


'''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,
)


A__		: List[Any]												=  logging.get_logger(__name__)  # pylint: disable=invalid-name

A__		: Tuple												=  """
    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       UpperCAmelCase__ (     UpperCAmelCase_   :    Dict				,   UpperCAmelCase_   :    Dict				,   UpperCAmelCase_   :    Tuple=8							)					->		int:
     __lowerCamelCase       :     List[Any]        =		height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     __lowerCamelCase       :     List[str]        =		width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor



def       UpperCAmelCase__ (     UpperCAmelCase_   :    Dict				,   UpperCAmelCase_   :    Tuple=5_12				,   UpperCAmelCase_   :    str=5_12							)					->		List[str]:
     __lowerCamelCase       :     List[Any]        =		pil_image.resize((w, h)				,   resample=Image.BICUBIC				,   reducing_gap=1							)
     __lowerCamelCase       :     Dict        =		np.array(pil_image.convert('RGB'							)							)
     __lowerCamelCase       :     Optional[Any]        =		arr.astype(np.floataa							) / 127.5 - 1
     __lowerCamelCase       :     int        =		np.transpose(UpperCAmelCase_				,   [2, 0, 1]							)
     __lowerCamelCase       :     Dict        =		torch.from_numpy(UpperCAmelCase_							).unsqueeze(0							)
     return image





class        UpperCAmelCase_  (lowerCAmelCase__  ):





    """simple docstring"""






    def __init__(							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					)    ->					Dict:
         super().__init__()

         self.register_modules(
             unet=_A      ,					scheduler=_A      ,					movq=_A      ,					)
         __lowerCamelCase       :     int        =		2 ** (len(self.movq.config.block_out_channels     ) - 1)







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )    ->					List[Any]:
         __lowerCamelCase       :     List[str]        =		min(int(num_inference_steps * strength     )      ,					_A     )

         __lowerCamelCase       :     str        =		max(num_inference_steps - init_timestep      ,					0     )
         __lowerCamelCase       :     Dict        =		self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=None     )    ->					Any:




         if not isinstance(_A      ,					(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(_A     )}'     )

         __lowerCamelCase       :     Optional[int]        =		image.to(device=_A      ,					dtype=_A     )

         __lowerCamelCase       :     List[Any]        =		batch_size * num_images_per_prompt

         if image.shape[1] == 4:
              __lowerCamelCase       :     Optional[Any]        =		image

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

              elif isinstance(_A      ,					_A     ):
                   __lowerCamelCase       :     str        =		[
                       self.movq.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(_A     )
                   ]
                   __lowerCamelCase       :     int        =		torch.cat(_A      ,					dim=0     )
              else:
                   __lowerCamelCase       :     Union[str, Any]        =		self.movq.encode(_A     ).latent_dist.sample(_A     )

              __lowerCamelCase       :     Optional[int]        =		self.movq.config.scaling_factor * init_latents

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

         __lowerCamelCase       :     List[str]        =		init_latents.shape
         __lowerCamelCase       :     Union[str, Any]        =		randn_tensor(_A      ,					generator=_A      ,					device=_A      ,					dtype=_A     )

         # get latents
         __lowerCamelCase       :     Union[str, Any]        =		self.scheduler.add_noise(_A      ,					_A      ,					_A     )

         __lowerCamelCase       :     Optional[int]        =		init_latents

         return latents







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_=0     )    ->					List[Any]:




         if is_accelerate_available():
              from accelerate import cpu_offload
         else:
              raise ImportError('Please install accelerate via `pip install accelerate`'     )

         __lowerCamelCase       :     Union[str, Any]        =		torch.device(f'cuda:{gpu_id}'     )

         __lowerCamelCase       :     Union[str, Any]        =		[
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
              if cpu_offloaded_model is not None:
                   cpu_offload(_A      ,					_A     )







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_=0     )    ->					Any:




         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.'     )

