code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import random
  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():
      import PIL
  
      from transformers import FlavaImageProcessor
      from transformers.image_utils import PILImageResampling
      from transformers.models.flava.image_processing_flava import (
          FLAVA_CODEBOOK_MEAN,
          FLAVA_CODEBOOK_STD,
          FLAVA_IMAGE_MEAN,
          FLAVA_IMAGE_STD,
      )
  else:
      A_			  =    A_			  =    A_			  =    A_			  =    None
  class 						__lowerCAmelCase   (			unittest.TestCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		Any				,			UpperCamelCase_:		Optional[int]				,			UpperCamelCase_:		Optional[int]=7				,			UpperCamelCase_:		str=3				,			UpperCamelCase_:		List[str]=30				,			UpperCamelCase_:		List[Any]=400				,			UpperCamelCase_:		Any=True				,			UpperCamelCase_:		Tuple=None				,			UpperCamelCase_:		str=True				,			UpperCamelCase_:		Optional[Any]=None				,			UpperCamelCase_:		Any=None				,			UpperCamelCase_:		Dict=True				,			UpperCamelCase_:		str=1 / 255				,			UpperCamelCase_:		List[Any]=True				,			UpperCamelCase_:		List[str]=FLAVA_IMAGE_MEAN				,			UpperCamelCase_:		Dict=FLAVA_IMAGE_STD				,			UpperCamelCase_:		List[Any]=14				,			UpperCamelCase_:		List[str]=75				,			UpperCamelCase_:		Dict=None				,			UpperCamelCase_:		List[Any]=16				,			UpperCamelCase_:		Optional[int]=0.3				,			UpperCamelCase_:		Any=None				,			UpperCamelCase_:		Union[str, Any]=True				,			UpperCamelCase_:		Optional[Any]=None				,			UpperCamelCase_:		List[Any]=None				,			UpperCamelCase_:		Any=True				,			UpperCamelCase_:		Optional[int]=None				,			UpperCamelCase_:		Optional[int]=True				,			UpperCamelCase_:		Optional[int]=True				,			UpperCamelCase_:		Union[str, Any]=FLAVA_CODEBOOK_MEAN				,			UpperCamelCase_:		Optional[int]=FLAVA_CODEBOOK_STD				,			):
       UpperCamelCase_     =size if size is not None else {"height": 224, "width": 224}
       UpperCamelCase_     =crop_size if crop_size is not None else {"height": 224, "width": 224}
       UpperCamelCase_     =codebook_size if codebook_size is not None else {"height": 112, "width": 112}
       UpperCamelCase_     =codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112}
  
       UpperCamelCase_     =parent
       UpperCamelCase_     =batch_size
       UpperCamelCase_     =num_channels
       UpperCamelCase_     =do_resize
       UpperCamelCase_     =do_rescale
       UpperCamelCase_     =rescale_factor
       UpperCamelCase_     =min_resolution
       UpperCamelCase_     =max_resolution
       UpperCamelCase_     =size
       UpperCamelCase_     =resample if resample is not None else PILImageResampling.BICUBIC
       UpperCamelCase_     =do_normalize
       UpperCamelCase_     =image_mean
       UpperCamelCase_     =image_std
       UpperCamelCase_     =do_center_crop
       UpperCamelCase_     =crop_size
  
       UpperCamelCase_     =input_size_patches
       UpperCamelCase_     =total_mask_patches
       UpperCamelCase_     =mask_group_max_patches
       UpperCamelCase_     =mask_group_min_patches
       UpperCamelCase_     =mask_group_min_aspect_ratio
       UpperCamelCase_     =mask_group_max_aspect_ratio
  
       UpperCamelCase_     =codebook_do_resize
       UpperCamelCase_     =codebook_size
       UpperCamelCase_     =codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
       UpperCamelCase_     =codebook_do_center_crop
       UpperCamelCase_     =codebook_crop_size
       UpperCamelCase_     =codebook_do_map_pixels
       UpperCamelCase_     =codebook_do_normalize
       UpperCamelCase_     =codebook_image_mean
       UpperCamelCase_     =codebook_image_std
    def 					UpperCamelCase__   (      self:		Optional[int]					):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "resample": self.resample,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "input_size_patches": self.input_size_patches,
           "total_mask_patches": self.total_mask_patches,
           "mask_group_max_patches": self.mask_group_max_patches,
           "mask_group_min_patches": self.mask_group_min_patches,
           "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
           "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
           "codebook_do_resize": self.codebook_do_resize,
           "codebook_size": self.codebook_size,
           "codebook_resample": self.codebook_resample,
           "codebook_do_center_crop": self.codebook_do_center_crop,
           "codebook_crop_size": self.codebook_crop_size,
           "codebook_do_map_pixels": self.codebook_do_map_pixels,
           "codebook_do_normalize": self.codebook_do_normalize,
           "codebook_image_mean": self.codebook_image_mean,
           "codebook_image_std": self.codebook_image_std,
       }
    def 					UpperCamelCase__   (      self:		Any					):
       return (self.size["height"], self.size["width"])
    def 					UpperCamelCase__   (      self:		int					):
       return (
           (self.input_size_patches, self.input_size_patches)
           if not isinstance(self.input_size_patches				,			UpperCamelCase_					)
           else self.input_size_patches
       )
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		Dict					):
       return (self.codebook_size["height"], self.codebook_size["width"])
  
