
"""simple docstring"""




from typing import List, Optional, Union

import torch

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,
)


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

lowerCamelCase_						     =					'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> 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 = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.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)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

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






def 						__magic_name__			(   __a      :	Dict	,		__a      :	List[str]	,		__a      :	Union[str, Any]=8						):



     '''simple docstring'''




     UpperCamelCase__				=    height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     UpperCamelCase__				=    width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor




class      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      ):
       super().__init__()

       self.register_modules(
           unet=SCREAMING_SNAKE_CASE_	,      scheduler=SCREAMING_SNAKE_CASE_	,      movq=SCREAMING_SNAKE_CASE_	,      )
       UpperCamelCase__				=    2 ** (len(self.movq.config.block_out_channels						) - 1)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       if latents is None:
            UpperCamelCase__				=    randn_tensor(SCREAMING_SNAKE_CASE_	,      generator=SCREAMING_SNAKE_CASE_	,      device=SCREAMING_SNAKE_CASE_	,      dtype=SCREAMING_SNAKE_CASE_						)
       else:
            if latents.shape != shape:
                 raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"						)
            UpperCamelCase__				=    latents.to(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    latents * scheduler.init_noise_sigma
       return latents







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_=0						):
       if is_accelerate_available():
            from accelerate import cpu_offload
       else:
            raise ImportError("""Please install accelerate via `pip install accelerate`"""						)

       UpperCamelCase__				=    torch.device(F"cuda:{gpu_id}"						)

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







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_=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."""						)

       UpperCamelCase__				=    torch.device(F"cuda:{gpu_id}"						)

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

       UpperCamelCase__				=    None
       for cpu_offloaded_model in [self.unet, self.movq]:
            UpperCamelCase__		,	UpperCamelCase__				=    cpu_offload_with_hook(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      prev_module_hook=SCREAMING_SNAKE_CASE_						)

       # We'll offload the last model manually.
       UpperCamelCase__				=    hook







  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 	UpperCAmelCase_ (self						):
       if not hasattr(self.unet	,      """_hf_hook"""						):
            return self.device
       for module in self.unet.modules():
            if (
                hasattr(SCREAMING_SNAKE_CASE_	,      """_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(SCREAMING_SNAKE_CASE_						)
  def __call__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = 5_12	,      SCREAMING_SNAKE_CASE_ = 5_12	,      SCREAMING_SNAKE_CASE_ = 1_00	,      SCREAMING_SNAKE_CASE_ = 4.0	,      SCREAMING_SNAKE_CASE_ = 1	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = "pil"	,      SCREAMING_SNAKE_CASE_ = True	,      ):
       UpperCamelCase__				=    self._execution_device

       UpperCamelCase__				=    guidance_scale > 1.0

       if isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    torch.cat(SCREAMING_SNAKE_CASE_	,      dim=0						)
       if isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    torch.cat(SCREAMING_SNAKE_CASE_	,      dim=0						)
       if isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    torch.cat(SCREAMING_SNAKE_CASE_	,      dim=0						)

       UpperCamelCase__				=    image_embeds.shape[0] * num_images_per_prompt

       if do_classifier_free_guidance:
            UpperCamelCase__				=    image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_	,      dim=0						)
            UpperCamelCase__				=    negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_	,      dim=0						)
            UpperCamelCase__				=    hint.repeat_interleave(SCREAMING_SNAKE_CASE_	,      dim=0						)

       UpperCamelCase__				=    torch.cat([negative_image_embeds, image_embeds]	,      dim=0						).to(dtype=self.unet.dtype	,      device=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    torch.cat([hint, hint]	,      dim=0						).to(dtype=self.unet.dtype	,      device=SCREAMING_SNAKE_CASE_						)
       self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	,      device=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    self.scheduler.timesteps

       UpperCamelCase__				=    self.movq.config.latent_channels

       UpperCamelCase__		,	UpperCamelCase__				=    downscale_height_and_width(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      self.movq_scale_factor						)

       # create initial latent
       UpperCamelCase__				=    self.prepare_latents(
           (batch_size, num_channels_latents, height, width)	,      image_embeds.dtype	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      self.scheduler	,      )

       for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_						)						):
            # expand the latents if we are doing classifier free guidance
            UpperCamelCase__				=    torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

            UpperCamelCase__				=    {"""image_embeds""": image_embeds, """hint""": hint}
            UpperCamelCase__				=    self.unet(
                sample=SCREAMING_SNAKE_CASE_	,      timestep=SCREAMING_SNAKE_CASE_	,      encoder_hidden_states=SCREAMING_SNAKE_CASE_	,      added_cond_kwargs=SCREAMING_SNAKE_CASE_	,      return_dict=SCREAMING_SNAKE_CASE_	,      )[0]

            if do_classifier_free_guidance:
                 UpperCamelCase__		,	UpperCamelCase__				=    noise_pred.split(latents.shape[1]	,      dim=1						)
                 UpperCamelCase__		,	UpperCamelCase__				=    noise_pred.chunk(2						)
                 UpperCamelCase__		,	UpperCamelCase__				=    variance_pred.chunk(2						)
                 UpperCamelCase__				=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                 UpperCamelCase__				=    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"]
            ):
                 UpperCamelCase__		,	UpperCamelCase__				=    noise_pred.split(latents.shape[1]	,      dim=1						)

