import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor






class 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
   def __init__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_=7							,     lowerCAmelCase_=3							,     lowerCAmelCase_=30							,     lowerCAmelCase_=4_00							,     lowerCAmelCase_=True							,     lowerCAmelCase_=None							,     lowerCAmelCase_=True							,     lowerCAmelCase_=[0.5, 0.5, 0.5]							,     lowerCAmelCase_=[0.5, 0.5, 0.5]							,     lowerCAmelCase_=False							,     )	->Dict:
        a  :    Any				=						size if size is not None else {'''height''': 30, '''width''': 30}
        a  :    int				=						parent
        a  :    Optional[int]				=						batch_size
        a  :    Any				=						num_channels
        a  :    str				=						min_resolution
        a  :    Tuple				=						max_resolution
        a  :    str				=						do_resize
        a  :    Tuple				=						size
        a  :    Optional[Any]				=						do_normalize
        a  :    Any				=						image_mean
        a  :    List[Any]				=						image_std
        a  :    Dict				=						do_reduce_labels


   def _lowercase(    self    )	->Any:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_reduce_labels": self.do_reduce_labels,
        }







def 	UpperCAmelCase       (		)	->	int:
     a  :    List[str]				=						load_dataset('''hf-internal-testing/fixtures_ade20k'''						,      split='''test'''       )

     a  :    str				=						Image.open(dataset[0]['''file''']       )
     a  :    Tuple				=						Image.open(dataset[1]['''file''']       )

     return image, map







def 	UpperCAmelCase       (		)	->	Optional[int]:
     a  :    Optional[int]				=						load_dataset('''hf-internal-testing/fixtures_ade20k'''						,      split='''test'''       )

     a  :    Union[str, Any]				=						Image.open(dataset[0]['''file''']       )
     a  :    Optional[Any]				=						Image.open(dataset[1]['''file''']       )
     a  :    Optional[int]				=						Image.open(dataset[2]['''file''']       )
     a  :    List[Any]				=						Image.open(dataset[3]['''file''']       )

     return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class 							_SCREAMING_SNAKE_CASE(   snake_case__       ,      unittest.TestCase	):
   UpperCamelCase_				:						Union[str, Any] 			=   SegformerImageProcessor if is_vision_available() else None
   def _lowercase(    self    )	->Dict:
        a  :    str				=						SegformerImageProcessingTester(self    )
   @property
   def _lowercase(    self    )	->Optional[int]:
        return self.image_processor_tester.prepare_image_processor_dict()
   def _lowercase(    self    )	->Dict:
        a  :    int				=						self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(lowerCAmelCase_							,     '''do_resize'''    )    )
        self.assertTrue(hasattr(lowerCAmelCase_							,     '''size'''    )    )
        self.assertTrue(hasattr(lowerCAmelCase_							,     '''do_normalize'''    )    )
        self.assertTrue(hasattr(lowerCAmelCase_							,     '''image_mean'''    )    )
        self.assertTrue(hasattr(lowerCAmelCase_							,     '''image_std'''    )    )
        self.assertTrue(hasattr(lowerCAmelCase_							,     '''do_reduce_labels'''    )    )
   def _lowercase(    self    )	->str:
        a  :    Any				=						self.image_processing_class.from_dict(self.image_processor_dict    )
        self.assertEqual(image_processor.size							,     {'''height''': 30, '''width''': 30}    )
        self.assertEqual(image_processor.do_reduce_labels							,     lowerCAmelCase_    )

        a  :    Dict				=						self.image_processing_class.from_dict(self.image_processor_dict							,     size=42							,     reduce_labels=lowerCAmelCase_    )
        self.assertEqual(image_processor.size							,     {'''height''': 42, '''width''': 42}    )
        self.assertEqual(image_processor.do_reduce_labels							,     lowerCAmelCase_    )
   def _lowercase(    self    )	->str:
        pass
   def _lowercase(    self    )	->Union[str, Any]:
        # Initialize image_processing
        a  :    List[Any]				=						self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        a  :    Dict				=						prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase_    )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase_							,     Image.Image    )

        # Test not batched input
        a  :    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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )

        # Test batched
        a  :    Any				=						image_processing(lowerCAmelCase_							,     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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
   def _lowercase(    self    )	->Union[str, Any]:
        # Initialize image_processing
        a  :    List[Any]				=						self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        a  :    int				=						prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase_							,     numpify=lowerCAmelCase_    )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase_							,     np.ndarray    )

        # Test not batched input
        a  :    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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )

        # Test batched
        a  :    int				=						image_processing(lowerCAmelCase_							,     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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
   def _lowercase(    self    )	->Dict:
        # Initialize image_processing
        a  :    int				=						self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        a  :    str				=						prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase_							,     torchify=lowerCAmelCase_    )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase_							,     torch.Tensor    )

