




'''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, 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				):




					'''simple docstring'''






					def __init__(       self,    __UpperCamelCase,    __UpperCamelCase=7,    __UpperCamelCase=3,    __UpperCamelCase=18,    __UpperCamelCase=30,    __UpperCamelCase=400,    __UpperCamelCase=True,    __UpperCamelCase=None,    __UpperCamelCase=True,    __UpperCamelCase=None,    ):
							_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






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							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_				(			__A,			unittest.TestCase				):




					'''simple docstring'''






					lowercase__       =					MobileNetVaImageProcessor if is_vision_available() else None






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			MobileNetVaImageProcessingTester(self					)






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






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_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"					)					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_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 							__SCREAMING_SNAKE_CASE      (       self					):
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# 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 							__SCREAMING_SNAKE_CASE      (       self					):
							# 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 							__SCREAMING_SNAKE_CASE      (       self					):
							# 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"],
							    ),    )






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase									=	{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	["LayoutLMTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	[
				    "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "LayoutLMForMaskedLM",
				    "LayoutLMForSequenceClassification",
				    "LayoutLMForTokenClassification",
				    "LayoutLMForQuestionAnswering",
				    "LayoutLMModel",
				    "LayoutLMPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	[
				    "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFLayoutLMForMaskedLM",
				    "TFLayoutLMForSequenceClassification",
				    "TFLayoutLMForTokenClassification",
				    "TFLayoutLMForQuestionAnswering",
				    "TFLayoutLMMainLayer",
				    "TFLayoutLMModel",
				    "TFLayoutLMPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
				from .tokenization_layoutlm import LayoutLMTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_layoutlm import (
								    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LayoutLMForMaskedLM,
								    LayoutLMForQuestionAnswering,
								    LayoutLMForSequenceClassification,
								    LayoutLMForTokenClassification,
								    LayoutLMModel,
								    LayoutLMPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_layoutlm import (
								    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFLayoutLMForMaskedLM,
								    TFLayoutLMForQuestionAnswering,
								    TFLayoutLMForSequenceClassification,
								    TFLayoutLMForTokenClassification,
								    TFLayoutLMMainLayer,
								    TFLayoutLMModel,
								    TFLayoutLMPreTrainedModel,
								)

else:
				import sys

				__lowerCamelCase									=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





