




"""simple docstring"""






import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import BeitImageProcessor






class lowerCAmelCase__			(  unittest.TestCase		):






			'''simple docstring'''



			def __init__(						self						,   lowercase						,   lowercase=7						,   lowercase=3						,   lowercase=18						,   lowercase=30						,   lowercase=400						,   lowercase=True						,   lowercase=None						,   lowercase=True						,   lowercase=None						,   lowercase=True						,   lowercase=[0.5, 0.5, 0.5]						,   lowercase=[0.5, 0.5, 0.5]						,   lowercase=False						,   ):
							_lowerCamelCase  :    Optional[Any]					=					size if size is not None else {'height': 20, 'width': 20}
							_lowerCamelCase  :    Any					=					crop_size if crop_size is not None else {'height': 18, 'width': 18}
							_lowerCamelCase  :    List[str]					=					parent
							_lowerCamelCase  :    Tuple					=					batch_size
							_lowerCamelCase  :    Any					=					num_channels
							_lowerCamelCase  :    Union[str, Any]					=					image_size
							_lowerCamelCase  :    int					=					min_resolution
							_lowerCamelCase  :    Any					=					max_resolution
							_lowerCamelCase  :    str					=					do_resize
							_lowerCamelCase  :    List[Any]					=					size
							_lowerCamelCase  :    List[str]					=					do_center_crop
							_lowerCamelCase  :    Dict					=					crop_size
							_lowerCamelCase  :    Any					=					do_normalize
							_lowerCamelCase  :    Union[str, Any]					=					image_mean
							_lowerCamelCase  :    Dict					=					image_std
							_lowerCamelCase  :    Any					=					do_reduce_labels






			def       A_					(						self						):
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_reduce_labels": self.do_reduce_labels,
							}







def   _snake_case    (       ):
				_lowerCamelCase  :    Union[str, Any]					=					load_dataset('hf-internal-testing/fixtures_ade20k'							,					split='test'	)

				_lowerCamelCase  :    str					=					Image.open(dataset[0]['file']	)
				_lowerCamelCase  :    Union[str, Any]					=					Image.open(dataset[1]['file']	)

				return image, map







def   _snake_case    (       ):
				_lowerCamelCase  :    Dict					=					load_dataset('hf-internal-testing/fixtures_ade20k'							,					split='test'	)

				_lowerCamelCase  :    Union[str, Any]					=					Image.open(ds[0]['file']	)
				_lowerCamelCase  :    Tuple					=					Image.open(ds[1]['file']	)
				_lowerCamelCase  :    Tuple					=					Image.open(ds[2]['file']	)
				_lowerCamelCase  :    Dict					=					Image.open(ds[3]['file']	)

				return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class lowerCAmelCase__			(  lowercase,			unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__   =    BeitImageProcessor if is_vision_available() else None
			def       A_					(						self						):
							_lowerCamelCase  :    List[Any]					=					BeitImageProcessingTester(self						)
			@property
			def       A_					(						self						):
							return self.image_processor_tester.prepare_image_processor_dict()
			def       A_					(						self						):
							_lowerCamelCase  :    Any					=					self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(lowercase						,   'do_resize'						)						)
							self.assertTrue(hasattr(lowercase						,   'size'						)						)
							self.assertTrue(hasattr(lowercase						,   'do_center_crop'						)						)
							self.assertTrue(hasattr(lowercase						,   'center_crop'						)						)
							self.assertTrue(hasattr(lowercase						,   'do_normalize'						)						)
							self.assertTrue(hasattr(lowercase						,   'image_mean'						)						)
							self.assertTrue(hasattr(lowercase						,   'image_std'						)						)
			def       A_					(						self						):
							_lowerCamelCase  :    Any					=					self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size						,   {'height': 20, 'width': 20}						)
							self.assertEqual(image_processor.crop_size						,   {'height': 18, 'width': 18}						)
							self.assertEqual(image_processor.do_reduce_labels						,   lowercase						)

							_lowerCamelCase  :    str					=					self.image_processing_class.from_dict(
							    self.image_processor_dict						,   size=42						,   crop_size=84						,   reduce_labels=lowercase						)
							self.assertEqual(image_processor.size						,   {'height': 42, 'width': 42}						)
							self.assertEqual(image_processor.crop_size						,   {'height': 84, 'width': 84}						)
							self.assertEqual(image_processor.do_reduce_labels						,   lowercase						)
			def       A_					(						self						):
							pass
			def       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    Any					=					self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							_lowerCamelCase  :    Union[str, Any]					=					prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowercase						)
							for image in image_inputs:
											self.assertIsInstance(lowercase						,   Image.Image						)

