

"""simple docstring"""





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


_SCREAMING_SNAKE_CASE       :  Optional[int]        = getLogger(__name__)


_SCREAMING_SNAKE_CASE       :  Tuple        = 4
_SCREAMING_SNAKE_CASE       :  Dict        = 7

_SCREAMING_SNAKE_CASE       :  List[Any]        = {
    """float16""": torch.floataa,
    """float32""": torch.floataa,
}



def        _lowerCAmelCase      (						UpperCAmelCase     :							Union[str, Any]				):


	'''simple docstring'''






	if isinstance(UpperCAmelCase      ,							torch.Tensor				):
		return image
	elif isinstance(UpperCAmelCase      ,							PIL.Image.Image				):
		UpperCamelCase__		:    int         =[image]

	if isinstance(image[0]      ,							PIL.Image.Image				):
		UpperCamelCase__		:    Optional[Any]         =image[0].size
		UpperCamelCase__		:    str         =(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__		:    List[str]         =np.concatenate(UpperCAmelCase      ,							axis=0				)
		UpperCamelCase__		:    str         =np.array(UpperCAmelCase				).astype(np.floataa				) / 255.0
		UpperCamelCase__		:    Union[str, Any]         =image.transpose(0      ,							3      ,							1      ,							2				)
		UpperCamelCase__		:    Tuple         =2.0 * image - 1.0
		UpperCamelCase__		:    str         =torch.from_numpy(UpperCAmelCase				)
	elif isinstance(image[0]      ,							torch.Tensor				):
		UpperCamelCase__		:    List[str]         =torch.cat(UpperCAmelCase      ,							dim=0				)

	return image







class     __a   (      snake_case__    ):

			"""simple docstring"""

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






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

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

				UpperCamelCase__		:    Dict         =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__		:    Dict         =self._encode_prompt(
				    lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						,  prompt_embeds=lowercase_						,  negative_prompt_embeds=lowercase_						,  )

				UpperCamelCase__		:    Dict         =ORT_TO_PT_TYPE[str(text_embeddings.dtype						)]

				# 4. Preprocess image
				UpperCamelCase__		:    Optional[int]         =preprocess(lowercase_						)
				UpperCamelCase__		:    List[str]         =image.cpu()

				# 5. set timesteps
				self.scheduler.set_timesteps(lowercase_						,  device=lowercase_						)
				UpperCamelCase__		:    str         =self.scheduler.timesteps

				# 5. Add noise to image
				UpperCamelCase__		:    Any         =torch.tensor([noise_level]						,  dtype=torch.long						,  device=lowercase_						)
				UpperCamelCase__		:    Optional[Any]         =torch.randn(image.shape						,  generator=lowercase_						,  device=lowercase_						,  dtype=lowercase_						)
				UpperCamelCase__		:    Optional[Any]         =self.low_res_scheduler.add_noise(lowercase_						,  lowercase_						,  lowercase_						)

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

				# 6. Prepare latent variables
				UpperCamelCase__		:    Optional[Any]         =image.shape[2:]
				UpperCamelCase__		:    str         =self.prepare_latents(
				    batch_size * num_images_per_prompt						,  lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						,  )

				# 7. Check that sizes of image and latents match
				UpperCamelCase__		:    Union[str, 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
				UpperCamelCase__		:    Union[str, Any]         =self.prepare_extra_step_kwargs(lowercase_						,  lowercase_						)

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

				# 9. Denoising loop
				UpperCamelCase__		:    str         =len(lowercase_						) - num_inference_steps * self.scheduler.order
				with self.progress_bar(total=lowercase_						) as progress_bar:
					for i, t in enumerate(lowercase_						):
						# 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(lowercase_						,  lowercase_						)
						UpperCamelCase__		:    str         =np.concatenate([latent_model_input, image]						,  axis=1						)

						# timestep to tensor
						UpperCamelCase__		:    List[Any]         =np.array([t]						,  dtype=lowercase_						)

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

						# perform guidance
						if do_classifier_free_guidance:
							UpperCamelCase__		:    Dict         =np.split(lowercase_						,  2						)
						UpperCamelCase__		:    List[str]         =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

						# compute the previous noisy sample x_t -> x_t-1
						UpperCamelCase__		:    List[Any]         =self.scheduler.step(
						    torch.from_numpy(lowercase_						)						,  lowercase_						,  lowercase_						,  **lowercase_						).prev_sample

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

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

				# 11. Convert to PIL
				if output_type == "pil":
					UpperCamelCase__		:    Dict         =self.numpy_to_pil(lowercase_						)

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=lowercase_						)






			def    _lowerCAmelCase			(							self     :					int						,  lowercase_     :					List[Any]						):
				UpperCamelCase__		:    List[str]         =1 / 0.0_8_3_3_3 * latents
				UpperCamelCase__		:    Optional[Any]         =self.vae(latent_sample=lowercase_						)[0]
				UpperCamelCase__		:    Dict         =np.clip(image / 2 + 0.5						,  0						,  1						)
				UpperCamelCase__		:    Optional[int]         =image.transpose((0, 2, 3, 1)						)
				return image






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

				if prompt_embeds is None:
					UpperCamelCase__		:    List[Any]         =self.tokenizer(
					    lowercase_						,  padding='''max_length'''						,  max_length=self.tokenizer.model_max_length						,  truncation=lowercase_						,  return_tensors='''pt'''						,  )
					UpperCamelCase__		:    Optional[int]         =text_inputs.input_ids
					UpperCamelCase__		:    int         =self.tokenizer(lowercase_						,  padding='''longest'''						,  return_tensors='''pt'''						).input_ids

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

				UpperCamelCase__		:    str         =prompt_embeds.shape
				# duplicate text embeddings for each generation per prompt, using mps friendly method
				UpperCamelCase__		:    Any         =prompt_embeds.repeat(1						,  lowercase_						)
				UpperCamelCase__		:    Any         =prompt_embeds.reshape(bs_embed * num_images_per_prompt						,  lowercase_						,  -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__		:    Optional[int]         =[''''''] * batch_size
					elif type(lowercase_						) is not type(lowercase_						):
						raise TypeError(
						    f'''`negative_prompt` should be the same type to `prompt`, but got {type(lowercase_						)} !='''
						    f''' {type(lowercase_						)}.'''						)
					elif isinstance(lowercase_						,  lowercase_						):
						UpperCamelCase__		:    str         =[negative_prompt]
					elif batch_size != len(lowercase_						):
						raise ValueError(
						    f'''`negative_prompt`: {negative_prompt} has batch size {len(lowercase_						)}, but `prompt`:'''
						    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
						    ''' the batch size of `prompt`.'''						)
					else:
						UpperCamelCase__		:    Tuple         =negative_prompt

					UpperCamelCase__		:    Any         =text_input_ids.shape[-1]
					UpperCamelCase__		:    List[str]         =self.tokenizer(
					    lowercase_						,  padding='''max_length'''						,  max_length=lowercase_						,  truncation=lowercase_						,  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(lowercase_						)						,  )
					UpperCamelCase__		:    Optional[Any]         =uncond_embeddings[0]

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


_SCREAMING_SNAKE_CASE       :  List[Any]        = {
    """configuration_deta""": ["""DETA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetaConfig"""],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  int        = ["""DetaImageProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """DETA_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """DetaForObjectDetection""",
				    """DetaModel""",
				    """DetaPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_deta import DetaImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deta import (
								    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetaForObjectDetection,
								    DetaModel,
								    DetaPreTrainedModel,
								)

else:
				import sys

				_SCREAMING_SNAKE_CASE       :  Optional[int]        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


