





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


__magic_name__					:				Optional[Any]	= getLogger(__name__)


__magic_name__					:				Optional[int]	= 4
__magic_name__					:				Dict	= 7

__magic_name__					:				str	= {
    """float16""": torch.floataa,
    """float32""": torch.floataa,
}

def     UpperCamelCase       (SCREAMING_SNAKE_CASE    ):
						if isinstance(UpperCamelCase__       ,    torch.Tensor    ):
												return image
						elif isinstance(UpperCamelCase__       ,    PIL.Image.Image    ):
												UpperCamelCase							:		Dict							      = [image]

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

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

						return image






class        lowercase__ (      __SCREAMING_SNAKE_CASE  ):

			"""simple docstring"""







			def __init__(			self	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A = 3_5_0	,      ):






									'''simple docstring'''







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







			def __call__(			self	,      _A	,      _A	,      _A = 7_5	,      _A = 9.0	,      _A = 2_0	,      _A = None	,      _A = 1	,      _A = 0.0	,      _A = None	,      _A = None	,      _A = None	,      _A = None	,      _A = "pil"	,      _A = True	,      _A = None	,      _A = 1	,      ):






									'''simple docstring'''







									self.check_inputs(__lowerCamelCase	,      __lowerCamelCase	,      __lowerCamelCase	,      __lowerCamelCase						)

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

									UpperCamelCase							:		Optional[int]							      = 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							:		Optional[Any]							      = guidance_scale > 1.0

									# 3. Encode input prompt
									UpperCamelCase							:		Tuple							      = self._encode_prompt(
									    __lowerCamelCase	,      __lowerCamelCase	,      __lowerCamelCase	,      __lowerCamelCase	,      __lowerCamelCase	,      prompt_embeds=__lowerCamelCase	,      negative_prompt_embeds=__lowerCamelCase	,      )

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

									# 4. Preprocess image
									UpperCamelCase							:		int							      = preprocess(__lowerCamelCase						)
									UpperCamelCase							:		Tuple							      = image.cpu()

									# 5. set timesteps
									self.scheduler.set_timesteps(__lowerCamelCase	,      device=__lowerCamelCase						)
									UpperCamelCase							:		str							      = self.scheduler.timesteps

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

									UpperCamelCase							:		Union[str, Any]							      = 2 if do_classifier_free_guidance else 1
									UpperCamelCase							:		Optional[Any]							      = np.concatenate([image] * batch_multiplier * num_images_per_prompt						)
									UpperCamelCase							:		Tuple							      = np.concatenate([noise_level] * image.shape[0]						)

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

									# 7. Check that sizes of image and latents match
									UpperCamelCase							:		List[str]							      = 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							:		Any							      = self.prepare_extra_step_kwargs(__lowerCamelCase	,      __lowerCamelCase						)

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

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

																					# concat latents, mask, masked_image_latents in the channel dimension
																					UpperCamelCase							:		Optional[int]							      = self.scheduler.scale_model_input(__lowerCamelCase	,      __lowerCamelCase						)
																					UpperCamelCase							:		Optional[int]							      = np.concatenate([latent_model_input, image]	,      axis=1						)

																					# timestep to tensor
																					UpperCamelCase							:		Optional[int]							      = np.array([t]	,      dtype=__lowerCamelCase						)

																					# predict the noise residual
																					UpperCamelCase							:		str							      = self.unet(
																					    sample=__lowerCamelCase	,      timestep=__lowerCamelCase	,      encoder_hidden_states=__lowerCamelCase	,      class_labels=noise_level.astype(np.intaa						)	,      )[0]

																					# perform guidance
																					if do_classifier_free_guidance:
																											UpperCamelCase							:		Optional[int]							      = np.split(__lowerCamelCase	,      2						)
																					UpperCamelCase							:		List[Any]							      = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																					# compute the previous noisy sample x_t -> x_t-1
																					UpperCamelCase							:		Dict							      = self.scheduler.step(
																					    torch.from_numpy(__lowerCamelCase						)	,      __lowerCamelCase	,      __lowerCamelCase	,      **__lowerCamelCase						).prev_sample

