






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

__a   :							Any						= "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Any      ,					__lowercase :      Dict      ,					__lowercase :      List[str]=8     )    ->   Any:





    """simple docstring"""

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



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Tuple      ,					__lowercase :      int=5_1_2      ,					__lowercase :      Optional[int]=5_1_2     )    ->   Union[str, Any]:





    """simple docstring"""

    __A       =						pil_image.resize((w, h)      ,					resample=Image.BICUBIC      ,					reducing_gap=1     )
    __A       =						np.array(pil_image.convert("""RGB"""     )     )
    __A       =						arr.astype(np.floataa     ) / 127.5 - 1
    __A       =						np.transpose(__lowercase      ,					[2, 0, 1]     )
    __A       =						torch.from_numpy(__lowercase     ).unsqueeze(0     )
    return image






class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     def __init__(					self							:      Tuple			,						UpperCamelCase_							:      UNetaDConditionModel			,						UpperCamelCase_							:      DDPMScheduler			,						UpperCamelCase_							:      VQModel			,						):


         """simple docstring"""



         super().__init__()

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




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      str					):


         """simple docstring"""



         __A       =						min(int(num_inference_steps * strength					)			,						UpperCamelCase_					)

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

         return timesteps, num_inference_steps - t_start




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      Optional[Any]=None					):


         """simple docstring"""



         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       =						image.to(device=UpperCamelCase_			,						dtype=UpperCamelCase_					)

         __A       =						batch_size * num_images_per_prompt

         if image.shape[1] == 4:
             __A       =						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       =						[
                     self.movq.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(UpperCamelCase_					)
                 ]
                 __A       =						torch.cat(UpperCamelCase_			,						dim=0					)
             else:
                 __A       =						self.movq.encode(UpperCamelCase_					).latent_dist.sample(UpperCamelCase_					)

             __A       =						self.movq.config.scaling_factor * init_latents

         __A       =						torch.cat([init_latents]			,						dim=0					)

         __A       =						init_latents.shape
         __A       =						randn_tensor(UpperCamelCase_			,						generator=UpperCamelCase_			,						device=UpperCamelCase_			,						dtype=UpperCamelCase_					)

         # get latents
         __A       =						self.scheduler.add_noise(UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_					)

         __A       =						init_latents

         return latents




     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      Optional[Any]=0					):


         """simple docstring"""



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

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

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




     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      Optional[Any]=0					):


         """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."""					)

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

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




     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         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							:      Union[str, 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_							:      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			,						):


         """simple docstring"""



         __A       =						self._execution_device

         __A       =						guidance_scale > 1.0

         if isinstance(UpperCamelCase_			,						UpperCamelCase_					):
             __A       =						torch.cat(UpperCamelCase_			,						dim=0					)
         __A       =						image_embeds.shape[0]
         if isinstance(UpperCamelCase_			,						UpperCamelCase_					):
             __A       =						torch.cat(UpperCamelCase_			,						dim=0					)

         if do_classifier_free_guidance:
             __A       =						image_embeds.repeat_interleave(UpperCamelCase_			,						dim=0					)
             __A       =						negative_image_embeds.repeat_interleave(UpperCamelCase_			,						dim=0					)

         __A       =						torch.cat([negative_image_embeds, image_embeds]			,						dim=0					).to(dtype=self.unet.dtype			,						device=UpperCamelCase_					)

         if not isinstance(UpperCamelCase_			,						UpperCamelCase_					):
             __A       =						[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       =						torch.cat([prepare_image(UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_					) for i in image]			,						dim=0					)
         __A       =						image.to(dtype=image_embeds.dtype			,						device=UpperCamelCase_					)

         __A       =						self.movq.encode(UpperCamelCase_					)["""latents"""]
         __A       =						latents.repeat_interleave(UpperCamelCase_			,						dim=0					)
         self.scheduler.set_timesteps(UpperCamelCase_			,						device=UpperCamelCase_					)
         __A     ,						__A       =						self.get_timesteps(UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_					)
         __A       =						timesteps[:1].repeat(batch_size * num_images_per_prompt					)
         __A     ,						__A       =						downscale_height_and_width(UpperCamelCase_			,						UpperCamelCase_			,						self.movq_scale_factor					)
         __A       =						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       =						torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

             __A       =						{"""image_embeds""": image_embeds}
             __A       =						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       =						noise_pred.split(latents.shape[1]			,						dim=1					)
                 __A     ,						__A       =						noise_pred.chunk(2					)
                 __A     ,						__A       =						variance_pred.chunk(2					)
                 __A       =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                 __A       =						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       =						noise_pred.split(latents.shape[1]			,						dim=1					)

             # compute the previous noisy sample x_t -> x_t-1
             __A       =						self.scheduler.step(
                 UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_			,						generator=UpperCamelCase_			,						)[0]

         # post-processing
         __A       =						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       =						image * 0.5 + 0.5
             __A       =						image.clamp(0			,						1					)
             __A       =						image.cpu().permute(0			,						2			,						3			,						1					).float().numpy()

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

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=UpperCamelCase_					)












from ....configuration_utils import PretrainedConfig
from ....utils import logging


__a   :							List[Any]						= logging.get_logger(__name__)

# TODO: upload to AWS
__a   :							Union[str, Any]						= {
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					"retribert"




     def __init__(					self							:      Any			,						UpperCamelCase_							:      int=30_522			,						UpperCamelCase_							:      Optional[int]=768			,						UpperCamelCase_							:      Union[str, Any]=8			,						UpperCamelCase_							:      Tuple=12			,						UpperCamelCase_							:      List[Any]=3_072			,						UpperCamelCase_							:      Tuple="gelu"			,						UpperCamelCase_							:      str=0.1			,						UpperCamelCase_							:      Dict=0.1			,						UpperCamelCase_							:      List[str]=512			,						UpperCamelCase_							:      Optional[int]=2			,						UpperCamelCase_							:      Tuple=0.02			,						UpperCamelCase_							:      List[str]=1e-12			,						UpperCamelCase_							:      Any=True			,						UpperCamelCase_							:      List[Any]=128			,						UpperCamelCase_							:      Union[str, Any]=0			,						**UpperCamelCase_							:      List[str]			,						):


         """simple docstring"""



         super().__init__(pad_token_id=UpperCamelCase_			,						**UpperCamelCase_					)

         __A       =						vocab_size
         __A       =						hidden_size
         __A       =						num_hidden_layers
         __A       =						num_attention_heads
         __A       =						hidden_act
         __A       =						intermediate_size
         __A       =						hidden_dropout_prob
         __A       =						attention_probs_dropout_prob
         __A       =						max_position_embeddings
         __A       =						type_vocab_size
         __A       =						initializer_range
         __A       =						layer_norm_eps
         __A       =						share_encoders
         __A       =						projection_dim




