
"""simple docstring"""
import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Union[str, Any]	=       (DDIMScheduler,)
    __lowercase				:			List[str]	=       (("""eta""", 0.0), ("""num_inference_steps""", 5_0))
    def        lowerCamelCase__						(     self    ,     **snake_case_  ):
      _snake_case  :    Tuple            = {
          "num_train_timesteps": 10_00,
          "beta_start": 0.0001,
          "beta_end": 0.02,
          "beta_schedule": "linear",
          "clip_sample": True,
      }

      config.update(**snake_case_  )
      return config
    def        lowerCamelCase__						(     self    ,     **snake_case_  ):
      _snake_case  :    Optional[int]            = self.scheduler_classes[0]
      _snake_case  :    Dict            = self.get_scheduler_config(**snake_case_  )
      _snake_case  :    Optional[int]            = scheduler_class(**snake_case_  )

      _snake_case      ,       _snake_case  :    List[Any]            = 10, 0.0

      _snake_case  :    List[Any]            = self.dummy_model()
      _snake_case  :    Tuple            = self.dummy_sample_deter

      scheduler.set_timesteps(snake_case_  )

      for t in scheduler.timesteps:
        _snake_case  :    Tuple            = model(snake_case_    ,     snake_case_  )
        _snake_case  :    Optional[Any]            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ).prev_sample

      return sample
    def        lowerCamelCase__						(     self  ):
      for timesteps in [1_00, 5_00, 10_00]:
        self.check_over_configs(num_train_timesteps=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=snake_case_  )

      _snake_case  :    Optional[int]            = self.scheduler_classes[0]
      _snake_case  :    Optional[Any]            = self.get_scheduler_config(steps_offset=1  )
      _snake_case  :    Tuple            = scheduler_class(**snake_case_  )
      scheduler.set_timesteps(5  )
      assert torch.equal(scheduler.timesteps    ,     torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]  )  )
    def        lowerCamelCase__						(     self  ):
      for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,     [0.002, 0.02, 0.2, 2]  ):
        self.check_over_configs(beta_start=snake_case_    ,     beta_end=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for timestep_spacing in ["trailing", "leading"]:
        self.check_over_configs(timestep_spacing=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      self.check_over_configs(thresholding=snake_case_  )
      for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(
              thresholding=snake_case_    ,     prediction_type=snake_case_    ,     sample_max_value=snake_case_    ,     )
    def        lowerCamelCase__						(     self  ):
      for t in [1, 10, 49]:
        self.check_over_forward(time_step=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for t, num_inference_steps in zip([1, 10, 50]    ,     [10, 50, 5_00]  ):
        self.check_over_forward(time_step=snake_case_    ,     num_inference_steps=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for t, eta in zip([1, 10, 49]    ,     [0.0, 0.5, 1.0]  ):
        self.check_over_forward(time_step=snake_case_    ,     eta=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = self.scheduler_classes[0]
      _snake_case  :    Optional[Any]            = self.get_scheduler_config()
      _snake_case  :    Optional[Any]            = scheduler_class(**snake_case_  )

      assert torch.sum(torch.abs(scheduler._get_variance(0    ,     0  ) - 0.0  )  ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_20    ,     4_00  ) - 0.14771  )  ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,     9_60  ) - 0.32460  )  ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(0    ,     0  ) - 0.0  )  ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_87    ,     4_86  ) - 0.00979  )  ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,     9_98  ) - 0.02  )  ) < 1E-5
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = self.full_loop()

      _snake_case  :    Union[str, Any]            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    Tuple            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_sum.item() - 172.0067  ) < 1E-2
      assert abs(result_mean.item() - 0.223967  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = self.full_loop(prediction_type="v_prediction"  )

      _snake_case  :    Optional[Any]            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    Optional[int]            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_sum.item() - 52.5302  ) < 1E-2
      assert abs(result_mean.item() - 0.0684  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      # We specify different beta, so that the first alpha is 0.99
      _snake_case  :    Tuple            = self.full_loop(set_alpha_to_one=snake_case_    ,     beta_start=0.01  )
      _snake_case  :    str            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    Union[str, Any]            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_sum.item() - 149.8295  ) < 1E-2
      assert abs(result_mean.item() - 0.1951  ) < 1E-3


    def        lowerCamelCase__						(     self  ):
      # We specify different beta, so that the first alpha is 0.99
      _snake_case  :    Dict            = self.full_loop(set_alpha_to_one=snake_case_    ,     beta_start=0.01  )
      _snake_case  :    Tuple            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    Dict            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_sum.item() - 149.0784  ) < 1E-2
      assert abs(result_mean.item() - 0.1941  ) < 1E-3



