






import math
import time

from transformers import Trainer, 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   lowerCamelCase				(_snake_case      ):



		def __init__(							self    ,		*_UpperCamelCase    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		**_UpperCamelCase       )     ->							Dict:
							super().__init__(*_UpperCamelCase    ,		**_UpperCamelCase       )
							UpperCAmelCase_   :				Optional[int]						=						eval_examples
							UpperCAmelCase_   :				Optional[int]						=						post_process_function



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase = "eval"       )     ->							List[str]:
							UpperCAmelCase_   :				Tuple						=						self.eval_dataset if eval_dataset is None else eval_dataset
							UpperCAmelCase_   :				Optional[int]						=						self.get_eval_dataloader(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						self.eval_examples if eval_examples is None else eval_examples

							# Temporarily disable metric computation, we will do it in the loop here.
							UpperCAmelCase_   :				int						=						self.compute_metrics
							UpperCAmelCase_   :				Union[str, Any]						=						None
							UpperCAmelCase_   :				Dict						=						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							UpperCAmelCase_   :				Optional[int]						=						time.time()
							try:
												UpperCAmelCase_   :				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:
												UpperCAmelCase_   :				List[str]						=						compute_metrics
							UpperCAmelCase_   :				Tuple						=						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
												UpperCAmelCase_   :				List[Any]						=						self.post_process_function(_UpperCamelCase    ,		_UpperCamelCase    ,		output.predictions       )
												UpperCAmelCase_   :				int						=						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}_"       ):
																						UpperCAmelCase_   :				List[str]						=						metrics.pop(_UpperCamelCase       )
												metrics.update(output.metrics       )
							else:
												UpperCAmelCase_   :				List[Any]						=						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()       )

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



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=None    ,		_UpperCamelCase = "test"       )     ->							List[str]:
							UpperCAmelCase_   :				Dict						=						self.get_test_dataloader(_UpperCamelCase       )

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

							UpperCAmelCase_   :				Dict						=						self.post_process_function(_UpperCamelCase    ,		_UpperCamelCase    ,		output.predictions    ,		'predict'       )
							UpperCAmelCase_   :				str						=						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}_"       ):
																	UpperCAmelCase_   :				List[str]						=						metrics.pop(_UpperCamelCase       )
							metrics.update(output.metrics       )
							return PredictionOutput(predictions=predictions.predictions    ,		label_ids=predictions.label_ids    ,		metrics=_UpperCamelCase       )














import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__UpperCAmelCase     							=						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__UpperCAmelCase     							=						'    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'



class   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				str						=						tempfile.mkdtemp()
							os.makedirs(os.path.join(self.diffusers_dir    ,		'schedulers/'       )       )
							UpperCAmelCase_   :				int						=						self.diffusers_dir
							shutil.copy(
							    os.path.join(_UpperCamelCase    ,		'src/diffusers/schedulers/scheduling_ddpm.py'       )    ,		os.path.join(self.diffusers_dir    ,		'schedulers/scheduling_ddpm.py'       )    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_   :				str						=						'src/diffusers'
							shutil.rmtree(self.diffusers_dir       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=None       )     ->							Optional[Any]:
							UpperCAmelCase_   :				Dict						=						comment + f"\nclass {class_name}(nn.Module):\n" + class_code
							if overwrite_result is not None:
												UpperCAmelCase_   :				Optional[Any]						=						comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
							UpperCAmelCase_   :				Any						=						black.Mode(target_versions={black.TargetVersion.PYaa}    ,		line_length=1_1_9       )
							UpperCAmelCase_   :				Optional[Any]						=						black.format_str(_UpperCamelCase    ,		mode=_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						os.path.join(self.diffusers_dir    ,		'new_code.py'       )
							with open(_UpperCamelCase    ,		'w'    ,		newline='\n'       ) as f:
												f.write(_UpperCamelCase       )
							if overwrite_result is None:
												self.assertTrue(len(check_copies.is_copy_consistent(_UpperCamelCase       )       ) == 0       )
							else:
												check_copies.is_copy_consistent(f.name    ,		overwrite=_UpperCamelCase       )
												with open(_UpperCamelCase    ,		'r'       ) as f:
																	self.assertTrue(f.read()    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				List[Any]						=						check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'       )
							self.assertEqual(_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							# Base copy consistency
							self.check_copy_consistency(
							    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'    ,		'DDPMSchedulerOutput'    ,		REFERENCE_CODE + '\n'    ,		)

							# With no empty line at the end
							self.check_copy_consistency(
							    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'    ,		'DDPMSchedulerOutput'    ,		_UpperCamelCase    ,		)

							# Copy consistency with rename
							self.check_copy_consistency(
							    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'    ,		'TestSchedulerOutput'    ,		re.sub('DDPM'    ,		'Test'    ,		_UpperCamelCase       )    ,		)

							# Copy consistency with a really long name
							UpperCAmelCase_   :				Optional[int]						=						'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
							self.check_copy_consistency(
							    f"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"    ,		f"{long_class_name}SchedulerOutput"    ,		re.sub('Bert'    ,		_UpperCamelCase    ,		_UpperCamelCase       )    ,		)

							# Copy consistency with overwrite
							self.check_copy_consistency(
							    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'    ,		'TestSchedulerOutput'    ,		_UpperCamelCase    ,		overwrite_result=re.sub('DDPM'    ,		'Test'    ,		_UpperCamelCase       )    ,		)






