
"""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 ):




				def __init__(							self       ,	UpperCAmelCase       ,	UpperCAmelCase=7       ,	UpperCAmelCase=3       ,	UpperCAmelCase=18       ,	UpperCAmelCase=30       ,	UpperCAmelCase=400       ,	UpperCAmelCase=True       ,	UpperCAmelCase=None       ,	UpperCAmelCase=True       ,	UpperCAmelCase=None       ,	):
							lowerCamelCase_ 		=     size if size is not None else {"shortest_edge": 20}
							lowerCamelCase_ 		=     crop_size if crop_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_center_crop
							lowerCamelCase_ 		=     crop_size





				def 					UpperCAmelCase__	(							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     (   UpperCAmelCase__	,  unittest.TestCase ):
				a__:			str									=						MobileNetVaImageProcessor if is_vision_available() else None




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     MobileNetVaImageProcessingTester(self      )




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




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(lowerCamelCase__       ,	'''do_resize'''      )      )
							self.assertTrue(hasattr(lowerCamelCase__       ,	'''size'''      )      )
							self.assertTrue(hasattr(lowerCamelCase__       ,	'''do_center_crop'''      )      )
							self.assertTrue(hasattr(lowerCamelCase__       ,	'''center_crop'''      )      )




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     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}      )

							lowerCamelCase_ 		=     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 					UpperCAmelCase__	(							self      ):
							pass




				def 					UpperCAmelCase__	(							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.crop_size['''height'''],
							        self.image_processor_tester.crop_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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )       ,	)




				def 					UpperCAmelCase__	(							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.crop_size['''height'''],
							        self.image_processor_tester.crop_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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )       ,	)





				def 					UpperCAmelCase__	(							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.crop_size['''height'''],
							        self.image_processor_tester.crop_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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )       ,	)





"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_						       =				logging.get_logger(__name__)

A_						       =				{
    """MBZUAI/swiftformer-xs""": """https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json""",
}




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			Any									=						'swiftformer'




				def __init__(							self       ,	UpperCAmelCase=3       ,	UpperCAmelCase=[3, 3, 6, 4]       ,	UpperCAmelCase=[48, 56, 112, 220]       ,	UpperCAmelCase=4       ,	UpperCAmelCase=[True, True, True, True]       ,	UpperCAmelCase="gelu"       ,	UpperCAmelCase=3       ,	UpperCAmelCase=2       ,	UpperCAmelCase=1       ,	UpperCAmelCase=0.0       ,	UpperCAmelCase=True       ,	UpperCAmelCase=1e-5       ,	UpperCAmelCase=1e-5       ,	**UpperCAmelCase       ,	):
							super().__init__(**UpperCAmelCase      )
							lowerCamelCase_ 		=     num_channels
							lowerCamelCase_ 		=     depths
							lowerCamelCase_ 		=     embed_dims
							lowerCamelCase_ 		=     mlp_ratio
							lowerCamelCase_ 		=     downsamples
							lowerCamelCase_ 		=     hidden_act
							lowerCamelCase_ 		=     down_patch_size
							lowerCamelCase_ 		=     down_stride
							lowerCamelCase_ 		=     down_pad
							lowerCamelCase_ 		=     drop_path_rate
							lowerCamelCase_ 		=     use_layer_scale
							lowerCamelCase_ 		=     layer_scale_init_value
							lowerCamelCase_ 		=     batch_norm_eps




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			str									=						version.parse('1.11' )




				@property
				def 					UpperCAmelCase__	(							self      ):
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							    ]      )





				@property
				def 					UpperCAmelCase__	(							self      ):
							return 1e-4



