



'''simple docstring'''

from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
			import numpy as np
			from decord import VideoReader


if is_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

SCREAMING_SNAKE_CASE__     :						str	=  logging.get_logger(__name__)

@add_end_docstrings(_UpperCamelCase     )
class 							a__( _UpperCamelCase     ):






			def __init__(     self					,   *_UpperCAmelCase					,   **_UpperCAmelCase						)							->							str:
					super().__init__(*lowercase__					,   **lowercase__						)
					requires_backends(self					,   'decord'						)
					self.check_model_type(lowercase__						)






			def 						_lowercase  (     self					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None						)							->							Dict:
					snake_case__	     ={}
					if frame_sampling_rate is not None:
							snake_case__	     =frame_sampling_rate
					if num_frames is not None:
							snake_case__	     =num_frames

					snake_case__	     ={}
					if top_k is not None:
							snake_case__	     =top_k
					return preprocess_params, {}, postprocess_params






			def __call__(     self					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							int:
					return super().__call__(lowercase__					,   **lowercase__						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase=None					,   _UpperCAmelCase=1						)							->							Tuple:
					if num_frames is None:
							snake_case__	     =self.model.config.num_frames

					if video.startswith('http://'						) or video.startswith('https://'						):
							snake_case__	     =BytesIO(requests.get(lowercase__						).content						)

					snake_case__	     =VideoReader(lowercase__						)
					videoreader.seek(0						)

					snake_case__	     =0
					snake_case__	     =num_frames * frame_sampling_rate - 1
					snake_case__	     =np.linspace(lowercase__					,   lowercase__					,   num=lowercase__					,   dtype=np.intaa						)

					snake_case__	     =videoreader.get_batch(lowercase__						).asnumpy()
					snake_case__	     =list(lowercase__						)

					snake_case__	     =self.image_processor(lowercase__					,   return_tensors=self.framework						)
					return model_inputs






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Optional[Any]:
					snake_case__	     =self.model(**lowercase__						)
					return model_outputs






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase=5						)							->							str:
					if top_k > self.model.config.num_labels:
							snake_case__	     =self.model.config.num_labels

					if self.framework == "pt":
							snake_case__	     =model_outputs.logits.softmax(-1						)[0]
							snake_case__	     =probs.topk(lowercase__						)
					else:
							raise ValueError(f"""Unsupported framework: {self.framework}"""						)

					snake_case__	     =scores.tolist()
					snake_case__	     =ids.tolist()
					return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowercase__					,   lowercase__						)]








from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		UNetaDModel
	_lowerCAmelCase     :		ScoreSdeVeScheduler


	def __init__(    self				,     lowercase__				,     lowercase__):
							super().__init__()
							self.register_modules(unet=lowercase__				,     scheduler=lowercase__)


	@torch.no_grad()
	def __call__(    self				,     lowercase__ = 1				,     lowercase__ = 2_0_0_0				,     lowercase__ = None				,     lowercase__ = "pil"				,     lowercase__ = True				,     **lowercase__				,     ):
							__UpperCAmelCase			:							int	    =	self.unet.config.sample_size
							__UpperCAmelCase			:							Optional[int]	    =	(batch_size, 3, img_size, img_size)

							__UpperCAmelCase			:							int	    =	self.unet

							__UpperCAmelCase			:							Tuple	    =	randn_tensor(lowercase__				,     generator=lowercase__) * self.scheduler.init_noise_sigma
							__UpperCAmelCase			:							List[str]	    =	sample.to(self.device)

							self.scheduler.set_timesteps(lowercase__)
							self.scheduler.set_sigmas(lowercase__)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
													__UpperCAmelCase			:							List[str]	    =	self.scheduler.sigmas[i] * torch.ones(shape[0]				,     device=self.device)

													# correction step
													for _ in range(self.scheduler.config.correct_steps):
																			__UpperCAmelCase			:							int	    =	self.unet(lowercase__				,     lowercase__).sample
																			__UpperCAmelCase			:							str	    =	self.scheduler.step_correct(lowercase__				,     lowercase__				,     generator=lowercase__).prev_sample

													# prediction step
													__UpperCAmelCase			:							Tuple	    =	model(lowercase__				,     lowercase__).sample
													__UpperCAmelCase			:							Dict	    =	self.scheduler.step_pred(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__)

													__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	output.prev_sample, output.prev_sample_mean

							__UpperCAmelCase			:							List[str]	    =	sample_mean.clamp(0				,     1)
							__UpperCAmelCase			:							Union[str, Any]	    =	sample.cpu().permute(0				,     2				,     3				,     1).numpy()
							if output_type == "pil":
													__UpperCAmelCase			:							List[Any]	    =	self.numpy_to_pil(lowercase__)

							if not return_dict:
													return (sample,)

							return ImagePipelineOutput(images=lowercase__)

