


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


A_														=      getLogger(__name__)


A_														=      4
A_														=      7

A_														=      {
    "float16": torch.floataa,
    "float32": torch.floataa,
}







def 							UpperCAmelCase				(     UpperCAmelCase      )->		Tuple:







					'''simple docstring'''



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

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

										SCREAMING_SNAKE_CASE_	      =       [np.array(i.resize((w, h)      )      )[None, :] for i in image]
										SCREAMING_SNAKE_CASE_	      =       np.concatenate(UpperCAmelCase				,axis=0      )
										SCREAMING_SNAKE_CASE_	      =       np.array(UpperCAmelCase      ).astype(np.floataa      ) / 2_5_5.0
										SCREAMING_SNAKE_CASE_	      =       image.transpose(0				,3				,1				,2      )
										SCREAMING_SNAKE_CASE_	      =       2.0 * image - 1.0
										SCREAMING_SNAKE_CASE_	      =       torch.from_numpy(UpperCAmelCase      )
					elif isinstance(image[0]				,torch.Tensor      ):
										SCREAMING_SNAKE_CASE_	      =       torch.cat(UpperCAmelCase				,dim=0      )

					return image






class 						snake_case			(       lowerCAmelCase__	):
			'''simple docstring'''

			def __init__(	self     :     int							,							lowerCAmelCase_     :     OnnxRuntimeModel							,							lowerCAmelCase_     :     OnnxRuntimeModel							,							lowerCAmelCase_     :     Any							,							lowerCAmelCase_     :     OnnxRuntimeModel							,							lowerCAmelCase_     :     DDPMScheduler							,							lowerCAmelCase_     :     Any							,							lowerCAmelCase_     :     int = 350							,							)					->					Optional[int]:




								"""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     :     List[str]							,							lowerCAmelCase_     :     Union[str, List[str]]							,							lowerCAmelCase_     :     Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]							,							lowerCAmelCase_     :     int = 75							,							lowerCAmelCase_     :     float = 9.0							,							lowerCAmelCase_     :     int = 20							,							lowerCAmelCase_     :     Optional[Union[str, List[str]]] = None							,							lowerCAmelCase_     :     Optional[int] = 1							,							lowerCAmelCase_     :     float = 0.0							,							lowerCAmelCase_     :     Optional[Union[torch.Generator, List[torch.Generator]]] = None							,							lowerCAmelCase_     :     Optional[torch.FloatTensor] = None							,							lowerCAmelCase_     :     Optional[np.ndarray] = None							,							lowerCAmelCase_     :     Optional[np.ndarray] = None							,							lowerCAmelCase_     :     Optional[str] = "pil"							,							lowerCAmelCase_     :     bool = True							,							lowerCAmelCase_     :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,							lowerCAmelCase_     :     Optional[int] = 1							,							)					->					List[str]:




								"""simple docstring"""






								self.check_inputs(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					)

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

								SCREAMING_SNAKE_CASE_	      =       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.
								SCREAMING_SNAKE_CASE_	      =       guidance_scale > 1.0

								# 3. Encode input prompt
								SCREAMING_SNAKE_CASE_	      =       self._encode_prompt(
								    lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							prompt_embeds=lowerCAmelCase_							,							negative_prompt_embeds=lowerCAmelCase_							,							)

								SCREAMING_SNAKE_CASE_	      =       ORT_TO_PT_TYPE[str(text_embeddings.dtype					)]

								# 4. Preprocess image
								SCREAMING_SNAKE_CASE_	      =       preprocess(lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       image.cpu()

								# 5. set timesteps
								self.scheduler.set_timesteps(lowerCAmelCase_							,							device=lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       self.scheduler.timesteps

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

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

								# 6. Prepare latent variables
								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       image.shape[2:]
								SCREAMING_SNAKE_CASE_	      =       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
								SCREAMING_SNAKE_CASE_	      =       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
								SCREAMING_SNAKE_CASE_	      =       self.prepare_extra_step_kwargs(lowerCAmelCase_							,							lowerCAmelCase_					)