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

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

									# 11. Convert to PIL
									if output_type == "pil":
															UpperCamelCase							:		List[str]							      = self.numpy_to_pil(__lowerCamelCase						)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=__lowerCamelCase						)







			def      _a			(			self	,      _A						):






									'''simple docstring'''







									UpperCamelCase							:		Tuple							      = 1 / 0.0_83_33 * latents
									UpperCamelCase							:		List[Any]							      = self.vae(latent_sample=__lowerCamelCase						)[0]
									UpperCamelCase							:		Any							      = np.clip(image / 2 + 0.5	,      0	,      1						)
									UpperCamelCase							:		Any							      = image.transpose((0, 2, 3, 1)						)
									return image







			def      _a			(			self	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A = None	,      _A = None	,      ):






									'''simple docstring'''







									if prompt is not None and isinstance(__lowerCamelCase	,      __lowerCamelCase						):
															UpperCamelCase							:		Optional[Any]							      = 1
									elif prompt is not None and isinstance(__lowerCamelCase	,      __lowerCamelCase						):
															UpperCamelCase							:		Dict							      = len(__lowerCamelCase						)
									else:
															UpperCamelCase							:		Union[str, Any]							      = prompt_embeds.shape[0]

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

															if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
															    __lowerCamelCase	,      __lowerCamelCase						):
																					UpperCamelCase							:		Union[str, 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
															UpperCamelCase							:		int							      = self.text_encoder(
															    input_ids=text_input_ids.int().to(__lowerCamelCase						)	,      )
															UpperCamelCase							:		List[Any]							      = prompt_embeds[0]

									UpperCamelCase							:		Dict							      = prompt_embeds.shape
									# duplicate text embeddings for each generation per prompt, using mps friendly method
									UpperCamelCase							:		Optional[Any]							      = prompt_embeds.repeat(1	,      __lowerCamelCase						)
									UpperCamelCase							:		Optional[Any]							      = prompt_embeds.reshape(bs_embed * num_images_per_prompt	,      __lowerCamelCase	,      -1						)

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

															UpperCamelCase							:		str							      = text_input_ids.shape[-1]
															UpperCamelCase							:		List[Any]							      = self.tokenizer(
															    __lowerCamelCase	,      padding="""max_length"""	,      max_length=__lowerCamelCase	,      truncation=__lowerCamelCase	,      return_tensors="""pt"""	,      )

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

															UpperCamelCase							:		int							      = self.text_encoder(
															    input_ids=uncond_input.input_ids.int().to(__lowerCamelCase						)	,      )
															UpperCamelCase							:		Any							      = uncond_embeddings[0]

									if do_classifier_free_guidance:
															UpperCamelCase							:		List[Any]							      = uncond_embeddings.shape[1]
															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
															UpperCamelCase							:		Tuple							      = uncond_embeddings.repeat(1	,      __lowerCamelCase						)
															UpperCamelCase							:		List[Any]							      = uncond_embeddings.reshape(batch_size * num_images_per_prompt	,      __lowerCamelCase	,      -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							:		Union[str, Any]							      = np.concatenate([uncond_embeddings, prompt_embeds]						)

									return prompt_embeds










"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__					:				str	= {"""configuration_levit""": ["""LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LevitConfig""", """LevitOnnxConfig"""]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__					:				Optional[int]	= ["""LevitFeatureExtractor"""]
		__magic_name__					:				Any	= ["""LevitImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__					:				List[Any]	= [
		    """LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LevitForImageClassification""",
		    """LevitForImageClassificationWithTeacher""",
		    """LevitModel""",
		    """LevitPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_levit import LevitFeatureExtractor
				from .image_processing_levit import LevitImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_levit import (
				    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LevitForImageClassification,
				    LevitForImageClassificationWithTeacher,
				    LevitModel,
				    LevitPreTrainedModel,
				)
else:
		import sys

		__magic_name__					:				Tuple	= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



