

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 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							def __init__(			self       ,							_a       ,							_a=7       ,							_a=3       ,							_a=1_8       ,							_a=3_0       ,							_a=4_0_0       ,							_a=True       ,							_a=None       ,							_a=True       ,							_a=None       ,							_a=True       ,							)    ->    List[str]:
												_a	:    Optional[Any]         =		size if size is not None else {'''shortest_edge''': 2_0}
												_a	:    List[Any]         =		crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
												_a	:    Tuple         =		parent
												_a	:    Union[str, Any]         =		batch_size
												_a	:    str         =		num_channels
												_a	:    Optional[Any]         =		image_size
												_a	:    Any         =		min_resolution
												_a	:    Optional[int]         =		max_resolution
												_a	:    Tuple         =		do_resize
												_a	:    Optional[int]         =		size
												_a	:    Any         =		do_center_crop
												_a	:    List[Any]         =		crop_size
												_a	:    Tuple         =		do_flip_channel_order




							def 				__lowercase				(			self       )    ->    str:
												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 				UpperCAmelCase_		(			__lowercase				,       unittest.TestCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[int]	    =    MobileViTImageProcessor if is_vision_available() else None


							def 				__lowercase				(			self       )    ->    str:
												_a	:    int         =		MobileViTImageProcessingTester(self       )


							@property
							def 				__lowercase				(			self       )    ->    Optional[int]:
												return self.image_processor_tester.prepare_image_processor_dict()


							def 				__lowercase				(			self       )    ->    List[str]:
												_a	:    str         =		self.image_processing_class(**self.image_processor_dict       )
												self.assertTrue(hasattr(_a       ,							'''do_resize'''       )       )
												self.assertTrue(hasattr(_a       ,							'''size'''       )       )
												self.assertTrue(hasattr(_a       ,							'''do_center_crop'''       )       )
												self.assertTrue(hasattr(_a       ,							'''center_crop'''       )       )
												self.assertTrue(hasattr(_a       ,							'''do_flip_channel_order'''       )       )


							def 				__lowercase				(			self       )    ->    Dict:
												_a	:    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}       )

												_a	:    Any         =		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 				__lowercase				(			self       )    ->    str:
												pass


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												# Initialize image_processing
												_a	:    Union[str, Any]         =		self.image_processing_class(**self.image_processor_dict       )
												# create random PIL images
												_a	:    List[Any]         =		prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=_a       )
												for image in image_inputs:
																	self.assertIsInstance(_a       ,							Image.Image       )

												# Test not batched input
												_a	:    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
												_a	:    int         =		image_processing(_a       ,							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 				__lowercase				(			self       )    ->    Optional[int]:
												# Initialize image_processing
												_a	:    Optional[Any]         =		self.image_processing_class(**self.image_processor_dict       )
												# create random numpy tensors
												_a	:    Optional[Any]         =		prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=_a       ,							numpify=_a       )
												for image in image_inputs:
																	self.assertIsInstance(_a       ,							np.ndarray       )

												# Test not batched input
												_a	:    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
												_a	:    Optional[Any]         =		image_processing(_a       ,							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 				__lowercase				(			self       )    ->    Dict:
												# Initialize image_processing
												_a	:    List[Any]         =		self.image_processing_class(**self.image_processor_dict       )
												# create random PyTorch tensors
												_a	:    Union[str, Any]         =		prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=_a       ,							torchify=_a       )
												for image in image_inputs:
																	self.assertIsInstance(_a       ,							torch.Tensor       )

												# Test not batched input
												_a	:    Optional[Any]         =		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
												_a	:    List[str]         =		image_processing(_a       ,							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'''],
												    )       ,							)






import logging

from transformers import PretrainedConfig


a__				=		logging.getLogger(__name__)


a__				=		{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "bertabs"


							def __init__(			self       ,							_a=3_0_5_2_2       ,							_a=5_1_2       ,							_a=6       ,							_a=5_1_2       ,							_a=8       ,							_a=5_1_2       ,							_a=0.2       ,							_a=6       ,							_a=7_6_8       ,							_a=8       ,							_a=2_0_4_8       ,							_a=0.2       ,							**_a       ,							)    ->    Any:
												super().__init__(**_a       )

												_a	:    int         =		vocab_size
												_a	:    List[str]         =		max_pos

												_a	:    Tuple         =		enc_layers
												_a	:    Optional[Any]         =		enc_hidden_size
												_a	:    int         =		enc_heads
												_a	:    Optional[Any]         =		enc_ff_size
												_a	:    List[str]         =		enc_dropout

												_a	:    Tuple         =		dec_layers
												_a	:    Optional[Any]         =		dec_hidden_size
												_a	:    Optional[Any]         =		dec_heads
												_a	:    Optional[Any]         =		dec_ff_size
												_a	:    List[Any]         =		dec_dropout



