




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


lowercase_			:       List[str]						       =				getLogger(__name__)


lowercase_			:       Optional[Any]						       =				4
lowercase_			:       Any						       =				7

lowercase_			:       List[Any]						       =				{
    'float16': torch.floataa,
    'float32': torch.floataa,
}


def 							__SCREAMING_SNAKE_CASE						(    snake_case_		):

						'''simple docstring'''


						if isinstance(snake_case_			,					torch.Tensor		):
												return image
						elif isinstance(snake_case_			,					PIL.Image.Image		):
												_UpperCAmelCase									=  [image]

						if isinstance(image[0]			,					PIL.Image.Image		):
												_UpperCAmelCase							,				_UpperCAmelCase									=  image[0].size
												_UpperCAmelCase							,				_UpperCAmelCase									=  (x - x % 64 for x in (w, h))  # resize to integer multiple of 32

												_UpperCAmelCase									=  [np.array(i.resize((w, h)		)		)[None, :] for i in image]
												_UpperCAmelCase									=  np.concatenate(snake_case_			,					axis=0		)
												_UpperCAmelCase									=  np.array(snake_case_		).astype(np.floataa		) / 255.0
												_UpperCAmelCase									=  image.transpose(0			,					3			,					1			,					2		)
												_UpperCAmelCase									=  2.0 * image - 1.0
												_UpperCAmelCase									=  torch.from_numpy(snake_case_		)
						elif isinstance(image[0]			,					torch.Tensor		):
												_UpperCAmelCase									=  torch.cat(snake_case_			,					dim=0		)

						return image






class 					__lowerCAmelCase				( UpperCAmelCase__				):







			def __init__( self					:    Tuple					,					snake_case__					:    OnnxRuntimeModel					,					snake_case__					:    OnnxRuntimeModel					,					snake_case__					:    Any					,					snake_case__					:    OnnxRuntimeModel					,					snake_case__					:    DDPMScheduler					,					snake_case__					:    Any					,					snake_case__					:    int = 350					,					):




									"""simple docstring"""


									super().__init__(
									    vae=snake_case__					,					text_encoder=snake_case__					,					tokenizer=snake_case__					,					unet=snake_case__					,					low_res_scheduler=snake_case__					,					scheduler=snake_case__					,					safety_checker=snake_case__					,					feature_extractor=snake_case__					,					watermarker=snake_case__					,					max_noise_level=snake_case__					,					)







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




									"""simple docstring"""


									self.check_inputs(snake_case__					,					snake_case__					,					snake_case__					,					snake_case__   )

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

									_UpperCAmelCase									=  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.
									_UpperCAmelCase									=  guidance_scale > 1.0

									# 3. Encode input prompt
									_UpperCAmelCase									=  self._encode_prompt(
									    snake_case__					,					snake_case__					,					snake_case__					,					snake_case__					,					snake_case__					,					prompt_embeds=snake_case__					,					negative_prompt_embeds=snake_case__					,					)

									_UpperCAmelCase									=  ORT_TO_PT_TYPE[str(text_embeddings.dtype   )]

									# 4. Preprocess image
									_UpperCAmelCase									=  preprocess(snake_case__   )
									_UpperCAmelCase									=  image.cpu()

									# 5. set timesteps
									self.scheduler.set_timesteps(snake_case__					,					device=snake_case__   )
									_UpperCAmelCase									=  self.scheduler.timesteps

									# 5. Add noise to image
									_UpperCAmelCase									=  torch.tensor([noise_level]					,					dtype=torch.long					,					device=snake_case__   )
									_UpperCAmelCase									=  torch.randn(image.shape					,					generator=snake_case__					,					device=snake_case__					,					dtype=snake_case__   )
									_UpperCAmelCase									=  self.low_res_scheduler.add_noise(snake_case__					,					snake_case__					,					snake_case__   )

									_UpperCAmelCase									=  2 if do_classifier_free_guidance else 1
									_UpperCAmelCase									=  np.concatenate([image] * batch_multiplier * num_images_per_prompt   )
									_UpperCAmelCase									=  np.concatenate([noise_level] * image.shape[0]   )

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

									# 7. Check that sizes of image and latents match
									_UpperCAmelCase									=  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
									_UpperCAmelCase									=  self.prepare_extra_step_kwargs(snake_case__					,					snake_case__   )

									_UpperCAmelCase									=  next(
									    (input.type for input in self.unet.model.get_inputs() if input.name == "timestep")					,					"tensor(float)"   )
									_UpperCAmelCase									=  ORT_TO_NP_TYPE[timestep_dtype]

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

																					# concat latents, mask, masked_image_latents in the channel dimension
																					_UpperCAmelCase									=  self.scheduler.scale_model_input(snake_case__					,					snake_case__   )
																					_UpperCAmelCase									=  np.concatenate([latent_model_input, image]					,					axis=1   )

																					# timestep to tensor
																					_UpperCAmelCase									=  np.array([t]					,					dtype=snake_case__   )

																					# predict the noise residual
																					_UpperCAmelCase									=  self.unet(
																					    sample=snake_case__					,					timestep=snake_case__					,					encoder_hidden_states=snake_case__					,					class_labels=noise_level.astype(np.intaa   )					,					)[0]