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

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

         __lowerCamelCase       :     List[Any]        =		None
         for cpu_offloaded_model in [self.unet, self.movq]:
              __lowerCamelCase       :     Union[str, Any]        =		cpu_offload_with_hook(_A      ,					_A      ,					prev_module_hook=_A     )

         # We'll offload the last model manually.
         __lowerCamelCase       :     int        =		hook







    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def 				lowercase_    (							self     )    ->					Union[str, Any]:




         if not hasattr(self.unet      ,					'_hf_hook'     ):
              return self.device
         for module in self.unet.modules():
              if (
                  hasattr(_A      ,					'_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(_A     )
    def __call__(							self      ,					SCREAMING_SNAKE_CASE_      ,					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_ = 0.3      ,					SCREAMING_SNAKE_CASE_ = 1      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = "pil"      ,					SCREAMING_SNAKE_CASE_ = True      ,					)    ->					Tuple:
         __lowerCamelCase       :     Dict        =		self._execution_device

         __lowerCamelCase       :     Optional[Any]        =		guidance_scale > 1.0

         if isinstance(_A      ,					_A     ):
              __lowerCamelCase       :     Any        =		torch.cat(_A      ,					dim=0     )
         if isinstance(_A      ,					_A     ):
              __lowerCamelCase       :     Union[str, Any]        =		torch.cat(_A      ,					dim=0     )
         if isinstance(_A      ,					_A     ):
              __lowerCamelCase       :     str        =		torch.cat(_A      ,					dim=0     )

         __lowerCamelCase       :     str        =		image_embeds.shape[0]

         if do_classifier_free_guidance:
              __lowerCamelCase       :     Tuple        =		image_embeds.repeat_interleave(_A      ,					dim=0     )
              __lowerCamelCase       :     int        =		negative_image_embeds.repeat_interleave(_A      ,					dim=0     )
              __lowerCamelCase       :     int        =		hint.repeat_interleave(_A      ,					dim=0     )

         __lowerCamelCase       :     List[str]        =		torch.cat([negative_image_embeds, image_embeds]      ,					dim=0     ).to(dtype=self.unet.dtype      ,					device=_A     )
         __lowerCamelCase       :     Union[str, Any]        =		torch.cat([hint, hint]      ,					dim=0     ).to(dtype=self.unet.dtype      ,					device=_A     )

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

         __lowerCamelCase       :     Any        =		torch.cat([prepare_image(_A      ,					_A      ,					_A     ) for i in image]      ,					dim=0     )
         __lowerCamelCase       :     Dict        =		image.to(dtype=image_embeds.dtype      ,					device=_A     )

         __lowerCamelCase       :     List[str]        =		self.movq.encode(_A     )['''latents''']
         __lowerCamelCase       :     Optional[int]        =		latents.repeat_interleave(_A      ,					dim=0     )
         self.scheduler.set_timesteps(_A      ,					device=_A     )
         __lowerCamelCase       :     Tuple        =		self.get_timesteps(_A      ,					_A      ,					_A     )
         __lowerCamelCase       :     Union[str, Any]        =		timesteps[:1].repeat(batch_size * num_images_per_prompt     )
         __lowerCamelCase       :     str        =		downscale_height_and_width(_A      ,					_A      ,					self.movq_scale_factor     )
         __lowerCamelCase       :     Optional[Any]        =		self.prepare_latents(
             _A      ,					_A      ,					_A      ,					_A      ,					image_embeds.dtype      ,					_A      ,					_A     )
         for i, t in enumerate(self.progress_bar(_A     )     ):
              # expand the latents if we are doing classifier free guidance
              __lowerCamelCase       :     Dict        =		torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

              __lowerCamelCase       :     List[str]        =		{'''image_embeds''': image_embeds, '''hint''': hint}
              __lowerCamelCase       :     Optional[Any]        =		self.unet(
                  sample=_A      ,					timestep=_A      ,					encoder_hidden_states=_A      ,					added_cond_kwargs=_A      ,					return_dict=_A      ,					)[0]

