

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Union[str, Any]             =       (DPMSolverSinglestepScheduler,)
							UpperCAmelCase__			:		Any             =       (("num_inference_steps", 25),)


							def 							__lowercase							(     self			,				**_a			)       ->       List[str]:
												_a      :       List[str]   =						{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''solver_order''': 2,
												    '''prediction_type''': '''epsilon''',
												    '''thresholding''': False,
												    '''sample_max_value''': 1.0,
												    '''algorithm_type''': '''dpmsolver++''',
												    '''solver_type''': '''midpoint''',
												    '''lambda_min_clipped''': -float('''inf'''			),
												    '''variance_type''': None,
												}

												config.update(**_a			)
												return config


							def 							__lowercase							(     self			,				_a=0			,				**_a			)       ->       Any:
												_a      :       List[str]   =						dict(self.forward_default_kwargs			)
												_a      :       Optional[Any]   =						kwargs.pop('''num_inference_steps'''			,				_a			)
												_a      :       Dict   =						self.dummy_sample
												_a      :       Any   =						0.1 * sample
												_a      :       Optional[Any]   =						[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

																	_a ,   _a      :       Any   =						sample, sample
																	for t in range(_a			,				time_step + scheduler.config.solver_order + 1			):
																						_a      :       Dict   =						scheduler.step(_a			,				_a			,				_a			,				**_a			).prev_sample
																						_a      :       List[str]   =						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 							__lowercase							(     self			)       ->       Dict:
												pass


							def 							__lowercase							(     self			,				_a=0			,				**_a			)       ->       List[Any]:
												_a      :       List[str]   =						dict(self.forward_default_kwargs			)
												_a      :       Union[str, Any]   =						kwargs.pop('''num_inference_steps'''			,				_a			)
												_a      :       Union[str, Any]   =						self.dummy_sample
												_a      :       List[str]   =						0.1 * sample
												_a      :       Optional[Any]   =						[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																	_a      :       int   =						self.get_scheduler_config()
																	_a      :       Optional[int]   =						scheduler_class(**_a			)
																	scheduler.set_timesteps(_a			)

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

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_a			)
																						_a      :       str   =						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)
																						_a      :       Optional[Any]   =						dummy_past_residuals[: new_scheduler.config.solver_order]

																	_a      :       int   =						scheduler.step(_a			,				_a			,				_a			,				**_a			).prev_sample
																	_a      :       int   =						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 							__lowercase							(     self			,				_a=None			,				**_a			)       ->       Dict:
												if scheduler is None:
																	_a      :       Optional[Any]   =						self.scheduler_classes[0]
																	_a      :       List[str]   =						self.get_scheduler_config(**_a			)
																	_a      :       Union[str, Any]   =						scheduler_class(**_a			)

												_a      :       int   =						self.scheduler_classes[0]
												_a      :       List[Any]   =						self.get_scheduler_config(**_a			)
												_a      :       Union[str, Any]   =						scheduler_class(**_a			)

												_a      :       Optional[Any]   =						1_0
												_a      :       List[Any]   =						self.dummy_model()
												_a      :       Union[str, Any]   =						self.dummy_sample_deter
												scheduler.set_timesteps(_a			)

												for i, t in enumerate(scheduler.timesteps			):
																	_a      :       Any   =						model(_a			,				_a			)
																	_a      :       Dict   =						scheduler.step(_a			,				_a			,				_a			).prev_sample

												return sample


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Optional[int]   =						DPMSolverSinglestepScheduler(**self.get_scheduler_config()			)
												_a      :       Union[str, Any]   =						5_0
												_a      :       Any   =						self.dummy_model()
												_a      :       Tuple   =						self.dummy_sample_deter
												scheduler.set_timesteps(_a			)

												# make sure that the first t is uneven
												for i, t in enumerate(scheduler.timesteps[3:]			):
																	_a      :       List[Any]   =						model(_a			,				_a			)
																	_a      :       Optional[int]   =						scheduler.step(_a			,				_a			,				_a			).prev_sample

												_a      :       Union[str, Any]   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.2574			) < 1e-3


							def 							__lowercase							(     self			)       ->       int:
												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 							__lowercase							(     self			)       ->       List[Any]:
												# make sure that iterating over schedulers with same config names gives same results
												# for defaults
												_a      :       Dict   =						DPMSolverSinglestepScheduler(**self.get_scheduler_config()			)
												_a      :       Dict   =						self.full_loop(scheduler=_a			)
												_a      :       Optional[Any]   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.2791			) < 1e-3

												_a      :       int   =						DEISMultistepScheduler.from_config(scheduler.config			)
												_a      :       Union[str, Any]   =						DPMSolverMultistepScheduler.from_config(scheduler.config			)
												_a      :       List[Any]   =						UniPCMultistepScheduler.from_config(scheduler.config			)
												_a      :       Union[str, Any]   =						DPMSolverSinglestepScheduler.from_config(scheduler.config			)

