





import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor





class 					A__       (				unittest.TestCase      ):




   """simple docstring"""



   def __init__(   self   ,    lowercase   ,    lowercase = True   ,    lowercase = None   ,    lowercase = 32   ,    lowercase = True   ,    lowercase = 1 / 255   ,    lowercase = True   ,    lowercase = True   ,    lowercase = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]   ,    lowercase = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]   ,    lowercase = True   ,    lowercase=7   ,    lowercase=30   ,    lowercase=400   ,    lowercase=3   ,    )     ->		Dict:







          '''simple docstring'''

          a__		:      Tuple					=							parent
          a__		:      Optional[int]					=							do_resize
          a__		:      List[str]					=							size if size is not None else {'shortest_edge': 288}
          a__		:      Tuple					=							size_divisor
          a__		:      Any					=							do_rescale
          a__		:      Optional[Any]					=							rescale_factor
          a__		:      int					=							do_normalize
          a__		:      Optional[Any]					=							do_center_crop
          a__		:      List[str]					=							image_mean
          a__		:      Dict					=							image_std
          a__		:      Optional[Any]					=							do_pad
          a__		:      Optional[Any]					=							batch_size
          a__		:      Optional[int]					=							num_channels
          a__		:      Dict					=							min_resolution
          a__		:      Tuple					=							max_resolution

   def 				__lowercase						(   self)     ->		int:







          '''simple docstring'''

          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "size_divisor": self.size_divisor,
          }

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase=False)     ->		Union[str, Any]:







          '''simple docstring'''

          if not batched:
                 a__		:      str					=							self.size['shortest_edge']
                 a__		:      Any					=							image_inputs[0]
                 if isinstance(__a   ,    Image.Image):
                        a__					,				a__		:      Optional[Any]					=							image.size
                 else:
                        a__					,				a__		:      Tuple					=							image.shape[1], image.shape[2]
                 a__		:      Tuple					=							size / min(__a   ,    __a)
                 if h < w:
                        a__					,				a__		:      Union[str, Any]					=							size, scale * w
                 else:
                        a__					,				a__		:      Optional[int]					=							scale * h, size

                 a__		:      Tuple					=							int((1333 / 800) * size)
                 if max(__a   ,    __a) > max_size:
                        a__		:      Optional[int]					=							max_size / max(__a   ,    __a)
                        a__		:      Tuple					=							newh * scale
                        a__		:      Optional[Any]					=							neww * scale

                 a__					,				a__		:      Any					=							int(newh + 0.5), int(neww + 0.5)
                 a__					,				a__		:      Any					=							(
                     newh // self.size_divisor * self.size_divisor,
                     neww // self.size_divisor * self.size_divisor,
                 )

          else:
                 a__		:      Optional[Any]					=							[]
                 for image in image_inputs:
                        a__					,				a__		:      int					=							self.get_expected_values([image])
                        expected_values.append((expected_height, expected_width))
                 a__		:      Union[str, Any]					=							max(__a   ,    key=lambda lowercase: item[0])[0]
                 a__		:      List[Any]					=							max(__a   ,    key=lambda lowercase: item[1])[1]

          return expected_height, expected_width





@require_torch
@require_vision
class 					A__       (				UpperCAmelCase_					,    unittest.TestCase      ):




   """simple docstring"""



   __A  :							str			    =    BridgeTowerImageProcessor if is_vision_available() else None

   def 				__lowercase						(   self)     ->		Dict:







          '''simple docstring'''

          a__		:      Optional[Any]					=							BridgeTowerImageProcessingTester(self)

   @property
   def 				__lowercase						(   self)     ->		Any:







          '''simple docstring'''

          return self.image_processor_tester.prepare_image_processor_dict()

   def 				__lowercase						(   self)     ->		str:







          '''simple docstring'''

          a__		:      Tuple					=							self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(__a   ,    'image_mean'))
          self.assertTrue(hasattr(__a   ,    'image_std'))
          self.assertTrue(hasattr(__a   ,    'do_normalize'))
          self.assertTrue(hasattr(__a   ,    'do_resize'))
          self.assertTrue(hasattr(__a   ,    'size'))
          self.assertTrue(hasattr(__a   ,    'size_divisor'))

   def 				__lowercase						(   self)     ->		str:







          '''simple docstring'''

          pass

   def 				__lowercase						(   self)     ->		Dict:







          '''simple docstring'''

          a__		:      Tuple					=							self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          a__		:      List[Any]					=							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__		:      str					=							image_processing(image_inputs[0]   ,    return_tensors='pt').pixel_values

          a__					,				a__		:      List[str]					=							self.image_processor_tester.get_expected_values(__a)
          self.assertEqual(
              encoded_images.shape   ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,    )

          # Test batched
          a__		:      List[Any]					=							image_processing(__a   ,    return_tensors='pt').pixel_values

          a__					,				a__		:      int					=							self.image_processor_tester.get_expected_values(__a   ,    batched=__a)
          self.assertEqual(
              encoded_images.shape   ,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )   ,    )

   def 				__lowercase						(   self)     ->		List[Any]:







