

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   lowercase_    (  unittest.TestCase     ):



      def __init__(			self      :				Tuple			,					A__      :				Dict			,					A__      :				int=7			,					A__      :				Dict=3			,					A__      :				Union[str, Any]=18			,					A__      :				str=30			,					A__      :				Union[str, Any]=400			,					A__      :				str=True			,					A__      :				Optional[Any]=None			,					A__      :				Any=True			,					A__      :				Dict=None			,					)    ->     Optional[int]:
        _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







      def 						UpperCamelCase_		(			self      :				Any		)    ->     Dict:
        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   lowercase_    (  __SCREAMING_SNAKE_CASE   ,							unittest.TestCase     ):
      UpperCamelCase_			:		Optional[Any]						      =				MobileNetVaImageProcessor if is_vision_available() else None



      def 						UpperCamelCase_		(			self      :				Dict		)    ->     Union[str, Any]:
        _snake_case						=							MobileNetVaImageProcessingTester(self		)



      @property
      def 						UpperCamelCase_		(			self      :				Dict		)    ->     Optional[int]:
        return self.image_processor_tester.prepare_image_processor_dict()



      def 						UpperCamelCase_		(			self      :				Optional[int]		)    ->     Optional[int]:
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(_snake_case			,					'''do_resize'''		)		)
        self.assertTrue(hasattr(_snake_case			,					'''size'''		)		)
        self.assertTrue(hasattr(_snake_case			,					'''do_center_crop'''		)		)
        self.assertTrue(hasattr(_snake_case			,					'''center_crop'''		)		)



      def 						UpperCamelCase_		(			self      :				str		)    ->     Any:
        _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 						UpperCamelCase_		(			self      :				Tuple		)    ->     Optional[int]:
        pass



      def 						UpperCamelCase_		(			self      :				Optional[int]		)    ->     Dict:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        _snake_case						=							prepare_image_inputs(self.image_processor_tester			,					equal_resolution=_snake_case		)
        for image in image_inputs:
          self.assertIsInstance(_snake_case			,					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(_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 						UpperCamelCase_		(			self      :				Optional[Any]		)    ->     Dict:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        _snake_case						=							prepare_image_inputs(self.image_processor_tester			,					equal_resolution=_snake_case			,					numpify=_snake_case		)
        for image in image_inputs:
          self.assertIsInstance(_snake_case			,					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(_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 						UpperCamelCase_		(			self      :				Tuple		)    ->     Optional[int]:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        _snake_case						=							prepare_image_inputs(self.image_processor_tester			,					equal_resolution=_snake_case			,					torchify=_snake_case		)
        for image in image_inputs:
          self.assertIsInstance(_snake_case			,					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(_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'''],
            )			,					)




'''simple docstring'''
from __future__ import annotations


def       a__    (  _SCREAMING_SNAKE_CASE					:    int							)			-> set[int]:


	"""simple docstring"""





	UpperCAmelCase_			:	Dict      =   {1}
	UpperCAmelCase_			:	int      =   [1]
	for _ in range(2     ,							depth + 1							):
		UpperCAmelCase_			:	Dict      =   [*previous_coefficients, 0]
		UpperCAmelCase_			:	Tuple      =   [0, *previous_coefficients]
		UpperCAmelCase_			:	Dict      =   []
		for x, y in zip(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE							):
			coefficients.add(x + y							)
			previous_coefficients.append(x + y							)
	return coefficients


def       a__    (  _SCREAMING_SNAKE_CASE					:    set[int]							)			-> set[int]:


	"""simple docstring"""





	UpperCAmelCase_			:	List[Any]      =   set()
	for number in unique_coefficients:
		UpperCAmelCase_			:	Tuple      =   2
		UpperCAmelCase_			:	Tuple      =   number
		while divisor**2 <= copy_number:
			UpperCAmelCase_			:	List[Any]      =   0
			while copy_number % divisor == 0:
				copy_number //= divisor
				multiplicity += 1
			if multiplicity >= 2:
				non_squarefrees.add(_SCREAMING_SNAKE_CASE							)
				break
			divisor += 1

	return unique_coefficients.difference(_SCREAMING_SNAKE_CASE							)


def       a__    (  _SCREAMING_SNAKE_CASE					:    int = 51							)			-> int:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[Any]      =   get_pascal_triangle_unique_coefficients(_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	Union[str, Any]      =   get_squarefrees(_SCREAMING_SNAKE_CASE							)
	return sum(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
					print(f"""{solution() = }""")




