



import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor






class SCREAMING_SNAKE_CASE__	(						unittest.TestCase       ):




    '''simple docstring'''






    def __init__(			self,						lowerCamelCase__,						lowerCamelCase__ = True,						lowerCamelCase__ = None,						lowerCamelCase__ = 32,						lowerCamelCase__ = True,						lowerCamelCase__ = 1 / 255,						lowerCamelCase__ = True,						lowerCamelCase__ = True,						lowerCamelCase__ = [0.4814_5466, 0.457_8275, 0.4082_1073],						lowerCamelCase__ = [0.2686_2954, 0.2613_0258, 0.2757_7711],						lowerCamelCase__ = True,						lowerCamelCase__=7,						lowerCamelCase__=30,						lowerCamelCase__=400,						lowerCamelCase__=3,						):
        A					:			str		=  parent
        A					:			List[Any]		=  do_resize
        A					:			Union[str, Any]		=  size if size is not None else {"""shortest_edge""": 288}
        A					:			Optional[Any]		=  size_divisor
        A					:			str		=  do_rescale
        A					:			Tuple		=  rescale_factor
        A					:			Optional[int]		=  do_normalize
        A					:			Tuple		=  do_center_crop
        A					:			Any		=  image_mean
        A					:			Union[str, Any]		=  image_std
        A					:			str		=  do_pad
        A					:			Any		=  batch_size
        A					:			Union[str, Any]		=  num_channels
        A					:			Dict		=  min_resolution
        A					:			Tuple		=  max_resolution






    def      _lowerCAmelCase   (			self						):
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }






    def      _lowerCAmelCase   (			self,						lowerCamelCase__,						lowerCamelCase__=False						):
        if not batched:
            A					:			Optional[Any]		=  self.size["""shortest_edge"""]
            A					:			Optional[int]		=  image_inputs[0]
            if isinstance(__A,						Image.Image						):
                A					:			Any		=  image.size
            else:
                A					:			Dict		=  image.shape[1], image.shape[2]
            A					:			Tuple		=  size / min(__A,						__A						)
            if h < w:
                A					:			Union[str, Any]		=  size, scale * w
            else:
                A					:			str		=  scale * h, size

            A					:			List[str]		=  int((1333 / 800) * size						)
            if max(__A,						__A						) > max_size:
                A					:			Optional[int]		=  max_size / max(__A,						__A						)
                A					:			Tuple		=  newh * scale
                A					:			Optional[Any]		=  neww * scale

            A					:			Tuple		=  int(newh + 0.5						), int(neww + 0.5						)
            A					:			List[Any]		=  (
                newh // self.size_divisor * self.size_divisor,
                neww // self.size_divisor * self.size_divisor,
            )

        else:
            A					:			Dict		=  []
            for image in image_inputs:
                A					:			List[str]		=  self.get_expected_values([image]						)
                expected_values.append((expected_height, expected_width)						)
            A					:			Dict		=  max(__A,						key=lambda lowerCamelCase__						: item[0]						)[0]
            A					:			Dict		=  max(__A,						key=lambda lowerCamelCase__						: item[1]						)[1]

        return expected_height, expected_width






@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__	(						_lowercase					,		unittest.TestCase       ):




    '''simple docstring'''






    __lowerCamelCase     :					str   = BridgeTowerImageProcessor if is_vision_available() else None






    def      _lowerCAmelCase   (			self						):
        A					:			str		=  BridgeTowerImageProcessingTester(self						)






