





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 ViTImageProcessor

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=None						,	_lowerCAmelCase=True						,	_lowerCAmelCase=[0.5, 0.5, 0.5]						,	_lowerCAmelCase=[0.5, 0.5, 0.5]						,	):
										lowerCamelCase__      =							size if size is not None else {"height": 18, "width": 18}
										lowerCamelCase__      =							parent
										lowerCamelCase__      =							batch_size
										lowerCamelCase__      =							num_channels
										lowerCamelCase__      =							image_size
										lowerCamelCase__      =							min_resolution
										lowerCamelCase__      =							max_resolution
										lowerCamelCase__      =							do_resize
										lowerCamelCase__      =							size
										lowerCamelCase__      =							do_normalize
										lowerCamelCase__      =							image_mean
										lowerCamelCase__      =							image_std






					def        __magic_name__			(			self						):
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										}

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





					"""simple docstring"""


					A__								=     ViTImageProcessor if is_vision_available() else None





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							ViTImageProcessingTester(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						,	"image_mean"						)						)
										self.assertTrue(hasattr(_lowerCAmelCase						,	"image_std"						)						)
										self.assertTrue(hasattr(_lowerCAmelCase						,	"do_normalize"						)						)
										self.assertTrue(hasattr(_lowerCAmelCase						,	"do_resize"						)						)
										self.assertTrue(hasattr(_lowerCAmelCase						,	"size"						)						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.image_processing_class.from_dict(self.image_processor_dict						)
										self.assertEqual(image_processor.size						,	{"height": 18, "width": 18}						)

										lowerCamelCase__      =							self.image_processing_class.from_dict(self.image_processor_dict						,	size=42						)
										self.assertEqual(image_processor.size						,	{"height": 42, "width": 42}						)





					def        __magic_name__			(			self						):
										pass





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

										# Test not batched input
										lowerCamelCase__      =							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.size["height"],
										        self.image_processor_tester.size["width"],
										    )						,	)

										# Test batched
										lowerCamelCase__      =							image_processing(_lowerCAmelCase						,	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.size["height"],
										        self.image_processor_tester.size["width"],
										    )						,	)





					def        __magic_name__			(			self						):
										# Initialize image_processing
										lowerCamelCase__      =							self.image_processing_class(**self.image_processor_dict						)
										# create random numpy tensors
										lowerCamelCase__      =							prepare_image_inputs(self.image_processor_tester						,	equal_resolution=_lowerCAmelCase						,	numpify=_lowerCAmelCase						)
										for image in image_inputs:
															self.assertIsInstance(_lowerCAmelCase						,	np.ndarray						)

										# Test not batched input
										lowerCamelCase__      =							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.size["height"],
										        self.image_processor_tester.size["width"],
										    )						,	)

										# Test batched
										lowerCamelCase__      =							image_processing(_lowerCAmelCase						,	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.size["height"],
										        self.image_processor_tester.size["width"],
										    )						,	)






					def        __magic_name__			(			self						):
										# Initialize image_processing
										lowerCamelCase__      =							self.image_processing_class(**self.image_processor_dict						)
										# create random PyTorch tensors
										lowerCamelCase__      =							prepare_image_inputs(self.image_processor_tester						,	equal_resolution=_lowerCAmelCase						,	torchify=_lowerCAmelCase						)
										for image in image_inputs:
															self.assertIsInstance(_lowerCAmelCase						,	torch.Tensor						)

										# Test not batched input
										lowerCamelCase__      =							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.size["height"],
										        self.image_processor_tester.size["width"],
										    )						,	)

										# Test batched
										lowerCamelCase__      =							image_processing(_lowerCAmelCase						,	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.size["height"],
										        self.image_processor_tester.size["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/

