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


lowerCAmelCase      :				Union[str, Any]     			=getLogger(__name__)


lowerCAmelCase      :				int     			=4
lowerCAmelCase      :				Optional[Any]     			=7

lowerCAmelCase      :				Optional[Any]     			={
    'float16': torch.floataa,
    'float32': torch.floataa,
}







def     A__	(       __A						):







    '''simple docstring'''
    if isinstance(snake_case_ ,			torch.Tensor						):
        return image
    elif isinstance(snake_case_ ,			PIL.Image.Image						):
        _lowerCamelCase  :		Union[str, Any]			    =    [image]

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

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

    return image




class 						__snake_case						(			SCREAMING_SNAKE_CASE__		):





      '''simple docstring'''






      def __init__(       self  :					Any     ,	_UpperCamelCase  :					List[Any]     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					Tuple     ,	_UpperCamelCase  :					Dict     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					List[str]     ,	_UpperCamelCase  :					str = 350     ,	)					->Union[str, Any]:


          """simple docstring"""


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



      def __call__(       self  :					Optional[Any]     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					Tuple     ,	_UpperCamelCase  :					Union[str, Any] = 75     ,	_UpperCamelCase  :					Union[str, Any] = 9.0     ,	_UpperCamelCase  :					Optional[Any] = 20     ,	_UpperCamelCase  :					List[Any] = None     ,	_UpperCamelCase  :					Tuple = 1     ,	_UpperCamelCase  :					Tuple = 0.0     ,	_UpperCamelCase  :					int = None     ,	_UpperCamelCase  :					Any = None     ,	_UpperCamelCase  :					List[Any] = None     ,	_UpperCamelCase  :					Optional[Any] = None     ,	_UpperCamelCase  :					Any = "pil"     ,	_UpperCamelCase  :					Tuple = True     ,	_UpperCamelCase  :					int = None     ,	_UpperCamelCase  :					Any = 1     ,	)					->Tuple:


          """simple docstring"""


          self.check_inputs(_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase)

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

          _lowerCamelCase  :		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.
          _lowerCamelCase  :		Union[str, Any]			    =    guidance_scale > 1.0

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

          _lowerCamelCase  :		Optional[int]			    =    ORT_TO_PT_TYPE[str(text_embeddings.dtype)]

          # 4. Preprocess image
          _lowerCamelCase  :		int			    =    preprocess(_UpperCamelCase)
          _lowerCamelCase  :		Tuple			    =    image.cpu()

          # 5. set timesteps
          self.scheduler.set_timesteps(_UpperCamelCase     ,	device=_UpperCamelCase)
          _lowerCamelCase  :		Any			    =    self.scheduler.timesteps

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

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

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

          # 7. Check that sizes of image and latents match
          _lowerCamelCase  :		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
          _lowerCamelCase  :		List[str]			    =    self.prepare_extra_step_kwargs(_UpperCamelCase     ,	_UpperCamelCase)

          _lowerCamelCase  :		Optional[Any]			    =    next(
              (input.type for input in self.unet.model.get_inputs() if input.name == """timestep""")     ,	"""tensor(float)""")
          _lowerCamelCase  :		str			    =    ORT_TO_NP_TYPE[timestep_dtype]

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

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

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

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

                  # perform guidance
                  if do_classifier_free_guidance:
                      _lowerCamelCase    ,				_lowerCamelCase  :		List[str]			    =    np.split(_UpperCamelCase     ,	2)
                  _lowerCamelCase  :		Tuple			    =    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                  # compute the previous noisy sample x_t -> x_t-1
                  _lowerCamelCase  :		Optional[int]			    =    self.scheduler.step(
                      torch.from_numpy(_UpperCamelCase)     ,	_UpperCamelCase     ,	_UpperCamelCase     ,	**_UpperCamelCase).prev_sample

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

        # 10. Post-processing
          _lowerCamelCase  :		int			    =    self.decode_latents(latents.float())

          # 11. Convert to PIL
          if output_type == "pil":
              _lowerCamelCase  :		List[Any]			    =    self.numpy_to_pil(_UpperCamelCase)

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int]     ,	_UpperCamelCase  :					Any)					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    1 / 0.0_8_3_3_3 * latents
          _lowerCamelCase  :		Union[str, Any]			    =    self.vae(latent_sample=_UpperCamelCase)[0]
          _lowerCamelCase  :		int			    =    np.clip(image / 2 + 0.5     ,	0     ,	1)
          _lowerCamelCase  :		Dict			    =    image.transpose((0, 2, 3, 1))
          return image




      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[str]     ,	_UpperCamelCase  :					Optional[int]     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					Optional[Any]     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					List[Any]     ,	_UpperCamelCase  :					str = None     ,	_UpperCamelCase  :					Any = None     ,	)					->Optional[int]:


          """simple docstring"""


          if prompt is not None and isinstance(_UpperCamelCase     ,	_UpperCamelCase):
              _lowerCamelCase  :		List[str]			    =    1
          elif prompt is not None and isinstance(_UpperCamelCase     ,	_UpperCamelCase):
              _lowerCamelCase  :		Tuple			    =    len(_UpperCamelCase)
          else:
              _lowerCamelCase  :		Any			    =    prompt_embeds.shape[0]

          if prompt_embeds is None:
              _lowerCamelCase  :		List[str]			    =    self.tokenizer(
                  _UpperCamelCase     ,	padding="""max_length"""     ,	max_length=self.tokenizer.model_max_length     ,	truncation=_UpperCamelCase     ,	return_tensors="""pt"""     ,	)
              _lowerCamelCase  :		Union[str, Any]			    =    text_inputs.input_ids
              _lowerCamelCase  :		List[Any]			    =    self.tokenizer(_UpperCamelCase     ,	padding="""longest"""     ,	return_tensors="""pt""").input_ids

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

          _lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase  :		Any			    =    prompt_embeds.shape
          # duplicate text embeddings for each generation per prompt, using mps friendly method
          _lowerCamelCase  :		str			    =    prompt_embeds.repeat(1     ,	_UpperCamelCase)
          _lowerCamelCase  :		List[str]			    =    prompt_embeds.reshape(bs_embed * num_images_per_prompt     ,	_UpperCamelCase     ,	-1)

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

              _lowerCamelCase  :		List[Any]			    =    text_input_ids.shape[-1]
              _lowerCamelCase  :		Any			    =    self.tokenizer(
                  _UpperCamelCase     ,	padding="""max_length"""     ,	max_length=_UpperCamelCase     ,	truncation=_UpperCamelCase     ,	return_tensors="""pt"""     ,	)

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

              _lowerCamelCase  :		Union[str, Any]			    =    self.text_encoder(
                  input_ids=uncond_input.input_ids.int().to(_UpperCamelCase)     ,	)
              _lowerCamelCase  :		Any			    =    uncond_embeddings[0]

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

          return prompt_embeds











def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						int						):
     if not isinstance(snake_case_ ,     snake_case_						):
          __magic_name__				=       f'Input value of [number={number}] must be an integer'
          raise TypeError(snake_case_						)
     if number < 0 or number % 2 == 1:
          return False
     __magic_name__				=       int(number**0.5						)
     return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()