






'''simple docstring'''





import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


_UpperCamelCase		:		Tuple								=			logging.get_logger(__name__)


if is_torch_available():
						import torch
						from torch import nn
						from torch.utils.data import Dataset

						from transformers import Trainer


						class 				snake_case__     (			_lowerCAmelCase):




											def __init__(       self       :						List[Any]							,				_A       :						int = 1_01     )  ->    Optional[Any]:
														UpperCAmelCase_     :   List[str]       =   length




											def __len__(       self       :						Optional[int]     )  ->    Optional[int]:
														return self.length





											def __getitem__(       self       :						List[str]							,				_A       :						Union[str, Any]     )  ->    Dict:
														return i






						class 				snake_case__     :




											def __call__(       self       :						Tuple							,				_A       :						Dict     )  ->    Optional[int]:
														return {"input_ids": torch.tensor(_lowerCamelCase     ), "labels": torch.tensor(_lowerCamelCase     )}






						class 				snake_case__     (			nn.Module):




											def __init__(       self       :						List[str]     )  ->    str:
														super().__init__()
														# Add some (unused) params otherwise DDP will complain.
														UpperCAmelCase_     :   Dict       =   nn.Linear(1_20							,				80     )





											def 						A     (       self       :						Optional[Any]							,				_A       :						Tuple							,				_A       :						Optional[Any]=None     )  ->    Any:
														if labels is not None:
																	return torch.tensor(0.0							,				device=input_ids.device     ), input_ids
														else:
																	return input_ids
def __UpperCAmelCase (			)  -> Dict:
			UpperCAmelCase_     :   int       =   HfArgumentParser((TrainingArguments,)						)
			sys.argv += ["--output_dir", "./examples"]
			UpperCAmelCase_     :   Tuple       =   parser.parse_args_into_dataclasses()[0]

			logger.warning(
			    F"Process rank: {training_args.local_rank}, device: {training_args.device}, "
			    F"tpu_num_cores: {training_args.tpu_num_cores}"	,   )

			# Essentially, what we want to verify in the distributed case is
			# that we get all samples back, in the right order.
			# (this is crucial for prediction for instance)
			for dataset_length in [1_0_0_1, 2_5_6, 1_5]:
						UpperCAmelCase_     :   List[str]       =   DummyDataset(__UpperCamelCase						)

						def compute_metrics(A			:	str						) -> Dict:
									UpperCAmelCase_     :   Any       =   list(range(len(__UpperCamelCase						)						)						)
									UpperCAmelCase_     :   Optional[int]       =   p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
									return {"success": success}

						UpperCAmelCase_     :   Any       =   Trainer(
						    model=DummyModel()	,   args=__UpperCamelCase	,   data_collator=DummyDataCollator()	,   eval_dataset=__UpperCamelCase	,   compute_metrics=__UpperCamelCase	,   )
						UpperCAmelCase_     :   Dict       =   trainer.evaluate()
						logger.info(__UpperCamelCase						)
						if metrics["eval_success"] is not True:
									logger.error(__UpperCamelCase						)
									exit(1						)

						UpperCAmelCase_     :   Any       =   trainer.predict(__UpperCamelCase						)
						logger.info(p.metrics						)
						if p.metrics["test_success"] is not True:
									logger.error(p.metrics						)
									exit(1						)

						UpperCAmelCase_     :   Dict       =   2

						UpperCAmelCase_     :   Union[str, Any]       =   trainer.evaluate()
						logger.info(__UpperCamelCase						)
						if metrics["eval_success"] is not True:
									logger.error(__UpperCamelCase						)
									exit(1						)

						UpperCAmelCase_     :   Dict       =   trainer.predict(__UpperCamelCase						)
						logger.info(p.metrics						)
						if p.metrics["test_success"] is not True:
									logger.error(p.metrics						)
									exit(1						)

						UpperCAmelCase_     :   List[str]       =   None

			logger.info('''🔥 All distributed tests successful'''						)
def __UpperCAmelCase (			A			:	Optional[int]						)  -> Optional[Any]:
			main()


if __name__ == "__main__":
						main()







'''simple docstring'''





import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class 				snake_case__     (			unittest.TestCase):




