean.item() - 0.223967		) < 1E-3





							def   UpperCamelCase_   (				self						:						Tuple		):

											'''simple docstring'''
											__a					       =						self.full_loop(prediction_type="""v_prediction"""		)

											__a					       =						torch.sum(torch.abs(__lowercase		)		)
											__a					       =						torch.mean(torch.abs(__lowercase		)		)

											assert abs(result_sum.item() - 52.5302		) < 1E-2
											assert abs(result_mean.item() - 0.0684		) < 1E-3





							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a					       =						self.full_loop(set_alpha_to_one=__lowercase ,			beta_start=0.01		)
											__a					       =						torch.sum(torch.abs(__lowercase		)		)
											__a					       =						torch.mean(torch.abs(__lowercase		)		)

											assert abs(result_sum.item() - 149.8295		) < 1E-2
											assert abs(result_mean.item() - 0.1951		) < 1E-3







							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											__a					       =						self.full_loop(set_alpha_to_one=__lowercase ,			beta_start=0.01		)
											__a					       =						torch.sum(torch.abs(__lowercase		)		)
											__a					       =						torch.mean(torch.abs(__lowercase		)		)

											assert abs(result_sum.item() - 149.0784		) < 1E-2
											assert abs(result_mean.item() - 0.1941		) < 1E-3





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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Dict=None			,       _SCREAMING_SNAKE_CASE    :     List[str]=None      ):
				"""simple docstring"""







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






@dataclass
class 				SCREAMING_SNAKE_CASE     :
							__lowerCamelCase		:		str      =field(
							    metadata={'help': 'The csv file to plot.'}     , )
							__lowerCamelCase		:		bool      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}     , )
							__lowerCamelCase		:		bool      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}     , )
							__lowerCamelCase		:		bool      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Disable logarithmic scale when plotting'}     , )
							__lowerCamelCase		:		bool      =field(
							    default=lowerCamelCase__     , metadata={
							        'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
							    }     , )
							__lowerCamelCase		:		Optional[str]      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}     , )
							__lowerCamelCase		:		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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Any      ):
				"""simple docstring"""







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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Tuple      ):
				"""simple docstring"""







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






class 				SCREAMING_SNAKE_CASE     :





							def __init__(				self						:						Optional[int] ,			__lowercase						:						str		):

											'''simple docstring'''
											__a					       =						args
											__a					       =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

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







							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a       ,   __a					       =						plt.subplots()
											__a					       =						"""Time usage""" if self.args.is_time else """Memory usage"""
											__a					       =						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()		):
															__a					       =						sorted(set(self.result_dict[model_name]["""bsz"""]		)		)
															__a					       =						sorted(set(self.result_dict[model_name]["""seq_len"""]		)		)
															__a					       =						self.result_dict[model_name]["""result"""]

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

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

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

																			__a					       =						np.asarray(__lowercase ,			__lowercase		)[: len(__lowercase		)]
																			plt.scatter(
																			    __lowercase ,			__lowercase ,			label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"		)
																			plt.plot(__lowercase ,			__lowercase ,			"""--"""		)

															title_str += F" {label_model_name} vs."

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

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

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



def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						HfArgumentParser(_SCREAMING_SNAKE_CASE      )
				__a					       =						parser.parse_args_into_dataclasses()[0]
				__a					       =						Plot(args=_SCREAMING_SNAKE_CASE      )
				plot.plot()


if __name__ == "__main__":
						main()
