


"""simple docstring"""







from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__lowercase          = logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowercase          = """
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")

        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""


def 	lowercase     (							A_    ,  A_    ,  A_=8			)-> str:
  '''simple docstring'''







  a		:						Optional[int] 				=     height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  a		:						int 				=     width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor


def 	lowercase     (							A_    ,  A_=512    ,  A_=512			)-> List[str]:
  '''simple docstring'''







  a		:						Union[str, Any] 				=     pil_image.resize((w, h)    ,  resample=Image.BICUBIC    ,  reducing_gap=1			)
  a		:						str 				=     np.array(pil_image.convert("RGB"			)			)
  a		:						str 				=     arr.astype(np.floataa			) / 1_2_7.5 - 1
  a		:						str 				=     np.transpose(A_    ,  [2, 0, 1]			)
  a		:						Dict 				=     torch.from_numpy(A_			).unsqueeze(0			)
  return image







class 		_A    (    _a    ):







    """simple docstring"""




    def __init__(   self							:       Optional[int]							,					__UpperCAmelCase							:       UNetaDConditionModel							,					__UpperCAmelCase							:       DDPMScheduler							,					__UpperCAmelCase							:       VQModel							,					):
      super().__init__()

      self.register_modules(
          unet=__UpperCAmelCase							,					scheduler=__UpperCAmelCase							,					movq=__UpperCAmelCase							,					)
      a		:						Optional[Any] 				=     2 ** (len(self.movq.config.block_out_channels) - 1)






    def       __snake_case				(   self							:       Union[str, Any]							,					__UpperCAmelCase							:       str							,					__UpperCAmelCase							:       Dict							,					__UpperCAmelCase							:       Optional[Any]):
      # get the original timestep using init_timestep
      a		:						Any 				=     min(int(num_inference_steps * strength)							,					__UpperCAmelCase)

      a		:						Optional[int] 				=     max(num_inference_steps - init_timestep							,					0)
      a		:						Any 				=     self.scheduler.timesteps[t_start:]

      return timesteps, num_inference_steps - t_start






    def       __snake_case				(   self							:       Optional[int]							,					__UpperCAmelCase							:       List[Any]							,					__UpperCAmelCase							:       Dict							,					__UpperCAmelCase							:       List[str]							,					__UpperCAmelCase							:       List[Any]							,					__UpperCAmelCase							:       Tuple							,					__UpperCAmelCase							:       Dict							,					__UpperCAmelCase							:       Dict=None):
      if not isinstance(__UpperCAmelCase							,					(torch.Tensor, PIL.Image.Image, list)):
        raise ValueError(
            f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__UpperCAmelCase)}''')

      a		:						str 				=     image.to(device=__UpperCAmelCase							,					dtype=__UpperCAmelCase)

      a		:						Optional[int] 				=     batch_size * num_images_per_prompt

      if image.shape[1] == 4:
        a		:						Any 				=     image

      else:
        if isinstance(__UpperCAmelCase							,					__UpperCAmelCase) and len(__UpperCAmelCase) != batch_size:
          raise ValueError(
              f'''You have passed a list of generators of length {len(__UpperCAmelCase)}, but requested an effective batch'''
              f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''')

        elif isinstance(__UpperCAmelCase							,					__UpperCAmelCase):
          a		:						Any 				=     [
              self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(__UpperCAmelCase)
          ]
          a		:						Dict 				=     torch.cat(__UpperCAmelCase							,					dim=0)
        else:
          a		:						Tuple 				=     self.movq.encode(__UpperCAmelCase).latent_dist.sample(__UpperCAmelCase)

        a		:						Dict 				=     self.movq.config.scaling_factor * init_latents

      a		:						Optional[Any] 				=     torch.cat([init_latents]							,					dim=0)

      a		:						List[Any] 				=     init_latents.shape
      a		:						Tuple 				=     randn_tensor(__UpperCAmelCase							,					generator=__UpperCAmelCase							,					device=__UpperCAmelCase							,					dtype=__UpperCAmelCase)

      # get latents
      a		:						str 				=     self.scheduler.add_noise(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase)

      a		:						str 				=     init_latents

      return latents






    def       __snake_case				(   self							:       Tuple							,					__UpperCAmelCase							:       Optional[Any]=0):
      if is_accelerate_available():
        from accelerate import cpu_offload
      else:
        raise ImportError("Please install accelerate via `pip install accelerate`")

      a		:						List[str] 				=     torch.device(f'''cuda:{gpu_id}''')

      a		:						Tuple 				=     [
          self.unet,
          self.movq,
      ]
      for cpu_offloaded_model in models:
        if cpu_offloaded_model is not None:
          cpu_offload(__UpperCAmelCase							,					__UpperCAmelCase)






    def       __snake_case				(   self							:       List[Any]							,					__UpperCAmelCase							:       List[str]=0):
      if is_accelerate_available() and is_accelerate_version(">="							,					"0.17.0.dev0"):
        from accelerate import cpu_offload_with_hook
      else:
        raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")

      a		:						List[Any] 				=     torch.device(f'''cuda:{gpu_id}''')

      if self.device.type != "cpu":
        self.to("cpu"							,					silence_dtype_warnings=__UpperCAmelCase)
        torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

      a		:						Dict 				=     None
      for cpu_offloaded_model in [self.unet, self.movq]:
        a				,						a		:						int 				=     cpu_offload_with_hook(__UpperCAmelCase							,					__UpperCAmelCase							,					prev_module_hook=__UpperCAmelCase)

