





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 SCREAMING_SNAKE_CASE_			(       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						,	):
										lowerCamelCase__      =							parent
										lowerCamelCase__      =							batch_size
										lowerCamelCase__      =							num_channels
										lowerCamelCase__      =							image_size
										lowerCamelCase__      =							min_resolution
										lowerCamelCase__      =							max_resolution
										lowerCamelCase__      =							do_rescale
										lowerCamelCase__      =							rescale_factor
										lowerCamelCase__      =							do_pad
										lowerCamelCase__      =							pad_size





					def        __magic_name__			(			self						):
										return {
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_pad": self.do_pad,
										    "pad_size": self.pad_size,
										}






					def        __magic_name__			(			self						,	_lowerCAmelCase=False						,	_lowerCAmelCase=False						,	_lowerCAmelCase=False						):
										assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

										if equal_resolution:
															lowerCamelCase__      =							[]
															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:
															lowerCamelCase__      =							[]
															for i in range(self.batch_size						):
																				lowerCamelCase__     ,		lowerCamelCase__      =							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
															lowerCamelCase__      =							[Image.fromarray(np.moveaxis(_lowerCAmelCase						,	0						,	-1						)						) for x in image_inputs]

										if torchify:
															lowerCamelCase__      =							[torch.from_numpy(_lowerCAmelCase						) for x in image_inputs]

										return image_inputs

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





					"""simple docstring"""


					A__								=     SwinaSRImageProcessor if is_vision_available() else None





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							SwinaSRImageProcessingTester(self						)





					@property
					def        __magic_name__			(			self						):
										return self.image_processor_tester.prepare_image_processor_dict()





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							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        __magic_name__			(			self						):
										pass





					def        __magic_name__			(			self						,	_lowerCAmelCase						):
										lowerCamelCase__     ,		lowerCamelCase__      =							get_image_size(_lowerCAmelCase						)
										lowerCamelCase__      =							self.image_processor_tester.pad_size

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





					def        __magic_name__			(			self						):
										# Initialize image_processor
										lowerCamelCase__      =							self.image_processing_class(**self.image_processor_dict						)
										# create random PIL images
										lowerCamelCase__      =							self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase						)
										for image in image_inputs:
															self.assertIsInstance(_lowerCAmelCase						,	Image.Image						)

										# Test not batched input
										lowerCamelCase__      =							image_processor(image_inputs[0]						,	return_tensors="pt"						).pixel_values
										lowerCamelCase__     ,		lowerCamelCase__      =							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        __magic_name__			(			self						):
										# Initialize image_processor
										lowerCamelCase__      =							self.image_processing_class(**self.image_processor_dict						)
										# create random numpy tensors
										lowerCamelCase__      =							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
										lowerCamelCase__      =							image_processor(image_inputs[0]						,	return_tensors="pt"						).pixel_values
										lowerCamelCase__     ,		lowerCamelCase__      =							self.calculate_expected_size(image_inputs[0]						)
										self.assertEqual(
										    encoded_images.shape						,	(
										        1,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )						,	)






					def        __magic_name__			(			self						):
										# Initialize image_processor
										lowerCamelCase__      =							self.image_processing_class(**self.image_processor_dict						)
										# create random PyTorch tensors
										lowerCamelCase__      =							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
										lowerCamelCase__      =							image_processor(image_inputs[0]						,	return_tensors="pt"						).pixel_values
										lowerCamelCase__     ,		lowerCamelCase__      =							self.calculate_expected_size(image_inputs[0]						)
										self.assertEqual(
										    encoded_images.shape						,	(
										        1,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )						,	)








# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    "--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/