					@property
					def 						A     (       self       :						Optional[Any]     )  ->    int:
								UpperCAmelCase_     :   Optional[int]       =   1
								UpperCAmelCase_     :   List[str]       =   3
								UpperCAmelCase_     :   Optional[int]       =   8
								UpperCAmelCase_     :   Optional[Any]       =   8

								UpperCAmelCase_     :   Any       =   torch.rand((batch_size, num_channels, height, width)     )

								return sample




					def 						A     (       self       :						Dict							,				_A       :						str     )  ->    List[Any]:
								if isinstance(image[0]							,				PIL.Image.Image     ):
											return np.stack([np.array(_A     ) for i in image]							,				axis=0     )
								elif isinstance(_A							,				torch.Tensor     ):
											return image.cpu().numpy().transpose(0							,				2							,				3							,				1     )
								return image




					def 						A     (       self       :						Tuple     )  ->    Optional[Any]:
								UpperCAmelCase_     :   int       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   List[Any]       =   self.dummy_sample
								UpperCAmelCase_     :   Optional[int]       =   self.to_np(_A     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   Any       =   image_processor.postprocess(
											    image_processor.preprocess(_A     )							,				output_type=_A							,				)
											UpperCAmelCase_     :   Union[str, Any]       =   self.to_np(_A     )
											UpperCAmelCase_     :   Union[str, Any]       =   (input_np * 2_55).round() if output_type == '''pil''' else input_np
											assert (
											    np.abs(in_np - out_np     ).max() < 1e-6
											), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Optional[Any]     )  ->    int:
								UpperCAmelCase_     :   Union[str, Any]       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )
								UpperCAmelCase_     :   List[Any]       =   self.dummy_sample.cpu().numpy().transpose(0							,				2							,				3							,				1     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   Union[str, Any]       =   image_processor.postprocess(image_processor.preprocess(_A     )							,				output_type=_A     )

											UpperCAmelCase_     :   List[Any]       =   self.to_np(_A     )
											UpperCAmelCase_     :   Any       =   (input_np * 2_55).round() if output_type == '''pil''' else input_np
											assert (
											    np.abs(in_np - out_np     ).max() < 1e-6
											), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Any     )  ->    Optional[Any]:
								UpperCAmelCase_     :   Tuple       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[Any]       =   self.dummy_sample.cpu().numpy().transpose(0							,				2							,				3							,				1     )
								UpperCAmelCase_     :   List[Any]       =   image_processor.numpy_to_pil(_A     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   str       =   image_processor.postprocess(image_processor.preprocess(_A     )							,				output_type=_A     )
											for i, o in zip(_A							,				_A     ):
														UpperCAmelCase_     :   Optional[int]       =   np.array(_A     )
														UpperCAmelCase_     :   Dict       =   self.to_np(_A     ) if output_type == '''pil''' else (self.to_np(_A     ) * 2_55).round()
														assert (
														    np.abs(in_np - out_np     ).max() < 1e-6
														), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Optional[Any]     )  ->    Optional[Any]:
								UpperCAmelCase_     :   int       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[Any]       =   self.dummy_sample
								UpperCAmelCase_     :   Optional[Any]       =   input_pt_ad.squeeze(0     )

								UpperCAmelCase_     :   Tuple       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)
								UpperCAmelCase_     :   Any       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   str       =   self.to_np(self.dummy_sample     )
								UpperCAmelCase_     :   Tuple       =   input_np_ad.squeeze(0     )

								UpperCAmelCase_     :   Optional[int]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)
								UpperCAmelCase_     :   Optional[Any]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								assert np.abs(out_pt_ad - out_pt_ad     ).max() < 1e-6
								assert np.abs(out_np_ad - out_np_ad     ).max() < 1e-6





					def 						A     (       self       :						Dict     )  ->    Dict:
								UpperCAmelCase_     :   List[Any]       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[int]       =   self.dummy_sample
								UpperCAmelCase_     :   Dict       =   list(_A     )

								UpperCAmelCase_     :   str       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Optional[Any]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Dict       =   self.to_np(self.dummy_sample     )
								list(_A     )

								UpperCAmelCase_     :   str       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Optional[int]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								assert np.abs(out_pt_ad - out_pt_list     ).max() < 1e-6
								assert np.abs(out_np_ad - out_np_list     ).max() < 1e-6


