



from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


snake_case     :    Dict						=    getLogger(__name__)


snake_case     :    str						=    4
snake_case     :    int						=    7

snake_case     :    int						=    {
    'float16': torch.floataa,
    'float32': torch.floataa,
}
def 						snake_case__							(       __lowercase							)		->		str:







						"""simple docstring"""

						if isinstance(__lowercase				,  torch.Tensor							):
												return image
						elif isinstance(__lowercase				,  PIL.Image.Image							):
												A__						:				Optional[int]         =			[image]

						if isinstance(image[0]				,  PIL.Image.Image							):
												A__     ,							A__						:				Any         =			image[0].size
												A__     ,							A__						:				Optional[int]         =			(x - x % 6_4 for x in (w, h))  # resize to integer multiple of 32

												A__						:				List[Any]         =			[np.array(i.resize((w, h)							)							)[None, :] for i in image]
												A__						:				Union[str, Any]         =			np.concatenate(__lowercase				,  axis=0							)
												A__						:				Any         =			np.array(__lowercase							).astype(np.floataa							) / 255.0
												A__						:				Optional[Any]         =			image.transpose(0				,  3				,  1				,  2							)
												A__						:				Optional[int]         =			2.0 * image - 1.0
												A__						:				Dict         =			torch.from_numpy(__lowercase							)
						elif isinstance(image[0]				,  torch.Tensor							):
												A__						:				Union[str, Any]         =			torch.cat(__lowercase				,  dim=0							)

						return image




class 				lowerCAmelCase__	(						UpperCamelCase    ):







					def __init__(							self   :  Dict      ,		_A   :  OnnxRuntimeModel      ,		_A   :  OnnxRuntimeModel      ,		_A   :  Any      ,		_A   :  OnnxRuntimeModel      ,		_A   :  DDPMScheduler      ,		_A   :  Any      ,		_A   :  int = 350      ,		):
											super().__init__(
											    vae=_A      ,		text_encoder=_A      ,		tokenizer=_A      ,		unet=_A      ,		low_res_scheduler=_A      ,		scheduler=_A      ,		safety_checker=_A      ,		feature_extractor=_A      ,		watermarker=_A      ,		max_noise_level=_A      ,		)







					def __call__(							self   :  List[Any]      ,		_A   :  Union[str, List[str]]      ,		_A   :  Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]      ,		_A   :  int = 75      ,		_A   :  float = 9.0      ,		_A   :  int = 20      ,		_A   :  Optional[Union[str, List[str]]] = None      ,		_A   :  Optional[int] = 1      ,		_A   :  float = 0.0      ,		_A   :  Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,		_A   :  Optional[torch.FloatTensor] = None      ,		_A   :  Optional[np.ndarray] = None      ,		_A   :  Optional[np.ndarray] = None      ,		_A   :  Optional[str] = "pil"      ,		_A   :  bool = True      ,		_A   :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,		_A   :  Optional[int] = 1      ,		):
											self.check_inputs(_A      ,		_A      ,		_A      ,		_A)

											# 2. Define call parameters
											if prompt is not None and isinstance(_A      ,		_A):
																	A__						:				Dict         =			1
											elif prompt is not None and isinstance(_A      ,		_A):
																	A__						:				Tuple         =			len(_A)
											else:
																	A__						:				Tuple         =			prompt_embeds.shape[0]

											A__						:				List[Any]         =			self._execution_device
											# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
											# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
											# corresponds to doing no classifier free guidance.
											A__						:				Optional[Any]         =			guidance_scale > 1.0

											# 3. Encode input prompt
											A__						:				int         =			self._encode_prompt(
											    _A      ,		_A      ,		_A      ,		_A      ,		_A      ,		prompt_embeds=_A      ,		negative_prompt_embeds=_A      ,		)

											A__						:				List[str]         =			ORT_TO_PT_TYPE[str(text_embeddings.dtype)]

											# 4. Preprocess image
											A__						:				Union[str, Any]         =			preprocess(_A)
											A__						:				Tuple         =			image.cpu()

											# 5. set timesteps
											self.scheduler.set_timesteps(_A      ,		device=_A)
											A__						:				int         =			self.scheduler.timesteps

											# 5. Add noise to image
											A__						:				Any         =			torch.tensor([noise_level]      ,		dtype=torch.long      ,		device=_A)
											A__						:				List[Any]         =			torch.randn(image.shape      ,		generator=_A      ,		device=_A      ,		dtype=_A)
											A__						:				int         =			self.low_res_scheduler.add_noise(_A      ,		_A      ,		_A)

											A__						:				List[Any]         =			2 if do_classifier_free_guidance else 1
											A__						:				Any         =			np.concatenate([image] * batch_multiplier * num_images_per_prompt)
											A__						:				Optional[Any]         =			np.concatenate([noise_level] * image.shape[0])

											# 6. Prepare latent variables
											A__     ,							A__						:				Any         =			image.shape[2:]
											A__						:				Any         =			self.prepare_latents(
											    batch_size * num_images_per_prompt      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		)

											# 7. Check that sizes of image and latents match
											A__						:				Any         =			image.shape[1]
											if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
																	raise ValueError(
																	    "Incorrect configuration settings! The config of `pipeline.unet` expects"
																	    F' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'
																	    F' `num_channels_image`: {num_channels_image} '
																	    F' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'
																	    " `pipeline.unet` or your `image` input.")

											# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
											A__						:				List[Any]         =			self.prepare_extra_step_kwargs(_A      ,		_A)

											A__						:				Optional[Any]         =			next(
											    (input.type for input in self.unet.model.get_inputs() if input.name == "timestep")      ,		"tensor(float)")
											A__						:				Union[str, Any]         =			ORT_TO_NP_TYPE[timestep_dtype]

											# 9. Denoising loop
											A__						:				Optional[int]         =			len(_A) - num_inference_steps * self.scheduler.order
											with self.progress_bar(total=_A) as progress_bar:
																	for i, t in enumerate(_A):
																							# expand the latents if we are doing classifier free guidance
																							A__						:				Optional[int]         =			np.concatenate([latents] * 2) if do_classifier_free_guidance else latents

																							# concat latents, mask, masked_image_latents in the channel dimension
																							A__						:				Optional[Any]         =			self.scheduler.scale_model_input(_A      ,		_A)
																							A__						:				Union[str, Any]         =			np.concatenate([latent_model_input, image]      ,		axis=1)

																							# timestep to tensor
																							A__						:				str         =			np.array([t]      ,		dtype=_A)

																							# predict the noise residual
																							A__						:				List[Any]         =			self.unet(
																							    sample=_A      ,		timestep=_A      ,		encoder_hidden_states=_A      ,		class_labels=noise_level.astype(np.intaa)      ,		)[0]

																							# perform guidance
																							if do_classifier_free_guidance:
																													A__     ,							A__						:				Tuple         =			np.split(_A      ,		2)
																							A__						:				Dict         =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																							# compute the previous noisy sample x_t -> x_t-1
																							A__						:				Optional[int]         =			self.scheduler.step(
																							    torch.from_numpy(_A)      ,		_A      ,		_A      ,		**_A).prev_sample

																							# call the callback, if provided
																							if i == len(_A) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
																													progress_bar.update()
																													if callback is not None and i % callback_steps == 0:
																																			callback(_A      ,		_A      ,		_A)

        # 10. Post-processing
											A__						:				Optional[Any]         =			self.decode_latents(latents.float())

											# 11. Convert to PIL
											if output_type == "pil":
																	A__						:				Optional[Any]         =			self.numpy_to_pil(_A)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=_A)







					def 			_lowercase      (							self   :  Union[str, Any]      ,		_A   :  Optional[int]):
											A__						:				List[Any]         =			1 / 0.0_83_33 * latents
											A__						:				List[Any]         =			self.vae(latent_sample=_A)[0]
											A__						:				Dict         =			np.clip(image / 2 + 0.5      ,		0      ,		1)
											A__						:				List[Any]         =			image.transpose((0, 2, 3, 1))
											return image







					def 			_lowercase      (							self   :  Union[str, Any]      ,		_A   :  Union[str, List[str]]      ,		_A   :  Any      ,		_A   :  Optional[int]      ,		_A   :  bool      ,		_A   :  Optional[str]      ,		_A   :  Optional[torch.FloatTensor] = None      ,		_A   :  Optional[torch.FloatTensor] = None      ,		):
											if prompt is not None and isinstance(_A      ,		_A):
																	A__						:				Tuple         =			1
											elif prompt is not None and isinstance(_A      ,		_A):
																	A__						:				Dict         =			len(_A)
											else:
																	A__						:				List[Any]         =			prompt_embeds.shape[0]

											if prompt_embeds is None:
																	A__						:				Union[str, Any]         =			self.tokenizer(
																	    _A      ,		padding="max_length"      ,		max_length=self.tokenizer.model_max_length      ,		truncation=_A      ,		return_tensors="pt"      ,		)
																	A__						:				Dict         =			text_inputs.input_ids
																	A__						:				Dict         =			self.tokenizer(_A      ,		padding="longest"      ,		return_tensors="pt").input_ids

																	if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
																	    _A      ,		_A):
																							A__						:				Any         =			self.tokenizer.batch_decode(
																							    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
																							logger.warning(
																							    "The following part of your input was truncated because CLIP can only handle sequences up to"
																							    F' {self.tokenizer.model_max_length} tokens: {removed_text}')

																	# no positional arguments to text_encoder
																	A__						:				Tuple         =			self.text_encoder(
																	    input_ids=text_input_ids.int().to(_A)      ,		)
																	A__						:				List[Any]         =			prompt_embeds[0]

