





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     :			int     						=	getLogger(__name__)


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

lowercase     :			Union[str, Any]     						=	{
    """float16""": torch.floataa,
    """float32""": torch.floataa,
}


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase       ):






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

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

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

       return image

class 			__A(    __lowercase			):

     def __init__(   self,							A,							A,							A,							A,							A,							A,							A = 350,							):





            """simple docstring"""

            super().__init__(
                vae=A__,							text_encoder=A__,							tokenizer=A__,							unet=A__,							low_res_scheduler=A__,							scheduler=A__,							safety_checker=A__,							feature_extractor=A__,							watermarker=A__,							max_noise_level=A__,							)

     def __call__(   self,							A,							A,							A = 75,							A = 9.0,							A = 20,							A = None,							A = 1,							A = 0.0,							A = None,							A = None,							A = None,							A = None,							A = "pil",							A = True,							A = None,							A = 1,							):





            """simple docstring"""

            self.check_inputs(A__,							A__,							A__,							A__   )

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

            _UpperCamelCase														=    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														=    guidance_scale > 1.0

            # 3. Encode input prompt
            _UpperCamelCase														=    self._encode_prompt(
                A__,							A__,							A__,							A__,							A__,							prompt_embeds=A__,							negative_prompt_embeds=A__,							)

            _UpperCamelCase														=    ORT_TO_PT_TYPE[str(text_embeddings.dtype   )]

            # 4. Preprocess image
            _UpperCamelCase														=    preprocess(A__   )
            _UpperCamelCase														=    image.cpu()

            # 5. set timesteps
            self.scheduler.set_timesteps(A__,							device=A__   )
            _UpperCamelCase														=    self.scheduler.timesteps

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

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

            # 6. Prepare latent variables
            _UpperCamelCase							,		_UpperCamelCase														=    image.shape[2:]
            _UpperCamelCase														=    self.prepare_latents(
                batch_size * num_images_per_prompt,							A__,							A__,							A__,							A__,							A__,							A__,							A__,							)

            # 7. Check that sizes of image and latents match
            _UpperCamelCase														=    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														=    self.prepare_extra_step_kwargs(A__,							A__   )

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

            # 9. Denoising loop
            _UpperCamelCase														=    len(A__   ) - num_inference_steps * self.scheduler.order
            with self.progress_bar(total=A__   ) as progress_bar:
                   for i, t in enumerate(A__   ):
                          # expand the latents if we are doing classifier free guidance
                          _UpperCamelCase														=    np.concatenate([latents] * 2   ) if do_classifier_free_guidance else latents

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

                          # timestep to tensor
                          _UpperCamelCase														=    np.array([t],							dtype=A__   )

                          # predict the noise residual
                          _UpperCamelCase														=    self.unet(
                              sample=A__,							timestep=A__,							encoder_hidden_states=A__,							class_labels=noise_level.astype(np.intaa   ),							)[0]

                          # perform guidance
                          if do_classifier_free_guidance:
                                 _UpperCamelCase							,		_UpperCamelCase														=    np.split(A__,							2   )
                          _UpperCamelCase														=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                          # compute the previous noisy sample x_t -> x_t-1
                          _UpperCamelCase														=    self.scheduler.step(
                              torch.from_numpy(A__   ),							A__,							A__,							**A__   ).prev_sample

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

        # 10. Post-processing
            _UpperCamelCase														=    self.decode_latents(latents.float()   )

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

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=A__   )

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

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



     def 						_UpperCamelCase  (   self,							A,							A,							A,							A,							A,							A = None,							A = None,							):





            """simple docstring"""

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

            if prompt_embeds is None:
                   _UpperCamelCase														=    self.tokenizer(
                       A__,							padding='''max_length''',							max_length=self.tokenizer.model_max_length,							truncation=A__,							return_tensors='''pt''',							)
                   _UpperCamelCase														=    text_inputs.input_ids
                   _UpperCamelCase														=    self.tokenizer(A__,							padding='''longest''',							return_tensors='''pt'''   ).input_ids

                   if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                       A__,							A__   ):
                          _UpperCamelCase														=    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														=    self.text_encoder(
                       input_ids=text_input_ids.int().to(A__   ),							)
                   _UpperCamelCase														=    prompt_embeds[0]

            _UpperCamelCase							,		_UpperCamelCase							,		_UpperCamelCase														=    prompt_embeds.shape
            # duplicate text embeddings for each generation per prompt, using mps friendly method
            _UpperCamelCase														=    prompt_embeds.repeat(1,							A__   )
            _UpperCamelCase														=    prompt_embeds.reshape(bs_embed * num_images_per_prompt,							A__,							-1   )

            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance and negative_prompt_embeds is None:
                   _UpperCamelCase														=    42
                   if negative_prompt is None:
                          _UpperCamelCase														=    [''''''] * batch_size
                   elif type(A__   ) is not type(A__   ):
                          raise TypeError(
                              F'''`negative_prompt` should be the same type to `prompt`, but got {type(A__   )} !='''
                              F''' {type(A__   )}.'''   )
                   elif isinstance(A__,							A__   ):
                          _UpperCamelCase														=    [negative_prompt]
                   elif batch_size != len(A__   ):
                          raise ValueError(
                              F'''`negative_prompt`: {negative_prompt} has batch size {len(A__   )}, but `prompt`:'''
                              F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                              ''' the batch size of `prompt`.'''   )
                   else:
                          _UpperCamelCase														=    negative_prompt

                   _UpperCamelCase														=    text_input_ids.shape[-1]
                   _UpperCamelCase														=    self.tokenizer(
                       A__,							padding='''max_length''',							max_length=A__,							truncation=A__,							return_tensors='''pt''',							)

                   # if hasattr(uncond_input, "attention_mask"):
                   #     attention_mask = uncond_input.attention_mask.to(device)
                   # else:
                   #     attention_mask = None

                   _UpperCamelCase														=    self.text_encoder(
                       input_ids=uncond_input.input_ids.int().to(A__   ),							)
                   _UpperCamelCase														=    uncond_embeddings[0]

            if do_classifier_free_guidance:
                   _UpperCamelCase														=    uncond_embeddings.shape[1]
                   # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                   _UpperCamelCase														=    uncond_embeddings.repeat(1,							A__   )
                   _UpperCamelCase														=    uncond_embeddings.reshape(batch_size * num_images_per_prompt,							A__,							-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														=    np.concatenate([uncond_embeddings, prompt_embeds]   )

            return prompt_embeds









import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


__A     						=      logging.get_logger(__name__)



class     lowercase_       (    __lowercase	):



      def __init__(     self :							List[str]     ,   *A__ :							Tuple     ,   **A__ :							int						)       ->	None:
        warnings.warn(
            '''The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use DetrImageProcessor instead.'''     ,   A__     ,   )
        super().__init__(*A__     ,   **A__						)