							# Test not batched input
							_lowerCamelCase  :    int					=					image_processing(image_inputs[0]						,   return_tensors='pt'						).pixel_values
							self.assertEqual(
							    encoded_images.shape						,   (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )						,   )

							# Test batched
							_lowerCamelCase  :    List[Any]					=					image_processing(lowercase						,   return_tensors='pt'						).pixel_values
							self.assertEqual(
							    encoded_images.shape						,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )						,   )
			def       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    Any					=					self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							_lowerCamelCase  :    int					=					prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowercase						,   numpify=lowercase						)
							for image in image_inputs:
											self.assertIsInstance(lowercase						,   np.ndarray						)

							# Test not batched input
							_lowerCamelCase  :    int					=					image_processing(image_inputs[0]						,   return_tensors='pt'						).pixel_values
							self.assertEqual(
							    encoded_images.shape						,   (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )						,   )

							# Test batched
							_lowerCamelCase  :    List[str]					=					image_processing(lowercase						,   return_tensors='pt'						).pixel_values
							self.assertEqual(
							    encoded_images.shape						,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )						,   )
			def       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    Optional[Any]					=					self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							_lowerCamelCase  :    List[Any]					=					prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowercase						,   torchify=lowercase						)
							for image in image_inputs:
											self.assertIsInstance(lowercase						,   torch.Tensor						)

							# Test not batched input
							_lowerCamelCase  :    int					=					image_processing(image_inputs[0]						,   return_tensors='pt'						).pixel_values
							self.assertEqual(
							    encoded_images.shape						,   (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )						,   )

							# Test batched
							_lowerCamelCase  :    Optional[int]					=					image_processing(lowercase						,   return_tensors='pt'						).pixel_values
							self.assertEqual(
							    encoded_images.shape						,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )						,   )
			def       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    str					=					self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							_lowerCamelCase  :    List[str]					=					prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowercase						,   torchify=lowercase						)
							_lowerCamelCase  :    List[str]					=					[]
							for image in image_inputs:
											self.assertIsInstance(lowercase						,   torch.Tensor						)
											maps.append(torch.zeros(image.shape[-2:]						).long()						)

							# Test not batched input
							_lowerCamelCase  :    str					=					image_processing(image_inputs[0]						,   maps[0]						,   return_tensors='pt'						)
							self.assertEqual(
							    encoding['pixel_values'].shape						,   (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )						,   )
							self.assertEqual(
							    encoding['labels'].shape						,   (
							        1,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )						,   )
							self.assertEqual(encoding['labels'].dtype						,   torch.long						)
							self.assertTrue(encoding['labels'].min().item() >= 0						)
							self.assertTrue(encoding['labels'].max().item() <= 255						)

							# Test batched
							_lowerCamelCase  :    Tuple					=					image_processing(lowercase						,   lowercase						,   return_tensors='pt'						)
							self.assertEqual(
							    encoding['pixel_values'].shape						,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )						,   )
							self.assertEqual(
							    encoding['labels'].shape						,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )						,   )
							self.assertEqual(encoding['labels'].dtype						,   torch.long						)
							self.assertTrue(encoding['labels'].min().item() >= 0						)
							self.assertTrue(encoding['labels'].max().item() <= 255						)

							# Test not batched input (PIL images)
							_lowerCamelCase,				_lowerCamelCase  :    Any					=					prepare_semantic_single_inputs()

							_lowerCamelCase  :    Tuple					=					image_processing(lowercase						,   lowercase						,   return_tensors='pt'						)
							self.assertEqual(
							    encoding['pixel_values'].shape						,   (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )						,   )
							self.assertEqual(
							    encoding['labels'].shape						,   (
							        1,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )						,   )
							self.assertEqual(encoding['labels'].dtype						,   torch.long						)
							self.assertTrue(encoding['labels'].min().item() >= 0						)
							self.assertTrue(encoding['labels'].max().item() <= 255						)

							# Test batched input (PIL images)
							_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					prepare_semantic_batch_inputs()

