
"""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


lowercase		:						List[Any] =					getLogger(__name__)


lowercase		:						str =					4
lowercase		:						Optional[Any] =					7

lowercase		:						int =					{
    'float16': torch.floataa,
    'float32': torch.floataa,
}


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			Any:


     '''simple docstring'''
     if isinstance(lowerCamelCase_	,				torch.Tensor ):
          return image
     elif isinstance(lowerCamelCase_	,				PIL.Image.Image ):
          __a:						List[Any]		    =					[image]

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

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

     return image




class    _snake_case  (						_lowercase       ):






       def __init__(					self						:			Optional[Any]						, snake_case__						:			OnnxRuntimeModel						, snake_case__						:			OnnxRuntimeModel						, snake_case__						:			Any						, snake_case__						:			OnnxRuntimeModel						, snake_case__						:			DDPMScheduler						, snake_case__						:			Any						, snake_case__						:			int = 3_5_0						, ):






            """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 = 7_5						, snake_case__						:			float = 9.0						, snake_case__						:			int = 2_0						, 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__    ):
                 __a:						List[Any]		    =					1
            elif prompt is not None and isinstance(snake_case__						, snake_case__    ):
                 __a:						List[str]		    =					len(snake_case__    )
            else:
                 __a:						List[str]		    =					prompt_embeds.shape[0]

            __a:						Union[str, Any]		    =					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:						Any		    =					guidance_scale > 1.0

            # 3. Encode input prompt
            __a:						Optional[Any]		    =					self._encode_prompt(
                snake_case__						, snake_case__						, snake_case__						, snake_case__						, snake_case__						, prompt_embeds=snake_case__						, negative_prompt_embeds=snake_case__						, )

            __a:						Tuple		    =					ORT_TO_PT_TYPE[str(text_embeddings.dtype    )]

            # 4. Preprocess image
            __a:						Any		    =					preprocess(snake_case__    )
            __a:						List[Any]		    =					image.cpu()

            # 5. set timesteps
            self.scheduler.set_timesteps(snake_case__						, device=snake_case__    )
            __a:						Optional[int]		    =					self.scheduler.timesteps

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

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

            # 6. Prepare latent variables
            __a:						List[Any]		    =					image.shape[2:]
            __a:						Optional[Any]		    =					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
            __a:						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
            __a:						Optional[int]		    =					self.prepare_extra_step_kwargs(snake_case__						, snake_case__    )

            __a:						Optional[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:						int		    =					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
                      __a:						Dict		    =					np.concatenate([latents] * 2    ) if do_classifier_free_guidance else latents

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

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

                      # predict the noise residual
                      __a:						List[str]		    =					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:
                           __a:						Tuple		    =					np.split(snake_case__						, 2    )
                      __a:						List[str]		    =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                      # compute the previous noisy sample x_t -> x_t-1
                      __a:						Dict		    =					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
            __a:						Tuple		    =					self.decode_latents(latents.float()    )

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

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=snake_case__    )






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






            """simple docstring"""



            __a:						int		    =					1 / 0.08_333 * latents
            __a:						Optional[Any]		    =					self.vae(latent_sample=snake_case__    )[0]
            __a:						Dict		    =					np.clip(image / 2 + 0.5						, 0						, 1    )
            __a:						str		    =					image.transpose((0, 2, 3, 1)    )
            return image






       def 							__lowercase  (					self						:			str						, snake_case__						:			Union[str, List[str]]						, snake_case__						:			Tuple						, 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__    ):
                 __a:						List[str]		    =					1
            elif prompt is not None and isinstance(snake_case__						, snake_case__    ):
                 __a:						Tuple		    =					len(snake_case__    )
            else:
                 __a:						List[str]		    =					prompt_embeds.shape[0]

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

            __a:						Any		    =					prompt_embeds.shape
            # duplicate text embeddings for each generation per prompt, using mps friendly method
            __a:						str		    =					prompt_embeds.repeat(1						, snake_case__    )
            __a:						Dict		    =					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:
                 __a:						List[str]
                 if negative_prompt is None:
                      __a:						List[str]		    =					[""""""] * 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__    ):
                      __a:						Dict		    =					[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:
                      __a:						str		    =					negative_prompt

                 __a:						List[Any]		    =					text_input_ids.shape[-1]
                 __a:						Tuple		    =					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

                 __a:						List[str]		    =					self.text_encoder(
                     input_ids=uncond_input.input_ids.int().to(snake_case__    )						, )
                 __a:						int		    =					uncond_embeddings[0]

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

            return prompt_embeds


"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase		:						int =					logging.get_logger(__name__)

lowercase		:						Any =					{
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						'''regnet'''
							SCREAMING_SNAKE_CASE		     =						['''x''', '''y''']






							def __init__(					self						:			Union[str, Any]						, snake_case__						:			Any=3						, snake_case__						:			str=3_2						, snake_case__						:			Tuple=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]						, snake_case__						:			Union[str, Any]=[2, 6, 1_2, 2]						, snake_case__						:			str=6_4						, snake_case__						:			Optional[int]="y"						, snake_case__						:			Tuple="relu"						, **snake_case__						:			str						, ):






												"""simple docstring"""



												super().__init__(**snake_case__    )
												if layer_type not in self.layer_types:
																	raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types    )}"""    )
												__a:						Any		    =					num_channels
												__a:						List[Any]		    =					embedding_size
												__a:						Optional[int]		    =					hidden_sizes
												__a:						Optional[int]		    =					depths
												__a:						Optional[Any]		    =					groups_width
												__a:						Tuple		    =					layer_type
												__a:						str		    =					hidden_act
												# always downsample in the first stage
												__a:						List[str]		    =					True
