import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
   import torch

if is_tf_available():
   import tensorflow as tf







@require_vision
@require_torchvision
class __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      tempfile.mkdtemp()
     SCREAMING_SNAKE_CASE__										=      SamImageProcessor()
     SCREAMING_SNAKE_CASE__										=      SamProcessor(UpperCamelCase__      )
     processor.save_pretrained(self.tmpdirname      )




   def 	__a						(    self			:				Optional[int]       ,	**_lowercase			:				Any      ):



     """simple docstring"""






     return AutoProcessor.from_pretrained(self.tmpdirname       ,	**UpperCamelCase__      ).image_processor




   def 	__a						(    self			:				str      ):



     """simple docstring"""






     shutil.rmtree(self.tmpdirname      )




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      [np.random.randint(2_55       ,	size=(3, 30, 4_00)       ,	dtype=np.uinta      )]

     SCREAMING_SNAKE_CASE__										=      [Image.fromarray(np.moveaxis(UpperCamelCase__       ,	0       ,	-1      )      ) for x in image_inputs]

     return image_inputs




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      SamProcessor(image_processor=self.get_image_processor()      )
     processor.save_pretrained(self.tmpdirname      )

     SCREAMING_SNAKE_CASE__										=      self.get_image_processor(do_normalize=UpperCamelCase__       ,	padding_value=1.0      )

     SCREAMING_SNAKE_CASE__										=      SamProcessor.from_pretrained(self.tmpdirname       ,	do_normalize=UpperCamelCase__       ,	padding_value=1.0      )

     self.assertEqual(processor.image_processor.to_json_string()       ,	image_processor_add_kwargs.to_json_string()      )
     self.assertIsInstance(processor.image_processor       ,	UpperCamelCase__      )




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.get_image_processor()

     SCREAMING_SNAKE_CASE__										=      SamProcessor(image_processor=UpperCamelCase__      )

     SCREAMING_SNAKE_CASE__										=      self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE__										=      image_processor(UpperCamelCase__       ,	return_tensors="""np"""      )
     SCREAMING_SNAKE_CASE__										=      processor(images=UpperCamelCase__       ,	return_tensors="""np"""      )

     input_feat_extract.pop("""original_sizes"""      )  # pop original_sizes as it is popped in the processor
     input_feat_extract.pop("""reshaped_input_sizes"""      )  # pop original_sizes as it is popped in the processor

     for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()       ,	input_processor[key].sum()       ,	delta=1E-2      )




   @require_torch
   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.get_image_processor()

     SCREAMING_SNAKE_CASE__										=      SamProcessor(image_processor=UpperCamelCase__      )
     SCREAMING_SNAKE_CASE__										=      [torch.ones((1, 3, 5, 5)      )]

     SCREAMING_SNAKE_CASE__										=      [[17_64, 26_46]]

     SCREAMING_SNAKE_CASE__										=      [[6_83, 10_24]]
     SCREAMING_SNAKE_CASE__										=      processor.post_process_masks(UpperCamelCase__       ,	UpperCamelCase__       ,	UpperCamelCase__      )
     self.assertEqual(masks[0].shape       ,	(1, 3, 17_64, 26_46)      )

     SCREAMING_SNAKE_CASE__										=      processor.post_process_masks(
         UpperCamelCase__       ,	torch.tensor(UpperCamelCase__      )       ,	torch.tensor(UpperCamelCase__      )      )
     self.assertEqual(masks[0].shape       ,	(1, 3, 17_64, 26_46)      )

     # should also work with np
     SCREAMING_SNAKE_CASE__										=      [np.ones((1, 3, 5, 5)      )]
     SCREAMING_SNAKE_CASE__										=      processor.post_process_masks(UpperCamelCase__       ,	np.array(UpperCamelCase__      )       ,	np.array(UpperCamelCase__      )      )

     self.assertEqual(masks[0].shape       ,	(1, 3, 17_64, 26_46)      )