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

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

              __lowerCamelCase       :     int        =		self.scheduler.step(
                  _A      ,					_A      ,					_A      ,					generator=_A      ,					)[0]

         # post-processing
         __lowerCamelCase       :     Dict        =		self.movq.decode(_A      ,					force_not_quantize=_A     )['''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"]:
              __lowerCamelCase       :     Dict        =		image * 0.5 + 0.5
              __lowerCamelCase       :     int        =		image.clamp(0      ,					1     )
              __lowerCamelCase       :     Optional[int]        =		image.cpu().permute(0      ,					2      ,					3      ,					1     ).float().numpy()

         if output_type == "pil":
              __lowerCamelCase       :     str        =		self.numpy_to_pil(_A     )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=_A     )










'''simple docstring'''



from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


A__		: Optional[int]												=  logging.get_logger(__name__)

A__		: Optional[Any]												=  {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

A__		: Optional[int]												=  {
    """vocab_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """openai/clip-vit-base-patch32""": (
            """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
        ),
    },
}

A__		: Any												=  {
    """openai/clip-vit-base-patch32""": 77,
}





class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : Union[str, Any]												=      VOCAB_FILES_NAMES
    lowerCamelCase  : Optional[int]												=      PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase  : str												=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase  : Union[str, Any]												=      ['input_ids', 'attention_mask']
    lowerCamelCase  : Optional[Any]												=      CLIPTokenizer







    def __init__(							self      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_="<|endoftext|>"      ,					SCREAMING_SNAKE_CASE_="<|startoftext|>"      ,					SCREAMING_SNAKE_CASE_="<|endoftext|>"      ,					SCREAMING_SNAKE_CASE_="<|endoftext|>"      ,					**SCREAMING_SNAKE_CASE_      ,					)    ->					Union[str, Any]:
         super().__init__(
             SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					tokenizer_file=SCREAMING_SNAKE_CASE_      ,					unk_token=SCREAMING_SNAKE_CASE_      ,					bos_token=SCREAMING_SNAKE_CASE_      ,					eos_token=SCREAMING_SNAKE_CASE_      ,					pad_token=SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_      ,					)

         if not isinstance(self.backend_tokenizer.pre_tokenizer      ,					pre_tokenizers.Sequence     ):
              raise ValueError(
                  'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
                  ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
                  ' to be compatible with this version.The easiest way to do so is'
                  ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
                  ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
                  ' transformers.'     )

         self._wrap_decode_method_backend_tokenizer()







    def 				lowercase_    (							self     )    ->					Optional[Any]:
         __lowerCamelCase       :     Union[str, Any]        =		self.backend_tokenizer.decode

         def new_decode_method(*SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_     ):
              __lowerCamelCase       :     int        =		orig_decode_method(*SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_     )
              __lowerCamelCase       :     List[str]        =		text.replace(self.backend_tokenizer.model.end_of_word_suffix      ,					' '     ).strip()
              return text

         __lowerCamelCase       :     Union[str, Any]        =		new_decode_method







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					List[int]:
         __lowerCamelCase       :     Optional[int]        =		[self.bos_token_id]
         __lowerCamelCase       :     Tuple        =		[self.eos_token_id]

         if token_ids_a is None:
              return bos_token + token_ids_a + eos_token
         return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					List[int]:
         __lowerCamelCase       :     Dict        =		[self.bos_token_id]
         __lowerCamelCase       :     Optional[Any]        =		[self.eos_token_id]

         if token_ids_a is None:
              return len(bos_token + token_ids_a + eos_token     ) * [0]
         return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token     ) * [0]







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					Tuple[str]:
         __lowerCamelCase       :     int        =		self._tokenizer.model.save(SCREAMING_SNAKE_CASE_      ,					name=SCREAMING_SNAKE_CASE_     )
         return tuple(SCREAMING_SNAKE_CASE_     )