  
  
  
  @require_torch
  @require_vision
  class 						__lowerCAmelCase   (			UpperCAmelCase       ,   unittest.TestCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    __lowerCamelCase       :    Optional[int]     	=			FlavaImageProcessor if is_vision_available() else None
    __lowerCamelCase       :    Union[str, Any]     	=			None
    def 					UpperCamelCase__   (      self:		str					):
       UpperCamelCase_     =FlavaImageProcessingTester(self					)
    @property
    def 					UpperCamelCase__   (      self:		Dict					):
       return self.image_processor_tester.prepare_image_processor_dict()
    def 					UpperCamelCase__   (      self:		Tuple					):
       UpperCamelCase_     =self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"image_mean"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"image_std"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"do_normalize"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"do_resize"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"resample"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"crop_size"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"do_center_crop"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"do_rescale"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"rescale_factor"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"masking_generator"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"codebook_do_resize"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"codebook_size"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"codebook_resample"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"codebook_do_center_crop"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"codebook_crop_size"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"codebook_do_map_pixels"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"codebook_do_normalize"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"codebook_image_mean"					)					)
       self.assertTrue(hasattr(UpperCamelCase_				,			"codebook_image_std"					)					)
    def 					UpperCamelCase__   (      self:		Tuple					):
       UpperCamelCase_     =self.image_processing_class.from_dict(self.image_processor_dict					)
       self.assertEqual(image_processor.size				,			{"height": 224, "width": 224}					)
       self.assertEqual(image_processor.crop_size				,			{"height": 224, "width": 224}					)
       self.assertEqual(image_processor.codebook_size				,			{"height": 112, "width": 112}					)
       self.assertEqual(image_processor.codebook_crop_size				,			{"height": 112, "width": 112}					)
  
       UpperCamelCase_     =self.image_processing_class.from_dict(
           self.image_processor_dict				,			size=42				,			crop_size=84				,			codebook_size=33				,			codebook_crop_size=66					)
       self.assertEqual(image_processor.size				,			{"height": 42, "width": 42}					)
       self.assertEqual(image_processor.crop_size				,			{"height": 84, "width": 84}					)
       self.assertEqual(image_processor.codebook_size				,			{"height": 33, "width": 33}					)
       self.assertEqual(image_processor.codebook_crop_size				,			{"height": 66, "width": 66}					)
    def 					UpperCamelCase__   (      self:		Union[str, Any]					):
       pass
    def 					UpperCamelCase__   (      self:		Union[str, Any]					):
       # Initialize image_processing
       UpperCamelCase_     =self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       UpperCamelCase_     =prepare_image_inputs(self.image_processor_tester				,			equal_resolution=UpperCamelCase_					)
       for image in image_inputs:
          self.assertIsInstance(UpperCamelCase_				,			PIL.Image.Image					)
  
       # Test not batched input
       UpperCamelCase_     =image_processing(image_inputs[0]				,			return_tensors="pt"					)
  
       # Test no bool masked pos
       self.assertFalse("bool_masked_pos" in encoded_images					)
  
       UpperCamelCase_    ,							UpperCamelCase_     =self.image_processor_tester.get_expected_image_size()
  
       self.assertEqual(
           encoded_images.pixel_values.shape				,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,			)
  
       # Test batched
       UpperCamelCase_     =image_processing(UpperCamelCase_				,			return_tensors="pt"					)
       UpperCamelCase_    ,							UpperCamelCase_     =self.image_processor_tester.get_expected_image_size()
  
       # Test no bool masked pos
       self.assertFalse("bool_masked_pos" in encoded_images					)
  
       self.assertEqual(
           encoded_images.pixel_values.shape				,			(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )				,			)
    def 					UpperCamelCase__   (      self:		Optional[Any]				,			UpperCamelCase_:		int				,			UpperCamelCase_:		Tuple					):
       # Initialize image_processing
       UpperCamelCase_     =self.image_processing_class(**self.image_processor_dict					)
       # create random tensors
       UpperCamelCase_     =prepare_image_inputs(self.image_processor_tester				,			equal_resolution=UpperCamelCase_				,			**UpperCamelCase_					)
       for image in image_inputs:
          self.assertIsInstance(UpperCamelCase_				,			UpperCamelCase_					)
  
