
"""simple docstring"""





import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
				from PIL import Image
else:






				class        _UpperCAmelCase:



						@staticmethod
						def 		UpperCAmelCase				(   *__a							,						**__a)			->			int:






											'''simple docstring'''
											pass



def  lowerCamelCase__						(     __snake_case      )				->						str:


					"""simple docstring"""





					_UpperCamelCase									=    hashlib.mda(image.tobytes()      )
					return m.hexdigest()[:10]



def  lowerCamelCase__						(     __snake_case      )				->						Dict:


					"""simple docstring"""





					_UpperCamelCase									=    np.array(__snake_case      )
					_UpperCamelCase									=    npimg.shape
					return {"hash": hashimage(__snake_case      ), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class        _UpperCAmelCase(       unittest.TestCase      ):
		lowercase__         = dict(
		    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])      )
		lowercase__         = dict(
		    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])      )



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    MaskGenerationPipeline(model=__a							,						image_processor=__a)
							return image_segmenter, [
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							]



		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			Optional[int]:






							'''simple docstring'''
							pass



		@require_tf
		@unittest.skip('''Image segmentation not implemented in TF''')
		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							pass



		@slow
		@require_torch
		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    pipeline('''mask-generation'''							,						model='''facebook/sam-vit-huge''')

							_UpperCamelCase									=    image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg'''							,						points_per_batch=2_56)

							# Shortening by hashing
							_UpperCamelCase									=    []
							for i, o in enumerate(outputs['''masks''']):
												new_outupt += [{"mask": mask_to_test_readable(__a), "scores": outputs["scores"][i]}]

							# fmt: off
							self.assertEqual(
							    nested_simplify(__a							,						decimals=4)							,						[
							        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0444},
							        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_80, 6_40)}, '''scores''': 1.021},
							        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0167},
							        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0132},
							        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0053},
							        {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9967},
							        {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.993},
							        {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9909},
							        {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9879},
							        {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9834},
							        {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9716},
							        {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9612},
							        {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9599},
							        {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9552},
							        {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9532},
							        {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9516},
							        {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9499},
							        {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9483},
							        {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9464},
							        {'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (4_80, 6_40)}, '''scores''': 0.943},
							        {'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (4_80, 6_40)}, '''scores''': 0.943},
							        {'''mask''': {'''hash''': '''c749b25868''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9408},
							        {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9335},
							        {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9326},
							        {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9262},
							        {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8999},
							        {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8986},
							        {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8984},
							        {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8873},
							        {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8871}
							    ]							,						)
							# fmt: on




		@require_torch
		@slow
		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    '''facebook/sam-vit-huge'''
							_UpperCamelCase									=    pipeline('''mask-generation'''							,						model=__a)

							_UpperCamelCase									=    image_segmenter(
							    '''http://images.cocodataset.org/val2017/000000039769.jpg'''							,						pred_iou_thresh=1							,						points_per_batch=2_56)

							# Shortening by hashing
							_UpperCamelCase									=    []
							for i, o in enumerate(outputs['''masks''']):
												new_outupt += [{"mask": mask_to_test_readable(__a), "scores": outputs["scores"][i]}]

							self.assertEqual(
							    nested_simplify(__a							,						decimals=4)							,						[
							        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0444},
							        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0210},
							        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0167},
							        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0132},
							        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0053},
							    ]							,						)








"""simple docstring"""





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = (DDPMScheduler,)



		def 		UpperCAmelCase				(   self							,						**__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''num_train_timesteps''': 10_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''variance_type''': '''fixed_small''',
							    '''clip_sample''': True,
							}

							config.update(**__a)
							return config



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							for timesteps in [1, 5, 1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							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=__a							,						beta_end=__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






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



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=__a)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=__a)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							self.check_over_configs(thresholding=__a)
							for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=__a							,						prediction_type=__a							,						sample_max_value=__a							,						)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






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



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							for t in [0, 5_00, 9_99]:
												self.check_over_forward(time_step=__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0979)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1e-5



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    len(__a)

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

							for t in reversed(range(__a)):
												# 1. predict noise residual
												_UpperCamelCase									=    model(__a							,						__a)

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

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												_UpperCamelCase									=    pred_prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							assert abs(result_sum.item() - 258.9606) < 1e-2
							assert abs(result_mean.item() - 0.3372) < 1e-3



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config(prediction_type='''v_prediction''')
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    len(__a)

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

							for t in reversed(range(__a)):
												# 1. predict noise residual
												_UpperCamelCase									=    model(__a							,						__a)

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

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												_UpperCamelCase									=    pred_prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							assert abs(result_sum.item() - 202.0296) < 1e-2
							assert abs(result_mean.item() - 0.2631) < 1e-3



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [1_00, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=__a)

							_UpperCamelCase									=    scheduler.timesteps

							for i, timestep in enumerate(__a):
												if i == len(__a) - 1:
																	_UpperCamelCase									=    -1
												else:
																	_UpperCamelCase									=    timesteps[i + 1]

												_UpperCamelCase									=    scheduler.previous_timestep(__a)
												_UpperCamelCase									=    prev_t.item()

												self.assertEqual(__a							,						__a)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [1_00, 87, 50, 51, 0]

							with self.assertRaises(__a							,						msg='''`custom_timesteps` must be in descending order.'''):
												scheduler.set_timesteps(timesteps=__a)



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [1_00, 87, 50, 1, 0]
							_UpperCamelCase									=    len(__a)

							with self.assertRaises(__a							,						msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
												scheduler.set_timesteps(num_inference_steps=__a							,						timesteps=__a)




		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    __a							,						msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''							,						):
												scheduler.set_timesteps(timesteps=__a)






