

"""simple docstring"""




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		List[str]       		=				(PNDMScheduler,)
			lowerCamelCase__ :		Dict       		=				(('num_inference_steps', 5_0),)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]     ,					**__UpperCAmelCase					:    Tuple							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   {
						    """num_train_timesteps""": 1_0_0_0,
						    """beta_start""": 0.0_001,
						    """beta_end""": 0.02,
						    """beta_schedule""": """linear""",
						}

						config.update(**__UpperCAmelCase							)
						return config

			def        SCREAMING_SNAKE_CASE					(       self					:    str     ,					__UpperCAmelCase					:    str=0     ,					**__UpperCAmelCase					:    Optional[int]							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   dict(self.forward_default_kwargs							)
						SCREAMING_SNAKE_CASE__										=   kwargs.pop("""num_inference_steps"""     ,					__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   self.dummy_sample
						SCREAMING_SNAKE_CASE__										=   0.1 * sample
						SCREAMING_SNAKE_CASE__										=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

						for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config(**__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   scheduler_class(**__UpperCAmelCase							)
									scheduler.set_timesteps(__UpperCAmelCase							)
									# copy over dummy past residuals
									SCREAMING_SNAKE_CASE__										=   dummy_past_residuals[:]

									with tempfile.TemporaryDirectory() as tmpdirname:
												scheduler.save_config(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   scheduler_class.from_pretrained(__UpperCAmelCase							)
												new_scheduler.set_timesteps(__UpperCAmelCase							)
												# copy over dummy past residuals
												SCREAMING_SNAKE_CASE__										=   dummy_past_residuals[:]

									SCREAMING_SNAKE_CASE__										=   scheduler.step_prk(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					**__UpperCAmelCase							).prev_sample
									SCREAMING_SNAKE_CASE__										=   new_scheduler.step_prk(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					**__UpperCAmelCase							).prev_sample

									assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"

									SCREAMING_SNAKE_CASE__										=   scheduler.step_plms(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					**__UpperCAmelCase							).prev_sample
									SCREAMING_SNAKE_CASE__										=   new_scheduler.step_plms(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					**__UpperCAmelCase							).prev_sample

									assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     int:
						pass

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]     ,					__UpperCAmelCase					:    Optional[int]=0     ,					**__UpperCAmelCase					:    Any							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   dict(self.forward_default_kwargs							)
						SCREAMING_SNAKE_CASE__										=   kwargs.pop("""num_inference_steps"""     ,					__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   self.dummy_sample
						SCREAMING_SNAKE_CASE__										=   0.1 * sample
						SCREAMING_SNAKE_CASE__										=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

						for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__										=   scheduler_class(**__UpperCAmelCase							)
									scheduler.set_timesteps(__UpperCAmelCase							)

									# copy over dummy past residuals (must be after setting timesteps)
									SCREAMING_SNAKE_CASE__										=   dummy_past_residuals[:]

									with tempfile.TemporaryDirectory() as tmpdirname:
												scheduler.save_config(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   scheduler_class.from_pretrained(__UpperCAmelCase							)
												# copy over dummy past residuals
												new_scheduler.set_timesteps(__UpperCAmelCase							)

												# copy over dummy past residual (must be after setting timesteps)
												SCREAMING_SNAKE_CASE__										=   dummy_past_residuals[:]

									SCREAMING_SNAKE_CASE__										=   scheduler.step_prk(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					**__UpperCAmelCase							).prev_sample
									SCREAMING_SNAKE_CASE__										=   new_scheduler.step_prk(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					**__UpperCAmelCase							).prev_sample

									assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"

									SCREAMING_SNAKE_CASE__										=   scheduler.step_plms(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					**__UpperCAmelCase							).prev_sample
									SCREAMING_SNAKE_CASE__										=   new_scheduler.step_plms(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					**__UpperCAmelCase							).prev_sample

									assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"

			def        SCREAMING_SNAKE_CASE					(       self					:    str     ,					**__UpperCAmelCase					:    Any							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config(**__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   scheduler_class(**__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   1_0
						SCREAMING_SNAKE_CASE__										=   self.dummy_model()
						SCREAMING_SNAKE_CASE__										=   self.dummy_sample_deter
						scheduler.set_timesteps(__UpperCAmelCase							)

						for i, t in enumerate(scheduler.prk_timesteps							):
									SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase     ,					__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   scheduler.step_prk(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase							).prev_sample

						for i, t in enumerate(scheduler.plms_timesteps							):
									SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase     ,					__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   scheduler.step_plms(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase							).prev_sample

						return sample

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   dict(self.forward_default_kwargs							)

						SCREAMING_SNAKE_CASE__										=   kwargs.pop("""num_inference_steps"""     ,					__UpperCAmelCase							)

						for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__										=   scheduler_class(**__UpperCAmelCase							)

									SCREAMING_SNAKE_CASE__										=   self.dummy_sample
									SCREAMING_SNAKE_CASE__										=   0.1 * sample

									if num_inference_steps is not None and hasattr(__UpperCAmelCase     ,					"""set_timesteps"""							):
												scheduler.set_timesteps(__UpperCAmelCase							)
									elif num_inference_steps is not None and not hasattr(__UpperCAmelCase     ,					"""set_timesteps"""							):
												SCREAMING_SNAKE_CASE__										=   num_inference_steps

									# copy over dummy past residuals (must be done after set_timesteps)
									SCREAMING_SNAKE_CASE__										=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
									SCREAMING_SNAKE_CASE__										=   dummy_past_residuals[:]