     SCREAMING_SNAKE_CASE__										=      [[1, 0], [0, 1]]
     with self.assertRaises(UpperCamelCase__      ):
       SCREAMING_SNAKE_CASE__										=      processor.post_process_masks(UpperCamelCase__       ,	np.array(UpperCamelCase__      )       ,	np.array(UpperCamelCase__      )      )







@require_vision
@require_tf
class __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      tempfile.mkdtemp()
     SCREAMING_SNAKE_CASE__										=      SamImageProcessor()
     SCREAMING_SNAKE_CASE__										=      SamProcessor(UpperCamelCase__      )
     processor.save_pretrained(self.tmpdirname      )




   def 	__a						(    self			:				Tuple       ,	**_lowercase			:				Any      ):



     """simple docstring"""






     return AutoProcessor.from_pretrained(self.tmpdirname       ,	**UpperCamelCase__      ).image_processor




   def 	__a						(    self			:				str      ):



     """simple docstring"""






     shutil.rmtree(self.tmpdirname      )




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      [np.random.randint(2_55       ,	size=(3, 30, 4_00)       ,	dtype=np.uinta      )]

     SCREAMING_SNAKE_CASE__										=      [Image.fromarray(np.moveaxis(UpperCamelCase__       ,	0       ,	-1      )      ) for x in image_inputs]

     return image_inputs




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      SamProcessor(image_processor=self.get_image_processor()      )
     processor.save_pretrained(self.tmpdirname      )

     SCREAMING_SNAKE_CASE__										=      self.get_image_processor(do_normalize=UpperCamelCase__       ,	padding_value=1.0      )

     SCREAMING_SNAKE_CASE__										=      SamProcessor.from_pretrained(self.tmpdirname       ,	do_normalize=UpperCamelCase__       ,	padding_value=1.0      )

     self.assertEqual(processor.image_processor.to_json_string()       ,	image_processor_add_kwargs.to_json_string()      )
     self.assertIsInstance(processor.image_processor       ,	UpperCamelCase__      )




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.get_image_processor()

     SCREAMING_SNAKE_CASE__										=      SamProcessor(image_processor=UpperCamelCase__      )

     SCREAMING_SNAKE_CASE__										=      self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE__										=      image_processor(UpperCamelCase__       ,	return_tensors="""np"""      )
     SCREAMING_SNAKE_CASE__										=      processor(images=UpperCamelCase__       ,	return_tensors="""np"""      )

     input_feat_extract.pop("""original_sizes"""      )  # pop original_sizes as it is popped in the processor
     input_feat_extract.pop("""reshaped_input_sizes"""      )  # pop reshaped_input_sizes as it is popped in the processor

     for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()       ,	input_processor[key].sum()       ,	delta=1E-2      )




   @require_tf
   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.get_image_processor()

     SCREAMING_SNAKE_CASE__										=      SamProcessor(image_processor=UpperCamelCase__      )
     SCREAMING_SNAKE_CASE__										=      [tf.ones((1, 3, 5, 5)      )]

     SCREAMING_SNAKE_CASE__										=      [[17_64, 26_46]]

     SCREAMING_SNAKE_CASE__										=      [[6_83, 10_24]]
     SCREAMING_SNAKE_CASE__										=      processor.post_process_masks(UpperCamelCase__       ,	UpperCamelCase__       ,	UpperCamelCase__       ,	return_tensors="""tf"""      )
     self.assertEqual(masks[0].shape       ,	(1, 3, 17_64, 26_46)      )

     SCREAMING_SNAKE_CASE__										=      processor.post_process_masks(
         UpperCamelCase__       ,	tf.convert_to_tensor(UpperCamelCase__      )       ,	tf.convert_to_tensor(UpperCamelCase__      )       ,	return_tensors="""tf"""       ,	)
     self.assertEqual(masks[0].shape       ,	(1, 3, 17_64, 26_46)      )

