






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 MobileViTImageProcessor


class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def __init__(     self:      str       ,	UpperCamelCase:      List[Any]       ,	UpperCamelCase:      List[Any]=7       ,	UpperCamelCase:      str=3       ,	UpperCamelCase:      List[Any]=18       ,	UpperCamelCase:      Optional[Any]=30       ,	UpperCamelCase:      int=4_00       ,	UpperCamelCase:      int=True       ,	UpperCamelCase:      Tuple=None       ,	UpperCamelCase:      str=True       ,	UpperCamelCase:      str=None       ,	UpperCamelCase:      Optional[int]=True       ,	)		->		str:
											snake_case__									=     size if size is not None else {'shortest_edge': 20}
											snake_case__									=     crop_size if crop_size is not None else {'height': 18, 'width': 18}
											snake_case__									=     parent
											snake_case__									=     batch_size
											snake_case__									=     num_channels
											snake_case__									=     image_size
											snake_case__									=     min_resolution
											snake_case__									=     max_resolution
											snake_case__									=     do_resize
											snake_case__									=     size
											snake_case__									=     do_center_crop
											snake_case__									=     crop_size
											snake_case__									=     do_flip_channel_order


							def lowerCAmelCase_ (     self:      List[str]						)		->		Any:
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											    "do_flip_channel_order": self.do_flip_channel_order,
											}


@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE(	a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       MobileViTImageProcessor if is_vision_available() else None
							def lowerCAmelCase_ (     self:      str						)		->		List[Any]:
											snake_case__									=     MobileViTImageProcessingTester(self						)
							@property
							def lowerCAmelCase_ (     self:      str						)		->		Tuple:
											return self.image_processor_tester.prepare_image_processor_dict()
							def lowerCAmelCase_ (     self:      Any						)		->		Tuple:
											snake_case__									=     self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(UpperCamelCase       ,	'do_resize'						)						)
											self.assertTrue(hasattr(UpperCamelCase       ,	'size'						)						)
											self.assertTrue(hasattr(UpperCamelCase       ,	'do_center_crop'						)						)
											self.assertTrue(hasattr(UpperCamelCase       ,	'center_crop'						)						)
											self.assertTrue(hasattr(UpperCamelCase       ,	'do_flip_channel_order'						)						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		int:
											snake_case__									=     self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size       ,	{'shortest_edge': 20}						)
											self.assertEqual(image_processor.crop_size       ,	{'height': 18, 'width': 18}						)

											snake_case__									=     self.image_processing_class.from_dict(self.image_processor_dict       ,	size=42       ,	crop_size=84						)
											self.assertEqual(image_processor.size       ,	{'shortest_edge': 42}						)
											self.assertEqual(image_processor.crop_size       ,	{'height': 84, 'width': 84}						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		Tuple:
											pass
							def lowerCAmelCase_ (     self:      int						)		->		Dict:
											# Initialize image_processing
											snake_case__									=     self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											snake_case__									=     prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=UpperCamelCase						)
											for image in image_inputs:
															self.assertIsInstance(UpperCamelCase       ,	Image.Image						)

											# Test not batched input
											snake_case__									=     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
											snake_case__									=     image_processing(UpperCamelCase       ,	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 lowerCAmelCase_ (     self:      List[str]						)		->		Union[str, Any]:
											# Initialize image_processing
											snake_case__									=     self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											snake_case__									=     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
											snake_case__									=     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
											snake_case__									=     image_processing(UpperCamelCase       ,	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 lowerCAmelCase_ (     self:      List[Any]						)		->		int:
											# Initialize image_processing
											snake_case__									=     self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											snake_case__									=     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
											snake_case__									=     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
											snake_case__									=     image_processing(UpperCamelCase       ,	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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase					:		str	  =						{"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""LayoutXLMTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		str	  =						["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
							from .processing_layoutxlm import LayoutXLMProcessor

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutxlm import LayoutXLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
							import sys

							__UpperCamelCase					:		Union[str, Any]	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




