
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      ="\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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__=8		):
      UpperCamelCase__    :   Optional[Any]					=				height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      UpperCamelCase__    :   Optional[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						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						)  ->   int:




           """simple docstring"""







           super().__init__()

           self.register_modules(
               unet=__SCREAMING_SNAKE_CASE						,						scheduler=__SCREAMING_SNAKE_CASE						,						movq=__SCREAMING_SNAKE_CASE						,						)
           UpperCamelCase__    :   Optional[Any]					=				2 ** (len(self.movq.config.block_out_channels  ) - 1)






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           if latents is None:
                 UpperCamelCase__    :   Any					=				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__    :   Optional[Any]					=				latents.to(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Tuple					=				latents * scheduler.init_noise_sigma
           return latents






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE=0  )  ->   Union[str, Any]:




           """simple docstring"""







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

           UpperCamelCase__    :   str					=				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(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE=0  )  ->   Tuple:




           """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__    :   Dict					=				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__    :   int					=				None
           for cpu_offloaded_model in [self.unet, self.movq]:
                 UpperCamelCase__    :   int					=				cpu_offload_with_hook(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						prev_module_hook=__SCREAMING_SNAKE_CASE  )

           # 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    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """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						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = 5_1_2						,						__SCREAMING_SNAKE_CASE = 5_1_2						,						__SCREAMING_SNAKE_CASE = 1_0_0						,						__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						,						)  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				self._execution_device

           UpperCamelCase__    :   int					=				guidance_scale > 1.0

           if isinstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ):
                 UpperCamelCase__    :   Optional[int]					=				torch.cat(__SCREAMING_SNAKE_CASE						,						dim=0  )
           if isinstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ):
                 UpperCamelCase__    :   Tuple					=				torch.cat(__SCREAMING_SNAKE_CASE						,						dim=0  )
           if isinstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ):
                 UpperCamelCase__    :   List[Any]					=				torch.cat(__SCREAMING_SNAKE_CASE						,						dim=0  )

           UpperCamelCase__    :   Dict					=				image_embeds.shape[0] * num_images_per_prompt

           if do_classifier_free_guidance:
                 UpperCamelCase__    :   Optional[Any]					=				image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE						,						dim=0  )
                 UpperCamelCase__    :   Tuple					=				negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE						,						dim=0  )
                 UpperCamelCase__    :   Optional[int]					=				hint.repeat_interleave(__SCREAMING_SNAKE_CASE						,						dim=0  )

           UpperCamelCase__    :   Any					=				torch.cat([negative_image_embeds, image_embeds]						,						dim=0  ).to(dtype=self.unet.dtype						,						device=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				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__    :   Dict					=				self.scheduler.timesteps

           UpperCamelCase__    :   str					=				self.movq.config.latent_channels

           UpperCamelCase__    :   Optional[Any]					=				downscale_height_and_width(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						self.movq_scale_factor  )

           # create initial latent
           UpperCamelCase__    :   Dict					=				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__    :   Any					=				torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents

                 UpperCamelCase__    :   Any					=				{'''image_embeds''': image_embeds, '''hint''': hint}
                 UpperCamelCase__    :   List[Any]					=				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__    :   Any					=				noise_pred.split(latents.shape[1]						,						dim=1  )
                       UpperCamelCase__    :   Optional[Any]					=				noise_pred.chunk(2  )
                       UpperCamelCase__    :   Union[str, Any]					=				variance_pred.chunk(2  )
                       UpperCamelCase__    :   Tuple					=				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                       UpperCamelCase__    :   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__    :   int					=				noise_pred.split(latents.shape[1]						,						dim=1  )

                 # compute the previous noisy sample x_t -> x_t-1
                 UpperCamelCase__    :   Optional[Any]					=				self.scheduler.step(
                     __SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						generator=__SCREAMING_SNAKE_CASE						,						)[0]
           # post-processing
           UpperCamelCase__    :   Optional[Any]					=				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__    :   List[Any]					=				image * 0.5 + 0.5
                 UpperCamelCase__    :   List[Any]					=				image.clamp(0						,						1  )
                 UpperCamelCase__    :   List[str]					=				image.cpu().permute(0						,						2						,						3						,						1  ).float().numpy()

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

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE  )




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


lowerCamelCase      =logging.get_logger(__name__)

lowerCamelCase      ={
    "google/realm-cc-news-pretrained-embedder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-encoder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-scorer": (
        "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-openqa": (
        "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"
    ),
    "google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json",
    "google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json",
    "google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json",
    "google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json",
    # See all REALM models at https://huggingface.co/models?filter=realm
}




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''realm'''






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=3_0_5_2_2						,						__SCREAMING_SNAKE_CASE=7_6_8						,						__SCREAMING_SNAKE_CASE=1_2_8						,						__SCREAMING_SNAKE_CASE=1_2						,						__SCREAMING_SNAKE_CASE=1_2						,						__SCREAMING_SNAKE_CASE=8						,						__SCREAMING_SNAKE_CASE=3_0_7_2						,						__SCREAMING_SNAKE_CASE="gelu_new"						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=5_1_2						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=0.02						,						__SCREAMING_SNAKE_CASE=1e-12						,						__SCREAMING_SNAKE_CASE=2_5_6						,						__SCREAMING_SNAKE_CASE=1_0						,						__SCREAMING_SNAKE_CASE=1e-3						,						__SCREAMING_SNAKE_CASE=5						,						__SCREAMING_SNAKE_CASE=3_2_0						,						__SCREAMING_SNAKE_CASE=1_3_3_5_3_7_1_8						,						__SCREAMING_SNAKE_CASE=5_0_0_0						,						__SCREAMING_SNAKE_CASE=1						,						__SCREAMING_SNAKE_CASE=0						,						__SCREAMING_SNAKE_CASE=2						,						**__SCREAMING_SNAKE_CASE						,						)  ->   List[str]:




           """simple docstring"""







           super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE						,						bos_token_id=__SCREAMING_SNAKE_CASE						,						eos_token_id=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )

           # Common config
           UpperCamelCase__    :   int					=				vocab_size
           UpperCamelCase__    :   Any					=				max_position_embeddings
           UpperCamelCase__    :   List[str]					=				hidden_size
           UpperCamelCase__    :   Union[str, Any]					=				retriever_proj_size
           UpperCamelCase__    :   int					=				num_hidden_layers
           UpperCamelCase__    :   Union[str, Any]					=				num_attention_heads
           UpperCamelCase__    :   str					=				num_candidates
           UpperCamelCase__    :   int					=				intermediate_size
           UpperCamelCase__    :   Dict					=				hidden_act
           UpperCamelCase__    :   Any					=				hidden_dropout_prob
           UpperCamelCase__    :   Any					=				attention_probs_dropout_prob
           UpperCamelCase__    :   Any					=				initializer_range
           UpperCamelCase__    :   Optional[Any]					=				type_vocab_size
           UpperCamelCase__    :   List[Any]					=				layer_norm_eps

           # Reader config
           UpperCamelCase__    :   List[Any]					=				span_hidden_size
           UpperCamelCase__    :   List[Any]					=				max_span_width
           UpperCamelCase__    :   Optional[Any]					=				reader_layer_norm_eps
           UpperCamelCase__    :   Optional[Any]					=				reader_beam_size
           UpperCamelCase__    :   int					=				reader_seq_len

           # Retrieval config
           UpperCamelCase__    :   List[str]					=				num_block_records
           UpperCamelCase__    :   Union[str, Any]					=				searcher_beam_size