       # Test not batched input
       UpperCamelCase_     =image_processing(image_inputs[0]				,			return_tensors="pt"					)
  
       UpperCamelCase_    ,							UpperCamelCase_     =self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.pixel_values.shape				,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,			)
  
       UpperCamelCase_     =image_processing(UpperCamelCase_				,			return_image_mask=UpperCamelCase_				,			return_tensors="pt"					)
  
       UpperCamelCase_    ,							UpperCamelCase_     =self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.pixel_values.shape				,			(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )				,			)
  
       UpperCamelCase_    ,							UpperCamelCase_     =self.image_processor_tester.get_expected_mask_size()
       self.assertEqual(
           encoded_images.bool_masked_pos.shape				,			(
               self.image_processor_tester.batch_size,
               expected_height,
               expected_width,
           )				,			)
  
       # Test batched
       UpperCamelCase_     =image_processing(UpperCamelCase_				,			return_tensors="pt"					).pixel_values
  
       UpperCamelCase_    ,							UpperCamelCase_     =self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.shape				,			(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )				,			)
  
       # Test masking
       UpperCamelCase_     =image_processing(UpperCamelCase_				,			return_image_mask=UpperCamelCase_				,			return_tensors="pt"					)
  
       UpperCamelCase_    ,							UpperCamelCase_     =self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.pixel_values.shape				,			(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )				,			)
  
       UpperCamelCase_    ,							UpperCamelCase_     =self.image_processor_tester.get_expected_mask_size()
       self.assertEqual(
           encoded_images.bool_masked_pos.shape				,			(
               self.image_processor_tester.batch_size,
               expected_height,
               expected_width,
           )				,			)
    def 					UpperCamelCase__   (      self:		List[str]					):
       self._test_call_framework(np.ndarray				,			prepare_kwargs={"numpify": True}					)
    def 					UpperCamelCase__   (      self:		Union[str, Any]					):
       self._test_call_framework(torch.Tensor				,			prepare_kwargs={"torchify": True}					)
    def 					UpperCamelCase__   (      self:		str					):
       # Initialize image_processing
       random.seed(1234					)
       UpperCamelCase_     =self.image_processing_class(**self.image_processor_dict					)
       UpperCamelCase_     =prepare_image_inputs(self.image_processor_tester				,			equal_resolution=UpperCamelCase_				,			torchify=UpperCamelCase_					)
  
       # Test not batched input
       UpperCamelCase_     =image_processing(image_inputs[0]				,			return_image_mask=UpperCamelCase_				,			return_tensors="pt"					)
       self.assertEqual(encoded_images.bool_masked_pos.sum().item()				,			75					)
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		List[str]					):
       # Initialize image_processing
       UpperCamelCase_     =self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       UpperCamelCase_     =prepare_image_inputs(self.image_processor_tester				,			equal_resolution=UpperCamelCase_					)
       for image in image_inputs:
          self.assertIsInstance(UpperCamelCase_				,			PIL.Image.Image					)
  
       # Test not batched input
       UpperCamelCase_     =image_processing(image_inputs[0]				,			return_codebook_pixels=UpperCamelCase_				,			return_tensors="pt"					)
       UpperCamelCase_    ,							UpperCamelCase_     =self.image_processor_tester.get_expected_codebook_image_size()
       self.assertEqual(
           encoded_images.codebook_pixel_values.shape				,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,			)
  
       # Test batched
       UpperCamelCase_     =image_processing(UpperCamelCase_				,			return_codebook_pixels=UpperCamelCase_				,			return_tensors="pt"					)
       UpperCamelCase_    ,							UpperCamelCase_     =self.image_processor_tester.get_expected_codebook_image_size()
       self.assertEqual(
           encoded_images.codebook_pixel_values.shape				,			(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )				,			)
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  from datetime import date
  
  import requests
  
  A_			  =    "https://api.carbonintensity.org.uk/intensity"
  
  
  
  
  def 						_UpperCamelCase			(  ):
     UpperCamelCase_     =requests.get(A			).json()["data"][0]
     return last_half_hour["intensity"]["actual"]
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A			):
     return requests.get(f"""{BASE_URL}/{start}/{end}"""			).json()["data"]
  
  
  if __name__ == "__main__":
      for entry in fetch_from_to(start=date(2020, 10, 1), end=date(2020, 10, 3)):
          print("from {from} to {to}: {intensity[actual]}".format(**entry))
      print(f'''{fetch_last_half_hour() = }''')
  