      # We'll offload the last model manually.
      a		:						str 				=     hook






    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def       __snake_case				(   self							:       Tuple):
      if not hasattr(self.unet							,					"_hf_hook"):
        return self.device
      for module in self.unet.modules():
        if (
            hasattr(__UpperCAmelCase							,					"_hf_hook")
            and hasattr(module._hf_hook							,					"execution_device")
            and module._hf_hook.execution_device is not None
        ):
          return torch.device(module._hf_hook.execution_device)
      return self.device






    @torch.no_grad()
    @replace_example_docstring(__UpperCAmelCase)
    def __call__(   self							:       List[Any]							,					__UpperCAmelCase							:       Union[torch.FloatTensor, List[torch.FloatTensor]]							,					__UpperCAmelCase							:       Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]							,					__UpperCAmelCase							:       Union[torch.FloatTensor, List[torch.FloatTensor]]							,					__UpperCAmelCase							:       torch.FloatTensor							,					__UpperCAmelCase							:       int = 512							,					__UpperCAmelCase							:       int = 512							,					__UpperCAmelCase							:       int = 100							,					__UpperCAmelCase							:       float = 4.0							,					__UpperCAmelCase							:       float = 0.3							,					__UpperCAmelCase							:       int = 1							,					__UpperCAmelCase							:       Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					__UpperCAmelCase							:       Optional[str] = "pil"							,					__UpperCAmelCase							:       bool = True							,					):
      a		:						List[str] 				=     self._execution_device

      a		:						Optional[Any] 				=     guidance_scale > 1.0

      if isinstance(__UpperCAmelCase							,					__UpperCAmelCase):
        a		:						List[Any] 				=     torch.cat(__UpperCAmelCase							,					dim=0)
      if isinstance(__UpperCAmelCase							,					__UpperCAmelCase):
        a		:						List[str] 				=     torch.cat(__UpperCAmelCase							,					dim=0)
      if isinstance(__UpperCAmelCase							,					__UpperCAmelCase):
        a		:						Optional[int] 				=     torch.cat(__UpperCAmelCase							,					dim=0)

      a		:						int 				=     image_embeds.shape[0]

      if do_classifier_free_guidance:
        a		:						int 				=     image_embeds.repeat_interleave(__UpperCAmelCase							,					dim=0)
        a		:						Union[str, Any] 				=     negative_image_embeds.repeat_interleave(__UpperCAmelCase							,					dim=0)
        a		:						Dict 				=     hint.repeat_interleave(__UpperCAmelCase							,					dim=0)

      a		:						int 				=     torch.cat([negative_image_embeds, image_embeds]							,					dim=0).to(dtype=self.unet.dtype							,					device=__UpperCAmelCase)
      a		:						List[Any] 				=     torch.cat([hint, hint]							,					dim=0).to(dtype=self.unet.dtype							,					device=__UpperCAmelCase)

      if not isinstance(__UpperCAmelCase							,					__UpperCAmelCase):
        a		:						Dict 				=     [image]
      if not all(isinstance(__UpperCAmelCase							,					(PIL.Image.Image, torch.Tensor)) for i in image):
        raise ValueError(
            f'''Input is in incorrect format: {[type(__UpperCAmelCase) for i in image]}. Currently, we only support  PIL image and pytorch tensor''')

      a		:						Dict 				=     torch.cat([prepare_image(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase) for i in image]							,					dim=0)
      a		:						Optional[int] 				=     image.to(dtype=image_embeds.dtype							,					device=__UpperCAmelCase)

      a		:						List[str] 				=     self.movq.encode(__UpperCAmelCase)["latents"]
      a		:						Dict 				=     latents.repeat_interleave(__UpperCAmelCase							,					dim=0)
      self.scheduler.set_timesteps(__UpperCAmelCase							,					device=__UpperCAmelCase)
      a				,						a		:						Tuple 				=     self.get_timesteps(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase)
      a		:						List[Any] 				=     timesteps[:1].repeat(batch_size * num_images_per_prompt)
      a				,						a		:						Union[str, Any] 				=     downscale_height_and_width(__UpperCAmelCase							,					__UpperCAmelCase							,					self.movq_scale_factor)
      a		:						Any 				=     self.prepare_latents(
          __UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					image_embeds.dtype							,					__UpperCAmelCase							,					__UpperCAmelCase)
      for i, t in enumerate(self.progress_bar(__UpperCAmelCase)):
        # expand the latents if we are doing classifier free guidance
        a		:						int 				=     torch.cat([latents] * 2) if do_classifier_free_guidance else latents