          '''simple docstring'''

          a__		:      Tuple					=							self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          a__		:      Optional[Any]					=							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__		:      Optional[int]					=							image_processing(image_inputs[0]   ,    return_tensors='pt').pixel_values

          a__					,				a__		:      List[str]					=							self.image_processor_tester.get_expected_values(__a)
          self.assertEqual(
              encoded_images.shape   ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,    )

          # Test batched
          a__		:      Optional[Any]					=							image_processing(__a   ,    return_tensors='pt').pixel_values

          a__					,				a__		:      int					=							self.image_processor_tester.get_expected_values(__a   ,    batched=__a)
          self.assertEqual(
              encoded_images.shape   ,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )   ,    )

   def 				__lowercase						(   self)     ->		List[Any]:







          '''simple docstring'''

          a__		:      List[Any]					=							self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          a__		:      List[str]					=							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__		:      Optional[Any]					=							image_processing(image_inputs[0]   ,    return_tensors='pt').pixel_values

          a__					,				a__		:      int					=							self.image_processor_tester.get_expected_values(__a)
          self.assertEqual(
              encoded_images.shape   ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,    )

          # Test batched
          a__		:      Optional[Any]					=							image_processing(__a   ,    return_tensors='pt').pixel_values

          a__					,				a__		:      Tuple					=							self.image_processor_tester.get_expected_values(__a   ,    batched=__a)
          self.assertEqual(
              encoded_images.shape   ,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )   ,    )







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     _A      =      (PNDMScheduler,)
     _A      =      (('num_inference_steps', 50),)







     def     _a   (self					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		{
               "num_train_timesteps": 10_00,
               "beta_start": 0.0001,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**__a     )
           return config







     def     _a   (self					,      __a=0					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		dict(self.forward_default_kwargs     )
           lowerCamelCase             =		kwargs.pop("num_inference_steps"					,      __a     )
           lowerCamelCase             =		self.dummy_sample
           lowerCamelCase             =		0.1 * sample
           lowerCamelCase             =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase             =		self.get_scheduler_config(**__a     )
                 lowerCamelCase             =		scheduler_class(**__a     )
                 scheduler.set_timesteps(__a     )
                 # copy over dummy past residuals
                 lowerCamelCase             =		dummy_past_residuals[:]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__a     )
                       lowerCamelCase             =		scheduler_class.from_pretrained(__a     )
                       new_scheduler.set_timesteps(__a     )
                       # copy over dummy past residuals
                       lowerCamelCase             =		dummy_past_residuals[:]

                 lowerCamelCase             =		scheduler.step_prk(__a					,      __a					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		new_scheduler.step_prk(__a					,      __a					,      __a					,      **__a     ).prev_sample

                 assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"

                 lowerCamelCase             =		scheduler.step_plms(__a					,      __a					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		new_scheduler.step_plms(__a					,      __a					,      __a					,      **__a     ).prev_sample

                 assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"







     def     _a   (self     ):






           '''simple docstring'''



           pass







     def     _a   (self					,      __a=0					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		dict(self.forward_default_kwargs     )
           lowerCamelCase             =		kwargs.pop("num_inference_steps"					,      __a     )
           lowerCamelCase             =		self.dummy_sample
           lowerCamelCase             =		0.1 * sample
           lowerCamelCase             =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase             =		self.get_scheduler_config()
                 lowerCamelCase             =		scheduler_class(**__a     )
                 scheduler.set_timesteps(__a     )

                 # copy over dummy past residuals (must be after setting timesteps)
                 lowerCamelCase             =		dummy_past_residuals[:]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__a     )
                       lowerCamelCase             =		scheduler_class.from_pretrained(__a     )
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(__a     )

                       # copy over dummy past residual (must be after setting timesteps)
                       lowerCamelCase             =		dummy_past_residuals[:]

                 lowerCamelCase             =		scheduler.step_prk(__a					,      __a					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		new_scheduler.step_prk(__a					,      __a					,      __a					,      **__a     ).prev_sample

                 assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"

                 lowerCamelCase             =		scheduler.step_plms(__a					,      __a					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		new_scheduler.step_plms(__a					,      __a					,      __a					,      **__a     ).prev_sample

                 assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"







     def     _a   (self					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.scheduler_classes[0]
           lowerCamelCase             =		self.get_scheduler_config(**__a     )
           lowerCamelCase             =		scheduler_class(**__a     )

           lowerCamelCase             =		10
           lowerCamelCase             =		self.dummy_model()
           lowerCamelCase             =		self.dummy_sample_deter
           scheduler.set_timesteps(__a     )

           for i, t in enumerate(scheduler.prk_timesteps     ):
                 lowerCamelCase             =		model(__a					,      __a     )
                 lowerCamelCase             =		scheduler.step_prk(__a					,      __a					,      __a     ).prev_sample

           for i, t in enumerate(scheduler.plms_timesteps     ):
                 lowerCamelCase             =		model(__a					,      __a     )
                 lowerCamelCase             =		scheduler.step_plms(__a					,      __a					,      __a     ).prev_sample

           return sample







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		dict(self.forward_default_kwargs     )

           lowerCamelCase             =		kwargs.pop("num_inference_steps"					,      __a     )