        # Test not batched input
        a  :    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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )

        # Test batched
        a  :    Any				=						image_processing(lowerCAmelCase_							,     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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
   def _lowercase(    self    )	->str:
        # Initialize image_processing
        a  :    Tuple				=						self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        a  :    Optional[Any]				=						prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase_							,     torchify=lowerCAmelCase_    )
        a  :    Dict				=						[]
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase_							,     torch.Tensor    )
             maps.append(torch.zeros(image.shape[-2:]    ).long()    )

        # Test not batched input
        a  :    List[Any]				=						image_processing(image_inputs[0]							,     maps[0]							,     return_tensors='''pt'''    )
        self.assertEqual(
            encoding['''pixel_values'''].shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
        self.assertEqual(
            encoding['''labels'''].shape							,     (
                1,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
        self.assertEqual(encoding['''labels'''].dtype							,     torch.long    )
        self.assertTrue(encoding['''labels'''].min().item() >= 0    )
        self.assertTrue(encoding['''labels'''].max().item() <= 2_55    )

        # Test batched
        a  :    List[str]				=						image_processing(lowerCAmelCase_							,     lowerCAmelCase_							,     return_tensors='''pt'''    )
        self.assertEqual(
            encoding['''pixel_values'''].shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
        self.assertEqual(
            encoding['''labels'''].shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
        self.assertEqual(encoding['''labels'''].dtype							,     torch.long    )
        self.assertTrue(encoding['''labels'''].min().item() >= 0    )
        self.assertTrue(encoding['''labels'''].max().item() <= 2_55    )

        # Test not batched input (PIL images)
        a			,							a  :    List[str]				=						prepare_semantic_single_inputs()

        a  :    Dict				=						image_processing(lowerCAmelCase_							,     lowerCAmelCase_							,     return_tensors='''pt'''    )
        self.assertEqual(
            encoding['''pixel_values'''].shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
        self.assertEqual(
            encoding['''labels'''].shape							,     (
                1,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
        self.assertEqual(encoding['''labels'''].dtype							,     torch.long    )
        self.assertTrue(encoding['''labels'''].min().item() >= 0    )
        self.assertTrue(encoding['''labels'''].max().item() <= 2_55    )

        # Test batched input (PIL images)
        a			,							a  :    int				=						prepare_semantic_batch_inputs()

        a  :    Optional[Any]				=						image_processing(lowerCAmelCase_							,     lowerCAmelCase_							,     return_tensors='''pt'''    )
        self.assertEqual(
            encoding['''pixel_values'''].shape							,     (
                2,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
        self.assertEqual(
            encoding['''labels'''].shape							,     (
                2,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
        self.assertEqual(encoding['''labels'''].dtype							,     torch.long    )
        self.assertTrue(encoding['''labels'''].min().item() >= 0    )
        self.assertTrue(encoding['''labels'''].max().item() <= 2_55    )


   def _lowercase(    self    )	->Optional[int]:
        # Initialize image_processing
        a  :    Optional[Any]				=						self.image_processing_class(**self.image_processor_dict    )

        # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
        a			,							a  :    Optional[int]				=						prepare_semantic_single_inputs()
        a  :    Optional[Any]				=						image_processing(lowerCAmelCase_							,     lowerCAmelCase_							,     return_tensors='''pt'''    )
        self.assertTrue(encoding['''labels'''].min().item() >= 0    )
        self.assertTrue(encoding['''labels'''].max().item() <= 1_50    )

        a  :    Optional[Any]				=						True
        a  :    Union[str, Any]				=						image_processing(lowerCAmelCase_							,     lowerCAmelCase_							,     return_tensors='''pt'''    )
        self.assertTrue(encoding['''labels'''].min().item() >= 0    )
        self.assertTrue(encoding['''labels'''].max().item() <= 2_55    )
from __future__ import annotations







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )	->	list[float | int]:
     if not all((nth_term, start_term_a, common_ratio_r)       ):
          return []
     a  :    list[float | int]				=						[]
     a  :    str				=						1
     a  :    Dict				=						common_ratio_r
     for _ in range(int(lowerCAmelCase_       )       ):
          if not series:
               series.append(lowerCAmelCase_       )
          else:
               power += 1
               series.append(float(start_term_a * multiple       )       )
               a  :    Dict				=						pow(float(lowerCAmelCase_       )						,      lowerCAmelCase_       )
     return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCAmelCase							   =      float(input("""Enter the last number (n term) of the Geometric Series"""))
  UpperCAmelCase							   =      float(input("""Enter the starting term (a) of the Geometric Series"""))
  UpperCAmelCase							   =      float(
      input("""Enter the common ratio between two terms (r) of the Geometric Series""")
  )
  print("""Formula of Geometric Series => a + ar + ar^2 ... +ar^n""")
  print(geometric_series(nth_term, start_term_a, common_ratio_r))