
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
       lowercase_ : Dict       =(DEISMultistepScheduler,)
       lowercase_ : Dict       =(('''num_inference_steps''', 25),)




       def 				A__   (       self     ,**A__):
            lowercase					=		{
                '''num_train_timesteps''': 1_0_0_0,
                '''beta_start''': 0.0001,
                '''beta_end''': 0.02,
                '''beta_schedule''': '''linear''',
                '''solver_order''': 2,
            }

            config.update(**A__)
            return config




       def 				A__   (       self     ,A__=0     ,**A__):
            lowercase					=		dict(self.forward_default_kwargs)
            lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
            lowercase					=		self.dummy_sample
            lowercase					=		0.1 * sample
            lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                 lowercase					=		self.get_scheduler_config(**A__)
                 lowercase					=		scheduler_class(**A__)
                 scheduler.set_timesteps(A__)
                 # copy over dummy past residuals
                 lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

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

                 lowercase			,		lowercase					=		sample, sample
                 for t in range(A__     ,time_step + scheduler.config.solver_order + 1):
                      lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
                      lowercase					=		new_scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample

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




       def 				A__   (       self):
            pass




       def 				A__   (       self     ,A__=0     ,**A__):
            lowercase					=		dict(self.forward_default_kwargs)
            lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
            lowercase					=		self.dummy_sample
            lowercase					=		0.1 * sample
            lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                 lowercase					=		self.get_scheduler_config()
                 lowercase					=		scheduler_class(**A__)
                 scheduler.set_timesteps(A__)

                 # copy over dummy past residuals (must be after setting timesteps)
                 lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

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

                      # copy over dummy past residual (must be after setting timesteps)
                      lowercase					=		dummy_past_residuals[: new_scheduler.config.solver_order]

                 lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
                 lowercase					=		new_scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample

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




       def 				A__   (       self     ,A__=None     ,**A__):
            if scheduler is None:
                 lowercase					=		self.scheduler_classes[0]
                 lowercase					=		self.get_scheduler_config(**A__)
                 lowercase					=		scheduler_class(**A__)

            lowercase					=		self.scheduler_classes[0]
            lowercase					=		self.get_scheduler_config(**A__)
            lowercase					=		scheduler_class(**A__)

            lowercase					=		1_0
            lowercase					=		self.dummy_model()
            lowercase					=		self.dummy_sample_deter
            scheduler.set_timesteps(A__)

            for i, t in enumerate(scheduler.timesteps):
                 lowercase					=		model(A__     ,A__)
                 lowercase					=		scheduler.step(A__     ,A__     ,A__).prev_sample

            return sample




       def 				A__   (       self):
            lowercase					=		dict(self.forward_default_kwargs)

            lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)

            for scheduler_class in self.scheduler_classes:
                 lowercase					=		self.get_scheduler_config()
                 lowercase					=		scheduler_class(**A__)

                 lowercase					=		self.dummy_sample
                 lowercase					=		0.1 * sample

                 if num_inference_steps is not None and hasattr(A__     ,'''set_timesteps'''):
                      scheduler.set_timesteps(A__)
                 elif num_inference_steps is not None and not hasattr(A__     ,'''set_timesteps'''):
                      lowercase					=		num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]
                 lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

                 lowercase					=		scheduler.timesteps[5]
                 lowercase					=		scheduler.timesteps[6]

                 lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
                 lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample

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




       def 				A__   (       self):
            # make sure that iterating over schedulers with same config names gives same results
            # for defaults
            lowercase					=		DEISMultistepScheduler(**self.get_scheduler_config())
            lowercase					=		self.full_loop(scheduler=A__)
            lowercase					=		torch.mean(torch.abs(A__))

            assert abs(result_mean.item() - 0.23916) < 1E-3

            lowercase					=		DPMSolverSinglestepScheduler.from_config(scheduler.config)
            lowercase					=		DPMSolverMultistepScheduler.from_config(scheduler.config)
            lowercase					=		UniPCMultistepScheduler.from_config(scheduler.config)
            lowercase					=		DEISMultistepScheduler.from_config(scheduler.config)

            lowercase					=		self.full_loop(scheduler=A__)
            lowercase					=		torch.mean(torch.abs(A__))

            assert abs(result_mean.item() - 0.23916) < 1E-3




       def 				A__   (       self):
            for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=A__)




       def 				A__   (       self):
            self.check_over_configs(thresholding=A__)
            for order in [1, 2, 3]:
                 for solver_type in ["logrho"]:
                      for threshold in [0.5, 1.0, 2.0]:
                           for prediction_type in ["epsilon", "sample"]:
                                self.check_over_configs(
                                    thresholding=A__     ,prediction_type=A__     ,sample_max_value=A__     ,algorithm_type='''deis'''     ,solver_order=A__     ,solver_type=A__     ,)