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase             =		self.get_scheduler_config()
                 lowerCamelCase             =		scheduler_class(**__a     )

                 lowerCamelCase             =		self.dummy_sample
                 lowerCamelCase             =		0.1 * sample

                 if num_inference_steps is not None and hasattr(__a					,      "set_timesteps"     ):
                       scheduler.set_timesteps(__a     )
                 elif num_inference_steps is not None and not hasattr(__a					,      "set_timesteps"     ):
                       lowerCamelCase             =		num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 lowerCamelCase             =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                 lowerCamelCase             =		dummy_past_residuals[:]

                 lowerCamelCase             =		scheduler.step_prk(__a					,      0					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		scheduler.step_prk(__a					,      1					,      __a					,      **__a     ).prev_sample

                 self.assertEqual(output_a.shape					,      sample.shape     )
                 self.assertEqual(output_a.shape					,      output_a.shape     )

                 lowerCamelCase             =		scheduler.step_plms(__a					,      0					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		scheduler.step_plms(__a					,      1					,      __a					,      **__a     ).prev_sample

                 self.assertEqual(output_a.shape					,      sample.shape     )
                 self.assertEqual(output_a.shape					,      output_a.shape     )







     def     _a   (self     ):






           '''simple docstring'''



           for timesteps in [1_00, 10_00]:
                 self.check_over_configs(num_train_timesteps=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=__a     )

           lowerCamelCase             =		self.scheduler_classes[0]
           lowerCamelCase             =		self.get_scheduler_config(steps_offset=1     )
           lowerCamelCase             =		scheduler_class(**__a     )
           scheduler.set_timesteps(10     )
           assert torch.equal(
               scheduler.timesteps					,      torch.LongTensor(
                   [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]     )					,      )







     def     _a   (self     ):






           '''simple docstring'''



           for beta_start, beta_end in zip([0.0001, 0.001]					,      [0.002, 0.02]     ):
                 self.check_over_configs(beta_start=__a					,      beta_end=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           for t in [1, 5, 10]:
                 self.check_over_forward(time_step=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           for t, num_inference_steps in zip([1, 5, 10]					,      [10, 50, 1_00]     ):
                 self.check_over_forward(num_inference_steps=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		27

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase             =		self.dummy_sample
                 lowerCamelCase             =		0.1 * sample

                 lowerCamelCase             =		self.get_scheduler_config()
                 lowerCamelCase             =		scheduler_class(**__a     )

                 scheduler.set_timesteps(__a     )

                 # before power of 3 fix, would error on first step, so we only need to do two
                 for i, t in enumerate(scheduler.prk_timesteps[:2]     ):
                       lowerCamelCase             =		scheduler.step_prk(__a					,      __a					,      __a     ).prev_sample







     def     _a   (self     ):






           '''simple docstring'''



           with self.assertRaises(__a     ):
                 lowerCamelCase             =		self.scheduler_classes[0]
                 lowerCamelCase             =		self.get_scheduler_config()
                 lowerCamelCase             =		scheduler_class(**__a     )

                 scheduler.step_plms(self.dummy_sample					,      1					,      self.dummy_sample     ).prev_sample







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.full_loop()
           lowerCamelCase             =		torch.sum(torch.abs(__a     )     )
           lowerCamelCase             =		torch.mean(torch.abs(__a     )     )

           assert abs(result_sum.item() - 198.1318     ) < 1E-2
           assert abs(result_mean.item() - 0.2580     ) < 1E-3







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.full_loop(prediction_type="v_prediction"     )
           lowerCamelCase             =		torch.sum(torch.abs(__a     )     )
           lowerCamelCase             =		torch.mean(torch.abs(__a     )     )

           assert abs(result_sum.item() - 67.3986     ) < 1E-2
           assert abs(result_mean.item() - 0.0878     ) < 1E-3







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.full_loop(set_alpha_to_one=__a					,      beta_start=0.01     )
           lowerCamelCase             =		torch.sum(torch.abs(__a     )     )
           lowerCamelCase             =		torch.mean(torch.abs(__a     )     )

           assert abs(result_sum.item() - 230.0399     ) < 1E-2
           assert abs(result_mean.item() - 0.2995     ) < 1E-3







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.full_loop(set_alpha_to_one=__a					,      beta_start=0.01     )
           lowerCamelCase             =		torch.sum(torch.abs(__a     )     )
           lowerCamelCase             =		torch.mean(torch.abs(__a     )     )

           assert abs(result_sum.item() - 186.9482     ) < 1E-2
           assert abs(result_mean.item() - 0.2434     ) < 1E-3