    @property
    def      _lowerCAmelCase   (			self						):
        return self.image_processor_tester.prepare_image_processor_dict()






    def      _lowerCAmelCase   (			self						):
        A					:			int		=  self.image_processing_class(**self.image_processor_dict						)
        self.assertTrue(hasattr(__A,						"""image_mean"""						)						)
        self.assertTrue(hasattr(__A,						"""image_std"""						)						)
        self.assertTrue(hasattr(__A,						"""do_normalize"""						)						)
        self.assertTrue(hasattr(__A,						"""do_resize"""						)						)
        self.assertTrue(hasattr(__A,						"""size"""						)						)
        self.assertTrue(hasattr(__A,						"""size_divisor"""						)						)






    def      _lowerCAmelCase   (			self						):
        pass






    def      _lowerCAmelCase   (			self						):
        # Initialize image processor
        A					:			Optional[int]		=  self.image_processing_class(**self.image_processor_dict						)
        # create random PIL images
        A					:			Optional[int]		=  prepare_image_inputs(self.image_processor_tester,						equal_resolution=__A						)
        for image in image_inputs:
            self.assertIsInstance(__A,						Image.Image						)

        # Test not batched input
        A					:			Dict		=  image_processing(image_inputs[0],						return_tensors="""pt"""						).pixel_values

        A					:			Optional[Any]		=  self.image_processor_tester.get_expected_values(__A						)
        self.assertEqual(
            encoded_images.shape,						(1, self.image_processor_tester.num_channels, expected_height, expected_width),						)

        # Test batched
        A					:			Union[str, Any]		=  image_processing(__A,						return_tensors="""pt"""						).pixel_values

        A					:			List[Any]		=  self.image_processor_tester.get_expected_values(__A,						batched=__A						)
        self.assertEqual(
            encoded_images.shape,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),						)






    def      _lowerCAmelCase   (			self						):
        # Initialize image processor
        A					:			Optional[int]		=  self.image_processing_class(**self.image_processor_dict						)
        # create random numpy tensors
        A					:			Optional[int]		=  prepare_image_inputs(self.image_processor_tester,						equal_resolution=__A,						numpify=__A						)
        for image in image_inputs:
            self.assertIsInstance(__A,						np.ndarray						)

        # Test not batched input
        A					:			Union[str, Any]		=  image_processing(image_inputs[0],						return_tensors="""pt"""						).pixel_values

        A					:			str		=  self.image_processor_tester.get_expected_values(__A						)
        self.assertEqual(
            encoded_images.shape,						(1, self.image_processor_tester.num_channels, expected_height, expected_width),						)

        # Test batched
        A					:			List[str]		=  image_processing(__A,						return_tensors="""pt"""						).pixel_values

        A					:			Dict		=  self.image_processor_tester.get_expected_values(__A,						batched=__A						)
        self.assertEqual(
            encoded_images.shape,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),						)






    def      _lowerCAmelCase   (			self						):
        # Initialize image processor
        A					:			Union[str, Any]		=  self.image_processing_class(**self.image_processor_dict						)
        # create random PyTorch tensors
        A					:			Any		=  prepare_image_inputs(self.image_processor_tester,						equal_resolution=__A,						torchify=__A						)
        for image in image_inputs:
            self.assertIsInstance(__A,						torch.Tensor						)

        # Test not batched input
        A					:			List[Any]		=  image_processing(image_inputs[0],						return_tensors="""pt"""						).pixel_values

        A					:			str		=  self.image_processor_tester.get_expected_values(__A						)
        self.assertEqual(
            encoded_images.shape,						(1, self.image_processor_tester.num_channels, expected_height, expected_width),						)

        # Test batched
        A					:			int		=  image_processing(__A,						return_tensors="""pt"""						).pixel_values

        A					:			Optional[Any]		=  self.image_processor_tester.get_expected_values(__A,						batched=__A						)
        self.assertEqual(
            encoded_images.shape,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),						)





from collections.abc import Sequence
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	return sum(c * (x**i) for i, c in enumerate(a__       )       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	__A :			Any		    =			0.0
	for coeff in reversed(a__       ):
		__A :			List[str]		    =			result * x + coeff
	return result


if __name__ == "__main__":
			UpperCAmelCase_							:					List[str]		 =   (0.0, 0.0, 5.0, 9.3, 7.0)
			UpperCAmelCase_							:					str		 =   10.0
			print(evaluate_poly(poly, x))
			print(horner(poly, x))





