





import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





def      _UpperCamelCase  (a__ :str							):






							"""simple docstring"""





							UpperCamelCase__					  =      [tensor.shape for tensor in tensor_list]
							return all(shape == shapes[0] for shape in shapes[1:]							)

class 							__SCREAMING_SNAKE_CASE       (  _a ,		_a ,		_a ,		unittest.TestCase							):
							snake_case  :   int      =							StableDiffusionLatentUpscalePipeline
							snake_case  :   Dict      =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
							    """height""",
							    """width""",
							    """cross_attention_kwargs""",
							    """negative_prompt_embeds""",
							    """prompt_embeds""",
							}
							snake_case  :   Optional[int]      =							PipelineTesterMixin.required_optional_params - {"""num_images_per_prompt"""}
							snake_case  :   Optional[int]      =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							snake_case  :   Dict      =							frozenset(
							    []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							snake_case  :   Dict      =							frozenset([]							)

							snake_case  :   Union[str, Any]      =							True






							@property
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      1
														UpperCamelCase__					  =      4
														UpperCamelCase__					  =      (16, 16)

														UpperCamelCase__					  =      floats_tensor((batch_size, num_channels) + sizes	,			rng=random.Random(0	)	).to(__lowerCAmelCase	)
														return image






							def 					_lowerCamelCase		(    self	):
														torch.manual_seed(0	)
														UpperCamelCase__					  =      UNetaDConditionModel(
														    act_fn="""gelu"""	,			attention_head_dim=8	,			norm_num_groups=__lowerCAmelCase	,			block_out_channels=[32, 32, 64, 64]	,			time_cond_proj_dim=160	,			conv_in_kernel=1	,			conv_out_kernel=1	,			cross_attention_dim=32	,			down_block_types=(
														        """KDownBlock2D""",
														        """KCrossAttnDownBlock2D""",
														        """KCrossAttnDownBlock2D""",
														        """KCrossAttnDownBlock2D""",
														    )	,			in_channels=8	,			mid_block_type=__lowerCAmelCase	,			only_cross_attention=__lowerCAmelCase	,			out_channels=5	,			resnet_time_scale_shift="""scale_shift"""	,			time_embedding_type="""fourier"""	,			timestep_post_act="""gelu"""	,			up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")	,			)
														UpperCamelCase__					  =      AutoencoderKL(
														    block_out_channels=[32, 32, 64, 64]	,			in_channels=3	,			out_channels=3	,			down_block_types=[
														        """DownEncoderBlock2D""",
														        """DownEncoderBlock2D""",
														        """DownEncoderBlock2D""",
														        """DownEncoderBlock2D""",
														    ]	,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]	,			latent_channels=4	,			)
														UpperCamelCase__					  =      EulerDiscreteScheduler(prediction_type="""sample"""	)
														UpperCamelCase__					  =      CLIPTextConfig(
														    bos_token_id=0	,			eos_token_id=2	,			hidden_size=32	,			intermediate_size=37	,			layer_norm_eps=1E-05	,			num_attention_heads=4	,			num_hidden_layers=5	,			pad_token_id=1	,			vocab_size=1000	,			hidden_act="""quick_gelu"""	,			projection_dim=512	,			)
														UpperCamelCase__					  =      CLIPTextModel(__lowerCAmelCase	)
														UpperCamelCase__					  =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

														UpperCamelCase__					  =      {
														    """unet""": model.eval(),
														    """vae""": vae.eval(),
														    """scheduler""": scheduler,
														    """text_encoder""": text_encoder,
														    """tokenizer""": tokenizer,
														}

														return components






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=0	):
														if str(__lowerCAmelCase	).startswith("""mps"""	):
																					UpperCamelCase__					  =      torch.manual_seed(__lowerCAmelCase	)
														else:
																					UpperCamelCase__					  =      torch.Generator(device=__lowerCAmelCase	).manual_seed(__lowerCAmelCase	)
														UpperCamelCase__					  =      {
														    """prompt""": """A painting of a squirrel eating a burger""",
														    """image""": self.dummy_image.cpu(),
														    """generator""": generator,
														    """num_inference_steps""": 2,
														    """output_type""": """numpy""",
														}
														return inputs






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      """cpu"""

														UpperCamelCase__					  =      self.get_dummy_components()
														UpperCamelCase__					  =      self.pipeline_class(**__lowerCAmelCase	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      self.get_dummy_inputs(__lowerCAmelCase	)
														UpperCamelCase__					  =      pipe(**__lowerCAmelCase	).images
														UpperCamelCase__					  =      image[0, -3:, -3:, -1]

														self.assertEqual(image.shape	,			(1, 256, 256, 3)	)
														UpperCamelCase__					  =      np.array(
														    [0.4722_2412, 0.4192_1633, 0.4471_7434, 0.4687_4192, 0.4258_8258, 0.4615_0726, 0.467_7534, 0.4558_3832, 0.4857_9055]	)
														UpperCamelCase__					  =      np.abs(image_slice.flatten() - expected_slice	).max()
														self.assertLessEqual(__lowerCAmelCase	,			1E-3	)






