





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					):







	'''simple docstring'''
	lowercase_	=    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

	def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int] ,				UpperCAmelCase_       :     List[Any] ,				UpperCAmelCase_       :     List[Any] ,				UpperCAmelCase_       :     List[str]) ->str:



			'''simple docstring'''



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

	def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[Any] ,				UpperCAmelCase_       :     Any ,				UpperCAmelCase_       :     str) ->Union[str, Any]:



			'''simple docstring'''



			for example in examples:
					lowerCamelCase__:   int    						=video_classifier(UpperCamelCase_)

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

	@require_torch
	def 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->Optional[int]:



			'''simple docstring'''



			lowerCamelCase__:   Union[str, Any]    						="hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
			lowerCamelCase__:   Optional[Any]    						=VideoMAEFeatureExtractor(
			    size={"shortest_edge": 10} ,				crop_size={"height": 10, "width": 10})
			lowerCamelCase__:   Optional[Any]    						=pipeline(
			    "video-classification" ,				model=UpperCamelCase_ ,				feature_extractor=UpperCamelCase_ ,				frame_sampling_rate=4)

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

			lowerCamelCase__:   str    						=video_classifier(
			    [
			        video_file_path,
			        video_file_path,
			    ] ,				top_k=2 ,				)
			self.assertEqual(
			    nested_simplify(UpperCamelCase_ ,				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 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int]) ->Dict:



			'''simple docstring'''



			pass








import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					,							unittest.TestCase					):







 '''simple docstring'''
 lowercase_	=    DiTPipeline
 lowercase_	=    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
 lowercase_	=    PipelineTesterMixin.required_optional_params - {
     "latents",
     "num_images_per_prompt",
     "callback",
     "callback_steps",
 }
 lowercase_	=    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
 lowercase_	=    False

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



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__:   Optional[int]    						=TransformeraDModel(
       sample_size=16 ,				num_layers=2 ,				patch_size=4 ,				attention_head_dim=8 ,				num_attention_heads=2 ,				in_channels=4 ,				out_channels=8 ,				attention_bias=UpperCAmelCase_ ,				activation_fn="gelu-approximate" ,				num_embeds_ada_norm=1_000 ,				norm_type="ada_norm_zero" ,				norm_elementwise_affine=UpperCAmelCase_ ,				)
   lowerCamelCase__:   List[str]    						=AutoencoderKL()
   lowerCamelCase__:   Tuple    						=DDIMScheduler()
   lowerCamelCase__:   Tuple    						={"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
   return components

 def 							SCREAMING_SNAKE_CASE_      (self       :     List[Any] ,				UpperCAmelCase_       :     int ,				UpperCAmelCase_       :     List[Any]=0) ->List[Any]:



   '''simple docstring'''



   if str(UpperCAmelCase_).startswith("mps"):
     lowerCamelCase__:   Any    						=torch.manual_seed(UpperCAmelCase_)
   else:
     lowerCamelCase__:   Union[str, Any]    						=torch.Generator(device=UpperCAmelCase_).manual_seed(UpperCAmelCase_)
   lowerCamelCase__:   List[Any]    						={
       "class_labels": [1],
       "generator": generator,
       "num_inference_steps": 2,
       "output_type": "numpy",
   }
   return inputs

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



   '''simple docstring'''



   lowerCamelCase__:   List[str]    						="cpu"

   lowerCamelCase__:   Union[str, Any]    						=self.get_dummy_components()
   lowerCamelCase__:   List[str]    						=self.pipeline_class(**UpperCAmelCase_)
   pipe.to(UpperCAmelCase_)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__:   List[str]    						=self.get_dummy_inputs(UpperCAmelCase_)
   lowerCamelCase__:   Any    						=pipe(**UpperCAmelCase_).images
   lowerCamelCase__:   List[str]    						=image[0, -3:, -3:, -1]

   self.assertEqual(image.shape ,				(1, 16, 16, 3))
   lowerCamelCase__:   Optional[Any]    						=np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457])
   lowerCamelCase__:   List[Any]    						=np.abs(image_slice.flatten() - expected_slice).max()
   self.assertLessEqual(UpperCAmelCase_ ,				1E-3)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->str:



   '''simple docstring'''



   self._test_inference_batch_single_identical(relax_max_difference=UpperCAmelCase_ ,				expected_max_diff=1E-3)







 @unittest.skipIf(
     torch_device != "cuda" or not is_xformers_available() ,				reason="XFormers attention is only available with CUDA and `xformers` installed" ,				)
 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict) ->int:



   '''simple docstring'''



   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)



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







 '''simple docstring'''

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



   '''simple docstring'''



   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()

 def 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:   List[Any]    						=torch.manual_seed(0)

   lowerCamelCase__:   Tuple    						=DiTPipeline.from_pretrained("facebook/DiT-XL-2-256")
   pipe.to("cuda")

   lowerCamelCase__:   Any    						=["vase", "umbrella", "white shark", "white wolf"]
   lowerCamelCase__:   Any    						=pipe.get_label_ids(UpperCAmelCase_)

   lowerCamelCase__:   Any    						=pipe(UpperCAmelCase_ ,				generator=UpperCAmelCase_ ,				num_inference_steps=40 ,				output_type="np").images

   for word, image in zip(UpperCAmelCase_ ,				UpperCAmelCase_):
     lowerCamelCase__:   Optional[int]    						=load_numpy(
         F"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy""")
     assert np.abs((expected_image - image).max()) < 1E-2







 def 							SCREAMING_SNAKE_CASE_      (self       :     int) ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:   Union[str, Any]    						=DiTPipeline.from_pretrained("facebook/DiT-XL-2-512")
   lowerCamelCase__:   List[str]    						=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
   pipe.to("cuda")

   lowerCamelCase__:   List[str]    						=["vase", "umbrella"]
   lowerCamelCase__:   List[Any]    						=pipe.get_label_ids(UpperCAmelCase_)

   lowerCamelCase__:   str    						=torch.manual_seed(0)
   lowerCamelCase__:   Optional[int]    						=pipe(UpperCAmelCase_ ,				generator=UpperCAmelCase_ ,				num_inference_steps=25 ,				output_type="np").images

   for word, image in zip(UpperCAmelCase_ ,				UpperCAmelCase_):
     lowerCamelCase__:   str    						=load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         F"""/dit/{word}_512.npy""")

     assert np.abs((expected_image - image).max()) < 1E-1

