




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       (IPNDMScheduler,)
						UpperCamelCase      =       (("""num_inference_steps""", 50),)

						def      A__    (		self :int     ,			**__snake_case :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										={"""num_train_timesteps""": 10_00}
										config.update(**__snake_case			)
										return config

						def      A__    (		self :int     ,			__snake_case :Any=0     ,			**__snake_case :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=dict(self.forward_default_kwargs			)
										__magic_name__						:						List[str]										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)
										__magic_name__						:						str										=self.dummy_sample
										__magic_name__						:						Dict										=0.1 * sample
										__magic_name__						:						Any										=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						int										=self.get_scheduler_config(**__snake_case			)
														__magic_name__						:						Optional[int]										=scheduler_class(**__snake_case			)
														scheduler.set_timesteps(__snake_case			)
														# copy over dummy past residuals
														__magic_name__						:						List[str]										=dummy_past_residuals[:]

														if time_step is None:
																		__magic_name__						:						Dict										=scheduler.timesteps[len(scheduler.timesteps			) // 2]

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

														__magic_name__						:						Optional[int]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						List[str]										=new_scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

														__magic_name__						:						Union[str, Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						List[str]										=new_scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										pass

						def      A__    (		self :Optional[int]     ,			__snake_case :Optional[Any]=0     ,			**__snake_case :Dict			):
										'''simple docstring'''

										__magic_name__						:						int										=dict(self.forward_default_kwargs			)
										__magic_name__						:						Dict										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)
										__magic_name__						:						str										=self.dummy_sample
										__magic_name__						:						Optional[Any]										=0.1 * sample
										__magic_name__						:						Union[str, Any]										=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						Dict										=self.get_scheduler_config()
														__magic_name__						:						Optional[Any]										=scheduler_class(**__snake_case			)
														scheduler.set_timesteps(__snake_case			)

														# copy over dummy past residuals (must be after setting timesteps)
														__magic_name__						:						Union[str, Any]										=dummy_past_residuals[:]

														if time_step is None:
																		__magic_name__						:						Optional[Any]										=scheduler.timesteps[len(scheduler.timesteps			) // 2]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(__snake_case			)
																		__magic_name__						:						List[Any]										=scheduler_class.from_pretrained(__snake_case			)
																		# copy over dummy past residuals
																		new_scheduler.set_timesteps(__snake_case			)

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

														__magic_name__						:						Union[str, Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						Union[str, Any]										=new_scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

														__magic_name__						:						Tuple										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						Optional[Any]										=new_scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

						def      A__    (		self :Optional[Any]     ,			**__snake_case :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.scheduler_classes[0]
										__magic_name__						:						List[str]										=self.get_scheduler_config(**__snake_case			)
										__magic_name__						:						Tuple										=scheduler_class(**__snake_case			)

										__magic_name__						:						List[str]										=10
										__magic_name__						:						Optional[Any]										=self.dummy_model()
										__magic_name__						:						Any										=self.dummy_sample_deter
										scheduler.set_timesteps(__snake_case			)

										for i, t in enumerate(scheduler.timesteps			):
														__magic_name__						:						Union[str, Any]										=model(__snake_case     ,			__snake_case			)
														__magic_name__						:						Union[str, Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case			).prev_sample

										for i, t in enumerate(scheduler.timesteps			):
														__magic_name__						:						Optional[Any]										=model(__snake_case     ,			__snake_case			)
														__magic_name__						:						Optional[Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case			).prev_sample

										return sample

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=dict(self.forward_default_kwargs			)

										__magic_name__						:						str										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						Union[str, Any]										=self.get_scheduler_config()
														__magic_name__						:						Optional[int]										=scheduler_class(**__snake_case			)

														__magic_name__						:						Tuple										=self.dummy_sample
														__magic_name__						:						Tuple										=0.1 * sample

														if num_inference_steps is not None and hasattr(__snake_case     ,			"""set_timesteps"""			):
																		scheduler.set_timesteps(__snake_case			)
														elif num_inference_steps is not None and not hasattr(__snake_case     ,			"""set_timesteps"""			):
																		__magic_name__						:						Dict										=num_inference_steps

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

														__magic_name__						:						Optional[Any]										=scheduler.timesteps[5]
														__magic_name__						:						List[str]										=scheduler.timesteps[6]

														__magic_name__						:						Union[str, Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						Tuple										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

														__magic_name__						:						Optional[Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						Union[str, Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										for timesteps in [1_00, 10_00]:
														self.check_over_configs(num_train_timesteps=__snake_case     ,			time_step=__snake_case			)

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										for t, num_inference_steps in zip([1, 5, 10]     ,			[10, 50, 1_00]			):
														self.check_over_forward(num_inference_steps=__snake_case     ,			time_step=__snake_case			)


						def      A__    (		self :Any			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self.full_loop()
										__magic_name__						:						str										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_mean.item() - 2_54_05_29			) < 10












import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm
			import torch_xla.debug.metrics as met




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						def __init__(self:					str ,					*__UpperCAmelCase:					Optional[int] ,					__UpperCAmelCase:					Optional[int]=None ,					__UpperCAmelCase:					Dict=None ,					**__UpperCAmelCase:					Tuple   )    ->     Dict:




