


"""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							, _a							, _a=7							, _a=3							, _a=30							, _a=400							, _a=True							, _a=None							, _a=0.9							, _a=None							, _a=True							, _a=[0.5, 0.5, 0.5]							, _a=[0.5, 0.5, 0.5]							, ):
				__a           =			size if size is not None else {'''shortest_edge''': 30}
				__a           =			crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
				__a           =			parent
				__a           =			batch_size
				__a           =			num_channels
				__a           =			min_resolution
				__a           =			max_resolution
				__a           =			do_resize_and_center_crop
				__a           =			size
				__a           =			crop_pct
				__a           =			crop_size
				__a           =			do_normalize
				__a           =			image_mean
				__a           =			image_std





		def 			__UpperCAmelCase       (  self      ):
				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					(   __SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			int										=				PoolFormerImageProcessor if is_vision_available() else None

		def 			__UpperCAmelCase       (  self      ):
				__a           =			PoolFormerImageProcessingTester(self      )

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

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.image_processing_class(**self.image_processor_dict      )
				self.assertTrue(hasattr(_a							, '''do_resize_and_center_crop'''      )      )
				self.assertTrue(hasattr(_a							, '''size'''      )      )
				self.assertTrue(hasattr(_a							, '''crop_pct'''      )      )
				self.assertTrue(hasattr(_a							, '''do_normalize'''      )      )
				self.assertTrue(hasattr(_a							, '''image_mean'''      )      )
				self.assertTrue(hasattr(_a							, '''image_std'''      )      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			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}      )

				__a           =			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      ):
				pass

		def 			__UpperCAmelCase       (  self      ):
				# Initialize image_processing
				__a           =			self.image_processing_class(**self.image_processor_dict      )
				# create random PIL images
				__a           =			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           =			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
				__a           =			image_processing(_a							, 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      ):
				# Initialize image_processing
				__a           =			self.image_processing_class(**self.image_processor_dict      )
				# create random numpy tensors
				__a           =			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           =			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
				__a           =			image_processing(_a							, 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      ):
				# Initialize image_processing
				__a           =			self.image_processing_class(**self.image_processor_dict      )
				# create random PyTorch tensors
				__a           =			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           =			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
				__a           =			image_processing(_a							, 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 pprint

import requests

UpperCamelCase__    					=      '''https://zenquotes.io/api'''





def 					a__       (					)    ->	list:
    return requests.get(API_ENDPOINT_URL + '''/today'''						).json()





def 					a__       (					)    ->	list:
    return requests.get(API_ENDPOINT_URL + '''/random'''						).json()


if __name__ == "__main__":
  UpperCamelCase__    					=      random_quotes()
  pprint.pprint(response)
