






'''simple docstring'''
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        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):
 snake_case_												=							MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING




 def        __magic_name__		( self		:							Tuple   , __lowercase		:							Tuple   , __lowercase		:							int   , __lowercase		:							Dict )		->  str:
       SCREAMING_SNAKE_CASE__       :			Any													=hf_hub_download(
           repo_id='''nateraw/video-demo'''   , filename='''archery.mp4'''   , repo_type='''dataset''' )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=VideoClassificationPipeline(model=__lowercase   , image_processor=__lowercase   , top_k=2 )
       SCREAMING_SNAKE_CASE__       :			Dict													=[
           example_video_filepath,
           '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
       ]
       return video_classifier, examples




 def        __magic_name__		( self		:							Dict   , __lowercase		:							Tuple   , __lowercase		:							Any )		->  Any:
       for example in examples:
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=video_classifier(__lowercase )

             self.assertEqual(
                 __lowercase   , [
                     {'''score''': ANY(__lowercase ), '''label''': ANY(__lowercase )},
                     {'''score''': ANY(__lowercase ), '''label''': ANY(__lowercase )},
                 ]   , )




 @require_torch
 def        __magic_name__		( self		:							Optional[int] )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													='''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
       SCREAMING_SNAKE_CASE__       :			Tuple													=VideoMAEFeatureExtractor(
           size={'''shortest_edge''': 10}   , crop_size={'''height''': 10, '''width''': 10} )
       SCREAMING_SNAKE_CASE__       :			Tuple													=pipeline(
           '''video-classification'''   , model=__lowercase   , feature_extractor=__lowercase   , frame_sampling_rate=4 )

       SCREAMING_SNAKE_CASE__       :			str													=hf_hub_download(repo_id='''nateraw/video-demo'''   , filename='''archery.mp4'''   , repo_type='''dataset''' )
       SCREAMING_SNAKE_CASE__       :			Dict													=video_classifier(__lowercase   , top_k=2 )
       self.assertEqual(
           nested_simplify(__lowercase   , decimals=4 )   , [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}]   , )

       SCREAMING_SNAKE_CASE__       :			str													=video_classifier(
           [
               video_file_path,
               video_file_path,
           ]   , top_k=2   , )
       self.assertEqual(
           nested_simplify(__lowercase   , decimals=4 )   , [
               [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
               [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
           ]   , )




 @require_tf
 def        __magic_name__		( self		:							Optional[Any] )		->  List[str]:
       pass







'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






@slow
@require_torch_gpu
class        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):




 def        __magic_name__		( self		:							List[str] )		->  Optional[Any]:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def        __magic_name__		( self		:							Union[str, Any] )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
       SCREAMING_SNAKE_CASE__       :			str													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_euler''' )

       SCREAMING_SNAKE_CASE__       :			List[Any]													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			List[str]													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=sd_pipe([prompt]   , generator=__lowercase   , guidance_scale=9.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=output.images

       SCREAMING_SNAKE_CASE__       :			int													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			Any													=np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2




 def        __magic_name__		( self		:							List[str] )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			List[str]													=StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			int													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_euler''' )

       SCREAMING_SNAKE_CASE__       :			Any													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			Dict													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Tuple													=sd_pipe([prompt]   , generator=__lowercase   , guidance_scale=9.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			Dict													=output.images

       SCREAMING_SNAKE_CASE__       :			Dict													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			List[str]													=np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1




 def        __magic_name__		( self		:							List[str] )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			Any													=StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			Tuple													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_dpmpp_2m''' )

       SCREAMING_SNAKE_CASE__       :			Tuple													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=sd_pipe(
           [prompt]   , generator=__lowercase   , guidance_scale=7.5   , num_inference_steps=15   , output_type='''np'''   , use_karras_sigmas=__lowercase   , )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=output.images

       SCREAMING_SNAKE_CASE__       :			Dict													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=np.array(
           [0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2