												_a      :       Optional[int]   =						self.full_loop(scheduler=_a			)
												_a      :       List[str]   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.2791			) < 1e-3


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												self.check_over_configs(thresholding=_a			)
												for order in [1, 2, 3]:
																	for solver_type in ["midpoint", "heun"]:
																						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='''dpmsolver++'''			,				solver_order=_a			,				solver_type=_a			,				)


							def 							__lowercase							(     self			)       ->       Tuple:
												for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=_a			)


							def 							__lowercase							(     self			)       ->       Dict:
												for algorithm_type in ["dpmsolver", "dpmsolver++"]:
																	for solver_type in ["midpoint", "heun"]:
																						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			,				)
																																_a      :       List[Any]   =						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 							__lowercase							(     self			)       ->       List[Any]:
												self.check_over_configs(lower_order_final=_a			)
												self.check_over_configs(lower_order_final=_a			)


							def 							__lowercase							(     self			)       ->       List[Any]:
												self.check_over_configs(lambda_min_clipped=-float('''inf'''			)			)
												self.check_over_configs(lambda_min_clipped=-5.1			)


							def 							__lowercase							(     self			)       ->       int:
												self.check_over_configs(variance_type=_a			)
												self.check_over_configs(variance_type='''learned_range'''			)


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												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 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Any   =						self.full_loop()
												_a      :       Optional[Any]   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.2791			) < 1e-3


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       Optional[Any]   =						self.full_loop(use_karras_sigmas=_a			)
												_a      :       int   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.2248			) < 1e-3


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Dict   =						self.full_loop(prediction_type='''v_prediction'''			)
												_a      :       Optional[int]   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.1453			) < 1e-3


							def 							__lowercase							(     self			)       ->       str:
												_a      :       List[str]   =						self.full_loop(prediction_type='''v_prediction'''			,				use_karras_sigmas=_a			)
												_a      :       Tuple   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.0649			) < 1e-3




							def 							__lowercase							(     self			)       ->       List[str]:
												_a      :       Union[str, Any]   =						self.scheduler_classes[0]
												_a      :       Dict   =						self.get_scheduler_config(thresholding=_a			,				dynamic_thresholding_ratio=0			)
												_a      :       str   =						scheduler_class(**_a			)

												_a      :       Dict   =						1_0
												_a      :       Optional[Any]   =						self.dummy_model()
												_a      :       List[str]   =						self.dummy_sample_deter.half()
												scheduler.set_timesteps(_a			)

												for i, t in enumerate(scheduler.timesteps			):
																	_a      :       Any   =						model(_a			,				_a			)
																	_a      :       int   =						scheduler.step(_a			,				_a			,				_a			).prev_sample

												assert sample.dtype == torch.floataa











import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


SCREAMING_SNAKE_CASE__   :      List[str]				=		logging.getLogger(__name__)

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







				def __init__(    self							,       *__lowerCAmelCase							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								super().__init__(*__lowerCAmelCase							,       **__lowerCAmelCase  )
								__magic_name__		:Any   =   eval_examples
								__magic_name__		:str   =   post_process_function
								__magic_name__		:int   =   quant_trainer_args
								__magic_name__		:List[str]   =   1_2_8  # default number of calibration samples







				def       A						(    self							,       __lowerCAmelCase=None  ):
								"""simple docstring"""
								if calib_dataset is None and self.calib_dataset is None:
												raise ValueError('''Trainer: calibration requires an calib_dataset.'''  )
								__magic_name__		:Optional[Any]   =   calib_dataset if calib_dataset is not None else self.calib_dataset

								__magic_name__		:Optional[int]   =   self._remove_unused_columns(__lowerCAmelCase							,       description='''Calibration'''  )

								return DataLoader(
								    __lowerCAmelCase							,       batch_size=self.args.eval_batch_size							,       collate_fn=self.data_collator							,       drop_last=self.args.dataloader_drop_last							,       num_workers=self.args.dataloader_num_workers							,       pin_memory=self.args.dataloader_pin_memory							,       shuffle=__lowerCAmelCase							,       )







				def       A						(    self							,       __lowerCAmelCase=None  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   self.train_dataset if calib_dataset is None else calib_dataset
								__magic_name__		:Any   =   self.get_calib_dataloader(__lowerCAmelCase  )

