






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,
)


A_		:		Optional[int]		     =			logging.get_logger(__name__)  # pylint: disable=invalid-name

A_		:		str		     =			'\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   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__=8  )	->    Union[str, Any]:
  UpperCamelCase_: Tuple   				=    height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  UpperCamelCase_: int   				=    width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







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

         self.register_modules(
             unet=_lowerCamelCase  ,      scheduler=_lowerCamelCase  ,      movq=_lowerCamelCase  ,      )
         UpperCamelCase_: Dict   				=    2 ** (len(self.movq.config.block_out_channels							) - 1)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         if latents is None:
           UpperCamelCase_: Dict   				=    randn_tensor(_lowerCamelCase  ,      generator=_lowerCamelCase  ,      device=_lowerCamelCase  ,      dtype=_lowerCamelCase							)
         else:
           if latents.shape != shape:
             raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''							)
           UpperCamelCase_: List[Any]   				=    latents.to(_lowerCamelCase							)

         UpperCamelCase_: int   				=    latents * scheduler.init_noise_sigma
         return latents





       def       _a   ( self  ,      _lowerCamelCase=0							):
         if is_accelerate_available():
           from accelerate import cpu_offload
         else:
           raise ImportError('Please install accelerate via `pip install accelerate`'							)

         UpperCamelCase_: int   				=    torch.device(f'''cuda:{gpu_id}'''							)

         UpperCamelCase_: Optional[int]   				=    [
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
             cpu_offload(_lowerCamelCase  ,      _lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase=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_: str   				=    torch.device(f'''cuda:{gpu_id}'''							)

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

         UpperCamelCase_: Tuple   				=    None
         for cpu_offloaded_model in [self.unet, self.movq]:
           UpperCamelCase_				,UpperCamelCase_: Optional[int]   				=    cpu_offload_with_hook(_lowerCamelCase  ,      _lowerCamelCase  ,      prev_module_hook=_lowerCamelCase							)

         # We'll offload the last model manually.
         UpperCamelCase_: Optional[int]   				=    hook





       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def       _a   ( self							):
         if not hasattr(self.unet  ,      '_hf_hook'							):
           return self.device
         for module in self.unet.modules():
           if (
               hasattr(_lowerCamelCase  ,      '_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(_lowerCamelCase							)
       def __call__( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = 5_1_2  ,      _lowerCamelCase = 5_1_2  ,      _lowerCamelCase = 1_0_0  ,      _lowerCamelCase = 4.0  ,      _lowerCamelCase = 1  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = "pil"  ,      _lowerCamelCase = True  ,      ):
         UpperCamelCase_: Optional[int]   				=    self._execution_device

         UpperCamelCase_: Tuple   				=    guidance_scale > 1.0

         if isinstance(_lowerCamelCase  ,      _lowerCamelCase							):
           UpperCamelCase_: Any   				=    torch.cat(_lowerCamelCase  ,      dim=0							)
         if isinstance(_lowerCamelCase  ,      _lowerCamelCase							):
           UpperCamelCase_: Union[str, Any]   				=    torch.cat(_lowerCamelCase  ,      dim=0							)
         if isinstance(_lowerCamelCase  ,      _lowerCamelCase							):
           UpperCamelCase_: Tuple   				=    torch.cat(_lowerCamelCase  ,      dim=0							)

         UpperCamelCase_: int   				=    image_embeds.shape[0] * num_images_per_prompt

         if do_classifier_free_guidance:
           UpperCamelCase_: List[Any]   				=    image_embeds.repeat_interleave(_lowerCamelCase  ,      dim=0							)
           UpperCamelCase_: Dict   				=    negative_image_embeds.repeat_interleave(_lowerCamelCase  ,      dim=0							)
           UpperCamelCase_: List[str]   				=    hint.repeat_interleave(_lowerCamelCase  ,      dim=0							)

