






"""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_								=	getLogger(__name__)


lowercase_								=	4
lowercase_								=	7

lowercase_								=	{
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







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

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

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

  return image





class    snake_case						(				UpperCamelCase__		):


   '''simple docstring'''

   def __init__(      self					:	int,			_lowerCamelCase					:	int,			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	Union[str, Any],			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	Optional[Any] = 3_50,			):

     '''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					:	Tuple,			_lowerCamelCase					:	int,			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	Union[str, Any] = 75,			_lowerCamelCase					:	str = 9.0,			_lowerCamelCase					:	Optional[int] = 20,			_lowerCamelCase					:	List[str] = None,			_lowerCamelCase					:	List[str] = 1,			_lowerCamelCase					:	int = 0.0,			_lowerCamelCase					:	Optional[Any] = None,			_lowerCamelCase					:	Any = None,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	Any = "pil",			_lowerCamelCase					:	Any = True,			_lowerCamelCase					:	Dict = None,			_lowerCamelCase					:	Optional[int] = 1,			):

     '''simple docstring'''
     self.check_inputs(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase    )

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

     __A		     =					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		     =					guidance_scale > 1.0

     # 3. Encode input prompt
     __A		     =					self._encode_prompt(
         _lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			prompt_embeds=_lowerCamelCase,			negative_prompt_embeds=_lowerCamelCase,			)

     __A		     =					ORT_TO_PT_TYPE[str(text_embeddings.dtype    )]

     # 4. Preprocess image
     __A		     =					preprocess(_lowerCamelCase    )
     __A		     =					image.cpu()

     # 5. set timesteps
     self.scheduler.set_timesteps(_lowerCamelCase,			device=_lowerCamelCase    )
     __A		     =					self.scheduler.timesteps

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

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

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

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

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

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

         # timestep to tensor
         __A		     =					np.array([t],			dtype=_lowerCamelCase    )

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

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

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

     if not return_dict:
       return (image,)

     return ImagePipelineOutput(images=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any],			_lowerCamelCase					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					1 / 0.0_83_33 * latents
     __A		     =					self.vae(latent_sample=_lowerCamelCase    )[0]
     __A		     =					np.clip(image / 2 + 0.5,			0,			1    )
     __A		     =					image.transpose((0, 2, 3, 1)    )
     return image






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Dict,			_lowerCamelCase					:	Dict,			_lowerCamelCase					:	Dict,			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	Union[str, Any] = None,			_lowerCamelCase					:	Dict = None,			):

     '''simple docstring'''
     if prompt is not None and isinstance(_lowerCamelCase,			_lowerCamelCase    ):
       __A		     =					1
     elif prompt is not None and isinstance(_lowerCamelCase,			_lowerCamelCase    ):
       __A		     =					len(_lowerCamelCase    )
     else:
       __A		     =					prompt_embeds.shape[0]

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

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

       __A		     =					text_input_ids.shape[-1]
       __A		     =					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

       __A		     =					self.text_encoder(
           input_ids=uncond_input.input_ids.int().to(_lowerCamelCase    ),			)
       __A		     =					uncond_embeddings[0]

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

     return prompt_embeds









'''simple docstring'''



lowerCAmelCase						:		Tuple						      = '''
# Diffusers installation
! pip install diffusers transformers datasets accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/diffusers.git
'''

lowerCAmelCase						:		Tuple						      = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]