																					# perform guidance
																					if do_classifier_free_guidance:
																											_UpperCAmelCase							,				_UpperCAmelCase									=  np.split(snake_case__					,					2   )
																					_UpperCAmelCase									=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																					# compute the previous noisy sample x_t -> x_t-1
																					_UpperCAmelCase									=  self.scheduler.step(
																					    torch.from_numpy(snake_case__   )					,					snake_case__					,					snake_case__					,					**snake_case__   ).prev_sample

																					# call the callback, if provided
																					if i == len(snake_case__   ) - 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(snake_case__					,					snake_case__					,					snake_case__   )

        # 10. Post-processing
									_UpperCAmelCase									=  self.decode_latents(latents.float()   )

									# 11. Convert to PIL
									if output_type == "pil":
															_UpperCAmelCase									=  self.numpy_to_pil(snake_case__   )

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=snake_case__   )







			def UpperCamelCase    ( self					:    List[Any]					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  1 / 0.08_333 * latents
									_UpperCAmelCase									=  self.vae(latent_sample=snake_case__   )[0]
									_UpperCAmelCase									=  np.clip(image / 2 + 0.5					,					0					,					1   )
									_UpperCAmelCase									=  image.transpose((0, 2, 3, 1)   )
									return image







			def UpperCamelCase    ( self					:    List[Any]					,					snake_case__					:    Union[str, List[str]]					,					snake_case__					:    Any					,					snake_case__					:    Optional[int]					,					snake_case__					:    bool					,					snake_case__					:    Optional[str]					,					snake_case__					:    Optional[torch.FloatTensor] = None					,					snake_case__					:    Optional[torch.FloatTensor] = None					,					):




									"""simple docstring"""


									if prompt is not None and isinstance(snake_case__					,					snake_case__   ):
															_UpperCAmelCase									=  1
									elif prompt is not None and isinstance(snake_case__					,					snake_case__   ):
															_UpperCAmelCase									=  len(snake_case__   )
									else:
															_UpperCAmelCase									=  prompt_embeds.shape[0]

									if prompt_embeds is None:
															_UpperCAmelCase									=  self.tokenizer(
															    snake_case__					,					padding="max_length"					,					max_length=self.tokenizer.model_max_length					,					truncation=snake_case__					,					return_tensors="pt"					,					)
															_UpperCAmelCase									=  text_inputs.input_ids
															_UpperCAmelCase									=  self.tokenizer(snake_case__					,					padding="longest"					,					return_tensors="pt"   ).input_ids

															if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
															    snake_case__					,					snake_case__   ):
																					_UpperCAmelCase									=  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
															_UpperCAmelCase									=  self.text_encoder(
															    input_ids=text_input_ids.int().to(snake_case__   )					,					)
															_UpperCAmelCase									=  prompt_embeds[0]

									_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase									=  prompt_embeds.shape
									# duplicate text embeddings for each generation per prompt, using mps friendly method
									_UpperCAmelCase									=  prompt_embeds.repeat(1					,					snake_case__   )
									_UpperCAmelCase									=  prompt_embeds.reshape(bs_embed * num_images_per_prompt					,					snake_case__					,					-1   )

									# get unconditional embeddings for classifier free guidance
									if do_classifier_free_guidance and negative_prompt_embeds is None:
															_UpperCAmelCase									=  42
															if negative_prompt is None:
																					_UpperCAmelCase									=  [""] * batch_size
															elif type(snake_case__   ) is not type(snake_case__   ):
																					raise TypeError(
																					    F"""`negative_prompt` should be the same type to `prompt`, but got {type(snake_case__   )} !="""
																					    F""" {type(snake_case__   )}."""   )
															elif isinstance(snake_case__					,					snake_case__   ):
																					_UpperCAmelCase									=  [negative_prompt]
															elif batch_size != len(snake_case__   ):
																					raise ValueError(
																					    F"""`negative_prompt`: {negative_prompt} has batch size {len(snake_case__   )}, but `prompt`:"""
																					    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																					    " the batch size of `prompt`."   )
															else:
																					_UpperCAmelCase									=  negative_prompt

															_UpperCAmelCase									=  text_input_ids.shape[-1]
															_UpperCAmelCase									=  self.tokenizer(
															    snake_case__					,					padding="max_length"					,					max_length=snake_case__					,					truncation=snake_case__					,					return_tensors="pt"					,					)

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

															_UpperCAmelCase									=  self.text_encoder(
															    input_ids=uncond_input.input_ids.int().to(snake_case__   )					,					)
															_UpperCAmelCase									=  uncond_embeddings[0]

									if do_classifier_free_guidance:
															_UpperCAmelCase									=  uncond_embeddings.shape[1]
															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
															_UpperCAmelCase									=  uncond_embeddings.repeat(1					,					snake_case__   )
															_UpperCAmelCase									=  uncond_embeddings.reshape(batch_size * num_images_per_prompt					,					snake_case__					,					-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
															_UpperCAmelCase									=  np.concatenate([uncond_embeddings, prompt_embeds]   )

									return prompt_embeds










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase_			:       List[str]						       =				{
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			:       Any						       =				[
				    'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'RwkvForCausalLM',
				    'RwkvModel',
				    'RwkvPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rwkv import (
								    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RwkvForCausalLM,
								    RwkvModel,
								    RwkvPreTrainedModel,
								)
else:
				import sys

				lowercase_			:       Union[str, Any]						       =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