         UpperCamelCase_: List[Any]   				=    torch.cat([negative_image_embeds, image_embeds]  ,      dim=0							).to(dtype=self.unet.dtype  ,      device=_lowerCamelCase							)
         UpperCamelCase_: List[Any]   				=    torch.cat([hint, hint]  ,      dim=0							).to(dtype=self.unet.dtype  ,      device=_lowerCamelCase							)
         self.scheduler.set_timesteps(_lowerCamelCase  ,      device=_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    self.scheduler.timesteps

         UpperCamelCase_: Dict   				=    self.movq.config.latent_channels

         UpperCamelCase_				,UpperCamelCase_: int   				=    downscale_height_and_width(_lowerCamelCase  ,      _lowerCamelCase  ,      self.movq_scale_factor							)

         # create initial latent
         UpperCamelCase_: int   				=    self.prepare_latents(
             (batch_size, num_channels_latents, height, width)  ,      image_embeds.dtype  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      self.scheduler  ,      )

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

           UpperCamelCase_: List[Any]   				=    {'image_embeds': image_embeds, 'hint': hint}
           UpperCamelCase_: Any   				=    self.unet(
               sample=_lowerCamelCase  ,      timestep=_lowerCamelCase  ,      encoder_hidden_states=_lowerCamelCase  ,      added_cond_kwargs=_lowerCamelCase  ,      return_dict=_lowerCamelCase  ,      )[0]

           if do_classifier_free_guidance:
             UpperCamelCase_				,UpperCamelCase_: int   				=    noise_pred.split(latents.shape[1]  ,      dim=1							)
             UpperCamelCase_				,UpperCamelCase_: Optional[int]   				=    noise_pred.chunk(2							)
             UpperCamelCase_				,UpperCamelCase_: Union[str, Any]   				=    variance_pred.chunk(2							)
             UpperCamelCase_: str   				=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
             UpperCamelCase_: List[Any]   				=    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_: int   				=    noise_pred.split(latents.shape[1]  ,      dim=1							)

           # compute the previous noisy sample x_t -> x_t-1
           UpperCamelCase_: Tuple   				=    self.scheduler.step(
               _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      generator=_lowerCamelCase  ,      )[0]
         # post-processing
         UpperCamelCase_: Union[str, Any]   				=    self.movq.decode(_lowerCamelCase  ,      force_not_quantize=_lowerCamelCase							)['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_: Optional[Any]   				=    image * 0.5 + 0.5
           UpperCamelCase_: int   				=    image.clamp(0  ,      1							)
           UpperCamelCase_: Optional[int]   				=    image.cpu().permute(0  ,      2  ,      3  ,      1							).float().numpy()

         if output_type == "pil":
           UpperCamelCase_: int   				=    self.numpy_to_pil(_lowerCamelCase							)

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=_lowerCamelCase							)







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_rjieba
@require_tokenizers
class     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Optional[int]									=RoFormerTokenizer
       a    :		int									=RoFormerTokenizerFast
       a    :		int									=True
       a    :		Optional[int]									=True





       def       _a   ( self							):
         super().setUp()





       def       _a   ( self  ,      **_lowerCamelCase							):
         return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'  ,      **_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    '永和服装饰品有限公司,今天天气非常好'
         UpperCamelCase_: Any   				=    '永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
         return input_text, output_text





       def       _a   ( self							):
         UpperCamelCase_: int   				=    self.get_tokenizer()
         UpperCamelCase_				,UpperCamelCase_: int   				=    self.get_chinese_input_output_texts()
         UpperCamelCase_: Tuple   				=    tokenizer.tokenize(_lowerCamelCase							)

         self.assertListEqual(_lowerCamelCase  ,      output_text.split()							)

         UpperCamelCase_: Dict   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: Dict   				=    [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    self.get_rust_tokenizer()
         UpperCamelCase_				,UpperCamelCase_: Tuple   				=    self.get_chinese_input_output_texts()
         UpperCamelCase_: Optional[Any]   				=    tokenizer.tokenize(_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase  ,      output_text.split()							)
         UpperCamelCase_: str   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: Optional[Any]   				=    [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       def       _a   ( self							):
         pass





       def       _a   ( self							):
         pass





       def       _a   ( self							):
         pass