






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


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import CLIPImageProcessor
class 							A_      (  unittest.TestCase     ):






 '''simple docstring'''




 def __init__(		self							,					_A							,					_A=7							,					_A=3							,					_A=18							,					_A=30							,					_A=400							,					_A=True							,					_A=None							,					_A=True							,					_A=None							,					_A=True							,					_A=[0.48145466, 0.4578275, 0.40821073]							,					_A=[0.26862954, 0.26130258, 0.27577711]							,					_A=True							,					)	->						str:

     """simple docstring"""



     _UpperCAmelCase			: int     					=			size if size is not None else {'''shortest_edge''': 20}
     _UpperCAmelCase			: Optional[int]     					=			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
     _UpperCAmelCase			: Union[str, Any]     					=			parent
     _UpperCAmelCase			: str     					=			batch_size
     _UpperCAmelCase			: Optional[int]     					=			num_channels
     _UpperCAmelCase			: Any     					=			image_size
     _UpperCAmelCase			: Tuple     					=			min_resolution
     _UpperCAmelCase			: Optional[Any]     					=			max_resolution
     _UpperCAmelCase			: List[Any]     					=			do_resize
     _UpperCAmelCase			: Any     					=			size
     _UpperCAmelCase			: Optional[int]     					=			do_center_crop
     _UpperCAmelCase			: Tuple     					=			crop_size
     _UpperCAmelCase			: int     					=			do_normalize
     _UpperCAmelCase			: Any     					=			image_mean
     _UpperCAmelCase			: Optional[Any]     					=			image_std
     _UpperCAmelCase			: int     					=			do_convert_rgb


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

     """simple docstring"""



     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_convert_rgb": self.do_convert_rgb,
     }


 def 	snake_case__			(		self							,					_A=False							,					_A=False							,					_A=False)	->						Dict:

     """simple docstring"""



     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     if equal_resolution:
         _UpperCAmelCase			: List[Any]     					=			[]
         for i in range(self.batch_size):
             image_inputs.append(
                 np.random.randint(
                     255							,					size=(self.num_channels, self.max_resolution, self.max_resolution)							,					dtype=np.uinta))
     else:
         _UpperCAmelCase			: Optional[int]     					=			[]
         for i in range(self.batch_size):
             _UpperCAmelCase					,	_UpperCAmelCase			: Any     					=			np.random.choice(np.arange(self.min_resolution							,					self.max_resolution)							,					2)
             image_inputs.append(np.random.randint(255							,					size=(self.num_channels, width, height)							,					dtype=np.uinta))

     if not numpify and not torchify:
         # PIL expects the channel dimension as last dimension
         _UpperCAmelCase			: Optional[int]     					=			[Image.fromarray(np.moveaxis(_A							,					0							,					-1)) for x in image_inputs]

     if torchify:
         _UpperCAmelCase			: Union[str, Any]     					=			[torch.from_numpy(_A) for x in image_inputs]

     return image_inputs



@require_torch
@require_vision
class 							A_      (  __lowercase	,				unittest.TestCase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Dict	=   CLIPImageProcessor if is_vision_available() else None


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

     """simple docstring"""



     _UpperCAmelCase			: Any     					=			CLIPImageProcessingTester(self)


 @property
 def 	snake_case__			(		self)	->						int:

     """simple docstring"""



     return self.image_processor_tester.prepare_image_processor_dict()


 def 	snake_case__			(		self)	->						Any:

     """simple docstring"""



     _UpperCAmelCase			: 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_normalize'''))
     self.assertTrue(hasattr(_A							,					'''image_mean'''))
     self.assertTrue(hasattr(_A							,					'''image_std'''))
     self.assertTrue(hasattr(_A							,					'''do_convert_rgb'''))


 def 	snake_case__			(		self)	->						Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase			: Union[str, Any]     					=			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})

     _UpperCAmelCase			: List[str]     					=			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 	snake_case__			(		self)	->						Tuple:

     """simple docstring"""



     pass


 def 	snake_case__			(		self)	->						Any:

     """simple docstring"""



     _UpperCAmelCase			: Tuple     					=			self.image_processing_class(**self.image_processor_dict)
     # create random PIL images
     _UpperCAmelCase			: int     					=			self.image_processor_tester.prepare_inputs(equal_resolution=_A)
     for image in image_inputs:
         self.assertIsInstance(_A							,					Image.Image)

     # Test not batched input
     _UpperCAmelCase			: 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			: 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'''],
         )							,					)


 def 	snake_case__			(		self)	->						Dict:

     """simple docstring"""