									SCREAMING_SNAKE_CASE__										=   scheduler.step_prk(__UpperCAmelCase     ,					0     ,					__UpperCAmelCase     ,					**__UpperCAmelCase							).prev_sample
									SCREAMING_SNAKE_CASE__										=   scheduler.step_prk(__UpperCAmelCase     ,					1     ,					__UpperCAmelCase     ,					**__UpperCAmelCase							).prev_sample

									self.assertEqual(output_a.shape     ,					sample.shape							)
									self.assertEqual(output_a.shape     ,					output_a.shape							)

									SCREAMING_SNAKE_CASE__										=   scheduler.step_plms(__UpperCAmelCase     ,					0     ,					__UpperCAmelCase     ,					**__UpperCAmelCase							).prev_sample
									SCREAMING_SNAKE_CASE__										=   scheduler.step_plms(__UpperCAmelCase     ,					1     ,					__UpperCAmelCase     ,					**__UpperCAmelCase							).prev_sample

									self.assertEqual(output_a.shape     ,					sample.shape							)
									self.assertEqual(output_a.shape     ,					output_a.shape							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Tuple:
						for timesteps in [1_0_0, 1_0_0_0]:
									self.check_over_configs(num_train_timesteps=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     List[str]:
						for steps_offset in [0, 1]:
									self.check_over_configs(steps_offset=__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config(steps_offset=1							)
						SCREAMING_SNAKE_CASE__										=   scheduler_class(**__UpperCAmelCase							)
						scheduler.set_timesteps(1_0							)
						assert torch.equal(
						    scheduler.timesteps     ,					torch.LongTensor(
						        [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]							)     ,					)

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     int:
						for beta_start, beta_end in zip([0.0_001, 0.001]     ,					[0.002, 0.02]							):
									self.check_over_configs(beta_start=__UpperCAmelCase     ,					beta_end=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Optional[Any]:
						for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Union[str, Any]:
						for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     List[str]:
						for t in [1, 5, 1_0]:
									self.check_over_forward(time_step=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     List[Any]:
						for t, num_inference_steps in zip([1, 5, 1_0]     ,					[1_0, 5_0, 1_0_0]							):
									self.check_over_forward(num_inference_steps=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     List[Any]:
						# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
						SCREAMING_SNAKE_CASE__										=   2_7

						for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE__										=   self.dummy_sample
									SCREAMING_SNAKE_CASE__										=   0.1 * sample

									SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__										=   scheduler_class(**__UpperCAmelCase							)

									scheduler.set_timesteps(__UpperCAmelCase							)

									# before power of 3 fix, would error on first step, so we only need to do two
									for i, t in enumerate(scheduler.prk_timesteps[:2]							):
												SCREAMING_SNAKE_CASE__										=   scheduler.step_prk(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase							).prev_sample

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Any:
						with self.assertRaises(__UpperCAmelCase							):
									SCREAMING_SNAKE_CASE__										=   self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__										=   scheduler_class(**__UpperCAmelCase							)

									scheduler.step_plms(self.dummy_sample     ,					1     ,					self.dummy_sample							).prev_sample

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   self.full_loop()
						SCREAMING_SNAKE_CASE__										=   torch.sum(torch.abs(__UpperCAmelCase							)							)
						SCREAMING_SNAKE_CASE__										=   torch.mean(torch.abs(__UpperCAmelCase							)							)

						assert abs(result_sum.item() - 198.1_318							) < 1e-2
						assert abs(result_mean.item() - 0.2_580							) < 1e-3

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   self.full_loop(prediction_type="""v_prediction"""							)
						SCREAMING_SNAKE_CASE__										=   torch.sum(torch.abs(__UpperCAmelCase							)							)
						SCREAMING_SNAKE_CASE__										=   torch.mean(torch.abs(__UpperCAmelCase							)							)

						assert abs(result_sum.item() - 67.3_986							) < 1e-2
						assert abs(result_mean.item() - 0.0_878							) < 1e-3

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Union[str, Any]:
						# We specify different beta, so that the first alpha is 0.99
						SCREAMING_SNAKE_CASE__										=   self.full_loop(set_alpha_to_one=__UpperCAmelCase     ,					beta_start=0.01							)
						SCREAMING_SNAKE_CASE__										=   torch.sum(torch.abs(__UpperCAmelCase							)							)
						SCREAMING_SNAKE_CASE__										=   torch.mean(torch.abs(__UpperCAmelCase							)							)

						assert abs(result_sum.item() - 230.0_399							) < 1e-2
						assert abs(result_mean.item() - 0.2_995							) < 1e-3





			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Union[str, Any]:
						# We specify different beta, so that the first alpha is 0.99
						SCREAMING_SNAKE_CASE__										=   self.full_loop(set_alpha_to_one=__UpperCAmelCase     ,					beta_start=0.01							)
						SCREAMING_SNAKE_CASE__										=   torch.sum(torch.abs(__UpperCAmelCase							)							)
						SCREAMING_SNAKE_CASE__										=   torch.mean(torch.abs(__UpperCAmelCase							)							)

						assert abs(result_sum.item() - 186.9_482							) < 1e-2
						assert abs(result_mean.item() - 0.2_434							) < 1e-3





"""simple docstring"""




import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A_				:      Optional[Any]           =			logging.getLogger(__name__)





@dataclass
class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=0.0      ,metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}      )
			lowerCamelCase__ :		bool       		=				field(default=A__      ,metadata={'help': 'Whether to SortishSamler or not.'}      )
			lowerCamelCase__ :		bool       		=				field(
			    default=A__      ,metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}      )
			lowerCamelCase__ :		bool       		=				field(default=A__      ,metadata={'help': 'whether to use adafactor'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(default=A__      ,metadata={'help': 'Dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Attention dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[str]       		=				field(
			    default='linear'      ,metadata={'help': f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()      )}"}      ,)


