
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 MobileNetVaImageProcessor





class 		__lowerCamelCase     (   unittest.TestCase		):



							def __init__(     self	,				__snake_case	,				__snake_case=7	,				__snake_case=3	,				__snake_case=1_8	,				__snake_case=3_0	,				__snake_case=4_0_0	,				__snake_case=True	,				__snake_case=None	,				__snake_case=True	,				__snake_case=None	,				)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     size if size is not None else {"shortest_edge": 2_0}
								UpperCAmelCase:					Optional[int] 						=     crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
								UpperCAmelCase:					Any 						=     parent
								UpperCAmelCase:					Optional[Any] 						=     batch_size
								UpperCAmelCase:					Tuple 						=     num_channels
								UpperCAmelCase:					int 						=     image_size
								UpperCAmelCase:					Optional[int] 						=     min_resolution
								UpperCAmelCase:					List[Any] 						=     max_resolution
								UpperCAmelCase:					Tuple 						=     do_resize
								UpperCAmelCase:					Optional[Any] 						=     size
								UpperCAmelCase:					Tuple 						=     do_center_crop
								UpperCAmelCase:					Dict 						=     crop_size




							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								}





@require_torch
@require_vision
class 		__lowerCamelCase     (   lowercase							, unittest.TestCase		):
							lowerCamelCase__:				Dict      =							MobileNetVaImageProcessor if is_vision_available() else None



							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     MobileNetVaImageProcessingTester(self							)



							@property
							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								return self.image_processor_tester.prepare_image_processor_dict()



							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     self.image_processing_class(**self.image_processor_dict							)
								self.assertTrue(hasattr(__snake_case	,				"do_resize"							)							)
								self.assertTrue(hasattr(__snake_case	,				"size"							)							)
								self.assertTrue(hasattr(__snake_case	,				"do_center_crop"							)							)
								self.assertTrue(hasattr(__snake_case	,				"center_crop"							)							)



							def     A__ (     self							)						->     Union[str, Any]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     self.image_processing_class.from_dict(self.image_processor_dict							)
								self.assertEqual(image_processor.size	,				{"shortest_edge": 2_0}							)
								self.assertEqual(image_processor.crop_size	,				{"height": 1_8, "width": 1_8}							)

								UpperCAmelCase:					Dict 						=     self.image_processing_class.from_dict(self.image_processor_dict	,				size=4_2	,				crop_size=8_4							)
								self.assertEqual(image_processor.size	,				{"shortest_edge": 4_2}							)
								self.assertEqual(image_processor.crop_size	,				{"height": 8_4, "width": 8_4}							)



							def     A__ (     self							)						->     str:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     self.image_processing_class(**self.image_processor_dict							)
								# create random PIL images
								UpperCAmelCase:					Union[str, Any] 						=     prepare_image_inputs(self.image_processor_tester	,				equal_resolution=__snake_case							)
								for image in image_inputs:
									self.assertIsInstance(__snake_case	,				Image.Image							)

								# Test not batched input
								UpperCAmelCase:					Dict 						=     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
								UpperCAmelCase:					List[str] 						=     image_processing(__snake_case	,				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							)						->     Union[str, Any]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     self.image_processing_class(**self.image_processor_dict							)
								# create random numpy tensors
								UpperCAmelCase:					Any 						=     prepare_image_inputs(self.image_processor_tester	,				equal_resolution=__snake_case	,				numpify=__snake_case							)
								for image in image_inputs:
									self.assertIsInstance(__snake_case	,				np.ndarray							)

								# Test not batched input
								UpperCAmelCase:					Optional[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
								UpperCAmelCase:					List[str] 						=     image_processing(__snake_case	,				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							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					int 						=     self.image_processing_class(**self.image_processor_dict							)
								# create random PyTorch tensors
								UpperCAmelCase:					List[str] 						=     prepare_image_inputs(self.image_processor_tester	,				equal_resolution=__snake_case	,				torchify=__snake_case							)
								for image in image_inputs:
									self.assertIsInstance(__snake_case	,				torch.Tensor							)

								# Test not batched input
								UpperCAmelCase:					List[str] 						=     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
								UpperCAmelCase:					List[str] 						=     image_processing(__snake_case	,				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"],
								    )	,				)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_      :					Optional[int]				      =     {
    'configuration_splinter': ['SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SplinterConfig'],
    'tokenization_splinter': ['SplinterTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Dict				      =     ['SplinterTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SplinterForQuestionAnswering',
	    'SplinterForPreTraining',
	    'SplinterLayer',
	    'SplinterModel',
	    'SplinterPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
	from .tokenization_splinter import SplinterTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_splinter_fast import SplinterTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_splinter import (
		    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SplinterForPreTraining,
		    SplinterForQuestionAnswering,
		    SplinterLayer,
		    SplinterModel,
		    SplinterPreTrainedModel,
		)


else:
	import sys

	snake_case_      :					Dict				      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
