






"""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,
         )				,			)








"""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() = }''')