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

								# 9. Denoising loop
								SCREAMING_SNAKE_CASE_	      =       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
																		SCREAMING_SNAKE_CASE_	      =       np.concatenate([latents] * 2					) if do_classifier_free_guidance else latents

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

																		# timestep to tensor
																		SCREAMING_SNAKE_CASE_	      =       np.array([t]							,							dtype=lowerCAmelCase_					)

																		# predict the noise residual
																		SCREAMING_SNAKE_CASE_	      =       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:
																							SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       np.split(lowerCAmelCase_							,							2					)
																		SCREAMING_SNAKE_CASE_	      =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE_	      =       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
								SCREAMING_SNAKE_CASE_	      =       self.decode_latents(latents.float()					)

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

								if not return_dict:
													return (image,)

								return ImagePipelineOutput(images=lowerCAmelCase_					)

			def _lowercase (	self     :     Dict							,							lowerCAmelCase_     :     Optional[int]					)					->					str:




								"""simple docstring"""






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




			def _lowercase (	self     :     Dict							,							lowerCAmelCase_     :     Union[str, List[str]]							,							lowerCAmelCase_     :     int							,							lowerCAmelCase_     :     Optional[int]							,							lowerCAmelCase_     :     bool							,							lowerCAmelCase_     :     Optional[str]							,							lowerCAmelCase_     :     Optional[torch.FloatTensor] = None							,							lowerCAmelCase_     :     Optional[torch.FloatTensor] = None							,							)					->					Tuple:




								"""simple docstring"""






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

								if prompt_embeds is None:
													SCREAMING_SNAKE_CASE_	      =       self.tokenizer(
													    lowerCAmelCase_							,							padding='''max_length'''							,							max_length=self.tokenizer.model_max_length							,							truncation=lowerCAmelCase_							,							return_tensors='''pt'''							,							)
													SCREAMING_SNAKE_CASE_	      =       text_inputs.input_ids
													SCREAMING_SNAKE_CASE_	      =       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_					):
																		SCREAMING_SNAKE_CASE_	      =       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
													SCREAMING_SNAKE_CASE_	      =       self.text_encoder(
													    input_ids=text_input_ids.int().to(lowerCAmelCase_					)							,							)
													SCREAMING_SNAKE_CASE_	      =       prompt_embeds[0]

								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       prompt_embeds.shape
								# duplicate text embeddings for each generation per prompt, using mps friendly method
								SCREAMING_SNAKE_CASE_	      =       prompt_embeds.repeat(1							,							lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       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:
													SCREAMING_SNAKE_CASE_	      =       42
													if negative_prompt is None:
																		SCREAMING_SNAKE_CASE_	      =       [''''''] * 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_					):
																		SCREAMING_SNAKE_CASE_	      =       [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:
																		SCREAMING_SNAKE_CASE_	      =       negative_prompt

													SCREAMING_SNAKE_CASE_	      =       text_input_ids.shape[-1]
													SCREAMING_SNAKE_CASE_	      =       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

													SCREAMING_SNAKE_CASE_	      =       self.text_encoder(
													    input_ids=uncond_input.input_ids.int().to(lowerCAmelCase_					)							,							)
													SCREAMING_SNAKE_CASE_	      =       uncond_embeddings[0]

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

								return prompt_embeds






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A_														=      {
    "configuration_switch_transformers": [
        "SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwitchTransformersConfig",
        "SwitchTransformersOnnxConfig",
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      [
		    "SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "SwitchTransformersEncoderModel",
		    "SwitchTransformersForConditionalGeneration",
		    "SwitchTransformersModel",
		    "SwitchTransformersPreTrainedModel",
		    "SwitchTransformersTop1Router",
		    "SwitchTransformersSparseMLP",
		]


if TYPE_CHECKING:
		from .configuration_switch_transformers import (
		    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SwitchTransformersConfig,
		    SwitchTransformersOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_switch_transformers import (
				    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SwitchTransformersEncoderModel,
				    SwitchTransformersForConditionalGeneration,
				    SwitchTransformersModel,
				    SwitchTransformersPreTrainedModel,
				    SwitchTransformersSparseMLP,
				    SwitchTransformersTopaRouter,
				)


else:
		import sys

		A_														=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


