

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     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING




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




							def        A__  ( self					,						_A					,						_A							):
												for example in examples:
																	__lowerCAmelCase					=							video_classifier(_A							)

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




							@require_torch
							def        A__  ( self							):
												__lowerCAmelCase					=							'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
												__lowerCAmelCase					=							VideoMAEFeatureExtractor(
												    size={'shortest_edge': 1_0}					,						crop_size={'height': 1_0, 'width': 1_0}							)
												__lowerCAmelCase					=							pipeline(
												    'video-classification'					,						model=_A					,						feature_extractor=_A					,						frame_sampling_rate=4							)

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

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









from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowerCamelCase      				=     logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase      				=     '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=8      ):

					"""simple docstring"""






					__lowerCAmelCase					=							height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					__lowerCAmelCase					=							width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=512		,					UpperCAmelCase__=512      ):

					"""simple docstring"""






					__lowerCAmelCase					=							pil_image.resize((w, h)		,					resample=Image.BICUBIC		,					reducing_gap=1      )
					__lowerCAmelCase					=							np.array(pil_image.convert('RGB'      )      )
					__lowerCAmelCase					=							arr.astype(np.floataa      ) / 127.5 - 1
					__lowerCAmelCase					=							np.transpose(UpperCAmelCase__		,					[2, 0, 1]      )
					__lowerCAmelCase					=							torch.from_numpy(UpperCAmelCase__      ).unsqueeze(0      )
					return image





class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A					,						_A					,						):
												super().__init__()

												self.register_modules(
												    unet=_A					,						scheduler=_A					,						movq=_A					,						)
												__lowerCAmelCase					=							2 ** (len(self.movq.config.block_out_channels							) - 1)




							def        A__  ( self					,						_A					,						_A					,						_A							):
												# get the original timestep using init_timestep
												__lowerCAmelCase					=							min(int(num_inference_steps * strength							)					,						_A							)

												__lowerCAmelCase					=							max(num_inference_steps - init_timestep					,						0							)
												__lowerCAmelCase					=							self.scheduler.timesteps[t_start:]

												return timesteps, num_inference_steps - t_start




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A=None							):
												if not isinstance(_A					,						(torch.Tensor, PIL.Image.Image, list)							):
																	raise ValueError(
																	    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A							)}"""							)

												__lowerCAmelCase					=							image.to(device=_A					,						dtype=_A							)

												__lowerCAmelCase					=							batch_size * num_images_per_prompt

												if image.shape[1] == 4:
																	__lowerCAmelCase					=							image

												else:
																	if isinstance(_A					,						_A							) and len(_A							) != batch_size:
																						raise ValueError(
																						    F"""You have passed a list of generators of length {len(_A							)}, but requested an effective batch"""
																						    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

																	elif isinstance(_A					,						_A							):
																						__lowerCAmelCase					=							[
																						    self.movq.encode(image[i : i + 1]							).latent_dist.sample(generator[i]							) for i in range(_A							)
																						]
																						__lowerCAmelCase					=							torch.cat(_A					,						dim=0							)
																	else:
																						__lowerCAmelCase					=							self.movq.encode(_A							).latent_dist.sample(_A							)

																	__lowerCAmelCase					=							self.movq.config.scaling_factor * init_latents

												__lowerCAmelCase					=							torch.cat([init_latents]					,						dim=0							)

												__lowerCAmelCase					=							init_latents.shape
												__lowerCAmelCase					=							randn_tensor(_A					,						generator=_A					,						device=_A					,						dtype=_A							)

												# get latents
												__lowerCAmelCase					=							self.scheduler.add_noise(_A					,						_A					,						_A							)

												__lowerCAmelCase					=							init_latents

												return latents




							def        A__  ( self					,						_A=0							):
												if is_accelerate_available():
																	from accelerate import cpu_offload
												else:
																	raise ImportError('Please install accelerate via `pip install accelerate`'							)

												__lowerCAmelCase					=							torch.device(F"""cuda:{gpu_id}"""							)

												__lowerCAmelCase					=							[
												    self.unet,
												    self.movq,
												]
												for cpu_offloaded_model in models:
																	if cpu_offloaded_model is not None:
																						cpu_offload(_A					,						_A							)




							def        A__  ( self					,						_A=0							):
												if is_accelerate_available() and is_accelerate_version('>='					,						'0.17.0.dev0'							):
																	from accelerate import cpu_offload_with_hook
												else:
																	raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'							)

												__lowerCAmelCase					=							torch.device(F"""cuda:{gpu_id}"""							)

												if self.device.type != "cpu":
																	self.to('cpu'					,						silence_dtype_warnings=_A							)
																	torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

												__lowerCAmelCase					=							None
												for cpu_offloaded_model in [self.unet, self.movq]:
																	__lowerCAmelCase,	__lowerCAmelCase					=							cpu_offload_with_hook(_A					,						_A					,						prev_module_hook=_A							)