							_lowerCamelCase  :    Tuple					=					image_processing(lowercase						,   lowercase						,   return_tensors='pt'						)
							self.assertEqual(
							    encoding['pixel_values'].shape						,   (
							        2,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )						,   )
							self.assertEqual(
							    encoding['labels'].shape						,   (
							        2,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )						,   )
							self.assertEqual(encoding['labels'].dtype						,   torch.long						)
							self.assertTrue(encoding['labels'].min().item() >= 0						)
							self.assertTrue(encoding['labels'].max().item() <= 255						)






			def       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    str					=					self.image_processing_class(**self.image_processor_dict						)

							# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
							_lowerCamelCase,				_lowerCamelCase  :    Union[str, Any]					=					prepare_semantic_single_inputs()
							_lowerCamelCase  :    List[Any]					=					image_processing(lowercase						,   lowercase						,   return_tensors='pt'						)
							self.assertTrue(encoding['labels'].min().item() >= 0						)
							self.assertTrue(encoding['labels'].max().item() <= 150						)

							_lowerCamelCase  :    List[Any]					=					True
							_lowerCamelCase  :    List[Any]					=					image_processing(lowercase						,   lowercase						,   return_tensors='pt'						)
							self.assertTrue(encoding['labels'].min().item() >= 0						)
							self.assertTrue(encoding['labels'].max().item() <= 255						)





"""simple docstring"""






from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


lowercase__  		=		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 lowerCAmelCase__			(  lowercase		):






						'''simple docstring'''



						def __init__(						self						,   lowercase = 101						):
										_lowerCamelCase  :    Optional[int]					=					length
						def __len__(						self						):
										return self.length






						def __getitem__(						self						,   lowercase						):
										return i






			class lowerCAmelCase__			:






						'''simple docstring'''



						def __call__(						self						,   lowercase						):
										return {"input_ids": torch.tensor(lowercase						), "labels": torch.tensor(lowercase						)}






			class lowerCAmelCase__			(  nn.Module		):






						'''simple docstring'''



						def __init__(						self						):
										super().__init__()
										# Add some (unused) params otherwise DDP will complain.
										_lowerCamelCase  :    int					=					nn.Linear(120						,   80						)






						def       A_					(						self						,   lowercase						,   lowercase=None						):
										if labels is not None:
														return torch.tensor(0.0						,   device=input_ids.device						), input_ids
										else:
														return input_ids






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			@require_torch_neuroncore
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					F'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
							_lowerCamelCase  :    int					=					self.get_auto_remove_tmp_dir()
							_lowerCamelCase  :    str					=					F'''--output_dir {output_dir}'''.split()
							_lowerCamelCase  :    Tuple					=					['torchrun'] + distributed_args + args
							execute_subprocess_async(lowercase						,   env=self.get_env()						)
							# successful return here == success - any errors would have caused an error in the sub-call






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			@require_torch_multi_gpu
			def       A_					(						self						):
							_lowerCamelCase  :    Union[str, Any]					=					F'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
							_lowerCamelCase  :    List[str]					=					self.get_auto_remove_tmp_dir()
							_lowerCamelCase  :    Any					=					F'''--output_dir {output_dir}'''.split()
							_lowerCamelCase  :    Any					=					['torchrun'] + distributed_args + args
							execute_subprocess_async(lowercase						,   env=self.get_env()						)
							# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
			# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
			#
			# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

			lowercase__  		=		HfArgumentParser((TrainingArguments,))
			lowercase__  		=		parser.parse_args_into_dataclasses()[0]

			logger.warning(
			    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
			    F"distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"
			)

			# 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 [101, 40, 7]:
						lowercase__  		=		DummyDataset(dataset_length)







						def   _snake_case    (       lowercase__	):
										_lowerCamelCase  :    int					=					list(range(len(lowercase__	)	)	)
										_lowerCamelCase  :    Optional[int]					=					p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
										if not success and training_args.local_rank == 0:
														logger.warning(
														    'Predictions and/or labels do not match expected results:\n  - predictions: '
														    f'''{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'''	)
										return {"success": success}

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

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

						lowercase__  		=		2

						lowercase__  		=		trainer.evaluate()
						logger.info(metrics)
						if metrics["eval_success"] is not True:
									logger.error(metrics)
									exit(1)

						lowercase__  		=		trainer.predict(dataset)
						logger.info(p.metrics)
						if p.metrics["test_success"] is not True:
									logger.error(p.metrics)
									exit(1)
						lowercase__  		=		None