								__magic_name__		:List[str]   =   self.model
								quant_trainer.configure_model(__lowerCAmelCase							,       self.quant_trainer_args							,       calib=__lowerCAmelCase  )
								model.eval()
								quant_trainer.enable_calibration(__lowerCAmelCase  )

								logger.info('''***** Running calibration *****'''  )
								logger.info(F'''  Num examples = {self.calib_num}'''  )
								logger.info(F'''  Batch size = {calib_dataloader.batch_size}'''  )

								for step, inputs in enumerate(__lowerCAmelCase  ):
												# Prediction step
												__magic_name__  ,					__magic_name__  ,					__magic_name__		:str   =   self.prediction_step(__lowerCAmelCase							,       __lowerCAmelCase							,       prediction_loss_only=__lowerCAmelCase  )
												if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
																break

								quant_trainer.finish_calibration(__lowerCAmelCase							,       self.quant_trainer_args  )
								__magic_name__		:Any   =   model







				def       A						(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase = "eval"  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   self.eval_dataset if eval_dataset is None else eval_dataset
								__magic_name__		:Optional[Any]   =   self.get_eval_dataloader(__lowerCAmelCase  )
								__magic_name__		:str   =   self.eval_examples if eval_examples is None else eval_examples

								# Temporarily disable metric computation, we will do it in the loop here.
								__magic_name__		:Any   =   self.compute_metrics
								__magic_name__		:List[Any]   =   None
								__magic_name__		:List[str]   =   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
								try:
												__magic_name__		:Optional[Any]   =   eval_loop(
												    __lowerCAmelCase							,       description='''Evaluation'''							,       prediction_loss_only=True if compute_metrics is None else None							,       ignore_keys=__lowerCAmelCase							,       )
								finally:
												__magic_name__		:Union[str, Any]   =   compute_metrics

								if self.post_process_function is not None and self.compute_metrics is not None:
												__magic_name__		:Union[str, Any]   =   self.post_process_function(__lowerCAmelCase							,       __lowerCAmelCase							,       output.predictions  )
												__magic_name__		:int   =   self.compute_metrics(__lowerCAmelCase  )

												# Prefix all keys with metric_key_prefix + '_'
												for key in list(metrics.keys()  ):
																if not key.startswith(F'''{metric_key_prefix}_'''  ):
																				__magic_name__		:Dict   =   metrics.pop(__lowerCAmelCase  )

												self.log(__lowerCAmelCase  )
								else:
												__magic_name__		:List[str]   =   {}

								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()  )

								__magic_name__		:Optional[Any]   =   self.callback_handler.on_evaluate(self.args							,       self.state							,       self.control							,       __lowerCAmelCase  )
								return metrics







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase=None							,       __lowerCAmelCase = "test"  ):
								"""simple docstring"""
								__magic_name__		:int   =   self.get_test_dataloader(__lowerCAmelCase  )

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

								if self.post_process_function is None or self.compute_metrics is None:
												return output

								__magic_name__		:Optional[Any]   =   self.post_process_function(__lowerCAmelCase							,       __lowerCAmelCase							,       output.predictions							,       '''predict'''  )
								__magic_name__		:Dict   =   self.compute_metrics(__lowerCAmelCase  )

								# Prefix all keys with metric_key_prefix + '_'
								for key in list(metrics.keys()  ):
												if not key.startswith(F'''{metric_key_prefix}_'''  ):
																__magic_name__		:List[str]   =   metrics.pop(__lowerCAmelCase  )

								return PredictionOutput(predictions=predictions.predictions							,       label_ids=predictions.label_ids							,       metrics=__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase="./"  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.eval_dataset
								__magic_name__		:Any   =   self.get_eval_dataloader(__lowerCAmelCase  )

								__magic_name__		:int   =   next(iter(__lowerCAmelCase  )  )

								# saving device - to make it consistent
								__magic_name__		:str   =   torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''  )

								# convert to tuple
								__magic_name__		:int   =   tuple(v.to(__lowerCAmelCase  ) for k, v in batch.items()  )

								logger.info('''Converting model to be onnx compatible'''  )
								from pytorch_quantization.nn import TensorQuantizer

								__magic_name__		:Any   =   True

								__magic_name__		:Optional[int]   =   self.model.to(__lowerCAmelCase  )

								model.eval()
								model.float()

								__magic_name__		:Any   =   model.module if hasattr(__lowerCAmelCase							,       '''module'''  ) else model
								quant_trainer.configure_model(__lowerCAmelCase							,       self.quant_trainer_args  )

								__magic_name__		:int   =   os.path.join(__lowerCAmelCase							,       '''model.onnx'''  )
								logger.info(F'''exporting model to {output_model_file}'''  )

								__magic_name__		:Dict   =   {0: '''batch_size''', 1: '''seq_len'''}

								torch.onnx.export(
								    __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       export_params=__lowerCAmelCase							,       opset_version=1_3							,       do_constant_folding=__lowerCAmelCase							,       input_names=['''input_ids''', '''attention_mask''', '''token_type_ids''']							,       output_names=['''output_start_logits''', '''output_end_logits''']							,       dynamic_axes={
								        '''input_ids''': axes,
								        '''attention_mask''': axes,
								        '''token_type_ids''': axes,
								        '''output_start_logits''': axes,
								        '''output_end_logits''': axes,
								    }							,       verbose=__lowerCAmelCase							,       )
								logger.info('''onnx export finished'''  )






