



import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class      __UpperCamelCase       (a__			):
	__A			  =					(KDPMaDiscreteScheduler,)
	__A			  =					10







	def   _a   (    self ,				**_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							{
							    "num_train_timesteps": 1100,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**lowerCAmelCase__						)
							return config







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							for timesteps in [10, 50, 100, 1000]:
													self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] ,				[0.0002, 0.002, 0.02]						):
													self.check_over_configs(beta_start=lowerCAmelCase__ ,				beta_end=lowerCAmelCase__						)







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							for schedule in ["linear", "scaled_linear"]:
													self.check_over_configs(beta_schedule=lowerCAmelCase__						)







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=lowerCAmelCase__						)







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							self.scheduler_classes[0]
							lowercase        =							self.get_scheduler_config(prediction_type="""v_prediction"""						)
							lowercase        =							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps						)

							lowercase        =							self.dummy_model()
							lowercase        =							self.dummy_sample_deter * scheduler.init_noise_sigma
							lowercase        =							sample.to(lowerCAmelCase__						)

							for i, t in enumerate(scheduler.timesteps						):
													lowercase        =							scheduler.scale_model_input(lowerCAmelCase__ ,				lowerCAmelCase__						)

													lowercase        =							model(lowerCAmelCase__ ,				lowerCAmelCase__						)

													lowercase        =							scheduler.step(lowerCAmelCase__ ,				lowerCAmelCase__ ,				lowerCAmelCase__						)
													lowercase        =							output.prev_sample

							lowercase        =							torch.sum(torch.abs(lowerCAmelCase__						)						)
							lowercase        =							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 4.69_34E-07						) < 1E-2
													assert abs(result_mean.item() - 6.11_12E-10						) < 1E-3
							else:
													# CUDA
													assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07						) < 1E-2
													assert abs(result_mean.item() - 0.0002						) < 1E-3







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							if torch_device == "mps":
													return
							lowercase        =							self.scheduler_classes[0]
							lowercase        =							self.get_scheduler_config()
							lowercase        =							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps						)

							lowercase        =							self.dummy_model()
							lowercase        =							self.dummy_sample_deter * scheduler.init_noise_sigma
							lowercase        =							sample.to(lowerCAmelCase__						)

							for i, t in enumerate(scheduler.timesteps						):
													lowercase        =							scheduler.scale_model_input(lowerCAmelCase__ ,				lowerCAmelCase__						)

													lowercase        =							model(lowerCAmelCase__ ,				lowerCAmelCase__						)

													lowercase        =							scheduler.step(lowerCAmelCase__ ,				lowerCAmelCase__ ,				lowerCAmelCase__						)
													lowercase        =							output.prev_sample

							lowercase        =							torch.sum(torch.abs(lowerCAmelCase__						)						)
							lowercase        =							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 20.4125						) < 1E-2
													assert abs(result_mean.item() - 0.0266						) < 1E-3
							else:
													# CUDA
													assert abs(result_sum.item() - 20.4125						) < 1E-2
													assert abs(result_mean.item() - 0.0266						) < 1E-3







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							if torch_device == "mps":
													return
							lowercase        =							self.scheduler_classes[0]
							lowercase        =							self.get_scheduler_config()
							lowercase        =							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps ,				device=lowerCAmelCase__						)

							lowercase        =							self.dummy_model()
							lowercase        =							self.dummy_sample_deter.to(lowerCAmelCase__						) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
													lowercase        =							scheduler.scale_model_input(lowerCAmelCase__ ,				lowerCAmelCase__						)

													lowercase        =							model(lowerCAmelCase__ ,				lowerCAmelCase__						)

													lowercase        =							scheduler.step(lowerCAmelCase__ ,				lowerCAmelCase__ ,				lowerCAmelCase__						)
													lowercase        =							output.prev_sample

							lowercase        =							torch.sum(torch.abs(lowerCAmelCase__						)						)
							lowercase        =							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if str(lowerCAmelCase__						).startswith("""cpu"""						):
													# The following sum varies between 148 and 156 on mps. Why?
													assert abs(result_sum.item() - 20.4125						) < 1E-2
													assert abs(result_mean.item() - 0.0266						) < 1E-3
							else:
													# CUDA
													assert abs(result_sum.item() - 20.4125						) < 1E-2
													assert abs(result_mean.item() - 0.0266						) < 1E-3







'''simple docstring'''


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_      :				int   					=    50_0000

lowercase_					,       lowercase_      :				Union[str, Any]   					=    os.path.split(__file__)
lowercase_      :				Optional[Any]   					=    os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))







@get_duration
def      SCREAMING_SNAKE_CASE				(				lowercase_    :      datasets.Dataset ,  **lowercase_    :      Dict						):
						lowercase        =							dataset.map(**lowercase_						)







@get_duration
def      SCREAMING_SNAKE_CASE				(				lowercase_    :      datasets.Dataset ,  **lowercase_    :      Optional[int]						):
						lowercase        =							dataset.filter(**lowercase_						)







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							{"""num examples""": SPEED_TEST_N_EXAMPLES}
						with tempfile.TemporaryDirectory() as tmp_dir:
												lowercase        =							datasets.Features({"""text""": datasets.Value("""string"""						), """numbers""": datasets.Value("""float32"""						)}						)
												lowercase        =							generate_example_dataset(
												    os.path.join(lowercase_ ,  """dataset.arrow"""						) ,  lowercase_ ,  num_examples=lowercase_						)

												lowercase        =							transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" ,  use_fast=lowercase_						)

												def tokenize(lowercase_    :      Dict						):
																		return tokenizer(examples["""text"""]						)

												lowercase        =							map(lowercase_						)

												lowercase        =							map(lowercase_ ,  batched=lowercase_						)

												lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												with dataset.formatted_as(type="""numpy"""						):
																		lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												with dataset.formatted_as(type="""pandas"""						):
																		lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												with dataset.formatted_as(type="""torch""" ,  columns="""numbers"""						):
																		lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												with dataset.formatted_as(type="""tensorflow""" ,  columns="""numbers"""						):
																		lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												lowercase        =							map(lowercase_ ,  function=lowercase_ ,  batched=lowercase_						)

												lowercase        =							filter(lowercase_						)

												# Activate later when tokenizer support batched inputs
												# with dataset.formatted_as(type='numpy'):
												#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

						with open(lowercase_ ,  """wb"""						) as f:
												f.write(json.dumps(lowercase_						).encode("""utf-8"""						)						)


if __name__ == "__main__":  # useful to run the profiler
						benchmark_map_filter()


