


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 MobileNetVaImageProcessor



class 							a       (      unittest.TestCase ):





     def __init__(     self       :					Optional[int]     ,						SCREAMING_SNAKE_CASE_       :					str     ,						SCREAMING_SNAKE_CASE_       :					List[str]=7     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]=3     ,						SCREAMING_SNAKE_CASE_       :					List[Any]=18     ,						SCREAMING_SNAKE_CASE_       :					List[str]=30     ,						SCREAMING_SNAKE_CASE_       :					List[Any]=400     ,						SCREAMING_SNAKE_CASE_       :					Dict=True     ,						SCREAMING_SNAKE_CASE_       :					Any=None     ,						SCREAMING_SNAKE_CASE_       :					List[str]=True     ,						SCREAMING_SNAKE_CASE_       :					Any=None     ,						):
        __lowerCamelCase:					List[Any]	 =						size if size is not None else {"""shortest_edge""": 20}
        __lowerCamelCase:					Tuple	 =						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
        __lowerCamelCase:					List[Any]	 =						parent
        __lowerCamelCase:					Optional[int]	 =						batch_size
        __lowerCamelCase:					str	 =						num_channels
        __lowerCamelCase:					Dict	 =						image_size
        __lowerCamelCase:					Tuple	 =						min_resolution
        __lowerCamelCase:					Union[str, Any]	 =						max_resolution
        __lowerCamelCase:					Tuple	 =						do_resize
        __lowerCamelCase:					List[str]	 =						size
        __lowerCamelCase:					int	 =						do_center_crop
        __lowerCamelCase:					Tuple	 =						crop_size







     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[Any]				):
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }



@require_torch
@require_vision
class 							a       (      _UpperCAmelCase  ,unittest.TestCase ):
     UpperCAmelCase__			:	int       =						MobileNetVaImageProcessor if is_vision_available() else None





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]				):
        __lowerCamelCase:					Tuple	 =						MobileNetVaImageProcessingTester(self				)





     @property
     def 			SCREAMING_SNAKE_CASE__							(     self       :					str				):
        return self.image_processor_tester.prepare_image_processor_dict()





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]				):
        __lowerCamelCase:					List[Any]	 =						self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""do_resize"""				)				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""size"""				)				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""do_center_crop"""				)				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""center_crop"""				)				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any				):
        __lowerCamelCase:					str	 =						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}				)

        __lowerCamelCase:					Optional[int]	 =						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 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[Any]				):
        pass





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        # Initialize image_processing
        __lowerCamelCase:					List[str]	 =						self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        __lowerCamelCase:					str	 =						prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=SCREAMING_SNAKE_CASE_				)
        for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						Image.Image				)

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

        # Test batched
        __lowerCamelCase:					Tuple	 =						image_processing(SCREAMING_SNAKE_CASE_     ,						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 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        # Initialize image_processing
        __lowerCamelCase:					Union[str, Any]	 =						self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        __lowerCamelCase:					Tuple	 =						prepare_image_inputs(self.image_processor_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
        __lowerCamelCase:					List[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
        __lowerCamelCase:					Optional[Any]	 =						image_processing(SCREAMING_SNAKE_CASE_     ,						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 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        # Initialize image_processing
        __lowerCamelCase:					str	 =						self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        __lowerCamelCase:					List[str]	 =						prepare_image_inputs(self.image_processor_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
        __lowerCamelCase:					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
        __lowerCamelCase:					Optional[int]	 =						image_processing(SCREAMING_SNAKE_CASE_     ,						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"""],
            )     ,						)






import sys



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

   __lowerCamelCase:					Union[str, Any]	 =						0
   __lowerCamelCase:					str	 =						sys.maxsize

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

   return 0 if min_len == sys.maxsize else min_len


