





"""simple docstring"""






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




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







					def 	_lowerCamelCase						(  self		,     **_UpperCAmelCase       ):
									__a	:  Union[str, Any]         =					{
									    '''num_train_timesteps''': 1000,
									    '''beta_start''': 0.0_0_0_1,
									    '''beta_end''': 0.0_2,
									    '''beta_schedule''': '''linear''',
									}

									config.update(**_UpperCAmelCase       )
									return config







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase=0		,     **_UpperCAmelCase       ):
									__a	:  str         =					dict(self.forward_default_kwargs       )
									__a	:  str         =					kwargs.pop('''num_inference_steps'''		,     _UpperCAmelCase       )
									__a	:  int         =					self.dummy_sample
									__a	:  Any         =					0.1 * sample
									__a	:  Dict         =					[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

													__a	:  Dict         =					scheduler.step_prk(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     **_UpperCAmelCase       ).prev_sample
													__a	:  Optional[Any]         =					new_scheduler.step_prk(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     **_UpperCAmelCase       ).prev_sample

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

													__a	:  Optional[Any]         =					scheduler.step_plms(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     **_UpperCAmelCase       ).prev_sample
													__a	:  Union[str, Any]         =					new_scheduler.step_plms(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     **_UpperCAmelCase       ).prev_sample

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







					def 	_lowerCamelCase						(  self       ):
									pass







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase=0		,     **_UpperCAmelCase       ):
									__a	:  Optional[Any]         =					dict(self.forward_default_kwargs       )
									__a	:  str         =					kwargs.pop('''num_inference_steps'''		,     _UpperCAmelCase       )
									__a	:  List[str]         =					self.dummy_sample
									__a	:  Optional[Any]         =					0.1 * sample
									__a	:  Optional[Any]         =					[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

									for scheduler_class in self.scheduler_classes:
													__a	:  List[Any]         =					self.get_scheduler_config()
													__a	:  Dict         =					scheduler_class(**_UpperCAmelCase       )
													scheduler.set_timesteps(_UpperCAmelCase       )

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

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

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

													__a	:  List[Any]         =					scheduler.step_prk(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     **_UpperCAmelCase       ).prev_sample
													__a	:  Optional[Any]         =					new_scheduler.step_prk(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     **_UpperCAmelCase       ).prev_sample

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

													__a	:  Union[str, Any]         =					scheduler.step_plms(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     **_UpperCAmelCase       ).prev_sample
													__a	:  int         =					new_scheduler.step_plms(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     **_UpperCAmelCase       ).prev_sample

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







					def 	_lowerCamelCase						(  self		,     **_UpperCAmelCase       ):
									__a	:  Optional[int]         =					self.scheduler_classes[0]
									__a	:  Any         =					self.get_scheduler_config(**_UpperCAmelCase       )
									__a	:  Union[str, Any]         =					scheduler_class(**_UpperCAmelCase       )

									__a	:  List[str]         =					10
									__a	:  Optional[int]         =					self.dummy_model()
									__a	:  Optional[Any]         =					self.dummy_sample_deter
									scheduler.set_timesteps(_UpperCAmelCase       )

									for i, t in enumerate(scheduler.prk_timesteps       ):
													__a	:  Union[str, Any]         =					model(_UpperCAmelCase		,     _UpperCAmelCase       )
													__a	:  str         =					scheduler.step_prk(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps       ):
													__a	:  Union[str, Any]         =					model(_UpperCAmelCase		,     _UpperCAmelCase       )
													__a	:  Tuple         =					scheduler.step_plms(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ).prev_sample

									return sample







					def 	_lowerCamelCase						(  self       ):
									__a	:  Any         =					dict(self.forward_default_kwargs       )

									__a	:  int         =					kwargs.pop('''num_inference_steps'''		,     _UpperCAmelCase       )

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

													__a	:  int         =					self.dummy_sample
													__a	:  str         =					0.1 * sample

													if num_inference_steps is not None and hasattr(_UpperCAmelCase		,     '''set_timesteps'''       ):
																	scheduler.set_timesteps(_UpperCAmelCase       )
													elif num_inference_steps is not None and not hasattr(_UpperCAmelCase		,     '''set_timesteps'''       ):
																	__a	:  Optional[int]         =					num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													__a	:  Any         =					[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
													__a	:  Optional[Any]         =					dummy_past_residuals[:]

