
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 ViTImageProcessor



class    _snake_case (						unittest.TestCase				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=13       ,	SCREAMING_SNAKE_CASE_=3       ,	SCREAMING_SNAKE_CASE_=2_24       ,	SCREAMING_SNAKE_CASE_=30       ,	SCREAMING_SNAKE_CASE_=4_00       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,	):


   '''simple docstring'''




   lowercase__	: List[str]     		=   size if size is not None else {"""height""": 18, """width""": 18}
   lowercase__	: int     		=   parent
   lowercase__	: Union[str, Any]     		=   batch_size
   lowercase__	: List[str]     		=   num_channels
   lowercase__	: str     		=   image_size
   lowercase__	: int     		=   min_resolution
   lowercase__	: Dict     		=   max_resolution
   lowercase__	: Tuple     		=   do_resize
   lowercase__	: Union[str, Any]     		=   size
   lowercase__	: Any     		=   do_normalize
   lowercase__	: Tuple     		=   image_mean
   lowercase__	: str     		=   image_std



 def   lowercase__  (       self):


   '''simple docstring'''




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



@require_torch
@require_vision
class    _snake_case (						UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Optional[Any]	   =      ViTImageProcessor if is_vision_available() else None


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   EfficientFormerImageProcessorTester(self)


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return self.image_proc_tester.prepare_image_processor_dict()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_mean"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_std"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_normalize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_resize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""size"""))


 def   lowercase__  (       self):


   '''simple docstring'''




   pass


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowercase__	: List[Any]     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	Image.Image)

   # Test not batched input
   lowercase__	: int     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: str     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowercase__	: str     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_       ,	numpify=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	np.ndarray)

   # Test not batched input
   lowercase__	: Optional[int]     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: Dict     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowercase__	: Dict     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_       ,	torchify=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	torch.Tensor)

   # Test not batched input
   lowercase__	: int     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: Any     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)








from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowerCamelCase__     :		Union[str, Any]        = logging.get_logger(__name__)



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Any	   =      ['pixel_values']


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = 1 / 2_55       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = 8       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   do_rescale
   lowercase__	: List[Any]     		=   rescale_factor
   lowercase__	: Tuple     		=   do_pad
   lowercase__	: Optional[Any]     		=   pad_size


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return rescale(SCREAMING_SNAKE_CASE_       ,	scale=SCREAMING_SNAKE_CASE_       ,	data_format=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   lowercase__		,     lowercase__	: Optional[int]     		=   get_image_size(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   (old_height // size + 1) * size - old_height
   lowercase__	: str     		=   (old_width // size + 1) * size - old_width

   return pad(SCREAMING_SNAKE_CASE_       ,	((0, pad_height), (0, pad_width))       ,	mode="""symmetric"""       ,	data_format=SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   do_rescale if do_rescale is not None else self.do_rescale
   lowercase__	: int     		=   rescale_factor if rescale_factor is not None else self.rescale_factor
   lowercase__	: Union[str, Any]     		=   do_pad if do_pad is not None else self.do_pad
   lowercase__	: Optional[Any]     		=   pad_size if pad_size is not None else self.pad_size

   lowercase__	: str     		=   make_list_of_images(SCREAMING_SNAKE_CASE_)

   if not valid_images(SCREAMING_SNAKE_CASE_):
     raise ValueError(
         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
         """torch.Tensor, tf.Tensor or jax.ndarray.""")

   if do_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

   # All transformations expect numpy arrays.
   lowercase__	: List[Any]     		=   [to_numpy_array(SCREAMING_SNAKE_CASE_) for image in images]

   if do_rescale:
     lowercase__	: str     		=   [self.rescale(image=SCREAMING_SNAKE_CASE_       ,	scale=SCREAMING_SNAKE_CASE_) for image in images]

   if do_pad:
     lowercase__	: List[str]     		=   [self.pad(SCREAMING_SNAKE_CASE_       ,	size=SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__	: Optional[Any]     		=   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__	: Dict     		=   {"""pixel_values""": images}
   return BatchFeature(data=SCREAMING_SNAKE_CASE_       ,	tensor_type=SCREAMING_SNAKE_CASE_)






