
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    _SCREAMING_SNAKE_CASE					( unittest.TestCase	):
      def __init__(     self						,		lowerCamelCase						,		lowerCamelCase=7						,		lowerCamelCase=3						,		lowerCamelCase=18						,		lowerCamelCase=30						,		lowerCamelCase=4_00						,		lowerCamelCase=True						,		lowerCamelCase=None						,		lowerCamelCase=True						,		lowerCamelCase=None						,		lowerCamelCase=True						,		lowerCamelCase=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]						,		lowerCamelCase=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]						,		lowerCamelCase=True						,		):
          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_normalize
          snake_case__  =			image_mean
          snake_case__  =			image_std
          snake_case__  =			do_convert_rgb
      def A_     (     self	):
          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 A_     (     self						,		lowerCamelCase=False						,		lowerCamelCase=False						,		lowerCamelCase=False	):
          assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

          if equal_resolution:
              snake_case__  =			[]
              for i in range(self.batch_size	):
                  image_inputs.append(
                      np.random.randint(
                          2_55						,		size=(self.num_channels, self.max_resolution, self.max_resolution)						,		dtype=np.uinta	)	)
          else:
              snake_case__  =			[]
              for i in range(self.batch_size	):
                  snake_case__  ,   snake_case__  =			np.random.choice(np.arange(self.min_resolution						,		self.max_resolution	)						,		2	)
                  image_inputs.append(np.random.randint(2_55						,		size=(self.num_channels, width, height)						,		dtype=np.uinta	)	)

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

          if torchify:
              snake_case__  =			[torch.from_numpy(lowerCamelCase	) for x in image_inputs]

          return image_inputs





@require_torch
@require_vision
class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase    ,   unittest.TestCase	):
      _A  :						Any      		=       CLIPImageProcessor if is_vision_available() else None
      def A_     (     self	):
          snake_case__  =			CLIPImageProcessingTester(self	)
      @property
      def A_     (     self	):
          return self.image_processor_tester.prepare_image_processor_dict()
      def A_     (     self	):
          snake_case__  =			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"	)	)
          self.assertTrue(hasattr(lowerCamelCase						,		"do_normalize"	)	)
          self.assertTrue(hasattr(lowerCamelCase						,		"image_mean"	)	)
          self.assertTrue(hasattr(lowerCamelCase						,		"image_std"	)	)
          self.assertTrue(hasattr(lowerCamelCase						,		"do_convert_rgb"	)	)
      def A_     (     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 A_     (     self	):
          pass
      def A_     (     self	):
          # Initialize image_processing
          snake_case__  =			self.image_processing_class(**self.image_processor_dict	)
          # create random PIL images
          snake_case__  =			self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase	)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase						,		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(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 A_     (     self	):
          # Initialize image_processing
          snake_case__  =			self.image_processing_class(**self.image_processor_dict	)
          # create random numpy tensors
          snake_case__  =			self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase						,		numpify=lowerCamelCase	)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase						,		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(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 A_     (     self	):
          # Initialize image_processing
          snake_case__  =			self.image_processing_class(**self.image_processor_dict	)
          # create random PyTorch tensors
          snake_case__  =			self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase						,		torchify=lowerCamelCase	)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase						,		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(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"],
              )						,		)





@require_torch
@require_vision
class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase    ,   unittest.TestCase	):
      _A  :						List[Any]      		=       CLIPImageProcessor if is_vision_available() else None
      def A_     (     self	):
          snake_case__  =			CLIPImageProcessingTester(self						,		num_channels=4	)
          snake_case__  =			3
      @property
      def A_     (     self	):
          return self.image_processor_tester.prepare_image_processor_dict()
      def A_     (     self	):
          snake_case__  =			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"	)	)
          self.assertTrue(hasattr(lowerCamelCase						,		"do_normalize"	)	)
          self.assertTrue(hasattr(lowerCamelCase						,		"image_mean"	)	)
          self.assertTrue(hasattr(lowerCamelCase						,		"image_std"	)	)
          self.assertTrue(hasattr(lowerCamelCase						,		"do_convert_rgb"	)	)
      def A_     (     self	):
          pass



      def A_     (     self	):
          # Initialize image_processing
          snake_case__  =			self.image_processing_class(**self.image_processor_dict	)
          # create random PIL images
          snake_case__  =			self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase	)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase						,		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.expected_encoded_image_num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )						,		)

          # Test batched
          snake_case__  =			image_processing(lowerCamelCase						,		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


__magic_name__           =					logging.get_logger(__name__)

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




class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						Any      		=       'swiftformer'
      def __init__(     self						,		lowerCamelCase=3						,		lowerCamelCase=[3, 3, 6, 4]						,		lowerCamelCase=[48, 56, 1_12, 2_20]						,		lowerCamelCase=4						,		lowerCamelCase=[True, True, True, True]						,		lowerCamelCase="gelu"						,		lowerCamelCase=3						,		lowerCamelCase=2						,		lowerCamelCase=1						,		lowerCamelCase=0.0						,		lowerCamelCase=True						,		lowerCamelCase=1e-5						,		lowerCamelCase=1e-5						,		**lowerCamelCase						,		):
          super().__init__(**lowerCamelCase	)
          snake_case__  =			num_channels
          snake_case__  =			depths
          snake_case__  =			embed_dims
          snake_case__  =			mlp_ratio
          snake_case__  =			downsamples
          snake_case__  =			hidden_act
          snake_case__  =			down_patch_size
          snake_case__  =			down_stride
          snake_case__  =			down_pad
          snake_case__  =			drop_path_rate
          snake_case__  =			use_layer_scale
          snake_case__  =			layer_scale_init_value
          snake_case__  =			batch_norm_eps





class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						int      		=       version.parse('1.11'	)
      @property
      def A_     (     self	):
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]	)



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