							def 					_lowerCamelCase		(    self	):
														super().test_attention_slicing_forward_pass(expected_max_diff=7E-3	)






							def 					_lowerCamelCase		(    self	):
														super().test_cpu_offload_forward_pass(expected_max_diff=3E-3	)






							def 					_lowerCamelCase		(    self	):
														super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3	)






							def 					_lowerCamelCase		(    self	):
														super().test_inference_batch_single_identical(expected_max_diff=7E-3	)






							def 					_lowerCamelCase		(    self	):
														super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3	)






							def 					_lowerCamelCase		(    self	):
														super().test_save_load_local(expected_max_difference=3E-3	)






							def 					_lowerCamelCase		(    self	):
														super().test_save_load_optional_components(expected_max_difference=3E-3	)







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      [
														    """DDIMScheduler""",
														    """DDPMScheduler""",
														    """PNDMScheduler""",
														    """HeunDiscreteScheduler""",
														    """EulerAncestralDiscreteScheduler""",
														    """KDPM2DiscreteScheduler""",
														    """KDPM2AncestralDiscreteScheduler""",
														    """DPMSolverSDEScheduler""",
														]
														UpperCamelCase__					  =      self.get_dummy_components()
														UpperCamelCase__					  =      self.pipeline_class(**__lowerCAmelCase	)

														# make sure that PNDM does not need warm-up
														pipe.scheduler.register_to_config(skip_prk_steps=__lowerCAmelCase	)

														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)
														UpperCamelCase__					  =      self.get_dummy_inputs(__lowerCAmelCase	)
														UpperCamelCase__					  =      2

														UpperCamelCase__					  =      []
														for scheduler_enum in KarrasDiffusionSchedulers:
																					if scheduler_enum.name in skip_schedulers:
																												# no sigma schedulers are not supported
																												# no schedulers
																												continue

																					UpperCamelCase__					  =      getattr(__lowerCAmelCase	,			scheduler_enum.name	)
																					UpperCamelCase__					  =      scheduler_cls.from_config(pipe.scheduler.config	)
																					UpperCamelCase__					  =      pipe(**__lowerCAmelCase	)[0]
																					outputs.append(__lowerCAmelCase	)

														assert check_same_shape(__lowerCAmelCase	)






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






							def 					_lowerCamelCase		(    self	):
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      torch.manual_seed(33	)

														UpperCamelCase__					  =      StableDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""	,			torch_dtype=torch.floataa	)
														pipe.to("""cuda"""	)

														UpperCamelCase__					  =      StableDiffusionLatentUpscalePipeline.from_pretrained(
														    """stabilityai/sd-x2-latent-upscaler"""	,			torch_dtype=torch.floataa	)
														upscaler.to("""cuda"""	)

														UpperCamelCase__					  =      """a photo of an astronaut high resolution, unreal engine, ultra realistic"""

														UpperCamelCase__					  =      pipe(__lowerCAmelCase	,			generator=__lowerCAmelCase	,			output_type="""latent"""	).images

														UpperCamelCase__					  =      upscaler(
														    prompt=__lowerCAmelCase	,			image=__lowerCAmelCase	,			num_inference_steps=20	,			guidance_scale=0	,			generator=__lowerCAmelCase	,			output_type="""np"""	,			).images[0]

														UpperCamelCase__					  =      load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"""	)
														assert np.abs((expected_image - image).mean()	) < 5E-2







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      torch.manual_seed(33	)

														UpperCamelCase__					  =      StableDiffusionLatentUpscalePipeline.from_pretrained(
														    """stabilityai/sd-x2-latent-upscaler"""	,			torch_dtype=torch.floataa	)
														upscaler.to("""cuda"""	)

														UpperCamelCase__					  =      """the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"""

														UpperCamelCase__					  =      load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"""	)

														UpperCamelCase__					  =      upscaler(
														    prompt=__lowerCAmelCase	,			image=__lowerCAmelCase	,			num_inference_steps=20	,			guidance_scale=0	,			generator=__lowerCAmelCase	,			output_type="""np"""	,			).images[0]

														UpperCamelCase__					  =      load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"""	)
														assert np.abs((expected_image - image).max()	) < 5E-2











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  :   List[str]      =							MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING






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






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														for example in examples:
																					UpperCamelCase__					  =      video_classifier(__lowerCAmelCase	)

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






							@require_torch
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
														UpperCamelCase__					  =      VideoMAEFeatureExtractor(
														    size={"""shortest_edge""": 10}	,			crop_size={"""height""": 10, """width""": 10}	)
														UpperCamelCase__					  =      pipeline(
														    """video-classification"""	,			model=__lowerCAmelCase	,			feature_extractor=__lowerCAmelCase	,			frame_sampling_rate=4	)

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

														UpperCamelCase__					  =      video_classifier(
														    [
														        video_file_path,
														        video_file_path,
														    ]	,			top_k=2	,			)
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase	,			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 					_lowerCamelCase		(    self	):
														pass