													__a	:  Dict         =					scheduler.step_prk(_UpperCAmelCase		,     0		,     _UpperCAmelCase		,     **_UpperCAmelCase       ).prev_sample
													__a	:  Union[str, Any]         =					scheduler.step_prk(_UpperCAmelCase		,     1		,     _UpperCAmelCase		,     **_UpperCAmelCase       ).prev_sample

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

													__a	:  Dict         =					scheduler.step_plms(_UpperCAmelCase		,     0		,     _UpperCAmelCase		,     **_UpperCAmelCase       ).prev_sample
													__a	:  Any         =					scheduler.step_plms(_UpperCAmelCase		,     1		,     _UpperCAmelCase		,     **_UpperCAmelCase       ).prev_sample

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







					def 	_lowerCamelCase						(  self       ):
									for timesteps in [100, 1000]:
													self.check_over_configs(num_train_timesteps=_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=_UpperCAmelCase       )

									__a	:  List[Any]         =					self.scheduler_classes[0]
									__a	:  Tuple         =					self.get_scheduler_config(steps_offset=1       )
									__a	:  int         =					scheduler_class(**_UpperCAmelCase       )
									scheduler.set_timesteps(10       )
									assert torch.equal(
									    scheduler.timesteps		,     torch.LongTensor(
									        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]       )		,     )







					def 	_lowerCamelCase						(  self       ):
									for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]		,     [0.0_0_2, 0.0_2]       ):
													self.check_over_configs(beta_start=_UpperCAmelCase		,     beta_end=_UpperCAmelCase       )







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







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







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







					def 	_lowerCamelCase						(  self       ):
									for t, num_inference_steps in zip([1, 5, 10]		,     [10, 50, 100]       ):
													self.check_over_forward(num_inference_steps=_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
									__a	:  int         =					27

									for scheduler_class in self.scheduler_classes:
													__a	:  Union[str, Any]         =					self.dummy_sample
													__a	:  Tuple         =					0.1 * sample

													__a	:  Union[str, Any]         =					self.get_scheduler_config()
													__a	:  Union[str, Any]         =					scheduler_class(**_UpperCAmelCase       )

													scheduler.set_timesteps(_UpperCAmelCase       )

													# 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]       ):
																	__a	:  Tuple         =					scheduler.step_prk(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ).prev_sample







					def 	_lowerCamelCase						(  self       ):
									with self.assertRaises(_UpperCAmelCase       ):
													__a	:  Optional[int]         =					self.scheduler_classes[0]
													__a	:  Optional[int]         =					self.get_scheduler_config()
													__a	:  Tuple         =					scheduler_class(**_UpperCAmelCase       )

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  Union[str, Any]         =					self.full_loop()
									__a	:  Optional[Any]         =					torch.sum(torch.abs(_UpperCAmelCase       )       )
									__a	:  int         =					torch.mean(torch.abs(_UpperCAmelCase       )       )

									assert abs(result_sum.item() - 1_9_8.1_3_1_8       ) < 1e-2
									assert abs(result_mean.item() - 0.2_5_8_0       ) < 1e-3







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[str]         =					self.full_loop(prediction_type='''v_prediction'''       )
									__a	:  Optional[Any]         =					torch.sum(torch.abs(_UpperCAmelCase       )       )
									__a	:  Union[str, Any]         =					torch.mean(torch.abs(_UpperCAmelCase       )       )

									assert abs(result_sum.item() - 6_7.3_9_8_6       ) < 1e-2
									assert abs(result_mean.item() - 0.0_8_7_8       ) < 1e-3







					def 	_lowerCamelCase						(  self       ):
									# We specify different beta, so that the first alpha is 0.99
									__a	:  Optional[int]         =					self.full_loop(set_alpha_to_one=_UpperCAmelCase		,     beta_start=0.0_1       )
									__a	:  List[Any]         =					torch.sum(torch.abs(_UpperCAmelCase       )       )
									__a	:  Union[str, Any]         =					torch.mean(torch.abs(_UpperCAmelCase       )       )

									assert abs(result_sum.item() - 2_3_0.0_3_9_9       ) < 1e-2
									assert abs(result_mean.item() - 0.2_9_9_5       ) < 1e-3







					def 	_lowerCamelCase						(  self       ):
									# We specify different beta, so that the first alpha is 0.99
									__a	:  Any         =					self.full_loop(set_alpha_to_one=_UpperCAmelCase		,     beta_start=0.0_1       )
									__a	:  str         =					torch.sum(torch.abs(_UpperCAmelCase       )       )
									__a	:  Optional[int]         =					torch.mean(torch.abs(_UpperCAmelCase       )       )

									assert abs(result_sum.item() - 1_8_6.9_4_8_2       ) < 1e-2
									assert abs(result_mean.item() - 0.2_4_3_4       ) < 1e-3