											A__     ,							A__     ,							A__						:				List[str]         =			prompt_embeds.shape
											# duplicate text embeddings for each generation per prompt, using mps friendly method
											A__						:				int         =			prompt_embeds.repeat(1      ,		_A)
											A__						:				Any         =			prompt_embeds.reshape(bs_embed * num_images_per_prompt      ,		_A      ,		-1)

											# get unconditional embeddings for classifier free guidance
											if do_classifier_free_guidance and negative_prompt_embeds is None:
																	A__						:				List[str]
																	if negative_prompt is None:
																							A__						:				str         =			[""] * batch_size
																	elif type(_A) is not type(_A):
																							raise TypeError(
																							    F'`negative_prompt` should be the same type to `prompt`, but got {type(_A)} !='
																							    F' {type(_A)}.')
																	elif isinstance(_A      ,		_A):
																							A__						:				Optional[Any]         =			[negative_prompt]
																	elif batch_size != len(_A):
																							raise ValueError(
																							    F'`negative_prompt`: {negative_prompt} has batch size {len(_A)}, but `prompt`:'
																							    F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
																							    " the batch size of `prompt`.")
																	else:
																							A__						:				Dict         =			negative_prompt

																	A__						:				Optional[int]         =			text_input_ids.shape[-1]
																	A__						:				Dict         =			self.tokenizer(
																	    _A      ,		padding="max_length"      ,		max_length=_A      ,		truncation=_A      ,		return_tensors="pt"      ,		)

																	# if hasattr(uncond_input, "attention_mask"):
																	#     attention_mask = uncond_input.attention_mask.to(device)
																	# else:
																	#     attention_mask = None

																	A__						:				int         =			self.text_encoder(
																	    input_ids=uncond_input.input_ids.int().to(_A)      ,		)
																	A__						:				int         =			uncond_embeddings[0]

											if do_classifier_free_guidance:
																	A__						:				List[str]         =			uncond_embeddings.shape[1]
																	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																	A__						:				str         =			uncond_embeddings.repeat(1      ,		_A)
																	A__						:				Tuple         =			uncond_embeddings.reshape(batch_size * num_images_per_prompt      ,		_A      ,		-1)

																	# For classifier free guidance, we need to do two forward passes.
																	# Here we concatenate the unconditional and text embeddings into a single batch
																	# to avoid doing two forward passes
																	A__						:				Optional[Any]         =			np.concatenate([uncond_embeddings, prompt_embeds])

											return prompt_embeds




from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case     :    Any						=    logging.get_logger(__name__)

snake_case     :    List[str]						=    {
    'MCG-NJU/videomae-base': 'https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json',
}




class 				lowerCAmelCase__	(						UpperCamelCase    ):
					__A  :     str     =    'videomae'







					def __init__(							self   :  Optional[Any]      ,		_A   :  str=224      ,		_A   :  Union[str, Any]=16      ,		_A   :  int=3      ,		_A   :  Tuple=16      ,		_A   :  int=2      ,		_A   :  List[Any]=768      ,		_A   :  List[Any]=12      ,		_A   :  Tuple=12      ,		_A   :  List[Any]=3072      ,		_A   :  Union[str, Any]="gelu"      ,		_A   :  Any=0.0      ,		_A   :  str=0.0      ,		_A   :  Tuple=0.02      ,		_A   :  Optional[Any]=1e-12      ,		_A   :  Tuple=True      ,		_A   :  Dict=True      ,		_A   :  str=6      ,		_A   :  Optional[int]=384      ,		_A   :  Any=4      ,		_A   :  Any=1536      ,		_A   :  Optional[int]=True      ,		**_A   :  Tuple      ,		):
											super().__init__(**_A)

											A__						:				List[str]         =			image_size
											A__						:				Union[str, Any]         =			patch_size
											A__						:				Optional[int]         =			num_channels
											A__						:				int         =			num_frames
											A__						:				List[Any]         =			tubelet_size

											A__						:				str         =			hidden_size
											A__						:				Optional[Any]         =			num_hidden_layers
											A__						:				int         =			num_attention_heads
											A__						:				List[Any]         =			intermediate_size
											A__						:				Optional[int]         =			hidden_act
											A__						:				Any         =			hidden_dropout_prob
											A__						:				str         =			attention_probs_dropout_prob
											A__						:				List[Any]         =			initializer_range
											A__						:				str         =			layer_norm_eps
											A__						:				Any         =			qkv_bias
											A__						:				Union[str, Any]         =			use_mean_pooling

											A__						:				int         =			decoder_num_attention_heads
											A__						:				Tuple         =			decoder_hidden_size
											A__						:				int         =			decoder_num_hidden_layers
											A__						:				int         =			decoder_intermediate_size
											A__						:				Any         =			norm_pix_loss