            # compute the previous noisy sample x_t -> x_t-1
            UpperCamelCase__				=    self.scheduler.step(
                SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      generator=SCREAMING_SNAKE_CASE_	,      )[0]
       # post-processing
       UpperCamelCase__				=    self.movq.decode(SCREAMING_SNAKE_CASE_	,      force_not_quantize=SCREAMING_SNAKE_CASE_						)["""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"]:
            UpperCamelCase__				=    image * 0.5 + 0.5
            UpperCamelCase__				=    image.clamp(0	,      1						)
            UpperCamelCase__				=    image.cpu().permute(0	,      2	,      3	,      1						).float().numpy()

       if output_type == "pil":
            UpperCamelCase__				=    self.numpy_to_pil(SCREAMING_SNAKE_CASE_						)

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_						)







from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image


lowerCamelCase_						     =					['''text''', '''image''', '''audio''']






def 						__magic_name__			(   __a      :	List[str]						):



     '''simple docstring'''




     UpperCamelCase__				=    []

     for input_type in input_types:
          if input_type == "text":
               inputs.append("""Text input"""						)
          elif input_type == "image":
               inputs.append(
                   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""						)						) / """000000039769.png"""						).resize((512, 512)						)						)
          elif input_type == "audio":
               inputs.append(torch.ones(3_000						)						)
          elif isinstance(__a	,		__a						):
               inputs.append(create_inputs(__a						)						)
          else:
               raise ValueError(f"Invalid type requested: {input_type}"						)

     return inputs






def 						__magic_name__			(   __a      :	List						):



     '''simple docstring'''




     UpperCamelCase__				=    []

     for output in outputs:
          if isinstance(__a	,		(str, AgentText)						):
               output_types.append("""text"""						)
          elif isinstance(__a	,		(Image.Image, AgentImage)						):
               output_types.append("""image"""						)
          elif isinstance(__a	,		(torch.Tensor, AgentAudio)						):
               output_types.append("""audio"""						)
          else:
               raise ValueError(f"Invalid output: {output}"						)

     return output_types




@is_tool_test
class      __A:

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       self.assertTrue(hasattr(self.tool	,      """inputs"""						)						)
       self.assertTrue(hasattr(self.tool	,      """outputs"""						)						)

       UpperCamelCase__				=    self.tool.inputs
       for _input in inputs:
            if isinstance(_input	,      SCREAMING_SNAKE_CASE_						):
                 for __input in _input:
                      self.assertTrue(__input in authorized_types						)
            else:
                 self.assertTrue(_input in authorized_types						)

       UpperCamelCase__				=    self.tool.outputs
       for _output in outputs:
            self.assertTrue(_output in authorized_types						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    create_inputs(self.tool.inputs						)
       UpperCamelCase__				=    self.tool(*SCREAMING_SNAKE_CASE_						)

       # There is a single output
       if len(self.tool.outputs						) == 1:
            UpperCamelCase__				=    [outputs]

       self.assertListEqual(output_types(SCREAMING_SNAKE_CASE_						)	,      self.tool.outputs						)







  def 	UpperCAmelCase_ (self						):
       self.assertTrue(hasattr(self.tool	,      """description"""						)						)
       self.assertTrue(hasattr(self.tool	,      """default_checkpoint"""						)						)
       self.assertTrue(self.tool.description.startswith("""This is a tool that"""						)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    create_inputs(self.tool.inputs						)
       UpperCamelCase__				=    self.tool(*SCREAMING_SNAKE_CASE_						)

       if not isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    [outputs]

       self.assertEqual(len(SCREAMING_SNAKE_CASE_						)	,      len(self.tool.outputs						)						)

       for output, output_type in zip(SCREAMING_SNAKE_CASE_	,      self.tool.outputs						):
            UpperCamelCase__				=    AGENT_TYPE_MAPPING[output_type]
            self.assertTrue(isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    create_inputs(self.tool.inputs						)

       UpperCamelCase__				=    []

       for _input, input_type in zip(SCREAMING_SNAKE_CASE_	,      self.tool.inputs						):
            if isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
                 _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input						) for _input_type in input_type]						)
            else:
                 _inputs.append(AGENT_TYPE_MAPPING[input_type](_input						)						)

        # Should not raise an error
       UpperCamelCase__				=    self.tool(*SCREAMING_SNAKE_CASE_						)

       if not isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    [outputs]

       self.assertEqual(len(SCREAMING_SNAKE_CASE_						)	,      len(self.tool.outputs						)						)