     _UpperCAmelCase			: Union[str, Any]     					=			self.image_processing_class(**self.image_processor_dict)
     # create random numpy tensors
     _UpperCAmelCase			: Dict     					=			self.image_processor_tester.prepare_inputs(equal_resolution=_A							,					numpify=_A)
     for image in image_inputs:
         self.assertIsInstance(_A							,					np.ndarray)

     # Test not batched input
     _UpperCAmelCase			: Union[str, 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
     _UpperCAmelCase			: Tuple     					=			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 	snake_case__			(		self)	->						Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase			: List[Any]     					=			self.image_processing_class(**self.image_processor_dict)
     # create random PyTorch tensors
     _UpperCAmelCase			: Optional[Any]     					=			self.image_processor_tester.prepare_inputs(equal_resolution=_A							,					torchify=_A)
     for image in image_inputs:
         self.assertIsInstance(_A							,					torch.Tensor)

     # 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			: Union[str, 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'''],
         )							,					)



@require_torch
@require_vision
class 							A_      (  __lowercase	,				unittest.TestCase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     int	=   CLIPImageProcessor if is_vision_available() else None


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

     """simple docstring"""



     _UpperCAmelCase			: Any     					=			CLIPImageProcessingTester(self							,					num_channels=4)
     _UpperCAmelCase			: int     					=			3


 @property
 def 	snake_case__			(		self)	->						Union[str, Any]:

     """simple docstring"""



     return self.image_processor_tester.prepare_image_processor_dict()


 def 	snake_case__			(		self)	->						Dict:

     """simple docstring"""



     _UpperCAmelCase			: List[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_normalize'''))
     self.assertTrue(hasattr(_A							,					'''image_mean'''))
     self.assertTrue(hasattr(_A							,					'''image_std'''))
     self.assertTrue(hasattr(_A							,					'''do_convert_rgb'''))


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

     """simple docstring"""



     pass


 def 	snake_case__			(		self)	->						Any:

     """simple docstring"""



     _UpperCAmelCase			: int     					=			self.image_processing_class(**self.image_processor_dict)
     # create random PIL images
     _UpperCAmelCase			: List[Any]     					=			self.image_processor_tester.prepare_inputs(equal_resolution=_A)
     for image in image_inputs:
         self.assertIsInstance(_A							,					Image.Image)

     # Test not batched input
     _UpperCAmelCase			: Optional[int]     					=			image_processing(image_inputs[0]							,					return_tensors='''pt''').pixel_values
     self.assertEqual(
         encoded_images.shape							,					(
             1,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )							,					)

     # Test batched
     _UpperCAmelCase			: Optional[int]     					=			image_processing(_A							,					return_tensors='''pt''').pixel_values
     self.assertEqual(
         encoded_images.shape							,					(
             self.image_processor_tester.batch_size,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )							,					)












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


SCREAMING_SNAKE_CASE								=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE								=  {
    'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
}
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Dict	=   "swiftformer"


 def __init__(		self							,					_A=3							,					_A=[3, 3, 6, 4]							,					_A=[48, 56, 112, 220]							,					_A=4							,					_A=[True, True, True, True]							,					_A="gelu"							,					_A=3							,					_A=2							,					_A=1							,					_A=0.0							,					_A=True							,					_A=1e-5							,					_A=1e-5							,					**_A							,					)	->						List[Any]:

     """simple docstring"""



     super().__init__(**_A)
     _UpperCAmelCase			: Tuple     					=			num_channels
     _UpperCAmelCase			: Optional[int]     					=			depths
     _UpperCAmelCase			: Union[str, Any]     					=			embed_dims
     _UpperCAmelCase			: Dict     					=			mlp_ratio
     _UpperCAmelCase			: Tuple     					=			downsamples
     _UpperCAmelCase			: str     					=			hidden_act
     _UpperCAmelCase			: int     					=			down_patch_size
     _UpperCAmelCase			: Dict     					=			down_stride
     _UpperCAmelCase			: Dict     					=			down_pad
     _UpperCAmelCase			: Tuple     					=			drop_path_rate
     _UpperCAmelCase			: Tuple     					=			use_layer_scale
     _UpperCAmelCase			: int     					=			layer_scale_init_value
     _UpperCAmelCase			: int     					=			batch_norm_eps



class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     List[Any]	=   version.parse("1.11"     )


 @property
 def 	snake_case__			(		self)	->						Mapping[str, Mapping[int, str]]:

     """simple docstring"""



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


 @property
 def 	snake_case__			(		self)	->						float:

     """simple docstring"""



     return 1e-4