"""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 			__lowercase     :







							'''simple docstring'''




							@staticmethod
							def 	_lowerCamelCase						(  *_UpperCAmelCase		,     **_UpperCAmelCase       ):
											pass

def       __A (     a_  :Image)						->						str:
				__a	:  List[str]         =					hashlib.mda(image.tobytes())
				return m.hexdigest()[:10]

def       __A (     a_  :Image)						->						Dict:
				__a	:  Any         =					np.array(a_)
				__a	:  Tuple         =					npimg.shape
				return {"hash": hashimage(a_), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					dict(
					    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if MODEL_FOR_MASK_GENERATION_MAPPING else [])							)
					__lowerCAmelCase					=					dict(
					    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])							)







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  List[str]         =					MaskGenerationPipeline(model=_UpperCAmelCase		,     image_processor=_UpperCAmelCase       )
									return image_segmenter, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									pass







					@require_tf
					@unittest.skip('''Image segmentation not implemented in TF'''       )
					def 	_lowerCamelCase						(  self       ):
									pass







					@slow
					@require_torch
					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					pipeline('''mask-generation'''		,     model='''facebook/sam-vit-huge'''       )

									__a	:  Optional[Any]         =					image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg'''		,     points_per_batch=256       )

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

									# fmt: off
									self.assertEqual(
									    nested_simplify(_UpperCAmelCase		,     decimals=4       )		,     [
									        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0_4_4_4},
									        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0_2_1},
									        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0_1_6_7},
									        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0_1_3_2},
									        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0_0_5_3},
									        {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (480, 640)}, '''scores''': 0.9_9_6_7},
									        {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (480, 640)}, '''scores''': 0.9_9_3},
									        {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (480, 640)}, '''scores''': 0.9_9_0_9},
									        {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (480, 640)}, '''scores''': 0.9_8_7_9},
									        {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (480, 640)}, '''scores''': 0.9_8_3_4},
									        {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (480, 640)}, '''scores''': 0.9_7_1_6},
									        {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (480, 640)}, '''scores''': 0.9_6_1_2},
									        {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (480, 640)}, '''scores''': 0.9_5_9_9},
									        {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (480, 640)}, '''scores''': 0.9_5_5_2},
									        {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (480, 640)}, '''scores''': 0.9_5_3_2},
									        {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (480, 640)}, '''scores''': 0.9_5_1_6},
									        {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (480, 640)}, '''scores''': 0.9_4_9_9},
									        {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (480, 640)}, '''scores''': 0.9_4_8_3},
									        {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (480, 640)}, '''scores''': 0.9_4_6_4},
									        {'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (480, 640)}, '''scores''': 0.9_4_3},
									        {'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (480, 640)}, '''scores''': 0.9_4_3},
									        {'''mask''': {'''hash''': '''c749b25868''', '''shape''': (480, 640)}, '''scores''': 0.9_4_0_8},
									        {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (480, 640)}, '''scores''': 0.9_3_3_5},
									        {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (480, 640)}, '''scores''': 0.9_3_2_6},
									        {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (480, 640)}, '''scores''': 0.9_2_6_2},
									        {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (480, 640)}, '''scores''': 0.8_9_9_9},
									        {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (480, 640)}, '''scores''': 0.8_9_8_6},
									        {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (480, 640)}, '''scores''': 0.8_9_8_4},
									        {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (480, 640)}, '''scores''': 0.8_8_7_3},
									        {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (480, 640)}, '''scores''': 0.8_8_7_1}
									    ]		,     )
									# fmt: on







					@require_torch
					@slow
					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					'''facebook/sam-vit-huge'''
									__a	:  Tuple         =					pipeline('''mask-generation'''		,     model=_UpperCAmelCase       )

									__a	:  List[Any]         =					image_segmenter(
									    '''http://images.cocodataset.org/val2017/000000039769.jpg'''		,     pred_iou_thresh=1		,     points_per_batch=256       )

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

									self.assertEqual(
									    nested_simplify(_UpperCAmelCase		,     decimals=4       )		,     [
									        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0_4_4_4},
									        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0_2_1_0},
									        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0_1_6_7},
									        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0_1_3_2},
									        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0_0_5_3},
									    ]		,     )