





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,
)


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

_UpperCAmelCase        =				'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> 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"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_=8  )     ->		str:
       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 							_UpperCamelCase					(	lowerCAmelCase_     ):

       def __init__(		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: UNetaDConditionModel					,   _SCREAMING_SNAKE_CASE: DDPMScheduler					,   _SCREAMING_SNAKE_CASE: VQModel					,   )       -> int:




              """simple docstring"""


              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 lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: List[Any]					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: Union[str, Any]		)       -> List[str]:




              """simple docstring"""


              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 lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: Tuple=0		)       -> Tuple:




              """simple docstring"""


              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 lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: Optional[int]=0		)       -> List[str]:




              """simple docstring"""


              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 lowercase (		self: Tuple		)       -> Dict:




              """simple docstring"""


              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: Any					,   _SCREAMING_SNAKE_CASE: Union[torch.FloatTensor, List[torch.FloatTensor]]					,   _SCREAMING_SNAKE_CASE: Union[torch.FloatTensor, List[torch.FloatTensor]]					,   _SCREAMING_SNAKE_CASE: torch.FloatTensor					,   _SCREAMING_SNAKE_CASE: int = 512					,   _SCREAMING_SNAKE_CASE: int = 512					,   _SCREAMING_SNAKE_CASE: int = 100					,   _SCREAMING_SNAKE_CASE: float = 4.0					,   _SCREAMING_SNAKE_CASE: int = 1					,   _SCREAMING_SNAKE_CASE: Optional[Union[torch.Generator, List[torch.Generator]]] = None					,   _SCREAMING_SNAKE_CASE: Optional[torch.FloatTensor] = None					,   _SCREAMING_SNAKE_CASE: Optional[str] = "pil"					,   _SCREAMING_SNAKE_CASE: bool = True					,   )       -> List[str]:




              """simple docstring"""


              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		)








import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 							_UpperCamelCase					(	lowerCAmelCase_  ,						unittest.TestCase     ):
       _UpperCamelCase				:					Union[str, Any]         =   XLMTokenizer
       _UpperCamelCase				:					Optional[int]         =   False

       def lowercase (		self: Dict		)       -> Union[str, Any]:




              """simple docstring"""


              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              UpperCamelCase_          =  [
                  "l",
                  "o",
                  "w",
                  "e",
                  "r",
                  "s",
                  "t",
                  "i",
                  "d",
                  "n",
                  "w</w>",
                  "r</w>",
                  "t</w>",
                  "lo",
                  "low",
                  "er</w>",
                  "low</w>",
                  "lowest</w>",
                  "newer</w>",
                  "wider</w>",
                  "<unk>",
              ]
              UpperCamelCase_          =  dict(zip(_SCREAMING_SNAKE_CASE					,   range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
              UpperCamelCase_          =  ["l o 123", "lo w 1456", "e r</w> 1789", ""]

              UpperCamelCase_          =  os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES["vocab_file"]		)
              UpperCamelCase_          =  os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES["merges_file"]		)
              with open(self.vocab_file					,   "w"		) as fp:
                     fp.write(json.dumps(_SCREAMING_SNAKE_CASE		)		)
              with open(self.merges_file					,   "w"		) as fp:
                     fp.write("\n".join(_SCREAMING_SNAKE_CASE		)		)

       def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: List[str]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  "lower newer"
              UpperCamelCase_          =  "lower newer"
              return input_text, output_text

       def lowercase (		self: int		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  XLMTokenizer(self.vocab_file					,   self.merges_file		)

              UpperCamelCase_          =  "lower"
              UpperCamelCase_          =  ["low", "er</w>"]
              UpperCamelCase_          =  tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  tokens + ["<unk>"]
              UpperCamelCase_          =  [14, 15, 20]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)



       @slow
       def lowercase (		self: Optional[int]		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  XLMTokenizer.from_pretrained("xlm-mlm-en-2048"		)

              UpperCamelCase_          =  tokenizer.encode("sequence builders"					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tokenizer.encode("multi-sequence build"					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              assert encoded_sentence == [0] + text + [1]
              assert encoded_pair == [0] + text + [1] + text_a + [1]