     # should also work with np
     SCREAMING_SNAKE_CASE__										=      [np.ones((1, 3, 5, 5)      )]
     SCREAMING_SNAKE_CASE__										=      processor.post_process_masks(
         UpperCamelCase__       ,	np.array(UpperCamelCase__      )       ,	np.array(UpperCamelCase__      )       ,	return_tensors="""tf"""      )

     self.assertEqual(masks[0].shape       ,	(1, 3, 17_64, 26_46)      )

     SCREAMING_SNAKE_CASE__										=      [[1, 0], [0, 1]]
     with self.assertRaises(tf.errors.InvalidArgumentError      ):
       SCREAMING_SNAKE_CASE__										=      processor.post_process_masks(
           UpperCamelCase__       ,	np.array(UpperCamelCase__      )       ,	np.array(UpperCamelCase__      )       ,	return_tensors="""tf"""      )







@require_vision
@require_torchvision
class __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      tempfile.mkdtemp()
     SCREAMING_SNAKE_CASE__										=      SamImageProcessor()
     SCREAMING_SNAKE_CASE__										=      SamProcessor(UpperCamelCase__      )
     processor.save_pretrained(self.tmpdirname      )




   def 	__a						(    self			:				Any       ,	**_lowercase			:				Any      ):



     """simple docstring"""






     return AutoProcessor.from_pretrained(self.tmpdirname       ,	**UpperCamelCase__      ).image_processor




   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     shutil.rmtree(self.tmpdirname      )




   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      [np.random.randint(2_55       ,	size=(3, 30, 4_00)       ,	dtype=np.uinta      )]

     SCREAMING_SNAKE_CASE__										=      [Image.fromarray(np.moveaxis(UpperCamelCase__       ,	0       ,	-1      )      ) for x in image_inputs]

     return image_inputs




   @is_pt_tf_cross_test
   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.get_image_processor()

     SCREAMING_SNAKE_CASE__										=      SamProcessor(image_processor=UpperCamelCase__      )
     SCREAMING_SNAKE_CASE__										=      np.random.randint(0       ,	2       ,	size=(1, 3, 5, 5)      ).astype(np.floataa      )
     SCREAMING_SNAKE_CASE__										=      [tf.convert_to_tensor(UpperCamelCase__      )]
     SCREAMING_SNAKE_CASE__										=      [torch.tensor(UpperCamelCase__      )]

     SCREAMING_SNAKE_CASE__										=      [[17_64, 26_46]]

     SCREAMING_SNAKE_CASE__										=      [[6_83, 10_24]]
     SCREAMING_SNAKE_CASE__										=      processor.post_process_masks(
         UpperCamelCase__       ,	UpperCamelCase__       ,	UpperCamelCase__       ,	return_tensors="""tf"""      )
     SCREAMING_SNAKE_CASE__										=      processor.post_process_masks(
         UpperCamelCase__       ,	UpperCamelCase__       ,	UpperCamelCase__       ,	return_tensors="""pt"""      )

     self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()      )      )




   @is_pt_tf_cross_test
   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.get_image_processor()

     SCREAMING_SNAKE_CASE__										=      SamProcessor(image_processor=UpperCamelCase__      )

     SCREAMING_SNAKE_CASE__										=      self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE__										=      image_processor(UpperCamelCase__       ,	return_tensors="""pt"""      )["""pixel_values"""].numpy()
     SCREAMING_SNAKE_CASE__										=      processor(images=UpperCamelCase__       ,	return_tensors="""pt"""      )["""pixel_values"""].numpy()

