
"""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  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=7   ,       SCREAMING_SNAKE_CASE__=3   ,       SCREAMING_SNAKE_CASE__=30   ,       SCREAMING_SNAKE_CASE__=4_00   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=0.9   ,       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]   ,       )					->		List[str]:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		List[str]:



											"""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  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[Any]					       =   PoolFormerImageProcessor if is_vision_available() else None



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   PoolFormerImageProcessingTester(self    )



				@property
				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											return self.image_processor_tester.prepare_image_processor_dict()



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[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}    )

											SCREAMING_SNAKE_CASE__						:       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 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											pass



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.image_processing_class(**self.image_processor_dict    )
											# create random PIL images
											SCREAMING_SNAKE_CASE__						:       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
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   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
											SCREAMING_SNAKE_CASE__						:       str           =   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 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.image_processing_class(**self.image_processor_dict    )
											# create random numpy tensors
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   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
											SCREAMING_SNAKE_CASE__						:       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
											SCREAMING_SNAKE_CASE__						:       str           =   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 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   self.image_processing_class(**self.image_processor_dict    )
											# create random PyTorch tensors
											SCREAMING_SNAKE_CASE__						:       Any           =   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
											SCREAMING_SNAKE_CASE__						:       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
											SCREAMING_SNAKE_CASE__						:       List[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"""],
											    )   ,       )






"""simple docstring"""




import mpmath  # for roots of unity
import numpy as np



class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=None    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   list(poly_a or [0]    )[:]
											SCREAMING_SNAKE_CASE__						:       Tuple           =   list(poly_b or [0]    )[:]

											# Remove leading zero coefficients
											while self.polyA[-1] == 0:
																		self.polyA.pop()
											SCREAMING_SNAKE_CASE__						:       int           =   len(self.polyA    )

											while self.polyB[-1] == 0:
																		self.polyB.pop()
											SCREAMING_SNAKE_CASE__						:       List[str]           =   len(self.polyB    )

											# Add 0 to make lengths equal a power of 2
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   int(
											    2 ** np.ceil(np.loga(len(self.polyA    ) + len(self.polyB    ) - 1    )    )    )

											while len(self.polyA    ) < self.c_max_length:
																		self.polyA.append(0    )
											while len(self.polyB    ) < self.c_max_length:
																		self.polyB.append(0    )
											# A complex root used for the fourier transform
											SCREAMING_SNAKE_CASE__						:       List[str]           =   complex(mpmath.root(x=1   ,       n=self.c_max_length   ,       k=1    )    )

											# The product
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.__multiply()



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   [[x] for x in self.polyA] if which == """A""" else [[x] for x in self.polyB]
											# Corner case
											if len(SCREAMING_SNAKE_CASE__    ) <= 1:
																		return dft[0]
											#
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.c_max_length // 2
											while next_ncol > 0:
																		SCREAMING_SNAKE_CASE__						:       Any           =   [[] for i in range(SCREAMING_SNAKE_CASE__    )]
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   self.root**next_ncol

																		# First half of next step
																		SCREAMING_SNAKE_CASE__						:       str           =   1
																		for j in range(self.c_max_length // (next_ncol * 2)    ):
																									for i in range(SCREAMING_SNAKE_CASE__    ):
																																new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]    )
																									current_root *= root
																		# Second half of next step
																		SCREAMING_SNAKE_CASE__						:       int           =   1
																		for j in range(self.c_max_length // (next_ncol * 2)    ):
																									for i in range(SCREAMING_SNAKE_CASE__    ):
																																new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]    )
																									current_root *= root
																		# Update
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   new_dft
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   next_ncol // 2
											return dft[0]



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.__dft("""A"""    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.__dft("""B"""    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   [[dft_a[i] * dft_b[i] for i in range(self.c_max_length    )]]
											del dft_a
											del dft_b

											# Corner Case
											if len(inverce_c[0]    ) <= 1:
																		return inverce_c[0]
											# Inverse DFT
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   2
											while next_ncol <= self.c_max_length:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   [[] for i in range(SCREAMING_SNAKE_CASE__    )]
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   self.root ** (next_ncol // 2)
																		SCREAMING_SNAKE_CASE__						:       Any           =   1
																		# First half of next step
																		for j in range(self.c_max_length // next_ncol    ):
																									for i in range(next_ncol // 2    ):
																																# Even positions
																																new_inverse_c[i].append(
																																    (
																																        inverce_c[i][j]
																																        + inverce_c[i][j + self.c_max_length // next_ncol]
																																    )
																																    / 2    )
																																# Odd positions
																																new_inverse_c[i + next_ncol // 2].append(
																																    (
																																        inverce_c[i][j]
																																        - inverce_c[i][j + self.c_max_length // next_ncol]
																																    )
																																    / (2 * current_root)    )
																									current_root *= root
																		# Update
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   new_inverse_c
																		next_ncol *= 2
											# Unpack
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [round(x[0].real   ,       8    ) + round(x[0].imag   ,       8    ) * 1j for x in inverce_c]

											# Remove leading 0's
											while inverce_c[-1] == 0:
																		inverce_c.pop()
											return inverce_c



				def __str__(self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   """A = """ + """ + """.join(
											    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A]    )    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """B = """ + """ + """.join(
											    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B]    )    )
											SCREAMING_SNAKE_CASE__						:       int           =   """A*B = """ + """ + """.join(
											    F'''{coef}*x^{i}''' for coef, i in enumerate(self.product    )    )

											return F'''{a}\n{b}\n{c}'''


# Unit tests
if __name__ == "__main__":
					import doctest

					doctest.testmod()




