



import unittest

import numpy as np

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 ViltImageProcessor


class 	lowerCamelCase__ (		unittest.TestCase   ):


						def __init__( self						:  Union[str, Any]      ,       _a						:  str      ,       _a						:  Optional[Any]=7      ,       _a						:  Any=3      ,       _a						:  int=1_8      ,       _a						:  Union[str, Any]=3_0      ,       _a						:  Union[str, Any]=4_0_0      ,       _a						:  Optional[int]=True      ,       _a						:  Union[str, Any]=None      ,       _a						:  Union[str, Any]=2      ,       _a						:  Optional[int]=True      ,       _a						:  Tuple=[0.5, 0.5, 0.5]      ,       _a						:  Any=[0.5, 0.5, 0.5]      ,       ):
													a__:       Optional[int]											=size if size is not None else {'shortest_edge': 3_0}
													a__:       Tuple											=parent
													a__:       List[str]											=batch_size
													a__:       int											=num_channels
													a__:       Dict											=image_size
													a__:       Dict											=min_resolution
													a__:       Optional[int]											=max_resolution
													a__:       Tuple											=do_resize
													a__:       List[Any]											=size
													a__:       Any											=size_divisor
													a__:       Optional[Any]											=do_normalize
													a__:       Optional[Any]											=image_mean
													a__:       str											=image_std


						def   _lowerCamelCase			( self						:  Optional[int]			):
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "size_divisor": self.size_divisor,
													}







						def   _lowerCamelCase			( self						:  Dict      ,       _a						:  Tuple      ,       _a						:  List[str]=False			):
													if not batched:
																				a__:       List[str]											=self.size['shortest_edge']
																				a__:       str											=image_inputs[0]
																				if isinstance(UpperCAmelCase_      ,       Image.Image			):
																											a__:       Any											=image.size
																				else:
																											a__:       Optional[int]											=image.shape[1], image.shape[2]
																				a__:       Union[str, Any]											=size / min(UpperCAmelCase_      ,       UpperCAmelCase_			)
																				if h < w:
																											a__:       Any											=size, scale * w
																				else:
																											a__:       int											=scale * h, size

																				a__:       Optional[int]											=int((1_3_3_3 / 8_0_0) * size			)
																				if max(UpperCAmelCase_      ,       UpperCAmelCase_			) > max_size:
																											a__:       List[Any]											=max_size / max(UpperCAmelCase_      ,       UpperCAmelCase_			)
																											a__:       Any											=newh * scale
																											a__:       str											=neww * scale

																				a__:       Optional[Any]											=int(newh + 0.5			), int(neww + 0.5			)
																				a__:       Optional[Any]											=(
																				    newh // self.size_divisor * self.size_divisor,
																				    neww // self.size_divisor * self.size_divisor,
																				)

													else:
																				a__:       Any											=[]
																				for image in image_inputs:
																											a__:       Dict											=self.get_expected_values([image]			)
																											expected_values.append((expected_height, expected_width)			)
																				a__:       str											=max(UpperCAmelCase_      ,       key=lambda _a			: item[0]			)[0]
																				a__:       List[Any]											=max(UpperCAmelCase_      ,       key=lambda _a			: item[1]			)[1]

													return expected_height, expected_width


@require_torch
@require_vision
class 	lowerCamelCase__ (		_a				,					unittest.TestCase   ):
						_lowerCAmelCase								= ViltImageProcessor if is_vision_available() else None


						def   _lowerCamelCase			( self						:  str			):
													a__:       List[str]											=ViltImageProcessingTester(self			)


						@property
						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													return self.image_processor_tester.prepare_image_processor_dict()


						def   _lowerCamelCase			( self						:  List[str]			):
													a__:       Optional[Any]											=self.image_processing_class(**self.image_processor_dict			)
													self.assertTrue(hasattr(UpperCAmelCase_      ,       "image_mean"			)			)
													self.assertTrue(hasattr(UpperCAmelCase_      ,       "image_std"			)			)
													self.assertTrue(hasattr(UpperCAmelCase_      ,       "do_normalize"			)			)
													self.assertTrue(hasattr(UpperCAmelCase_      ,       "do_resize"			)			)
													self.assertTrue(hasattr(UpperCAmelCase_      ,       "size"			)			)
													self.assertTrue(hasattr(UpperCAmelCase_      ,       "size_divisor"			)			)


						def   _lowerCamelCase			( self						:  List[str]			):
													a__:       Union[str, Any]											=self.image_processing_class.from_dict(self.image_processor_dict			)
													self.assertEqual(image_processor.size      ,       {"shortest_edge": 3_0}			)

													a__:       str											=self.image_processing_class.from_dict(self.image_processor_dict      ,       size=4_2			)
													self.assertEqual(image_processor.size      ,       {"shortest_edge": 4_2}			)


						def   _lowerCamelCase			( self						:  Dict			):
													pass


						def   _lowerCamelCase			( self						:  Optional[Any]			):
													# Initialize image_processing
													a__:       List[Any]											=self.image_processing_class(**self.image_processor_dict			)
													# create random PIL images
													a__:       int											=prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=UpperCAmelCase_			)
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase_      ,       Image.Image			)