											'''simple docstring'''



											super().__init__(*__UpperCAmelCase ,					**__UpperCAmelCase   )
											__a					:						str      =					eval_examples
											__a					:						List[Any]      =					post_process_function



						def        UpperCAmelCase__					(self:					Optional[int] ,					__UpperCAmelCase:					Optional[Dataset] = None ,					__UpperCAmelCase:					Optional[int]=None ,					__UpperCAmelCase:					Optional[List[str]] = None ,					__UpperCAmelCase:					str = "eval" ,					**__UpperCAmelCase:					Any ,					)    ->     Dict[str, float]:




											'''simple docstring'''



											__a					:						Optional[int]      =					gen_kwargs.copy()
											__a					:						Union[str, Any]      =					(
											    gen_kwargs["max_length"] if gen_kwargs.get("max_length"   ) is not None else self.args.generation_max_length
											)
											__a					:						Dict      =					(
											    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"   ) is not None else self.args.generation_num_beams
											)
											__a					:						Optional[Any]      =					gen_kwargs

											__a					:						Union[str, Any]      =					self.eval_dataset if eval_dataset is None else eval_dataset
											__a					:						Optional[int]      =					self.get_eval_dataloader(__UpperCAmelCase   )
											__a					:						Tuple      =					self.eval_examples if eval_examples is None else eval_examples

											# Temporarily disable metric computation, we will do it in the loop here.
											__a					:						int      =					self.compute_metrics
											__a					:						Dict      =					None
											__a					:						List[Any]      =					time.time()
											__a					:						str      =					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																__a					:						str      =					eval_loop(
																    __UpperCAmelCase ,					description="Evaluation" ,					prediction_loss_only=True if compute_metrics is None else None ,					ignore_keys=__UpperCAmelCase ,					metric_key_prefix=__UpperCAmelCase ,					)
											finally:
																__a					:						Union[str, Any]      =					compute_metrics
											__a					:						str      =					self.args.eval_batch_size * self.args.world_size
											if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
																start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
											output.metrics.update(
											    speed_metrics(
											        __UpperCAmelCase ,					__UpperCAmelCase ,					num_samples=output.num_samples ,					num_steps=math.ceil(output.num_samples / total_batch_size   ) ,					)   )

											if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
																# Only the main node write the results by default
																__a					:						Union[str, Any]      =					self.post_process_function(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )
																__a					:						Dict      =					self.compute_metrics(__UpperCAmelCase   )

																# Prefix all keys with metric_key_prefix + '_'
																for key in list(metrics.keys()   ):
																					if not key.startswith(f'{metric_key_prefix}_'   ):
																										__a					:						int      =					metrics.pop(__UpperCAmelCase   )

																metrics.update(output.metrics   )
											else:
																__a					:						Optional[int]      =					output.metrics

											if self.args.should_log:
																# Only the main node log the results by default
																self.log(__UpperCAmelCase   )

											if self.args.tpu_metrics_debug or self.args.debug:
																# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
																xm.master_print(met.metrics_report()   )

											__a					:						Tuple      =					self.callback_handler.on_evaluate(self.args ,					self.state ,					self.control ,					__UpperCAmelCase   )
											return metrics



						def        UpperCAmelCase__					(self:					Dict ,					__UpperCAmelCase:					List[str] ,					__UpperCAmelCase:					List[str] ,					__UpperCAmelCase:					Union[str, Any]=None ,					__UpperCAmelCase:					str = "test" ,					**__UpperCAmelCase:					List[Any]   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						Any      =					gen_kwargs.copy()

											__a					:						Tuple      =					self.get_test_dataloader(__UpperCAmelCase   )

											# Temporarily disable metric computation, we will do it in the loop here.
											__a					:						Optional[Any]      =					self.compute_metrics
											__a					:						int      =					None
											__a					:						List[Any]      =					time.time()
											__a					:						Dict      =					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																__a					:						Optional[Any]      =					eval_loop(
																    __UpperCAmelCase ,					description="Prediction" ,					prediction_loss_only=True if compute_metrics is None else None ,					ignore_keys=__UpperCAmelCase ,					metric_key_prefix=__UpperCAmelCase ,					)
											finally:
																__a					:						List[Any]      =					compute_metrics

											__a					:						Optional[Any]      =					self.args.eval_batch_size * self.args.world_size
											if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
																start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
											output.metrics.update(
											    speed_metrics(
											        __UpperCAmelCase ,					__UpperCAmelCase ,					num_samples=output.num_samples ,					num_steps=math.ceil(output.num_samples / total_batch_size   ) ,					)   )
											if self.post_process_function is None or self.compute_metrics is None:
																return output

											__a					:						Optional[int]      =					self.post_process_function(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					"predict"   )
											__a					:						Dict      =					self.compute_metrics(__UpperCAmelCase   )

											# Prefix all keys with metric_key_prefix + '_'
											for key in list(metrics.keys()   ):
																if not key.startswith(f'{metric_key_prefix}_'   ):
																					__a					:						List[str]      =					metrics.pop(__UpperCAmelCase   )
											metrics.update(output.metrics   )
											return PredictionOutput(predictions=predictions.predictions ,					label_ids=predictions.label_ids ,					metrics=__UpperCAmelCase   )