												# We'll offload the last model manually.
												__lowerCAmelCase					=							hook




							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def        A__  ( self							):
												if not hasattr(self.unet					,						'_hf_hook'							):
																	return self.device
												for module in self.unet.modules():
																	if (
																	    hasattr(_A					,						'_hf_hook'							)
																	    and hasattr(module._hf_hook					,						'execution_device'							)
																	    and module._hf_hook.execution_device is not None
																	):
																						return torch.device(module._hf_hook.execution_device							)
												return self.device




							@torch.no_grad()
							@replace_example_docstring(_A							)
							def __call__( self					,						_A					,						_A					,						_A					,						_A = 5_1_2					,						_A = 5_1_2					,						_A = 1_0_0					,						_A = 4.0					,						_A = 0.3					,						_A = 1					,						_A = None					,						_A = "pil"					,						_A = True					,						):
												__lowerCAmelCase					=							self._execution_device

												__lowerCAmelCase					=							guidance_scale > 1.0

												if isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							torch.cat(_A					,						dim=0							)
												__lowerCAmelCase					=							image_embeds.shape[0]
												if isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							torch.cat(_A					,						dim=0							)

												if do_classifier_free_guidance:
																	__lowerCAmelCase					=							image_embeds.repeat_interleave(_A					,						dim=0							)
																	__lowerCAmelCase					=							negative_image_embeds.repeat_interleave(_A					,						dim=0							)

												__lowerCAmelCase					=							torch.cat([negative_image_embeds, image_embeds]					,						dim=0							).to(dtype=self.unet.dtype					,						device=_A							)

												if not isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							[image]
												if not all(isinstance(_A					,						(PIL.Image.Image, torch.Tensor)							) for i in image							):
																	raise ValueError(
																	    F"""Input is in incorrect format: {[type(_A							) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""							)

												__lowerCAmelCase					=							torch.cat([prepare_image(_A					,						_A					,						_A							) for i in image]					,						dim=0							)
												__lowerCAmelCase					=							image.to(dtype=image_embeds.dtype					,						device=_A							)

												__lowerCAmelCase					=							self.movq.encode(_A							)['latents']
												__lowerCAmelCase					=							latents.repeat_interleave(_A					,						dim=0							)
												self.scheduler.set_timesteps(_A					,						device=_A							)
												__lowerCAmelCase,	__lowerCAmelCase					=							self.get_timesteps(_A					,						_A					,						_A							)
												__lowerCAmelCase					=							timesteps[:1].repeat(batch_size * num_images_per_prompt							)
												__lowerCAmelCase,	__lowerCAmelCase					=							downscale_height_and_width(_A					,						_A					,						self.movq_scale_factor							)
												__lowerCAmelCase					=							self.prepare_latents(
												    _A					,						_A					,						_A					,						_A					,						image_embeds.dtype					,						_A					,						_A							)
												for i, t in enumerate(self.progress_bar(_A							)							):
																	# expand the latents if we are doing classifier free guidance
																	__lowerCAmelCase					=							torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

																	__lowerCAmelCase					=							{'image_embeds': image_embeds}
																	__lowerCAmelCase					=							self.unet(
																	    sample=_A					,						timestep=_A					,						encoder_hidden_states=_A					,						added_cond_kwargs=_A					,						return_dict=_A					,						)[0]

																	if do_classifier_free_guidance:
																						__lowerCAmelCase,	__lowerCAmelCase					=							noise_pred.split(latents.shape[1]					,						dim=1							)
																						__lowerCAmelCase,	__lowerCAmelCase					=							noise_pred.chunk(2							)
																						__lowerCAmelCase,	__lowerCAmelCase					=							variance_pred.chunk(2							)
																						__lowerCAmelCase					=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						__lowerCAmelCase					=							torch.cat([noise_pred, variance_pred_text]					,						dim=1							)

																	if not (
																	    hasattr(self.scheduler.config					,						'variance_type'							)
																	    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																	):
																						__lowerCAmelCase,	__lowerCAmelCase					=							noise_pred.split(latents.shape[1]					,						dim=1							)

																	# compute the previous noisy sample x_t -> x_t-1
																	__lowerCAmelCase					=							self.scheduler.step(
																	    _A					,						_A					,						_A					,						generator=_A					,						)[0]

												# post-processing
												__lowerCAmelCase					=							self.movq.decode(_A					,						force_not_quantize=_A							)['sample']

												if output_type not in ["pt", "np", "pil"]:
																	raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""							)

												if output_type in ["np", "pil"]:
																	__lowerCAmelCase					=							image * 0.5 + 0.5
																	__lowerCAmelCase					=							image.clamp(0					,						1							)
																	__lowerCAmelCase					=							image.cpu().permute(0					,						2					,						3					,						1							).float().numpy()

												if output_type == "pil":
																	__lowerCAmelCase					=							self.numpy_to_pil(_A							)

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=_A							)