     SCREAMING_SNAKE_CASE__										=      image_processor(UpperCamelCase__       ,	return_tensors="""tf"""      )["""pixel_values"""].numpy()
     SCREAMING_SNAKE_CASE__										=      processor(images=UpperCamelCase__       ,	return_tensors="""tf"""      )["""pixel_values"""].numpy()

     self.assertTrue(np.allclose(UpperCamelCase__       ,	UpperCamelCase__      )      )
     self.assertTrue(np.allclose(UpperCamelCase__       ,	UpperCamelCase__      )      )
     self.assertTrue(np.allclose(UpperCamelCase__       ,	UpperCamelCase__      )      )






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   (UnCLIPScheduler,)



		def        SCREAMING_SNAKE_CASE__			(     self,	**UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  {
								    '''num_train_timesteps''': 1000,
								    '''variance_type''': '''fixed_small_log''',
								    '''clip_sample''': True,
								    '''clip_sample_range''': 1.0,
								    '''prediction_type''': '''epsilon''',
								}

								config.update(**UpperCamelCase__   )
								return config



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for timesteps in [1, 5, 100, 1000]:
														self.check_over_configs(num_train_timesteps=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for variance in ["fixed_small_log", "learned_range"]:
														self.check_over_configs(variance_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for clip_sample_range in [1, 5, 10, 20]:
														self.check_over_configs(clip_sample_range=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for prediction_type in ["epsilon", "sample"]:
														self.check_over_configs(prediction_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for time_step in [0, 500, 999]:
														for prev_timestep in [None, 5, 100, 250, 500, 750]:
																				if prev_timestep is not None and prev_timestep >= time_step:
																										continue

																				self.check_over_forward(time_step=UpperCamelCase__,	prev_timestep=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(variance_type='''fixed_small_log'''   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0000E-10   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_549_625   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.9_994_987   )   ) < 1E-5



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(variance_type='''learned_range'''   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_      			=  0.5

								assert scheduler._get_variance(1,	predicted_variance=UpperCamelCase__   ) - -10.1_712_790 < 1E-5
								assert scheduler._get_variance(487,	predicted_variance=UpperCamelCase__   ) - -5.7_998_052 < 1E-5
								assert scheduler._get_variance(999,	predicted_variance=UpperCamelCase__   ) - -0.0_010_011 < 1E-5



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_      			=  scheduler.timesteps

								lowerCAmelCase_      			=  self.dummy_model()
								lowerCAmelCase_      			=  self.dummy_sample_deter
								lowerCAmelCase_      			=  torch.manual_seed(0   )

								for i, t in enumerate(UpperCamelCase__   ):
														# 1. predict noise residual
														lowerCAmelCase_      			=  model(UpperCamelCase__,	UpperCamelCase__   )

														# 2. predict previous mean of sample x_t-1
														lowerCAmelCase_      			=  scheduler.step(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	generator=UpperCamelCase__   ).prev_sample

														lowerCAmelCase_      			=  pred_prev_sample

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 252.2_682_495   ) < 1E-2
								assert abs(result_mean.item() - 0.3_284_743   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								scheduler.set_timesteps(25   )

								lowerCAmelCase_      			=  scheduler.timesteps

								lowerCAmelCase_      			=  self.dummy_model()
								lowerCAmelCase_      			=  self.dummy_sample_deter
								lowerCAmelCase_      			=  torch.manual_seed(0   )

								for i, t in enumerate(UpperCamelCase__   ):
														# 1. predict noise residual
														lowerCAmelCase_      			=  model(UpperCamelCase__,	UpperCamelCase__   )

														if i + 1 == timesteps.shape[0]:
																				lowerCAmelCase_      			=  None
														else:
																				lowerCAmelCase_      			=  timesteps[i + 1]

														# 2. predict previous mean of sample x_t-1
														lowerCAmelCase_      			=  scheduler.step(
														    UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	prev_timestep=UpperCamelCase__,	generator=UpperCamelCase__   ).prev_sample

														lowerCAmelCase_      			=  pred_prev_sample

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 258.2_044_983   ) < 1E-2
								assert abs(result_mean.item() - 0.3_362_038   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass




