

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							:     str 						=	getLogger(__name__)


__snake_case							:     int 						=	4
__snake_case							:     int 						=	7

__snake_case							:     Optional[Any] 						=	{
    """float16""": torch.floataa,
    """float32""": torch.floataa,
}


def 			_UpperCAmelCase							(		a__):

			'''simple docstring'''



			if isinstance(a__       ,					torch.Tensor):
						return image
			elif isinstance(a__       ,					PIL.Image.Image):
						a_		:		Any				    =				[image]

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

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

			return image



class    A__(a_    ):
						"""simple docstring"""






						def __init__(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase = 350       ,     )      ->				int:
									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       ,     _lowercase       ,     _lowercase       ,     _lowercase = 75       ,     _lowercase = 9.0       ,     _lowercase = 20       ,     _lowercase = None       ,     _lowercase = 1       ,     _lowercase = 0.0       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = "pil"       ,     _lowercase = True       ,     _lowercase = None       ,     _lowercase = 1       ,     )      ->				int:
									self.check_inputs(_lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )

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

									a_		:		List[str]				    =				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_		:		Union[str, Any]				    =				guidance_scale > 1.0

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

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

									# 4. Preprocess image
									a_		:		Union[str, Any]				    =				preprocess(_lowercase  )
									a_		:		str				    =				image.cpu()

									# 5. set timesteps
									self.scheduler.set_timesteps(_lowercase       ,     device=_lowercase  )
									a_		:		str				    =				self.scheduler.timesteps

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

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

									# 6. Prepare latent variables
									a_    ,			a_		:		Union[str, Any]				    =				image.shape[2:]
									a_		:		Any				    =				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
									a_		:		Dict				    =				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_		:		Union[str, Any]				    =				self.prepare_extra_step_kwargs(_lowercase       ,     _lowercase  )

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

									# 9. Denoising loop
									a_		:		Optional[Any]				    =				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
															a_		:		List[Any]				    =				np.concatenate([latents] * 2  ) if do_classifier_free_guidance else latents

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

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

															# predict the noise residual
															a_		:		List[Any]				    =				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:
																		a_    ,			a_		:		Optional[int]				    =				np.split(_lowercase       ,     2  )
															a_		:		str				    =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# compute the previous noisy sample x_t -> x_t-1
															a_		:		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
									a_		:		Optional[Any]				    =				self.decode_latents(latents.float()  )

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

									if not return_dict:
												return (image,)

									return ImagePipelineOutput(images=_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				Any:
									a_		:		Dict				    =				1 / 0.0_8_3_3_3 * latents
									a_		:		int				    =				self.vae(latent_sample=_lowercase  )[0]
									a_		:		Any				    =				np.clip(image / 2 + 0.5       ,     0       ,     1  )
									a_		:		int				    =				image.transpose((0, 2, 3, 1)  )
									return image





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase = None       ,     _lowercase = None       ,     )      ->				Tuple:
									if prompt is not None and isinstance(_lowercase       ,     _lowercase  ):
												a_		:		int				    =				1
									elif prompt is not None and isinstance(_lowercase       ,     _lowercase  ):
												a_		:		Dict				    =				len(_lowercase  )
									else:
												a_		:		Any				    =				prompt_embeds.shape[0]

									if prompt_embeds is None:
												a_		:		int				    =				self.tokenizer(
												    _lowercase       ,     padding="""max_length"""       ,     max_length=self.tokenizer.model_max_length       ,     truncation=_lowercase       ,     return_tensors="""pt"""       ,     )
												a_		:		int				    =				text_inputs.input_ids
												a_		:		Optional[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  ):
															a_		:		Tuple				    =				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_		:		List[Any]				    =				self.text_encoder(
												    input_ids=text_input_ids.int().to(_lowercase  )       ,     )
												a_		:		str				    =				prompt_embeds[0]

									a_    ,			a_    ,			a_		:		Optional[int]				    =				prompt_embeds.shape
									# duplicate text embeddings for each generation per prompt, using mps friendly method
									a_		:		Optional[Any]				    =				prompt_embeds.repeat(1       ,     _lowercase  )
									a_		:		List[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:
												a_		:		List[str]
												if negative_prompt is None:
															a_		:		Tuple				    =				[""""""] * 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  ):
															a_		:		Optional[int]				    =				[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:
															a_		:		Optional[int]				    =				negative_prompt

												a_		:		int				    =				text_input_ids.shape[-1]
												a_		:		Dict				    =				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

												a_		:		Any				    =				self.text_encoder(
												    input_ids=uncond_input.input_ids.int().to(_lowercase  )       ,     )
												a_		:		Tuple				    =				uncond_embeddings[0]

									if do_classifier_free_guidance:
												a_		:		str				    =				uncond_embeddings.shape[1]
												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												a_		:		str				    =				uncond_embeddings.repeat(1       ,     _lowercase  )
												a_		:		str				    =				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
												a_		:		Optional[Any]				    =				np.concatenate([uncond_embeddings, prompt_embeds]  )

									return prompt_embeds





from collections import namedtuple

__snake_case							:     Optional[Any] 						=	namedtuple("""from_to""", """from_ to""")

__snake_case							:     Tuple 						=	{
    """millimeter""": """mm""",
    """centimeter""": """cm""",
    """meter""": """m""",
    """kilometer""": """km""",
    """inch""": """in""",
    """inche""": """in""",  # Trailing 's' has been stripped off
    """feet""": """ft""",
    """foot""": """ft""",
    """yard""": """yd""",
    """mile""": """mi""",
}

__snake_case							:     Optional[int] 						=	{
    """mm""": from_to(0.001, 10_00),
    """cm""": from_to(0.01, 1_00),
    """m""": from_to(1, 1),
    """km""": from_to(10_00, 0.001),
    """in""": from_to(0.0254, 39.3701),
    """ft""": from_to(0.3048, 3.2_8084),
    """yd""": from_to(0.9144, 1.0_9361),
    """mi""": from_to(1609.34, 0.0_0062_1371),
}


def 			_UpperCAmelCase							(		a__       ,					a__       ,					a__):

			'''simple docstring'''



			a_		:		List[str]				    =				from_type.lower().rstrip("""s""")
			a_		:		Union[str, Any]				    =				TYPE_CONVERSION.get(a__       ,					a__)
			a_		:		Optional[int]				    =				to_type.lower().rstrip("""s""")
			a_		:		Optional[int]				    =				TYPE_CONVERSION.get(a__       ,					a__)
			if new_from not in METRIC_CONVERSION:
						a_		:		Any				    =				(
						    f'''Invalid \'from_type\' value: {from_type!r}.\n'''
						    f'''Conversion abbreviations are: {", ".join(a__)}'''
						)
						raise ValueError(a__)
			if new_to not in METRIC_CONVERSION:
						a_		:		Union[str, Any]				    =				(
						    f'''Invalid \'to_type\' value: {to_type!r}.\n'''
						    f'''Conversion abbreviations are: {", ".join(a__)}'''
						)
						raise ValueError(a__)
			return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
	import doctest

	doctest.testmod()