"""simple docstring"""
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   _UpperCAmelCase					(       unittest.TestCase):
    def __init__(     self    ,     snake_case_    ,     snake_case_=7    ,     snake_case_=3    ,     snake_case_=30    ,     snake_case_=4_00    ,     snake_case_=True    ,     snake_case_=None    ,     snake_case_=True    ,     snake_case_=[0.5, 0.5, 0.5]    ,     snake_case_=[0.5, 0.5, 0.5]    ,     snake_case_=False    ,     ):
      _snake_case  :    List[str]            = size if size is not None else {"height": 30, "width": 30}
      _snake_case  :    Optional[int]            = parent
      _snake_case  :    Dict            = batch_size
      _snake_case  :    Any            = num_channels
      _snake_case  :    Optional[Any]            = min_resolution
      _snake_case  :    int            = max_resolution
      _snake_case  :    int            = do_resize
      _snake_case  :    List[Any]            = size
      _snake_case  :    int            = do_normalize
      _snake_case  :    Any            = image_mean
      _snake_case  :    str            = image_std
      _snake_case  :    List[Any]            = do_reduce_labels


    def        lowerCamelCase__						(     self  ):
      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 					a__ ( ):

  """simple docstring"""







  _snake_case  :    List[str]            = load_dataset("hf-internal-testing/fixtures_ade20k"							,  split="test"		)

  _snake_case  :    Any            = Image.open(dataset[0]["file"]		)
  _snake_case  :    Optional[int]            = Image.open(dataset[1]["file"]		)

  return image, map
def 					a__ ( ):

  """simple docstring"""







  _snake_case  :    Optional[int]            = load_dataset("hf-internal-testing/fixtures_ade20k"							,  split="test"		)

  _snake_case  :    Optional[int]            = Image.open(dataset[0]["file"]		)
  _snake_case  :    List[str]            = Image.open(dataset[1]["file"]		)
  _snake_case  :    Dict            = Image.open(dataset[2]["file"]		)
  _snake_case  :    List[str]            = Image.open(dataset[3]["file"]		)

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






@require_torch
@require_vision
class   _UpperCAmelCase					(       _snake_case		,       unittest.TestCase):
    __lowercase				:			Dict	=       SegformerImageProcessor if is_vision_available() else None
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = SegformerImageProcessingTester(self  )
    @property
    def        lowerCamelCase__						(     self  ):
      return self.image_processor_tester.prepare_image_processor_dict()
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = self.image_processing_class(**self.image_processor_dict  )
      self.assertTrue(hasattr(snake_case_    ,     "do_resize"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "size"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "do_normalize"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "image_mean"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "image_std"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "do_reduce_labels"  )  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = 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    ,     snake_case_  )

      _snake_case  :    int            = self.image_processing_class.from_dict(self.image_processor_dict    ,     size=42    ,     reduce_labels=snake_case_  )
      self.assertEqual(image_processor.size    ,     {"height": 42, "width": 42}  )
      self.assertEqual(image_processor.do_reduce_labels    ,     snake_case_  )
    def        lowerCamelCase__						(     self  ):
      pass
    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    List[Any]            = self.image_processing_class(**self.image_processor_dict  )
      # create random PIL images
      _snake_case  :    List[str]            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_  )
      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     Image.Image  )

      # Test not batched input
      _snake_case  :    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
      _snake_case  :    List[Any]            = image_processing(snake_case_    ,     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        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    Optional[Any]            = self.image_processing_class(**self.image_processor_dict  )
      # create random numpy tensors
      _snake_case  :    Any            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_    ,     numpify=snake_case_  )
      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     np.ndarray  )

      # Test not batched input
      _snake_case  :    Optional[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
      _snake_case  :    Optional[int]            = image_processing(snake_case_    ,     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        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    Optional[int]            = self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _snake_case  :    Optional[Any]            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_    ,     torchify=snake_case_  )
      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     torch.Tensor  )

      # Test not batched input
      _snake_case  :    Dict            = 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
      _snake_case  :    List[Any]            = image_processing(snake_case_    ,     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        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    int            = self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _snake_case  :    List[str]            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_    ,     torchify=snake_case_  )
      _snake_case  :    Optional[int]            = []
      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     torch.Tensor  )
        maps.append(torch.zeros(image.shape[-2:]  ).long()  )

      # Test not batched input
      _snake_case  :    Optional[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
      _snake_case  :    List[Any]            = image_processing(snake_case_    ,     snake_case_    ,     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)
      _snake_case      ,       _snake_case  :    int            = prepare_semantic_single_inputs()

      _snake_case  :    List[str]            = image_processing(snake_case_    ,     snake_case_    ,     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)
      _snake_case      ,       _snake_case  :    str            = prepare_semantic_batch_inputs()

      _snake_case  :    List[Any]            = image_processing(snake_case_    ,     snake_case_    ,     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        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    List[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
      _snake_case      ,       _snake_case  :    List[Any]            = prepare_semantic_single_inputs()
      _snake_case  :    Dict            = image_processing(snake_case_    ,     snake_case_    ,     return_tensors="pt"  )
      self.assertTrue(encoding["labels"].min().item() >= 0  )
      self.assertTrue(encoding["labels"].max().item() <= 1_50  )

      _snake_case  :    Tuple            = True
      _snake_case  :    Union[str, Any]            = image_processing(snake_case_    ,     snake_case_    ,     return_tensors="pt"  )
      self.assertTrue(encoding["labels"].min().item() >= 0  )
      self.assertTrue(encoding["labels"].max().item() <= 2_55  )

