





'''simple docstring'''


import sys
def 							UpperCAmelCase_						(		__lowerCamelCase      :      int			,__lowerCamelCase      :      list[int]				):
   if not numbers:
      return 0
   if target == 0 and target in numbers:
      return 0
   if not isinstance(__lowerCamelCase			,(list, tuple)				) or not all(
       isinstance(__lowerCamelCase			,__lowerCamelCase				) for number in numbers				):
      raise ValueError("numbers must be an iterable of integers"				)

   lowercase_    :List[str]					    =				0
   lowercase_    :int					    =				sys.maxsize

   while right < len(__lowerCamelCase				):
      curr_sum += numbers[right]
      while curr_sum >= target and left <= right:
         lowercase_    :Any					    =				min(__lowerCamelCase			,right - left + 1				)
         curr_sum -= numbers[left]
         left += 1
      right += 1

   return 0 if min_len == sys.maxsize else min_len









'''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 EfficientNetImageProcessor
class    a_     (       unittest.TestCase							):





     def __init__(				self	:      int			,   lowercase	:      Optional[int]			,   lowercase	:      Tuple=13			,   lowercase	:      str=3			,   lowercase	:      List[str]=18			,   lowercase	:      Union[str, Any]=30			,   lowercase	:      Any=400			,   lowercase	:      Tuple=True			,   lowercase	:      Union[str, Any]=None			,   lowercase	:      Optional[Any]=True			,   lowercase	:      Optional[int]=[0.5, 0.5, 0.5]			,   lowercase	:      List[str]=[0.5, 0.5, 0.5]			,   ):





        """simple docstring"""




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







     def 							lowercase__   (				self	:      Union[str, Any]		):





        """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    a_     (       _lowerCAmelCase			,		unittest.TestCase							):
     __A  				=    EfficientNetImageProcessor if is_vision_available() else None





     def 							lowercase__   (				self	:      Optional[int]		):





        """simple docstring"""




        lowercase_    :List[Any]					    =				EfficientNetImageProcessorTester(self		)





     @property
     def 							lowercase__   (				self	:      Optional[Any]		):





        """simple docstring"""




        return self.image_processor_tester.prepare_image_processor_dict()





     def 							lowercase__   (				self	:      Union[str, Any]		):





        """simple docstring"""




        lowercase_    :Optional[Any]					    =				self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(lowercase			,   "image_mean"		)		)
        self.assertTrue(hasattr(lowercase			,   "image_std"		)		)
        self.assertTrue(hasattr(lowercase			,   "do_normalize"		)		)
        self.assertTrue(hasattr(lowercase			,   "do_resize"		)		)
        self.assertTrue(hasattr(lowercase			,   "size"		)		)





     def 							lowercase__   (				self	:      str		):





        """simple docstring"""




        lowercase_    :Tuple					    =				self.image_processing_class.from_dict(self.image_processor_dict		)
        self.assertEqual(image_processor.size			,   {"height": 18, "width": 18}		)

        lowercase_    :List[str]					    =				self.image_processing_class.from_dict(self.image_processor_dict			,   size=42		)
        self.assertEqual(image_processor.size			,   {"height": 42, "width": 42}		)





     def 							lowercase__   (				self	:      Optional[int]		):





        """simple docstring"""




        lowercase_    :List[Any]					    =				self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        lowercase_    :Tuple					    =				prepare_image_inputs(self.image_processor_tester			,   equal_resolution=lowercase		)
        for image in image_inputs:
           self.assertIsInstance(lowercase			,   Image.Image		)

        # Test not batched input
        lowercase_    :Optional[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.size["height"],
                self.image_processor_tester.size["width"],
            )			,   )

        # Test batched
        lowercase_    :Optional[Any]					    =				image_processing(lowercase			,   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.size["height"],
                self.image_processor_tester.size["width"],
            )			,   )





     def 							lowercase__   (				self	:      Union[str, Any]		):





        """simple docstring"""




        lowercase_    :int					    =				self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        lowercase_    :List[Any]					    =				prepare_image_inputs(self.image_processor_tester			,   equal_resolution=lowercase			,   numpify=lowercase		)
        for image in image_inputs:
           self.assertIsInstance(lowercase			,   np.ndarray		)

        # Test not batched input
        lowercase_    :Optional[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.size["height"],
                self.image_processor_tester.size["width"],
            )			,   )

        # Test batched
        lowercase_    :Any					    =				image_processing(lowercase			,   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.size["height"],
                self.image_processor_tester.size["width"],
            )			,   )







     def 							lowercase__   (				self	:      Union[str, Any]		):





        """simple docstring"""




        lowercase_    :str					    =				self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        lowercase_    :Tuple					    =				prepare_image_inputs(self.image_processor_tester			,   equal_resolution=lowercase			,   torchify=lowercase		)
        for image in image_inputs:
           self.assertIsInstance(lowercase			,   torch.Tensor		)

        # Test not batched input
        lowercase_    :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.size["height"],
                self.image_processor_tester.size["width"],
            )			,   )

        # Test batched
        lowercase_    :Optional[int]					    =				image_processing(lowercase			,   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.size["height"],
                self.image_processor_tester.size["width"],
            )			,   )