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




       def 				A__   (       self):
            for algorithm_type in ["deis"]:
                 for solver_type in ["logrho"]:
                      for order in [1, 2, 3]:
                           for prediction_type in ["epsilon", "sample"]:
                                self.check_over_configs(
                                    solver_order=A__     ,solver_type=A__     ,prediction_type=A__     ,algorithm_type=A__     ,)
                                lowercase					=		self.full_loop(
                                    solver_order=A__     ,solver_type=A__     ,prediction_type=A__     ,algorithm_type=A__     ,)
                                assert not torch.isnan(A__).any(), "Samples have nan numbers"




       def 				A__   (       self):
            self.check_over_configs(lower_order_final=A__)
            self.check_over_configs(lower_order_final=A__)




       def 				A__   (       self):
            for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_forward(num_inference_steps=A__     ,time_step=0)




       def 				A__   (       self):
            lowercase					=		self.full_loop()
            lowercase					=		torch.mean(torch.abs(A__))

            assert abs(result_mean.item() - 0.23916) < 1E-3




       def 				A__   (       self):
            lowercase					=		self.full_loop(prediction_type='''v_prediction''')
            lowercase					=		torch.mean(torch.abs(A__))

            assert abs(result_mean.item() - 0.091) < 1E-3





       def 				A__   (       self):
            lowercase					=		self.scheduler_classes[0]
            lowercase					=		self.get_scheduler_config(thresholding=A__     ,dynamic_thresholding_ratio=0)
            lowercase					=		scheduler_class(**A__)

            lowercase					=		1_0
            lowercase					=		self.dummy_model()
            lowercase					=		self.dummy_sample_deter.half()
            scheduler.set_timesteps(A__)

            for i, t in enumerate(scheduler.timesteps):
                 lowercase					=		model(A__     ,A__)
                 lowercase					=		scheduler.step(A__     ,A__     ,A__).prev_sample

            assert sample.dtype == torch.floataa






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        lowercase	(      SCREAMING_SNAKE_CASE__	):




							def __init__(       self     ,*A__     ,A__=None     ,A__=None     ,**A__):
												super().__init__(*A__     ,**A__)
												lowercase					=		eval_examples
												lowercase					=		post_process_function




							def 				A__   (       self     ,A__ = None     ,A__=None     ,A__ = None     ,A__ = "eval"     ,**A__     ,):
												lowercase					=		gen_kwargs.copy()
												lowercase					=		(
												    gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length''') is not None else self.args.generation_max_length
												)
												lowercase					=		(
												    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams''') is not None else self.args.generation_num_beams
												)
												lowercase					=		gen_kwargs

												lowercase					=		self.eval_dataset if eval_dataset is None else eval_dataset
												lowercase					=		self.get_eval_dataloader(A__)
												lowercase					=		self.eval_examples if eval_examples is None else eval_examples

												# Temporarily disable metric computation, we will do it in the loop here.
												lowercase					=		self.compute_metrics
												lowercase					=		None
												lowercase					=		time.time()
												lowercase					=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
												try:
																	lowercase					=		eval_loop(
																	    A__     ,description='''Evaluation'''     ,prediction_loss_only=True if compute_metrics is None else None     ,ignore_keys=A__     ,metric_key_prefix=A__     ,)
												finally:
																	lowercase					=		compute_metrics
												lowercase					=		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(
												        A__     ,A__     ,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
																	lowercase					=		self.post_process_function(A__     ,A__     ,A__)
																	lowercase					=		self.compute_metrics(A__)

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

																	metrics.update(output.metrics)
												else:
																	lowercase					=		output.metrics

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

												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())

												lowercase					=		self.callback_handler.on_evaluate(self.args     ,self.state     ,self.control     ,A__)
												return metrics





							def 				A__   (       self     ,A__     ,A__     ,A__=None     ,A__ = "test"     ,**A__):
												lowercase					=		gen_kwargs.copy()

												lowercase					=		self.get_test_dataloader(A__)

												# Temporarily disable metric computation, we will do it in the loop here.
												lowercase					=		self.compute_metrics
												lowercase					=		None
												lowercase					=		time.time()
												lowercase					=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
												try:
																	lowercase					=		eval_loop(
																	    A__     ,description='''Prediction'''     ,prediction_loss_only=True if compute_metrics is None else None     ,ignore_keys=A__     ,metric_key_prefix=A__     ,)
												finally:
																	lowercase					=		compute_metrics

												lowercase					=		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(
												        A__     ,A__     ,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

												lowercase					=		self.post_process_function(A__     ,A__     ,A__     ,'''predict''')
												lowercase					=		self.compute_metrics(A__)

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