        a		:						List[Any] 				=     {"image_embeds": image_embeds, "hint": hint}
        a		:						Union[str, Any] 				=     self.unet(
            sample=__UpperCAmelCase							,					timestep=__UpperCAmelCase							,					encoder_hidden_states=__UpperCAmelCase							,					added_cond_kwargs=__UpperCAmelCase							,					return_dict=__UpperCAmelCase							,					)[0]

        if do_classifier_free_guidance:
          a				,						a		:						str 				=     noise_pred.split(latents.shape[1]							,					dim=1)
          a				,						a		:						Tuple 				=     noise_pred.chunk(2)
          a				,						a		:						Dict 				=     variance_pred.chunk(2)
          a		:						int 				=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
          a		:						List[str] 				=     torch.cat([noise_pred, variance_pred_text]							,					dim=1)

        if not (
            hasattr(self.scheduler.config							,					"variance_type")
            and self.scheduler.config.variance_type in ["learned", "learned_range"]
        ):
          a				,						a		:						List[Any] 				=     noise_pred.split(latents.shape[1]							,					dim=1)

        # compute the previous noisy sample x_t -> x_t-1

        a		:						int 				=     self.scheduler.step(
            __UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					generator=__UpperCAmelCase							,					)[0]

      # post-processing
      a		:						Tuple 				=     self.movq.decode(__UpperCAmelCase							,					force_not_quantize=__UpperCAmelCase)["sample"]

      if output_type not in ["pt", "np", "pil"]:
        raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''')

      if output_type in ["np", "pil"]:
        a		:						Tuple 				=     image * 0.5 + 0.5
        a		:						Union[str, Any] 				=     image.clamp(0							,					1)
        a		:						List[Any] 				=     image.cpu().permute(0							,					2							,					3							,					1).float().numpy()

      if output_type == "pil":
        a		:						Any 				=     self.numpy_to_pil(__UpperCAmelCase)

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=__UpperCAmelCase)










"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowercase          = logging.get_logger(__name__)

__lowercase          = {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}






class 		_A    (    _a    ):







    """simple docstring"""




    UpperCAmelCase			:				Tuple      =			SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
    UpperCAmelCase			:				Any      =			"""squeezebert"""






    def __init__(   self							:       List[str]							,					__UpperCAmelCase							:       Tuple=30522							,					__UpperCAmelCase							:       Tuple=768							,					__UpperCAmelCase							:       int=12							,					__UpperCAmelCase							:       str=12							,					__UpperCAmelCase							:       List[Any]=3072							,					__UpperCAmelCase							:       Dict="gelu"							,					__UpperCAmelCase							:       List[Any]=0.1							,					__UpperCAmelCase							:       Optional[int]=0.1							,					__UpperCAmelCase							:       Optional[Any]=512							,					__UpperCAmelCase							:       int=2							,					__UpperCAmelCase							:       Dict=0.02							,					__UpperCAmelCase							:       Optional[int]=1e-12							,					__UpperCAmelCase							:       Optional[Any]=0							,					__UpperCAmelCase							:       Optional[Any]=768							,					__UpperCAmelCase							:       Tuple=4							,					__UpperCAmelCase							:       Dict=4							,					__UpperCAmelCase							:       str=4							,					__UpperCAmelCase							:       List[Any]=1							,					__UpperCAmelCase							:       Optional[int]=4							,					__UpperCAmelCase							:       Optional[int]=4							,					**__UpperCAmelCase							:       Tuple							,					):
      super().__init__(pad_token_id=__UpperCAmelCase							,					**__UpperCAmelCase)

      a		:						List[Any] 				=     vocab_size
      a		:						List[Any] 				=     hidden_size
      a		:						Dict 				=     num_hidden_layers
      a		:						int 				=     num_attention_heads
      a		:						Dict 				=     hidden_act
      a		:						Tuple 				=     intermediate_size
      a		:						int 				=     hidden_dropout_prob
      a		:						List[Any] 				=     attention_probs_dropout_prob
      a		:						Union[str, Any] 				=     max_position_embeddings
      a		:						Union[str, Any] 				=     type_vocab_size
      a		:						Optional[Any] 				=     initializer_range
      a		:						Any 				=     layer_norm_eps
      a		:						Any 				=     embedding_size
      a		:						Tuple 				=     q_groups
      a		:						List[str] 				=     k_groups
      a		:						str 				=     v_groups
      a		:						Optional[Any] 				=     post_attention_groups
      a		:						List[str] 				=     intermediate_groups
      a		:						Optional[int] 				=     output_groups






class 		_A    (    _a    ):







    """simple docstring"""




    @property
    def       __snake_case				(   self							:       Optional[Any]):
      if self.task == "multiple-choice":
        a		:						Optional[int] 				=     {0: "batch", 1: "choice", 2: "sequence"}
      else:
        a		:						Tuple 				=     {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("token_type_ids", dynamic_axis),
          ])






