




"""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 PoolFormerImageProcessor


class _lowerCamelCase    (					unittest.TestCase						):






   def __init__(		self       :		Union[str, Any]   ,     UpperCamelCase       :		List[str]   ,     UpperCamelCase       :		Any=7   ,     UpperCamelCase       :		Optional[Any]=3   ,     UpperCamelCase       :		List[str]=30   ,     UpperCamelCase       :		Any=4_00   ,     UpperCamelCase       :		Dict=True   ,     UpperCamelCase       :		Dict=None   ,     UpperCamelCase       :		Dict=0.9   ,     UpperCamelCase       :		str=None   ,     UpperCamelCase       :		int=True   ,     UpperCamelCase       :		Optional[Any]=[0.5, 0.5, 0.5]   ,     UpperCamelCase       :		str=[0.5, 0.5, 0.5]   ,     )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	size if size is not None else {"""shortest_edge""": 30}
         lowerCAmelCase__      :			str   =	crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
         lowerCAmelCase__      :			List[Any]   =	parent
         lowerCAmelCase__      :			Optional[Any]   =	batch_size
         lowerCAmelCase__      :			List[str]   =	num_channels
         lowerCAmelCase__      :			Dict   =	min_resolution
         lowerCAmelCase__      :			Optional[int]   =	max_resolution
         lowerCAmelCase__      :			str   =	do_resize_and_center_crop
         lowerCAmelCase__      :			Tuple   =	size
         lowerCAmelCase__      :			List[Any]   =	crop_pct
         lowerCAmelCase__      :			Union[str, Any]   =	crop_size
         lowerCAmelCase__      :			Optional[int]   =	do_normalize
         lowerCAmelCase__      :			str   =	image_mean
         lowerCAmelCase__      :			Dict   =	image_std






   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Optional[Any]:

         """simple docstring"""
         return {
             "size": self.size,
             "do_resize_and_center_crop": self.do_resize_and_center_crop,
             "crop_pct": self.crop_pct,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }






@require_torch
@require_vision
class _lowerCamelCase    (					a_       ,	unittest.TestCase						):
   _lowerCamelCase    :Dict     				=     PoolFormerImageProcessor if is_vision_available() else None






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	PoolFormerImageProcessingTester(self   )






   @property
   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						str:

         """simple docstring"""
         return self.image_processor_tester.prepare_image_processor_dict()






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	self.image_processing_class(**self.image_processor_dict   )
         self.assertTrue(hasattr(UpperCamelCase   ,     """do_resize_and_center_crop"""   )   )
         self.assertTrue(hasattr(UpperCamelCase   ,     """size"""   )   )
         self.assertTrue(hasattr(UpperCamelCase   ,     """crop_pct"""   )   )
         self.assertTrue(hasattr(UpperCamelCase   ,     """do_normalize"""   )   )
         self.assertTrue(hasattr(UpperCamelCase   ,     """image_mean"""   )   )
         self.assertTrue(hasattr(UpperCamelCase   ,     """image_std"""   )   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	self.image_processing_class.from_dict(self.image_processor_dict   )
         self.assertEqual(image_processor.size   ,     {"""shortest_edge""": 30}   )
         self.assertEqual(image_processor.crop_size   ,     {"""height""": 30, """width""": 30}   )

         lowerCAmelCase__      :			Union[str, Any]   =	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 	_lowerCAmelCase   (		self       :		Any   )			->						Optional[int]:

         """simple docstring"""
         pass






   def 	_lowerCAmelCase   (		self       :		str   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	self.image_processing_class(**self.image_processor_dict   )
         # create random PIL images
         lowerCAmelCase__      :			Dict   =	prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=UpperCamelCase   )
         for image in image_inputs:
               self.assertIsInstance(UpperCamelCase   ,     Image.Image   )

         # Test not batched input
         lowerCAmelCase__      :			Tuple   =	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__      :			Union[str, Any]   =	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 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.image_processing_class(**self.image_processor_dict   )
         # create random numpy tensors
         lowerCAmelCase__      :			int   =	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
         lowerCAmelCase__      :			List[str]   =	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__      :			Tuple   =	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 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	self.image_processing_class(**self.image_processor_dict   )
         # create random PyTorch tensors
         lowerCAmelCase__      :			int   =	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
         lowerCAmelCase__      :			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
         lowerCAmelCase__      :			Any   =	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"""


import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_A													=      logging.get_logger(__name__)


class _lowerCamelCase    (					a_						):






   def __init__(		self       :		Union[str, Any]   ,     *UpperCamelCase       :		int   ,     **UpperCamelCase       :		List[Any]   )			->						None:

         """simple docstring"""
         warnings.warn(
             """The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
             """ use CLIPImageProcessor instead."""   ,     UpperCamelCase   ,     )
         super().__init__(*UpperCamelCase   ,     **UpperCamelCase   )