													# Test not batched input
													a__:       Optional[int]											=image_processing(image_inputs[0]      ,       return_tensors="pt"			).pixel_values

													a__:       int											=self.image_processor_tester.get_expected_values(UpperCAmelCase_			)
													self.assertEqual(
													    encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

													# Test batched
													a__:       str											=image_processing(UpperCAmelCase_      ,       return_tensors="pt"			).pixel_values

													a__:       Dict											=self.image_processor_tester.get_expected_values(UpperCAmelCase_      ,       batched=UpperCAmelCase_			)
													self.assertEqual(
													    encoded_images.shape      ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )      ,       )


						def   _lowerCamelCase			( self						:  Optional[int]			):
													# Initialize image_processing
													a__:       int											=self.image_processing_class(**self.image_processor_dict			)
													# create random numpy tensors
													a__:       Union[str, Any]											=prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=UpperCAmelCase_      ,       numpify=UpperCAmelCase_			)
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase_      ,       np.ndarray			)

													# Test not batched input
													a__:       List[Any]											=image_processing(image_inputs[0]      ,       return_tensors="pt"			).pixel_values

													a__:       Dict											=self.image_processor_tester.get_expected_values(UpperCAmelCase_			)
													self.assertEqual(
													    encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

													# Test batched
													a__:       str											=image_processing(UpperCAmelCase_      ,       return_tensors="pt"			).pixel_values

													a__:       Dict											=self.image_processor_tester.get_expected_values(UpperCAmelCase_      ,       batched=UpperCAmelCase_			)
													self.assertEqual(
													    encoded_images.shape      ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )      ,       )







						def   _lowerCamelCase			( self						:  List[str]			):
													# Initialize image_processing
													a__:       Optional[int]											=self.image_processing_class(**self.image_processor_dict			)
													# create random PyTorch tensors
													a__:       Tuple											=prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=UpperCAmelCase_      ,       torchify=UpperCAmelCase_			)
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase_      ,       torch.Tensor			)

													# Test not batched input
													a__:       List[str]											=image_processing(image_inputs[0]      ,       return_tensors="pt"			).pixel_values

													a__:       int											=self.image_processor_tester.get_expected_values(UpperCAmelCase_			)
													self.assertEqual(
													    encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

													# Test batched
													a__:       List[Any]											=image_processing(UpperCAmelCase_      ,       return_tensors="pt"			).pixel_values

													a__:       Optional[int]											=self.image_processor_tester.get_expected_values(UpperCAmelCase_      ,       batched=UpperCAmelCase_			)
													self.assertEqual(
													    encoded_images.shape      ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )      ,       )










# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

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


__UpperCAmelCase						  =     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__ (		_a   ):


								def __init__( self						:  Any      ,       _a						:  int = 1_0_1			):
															a__:       Dict											=length


								def __len__( self						:  Optional[Any]			):
															return self.length







								def __getitem__( self						:  Dict      ,       _a						:  int			):
															return i


		class 	lowerCamelCase__ :


								def __call__( self						:  Any      ,       _a						:  List[str]			):
															return {"input_ids": torch.tensor(_a			), "labels": torch.tensor(_a			)}


		class 	lowerCamelCase__ (		nn.Module   ):


								def __init__( self						:  Tuple			):
															super().__init__()
															# Add some (unused) params otherwise DDP will complain.
															a__:       int											=nn.Linear(1_2_0      ,       8_0			)







								def   _lowerCamelCase			( self						:  str      ,       _a						:  List[Any]      ,       _a						:  List[str]=None			):
															if labels is not None:
																						return torch.tensor(0.0      ,       device=input_ids.device			), input_ids
															else:
																						return input_ids





def 						__lowerCamelCase       (      ):
							a__:       List[str]											=HfArgumentParser((TrainingArguments,)						)
							sys.argv += ["--output_dir", "./examples"]
							a__:       int											=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_001, 256, 15]:
														a__:       Optional[Any]											=DummyDataset(__magic_name__						)

														def compute_metrics(__magic_name__	:  EvalPrediction						) -> Dict:
																					a__:       List[str]											=list(range(len(__magic_name__						)						)						)
																					a__:       Union[str, Any]											=p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																					return {"success": success}

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

														a__:       List[str]											=trainer.predict(__magic_name__						)
														logger.info(p.metrics						)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics						)
																					exit(1						)

														a__:       Tuple											=2

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

														a__:       List[str]											=trainer.predict(__magic_name__						)
														logger.info(p.metrics						)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics						)
																					exit(1						)

														a__:       List[str]											=None

							logger.info("🔥 All distributed tests successful"						)





def 						__lowerCamelCase       (      __magic_name__	:  Optional[Any]						):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
		main()





