



'''simple docstring'''





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


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import SwinaSRImageProcessor
				from transformers.image_transforms import get_image_size



class 			_lowercase							(     unittest.TestCase			):





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	, lowerCamelCase_=7	, lowerCamelCase_=3	, lowerCamelCase_=18	, lowerCamelCase_=30	, lowerCamelCase_=400	, lowerCamelCase_=True	, lowerCamelCase_=1 / 255	, lowerCamelCase_=True	, lowerCamelCase_=8	, ):
						"""simple docstring"""

						a        =     parent
						a        =     batch_size
						a        =     num_channels
						a        =     image_size
						a        =     min_resolution
						a        =     max_resolution
						a        =     do_rescale
						a        =     rescale_factor
						a        =     do_pad
						a        =     pad_size




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return {
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						    "pad_size": self.pad_size,
						}




	def   UpperCamelCase_ (self	, lowerCamelCase_=False	, lowerCamelCase_=False	, lowerCamelCase_=False	):
						"""simple docstring"""

						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
											a        =     []
											for i in range(self.batch_size	):
																image_inputs.append(
																    np.random.randint(
																        255	, size=(self.num_channels, self.max_resolution, self.max_resolution)	, dtype=np.uinta	)	)
						else:
											a        =     []
											for i in range(self.batch_size	):
																a  ,       a        =     np.random.choice(np.arange(self.min_resolution	, self.max_resolution	)	, 2	)
																image_inputs.append(np.random.randint(255	, size=(self.num_channels, width, height)	, dtype=np.uinta	)	)

						if not numpify and not torchify:
											# PIL expects the channel dimension as last dimension
											a        =     [Image.fromarray(np.moveaxis(lowerCamelCase_	, 0	, -1	)	) for x in image_inputs]

						if torchify:
											a        =     [torch.from_numpy(lowerCamelCase_	) for x in image_inputs]

						return image_inputs




@require_torch
@require_vision
class 			_lowercase							(     lowercase_,   unittest.TestCase			):





	"""simple docstring"""
	__A					=							SwinaSRImageProcessor if is_vision_available() else None




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     SwinaSRImageProcessingTester(self	)




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return self.image_processor_tester.prepare_image_processor_dict()




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.image_processing_class(**self.image_processor_dict	)
						self.assertTrue(hasattr(lowerCamelCase_	, "do_rescale"	)	)
						self.assertTrue(hasattr(lowerCamelCase_	, "rescale_factor"	)	)
						self.assertTrue(hasattr(lowerCamelCase_	, "do_pad"	)	)
						self.assertTrue(hasattr(lowerCamelCase_	, "pad_size"	)	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						pass




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						a  ,       a        =     get_image_size(lowerCamelCase_	)
						a        =     self.image_processor_tester.pad_size

						a        =     (old_height // size + 1) * size - old_height
						a        =     (old_width // size + 1) * size - old_width
						return old_height + pad_height, old_width + pad_width




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						a        =     self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase_	)
						for image in image_inputs:
											self.assertIsInstance(lowerCamelCase_	, Image.Image	)

						# Test not batched input
						a        =     image_processor(image_inputs[0]	, return_tensors="pt"	).pixel_values
						a  ,       a        =     self.calculate_expected_size(np.array(image_inputs[0]	)	)
						self.assertEqual(
						    encoded_images.shape	, (
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )	, )




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						a        =     self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase_	, numpify=lowerCamelCase_	)
						for image in image_inputs:
											self.assertIsInstance(lowerCamelCase_	, np.ndarray	)

						# Test not batched input
						a        =     image_processor(image_inputs[0]	, return_tensors="pt"	).pixel_values
						a  ,       a        =     self.calculate_expected_size(image_inputs[0]	)
						self.assertEqual(
						    encoded_images.shape	, (
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )	, )




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						a        =     self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase_	, torchify=lowerCamelCase_	)
						for image in image_inputs:
											self.assertIsInstance(lowerCamelCase_	, torch.Tensor	)

						# Test not batched input
						a        =     image_processor(image_inputs[0]	, return_tensors="pt"	).pixel_values
						a  ,       a        =     self.calculate_expected_size(image_inputs[0]	)
						self.assertEqual(
						    encoded_images.shape	, (
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )	, )





# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls




	@slow
	@require_torch_gpu
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.get_auto_remove_tmp_dir()
						a        =     F'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

						# we want this test to run even if there is only one GPU, but if there are more we use them all
						a        =     get_gpu_count()
						a        =     F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
						a        =     [sys.executable] + distributed_args + args
						execute_subprocess_async(lowerCamelCase_	, env=self.get_env()	)

						a        =     os.path.join(lowerCamelCase_	, "test_bleu.json"	)
						a        =     load_json(lowerCamelCase_	)
						# print(metrics)
						self.assertGreaterEqual(metrics["bleu"]	, 25	)
