


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					(UnCLIPScheduler,)

					def     lowerCamelCase__	(self       :		Union[str, Any] ,   **_UpperCAmelCase       :		Union[str, Any]						)		->  Dict:




							"""simple docstring"""


							lowercase__	     =						{
							    """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     lowerCamelCase__	(self       :		Union[str, Any]						)		->  str:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		Tuple						)		->  Union[str, Any]:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		Tuple						)		->  List[Any]:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		Tuple						)		->  Any:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		str						)		->  List[Any]:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		str						)		->  Union[str, Any]:




							"""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     lowerCamelCase__	(self       :		Optional[Any]						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						self.scheduler_classes[0]
							lowercase__	     =						self.get_scheduler_config(variance_type="""fixed_small_log"""						)
							lowercase__	     =						scheduler_class(**_UpperCAmelCase						)

							assert torch.sum(torch.abs(scheduler._get_variance(0						) - 1.0_0_0_0E-1_0						)						) < 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     lowerCamelCase__	(self       :		Union[str, Any]						)		->  int:




							"""simple docstring"""


							lowercase__	     =						self.scheduler_classes[0]
							lowercase__	     =						self.get_scheduler_config(variance_type="""learned_range"""						)
							lowercase__	     =						scheduler_class(**_UpperCAmelCase						)

							lowercase__	     =						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     lowerCamelCase__	(self       :		str						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						self.scheduler_classes[0]
							lowercase__	     =						self.get_scheduler_config()
							lowercase__	     =						scheduler_class(**_UpperCAmelCase						)

							lowercase__	     =						scheduler.timesteps

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

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

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

									lowercase__	     =						pred_prev_sample

							lowercase__	     =						torch.sum(torch.abs(_UpperCAmelCase						)						)
							lowercase__	     =						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     lowerCamelCase__	(self       :		str						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						self.scheduler_classes[0]
							lowercase__	     =						self.get_scheduler_config()
							lowercase__	     =						scheduler_class(**_UpperCAmelCase						)

							scheduler.set_timesteps(25						)

							lowercase__	     =						scheduler.timesteps

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

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

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

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

									lowercase__	     =						pred_prev_sample

							lowercase__	     =						torch.sum(torch.abs(_UpperCAmelCase						)						)
							lowercase__	     =						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     lowerCamelCase__	(self       :		Optional[int]						)		->  int:




							"""simple docstring"""


							pass


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




							"""simple docstring"""


							pass





import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY



@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					A__     =					MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Tuple						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						hf_hub_download(
							    repo_id="""nateraw/video-demo""" ,   filename="""archery.mp4""" ,   repo_type="""dataset"""						)
							lowercase__	     =						VideoClassificationPipeline(model=_UpperCAmelCase ,   image_processor=_UpperCAmelCase ,   top_k=2						)
							lowercase__	     =						[
							    example_video_filepath,
							    """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
							]
							return video_classifier, examples

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Tuple						)		->  List[Any]:




							"""simple docstring"""


							for example in examples:
									lowercase__	     =						video_classifier(_UpperCAmelCase						)

									self.assertEqual(
									    _UpperCAmelCase ,   [
									        {"""score""": ANY(_UpperCAmelCase						), """label""": ANY(_UpperCAmelCase						)},
									        {"""score""": ANY(_UpperCAmelCase						), """label""": ANY(_UpperCAmelCase						)},
									    ] ,   )

					@require_torch
					def     lowerCamelCase__	(self       :		str						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						"""hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
							lowercase__	     =						VideoMAEFeatureExtractor(
							    size={"""shortest_edge""": 10} ,   crop_size={"""height""": 10, """width""": 10}						)
							lowercase__	     =						pipeline(
							    """video-classification""" ,   model=_UpperCAmelCase ,   feature_extractor=_UpperCAmelCase ,   frame_sampling_rate=4						)

							lowercase__	     =						hf_hub_download(repo_id="""nateraw/video-demo""" ,   filename="""archery.mp4""" ,   repo_type="""dataset"""						)
							lowercase__	     =						video_classifier(_UpperCAmelCase ,   top_k=2						)
							self.assertEqual(
							    nested_simplify(_UpperCAmelCase ,   decimals=4						) ,   [{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}] ,   )

							lowercase__	     =						video_classifier(
							    [
							        video_file_path,
							        video_file_path,
							    ] ,   top_k=2 ,   )
							self.assertEqual(
							    nested_simplify(_UpperCAmelCase ,   decimals=4						) ,   [
							        [{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}],
							        [{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}],
							    ] ,   )


					@require_tf
					def     lowerCamelCase__	(self       :		Any						)		->  Union[str, Any]:




							"""simple docstring"""


							pass

