
"""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,
)


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

SCREAMING_SNAKE_CASE_         =  '''
    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       lowercase__  (   lowerCAmelCase			:   Optional[int]	,			lowerCAmelCase			:   int	,			lowerCAmelCase			:   Tuple=8	)	-> Tuple:






    """simple docstring"""




    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




def       lowercase__  (   lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Union[str, Any]=512	,			lowerCAmelCase			:   Optional[Any]=512	)	-> str:






    """simple docstring"""




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







class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):




      def __init__(							self				,    lowercase_				,    lowercase_				,    lowercase_				,    )  ->   List[str]:
          super().__init__()

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




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Tuple:
          # get the original timestep using init_timestep
          UpperCAmelCase       =   min(int(num_inference_steps * strength							)				,    lowercase_							)

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

          return timesteps, num_inference_steps - t_start




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_=None							)  ->   Optional[int]:
          if not isinstance(lowercase_				,    (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(lowercase_							)}"							)

          UpperCAmelCase       =   image.to(device=lowercase_				,    dtype=lowercase_							)

          UpperCAmelCase       =   batch_size * num_images_per_prompt

          if image.shape[1] == 4:
              UpperCAmelCase       =   image

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

              elif isinstance(lowercase_				,    lowercase_							):
                  UpperCAmelCase       =   [
                      self.movq.encode(image[i : i + 1]							).latent_dist.sample(generator[i]							) for i in range(lowercase_							)
                  ]
                  UpperCAmelCase       =   torch.cat(lowercase_				,    dim=0							)
              else:
                  UpperCAmelCase       =   self.movq.encode(lowercase_							).latent_dist.sample(lowercase_							)

              UpperCAmelCase       =   self.movq.config.scaling_factor * init_latents

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

          UpperCAmelCase       =   init_latents.shape
          UpperCAmelCase       =   randn_tensor(lowercase_				,    generator=lowercase_				,    device=lowercase_				,    dtype=lowercase_							)

          # get latents
          UpperCAmelCase       =   self.scheduler.add_noise(lowercase_				,    lowercase_				,    lowercase_							)

          UpperCAmelCase       =   init_latents

          return latents




      def       a_ (							self				,    lowercase_=0							)  ->   Optional[int]:
          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(lowercase_				,    lowercase_							)




      def       a_ (							self				,    lowercase_=0							)  ->   Tuple:
          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=lowercase_							)
              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(lowercase_				,    lowercase_				,    prev_module_hook=lowercase_							)

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




      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def       a_ (							self							)  ->   Optional[int]:
          if not hasattr(self.unet				,    '_hf_hook'							):
              return self.device
          for module in self.unet.modules():
              if (
                  hasattr(lowercase_				,    '_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(lowercase_							)
      def __call__(							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = 5_1_2				,    lowercase_ = 5_1_2				,    lowercase_ = 1_0_0				,    lowercase_ = 4.0				,    lowercase_ = 0.3				,    lowercase_ = 1				,    lowercase_ = None				,    lowercase_ = "pil"				,    lowercase_ = True				,    )  ->   Dict:
          UpperCAmelCase       =   self._execution_device

          UpperCAmelCase       =   guidance_scale > 1.0

          if isinstance(lowercase_				,    lowercase_							):
              UpperCAmelCase       =   torch.cat(lowercase_				,    dim=0							)
          if isinstance(lowercase_				,    lowercase_							):
              UpperCAmelCase       =   torch.cat(lowercase_				,    dim=0							)
          if isinstance(lowercase_				,    lowercase_							):
              UpperCAmelCase       =   torch.cat(lowercase_				,    dim=0							)

          UpperCAmelCase       =   image_embeds.shape[0]

          if do_classifier_free_guidance:
              UpperCAmelCase       =   image_embeds.repeat_interleave(lowercase_				,    dim=0							)
              UpperCAmelCase       =   negative_image_embeds.repeat_interleave(lowercase_				,    dim=0							)
              UpperCAmelCase       =   hint.repeat_interleave(lowercase_				,    dim=0							)

          UpperCAmelCase       =   torch.cat([negative_image_embeds, image_embeds]				,    dim=0							).to(dtype=self.unet.dtype				,    device=lowercase_							)
          UpperCAmelCase       =   torch.cat([hint, hint]				,    dim=0							).to(dtype=self.unet.dtype				,    device=lowercase_							)

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

          UpperCAmelCase       =   torch.cat([prepare_image(lowercase_				,    lowercase_				,    lowercase_							) for i in image]				,    dim=0							)
          UpperCAmelCase       =   image.to(dtype=image_embeds.dtype				,    device=lowercase_							)

          UpperCAmelCase       =   self.movq.encode(lowercase_							)['latents']
          UpperCAmelCase       =   latents.repeat_interleave(lowercase_				,    dim=0							)
          self.scheduler.set_timesteps(lowercase_				,    device=lowercase_							)
          UpperCAmelCase     ,							UpperCAmelCase       =   self.get_timesteps(lowercase_				,    lowercase_				,    lowercase_							)
          UpperCAmelCase       =   timesteps[:1].repeat(batch_size * num_images_per_prompt							)
          UpperCAmelCase     ,							UpperCAmelCase       =   downscale_height_and_width(lowercase_				,    lowercase_				,    self.movq_scale_factor							)
          UpperCAmelCase       =   self.prepare_latents(
              lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    image_embeds.dtype				,    lowercase_				,    lowercase_							)
          for i, t in enumerate(self.progress_bar(lowercase_							)							):
              # 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=lowercase_				,    timestep=lowercase_				,    encoder_hidden_states=lowercase_				,    added_cond_kwargs=lowercase_				,    return_dict=lowercase_				,    )[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(
                  lowercase_				,    lowercase_				,    lowercase_				,    generator=lowercase_				,    )[0]

          # post-processing
          UpperCAmelCase       =   self.movq.decode(lowercase_				,    force_not_quantize=lowercase_							)['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(lowercase_							)

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=lowercase_							)





"""simple docstring"""

import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()




def       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Optional[Any]	)	-> Optional[Any]:






    """simple docstring"""




    UpperCAmelCase       =   FNetConfig.from_json_file(lowerCAmelCase	)
    print(F"Building PyTorch model from configuration: {config}"	)
    UpperCAmelCase       =   FNetForPreTraining(lowerCAmelCase	)

    UpperCAmelCase       =   restore_checkpoint(lowerCAmelCase	,			lowerCAmelCase	)
    UpperCAmelCase       =   checkpoint_dict['target']

    # Embeddings
    # Position IDs
    UpperCAmelCase       =   fnet_pretraining_model.state_dict()

    UpperCAmelCase       =   state_dict['fnet.embeddings.position_ids']
    UpperCAmelCase       =   {'fnet.embeddings.position_ids': position_ids}
    # Embedding Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['position']['embedding'][0]	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['type']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['scale']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['bias']	)

    # Encoder Layers
    for layer in range(config.num_hidden_layers	):
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['scale']	)
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['kernel']	).T
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['kernel']	).T
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['scale']	)
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['bias']	)

    # Pooler Layers
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['encoder']['pooler']['bias']	)

    # Masked LM Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_dense']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_dense']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_layer_norm']['scale']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_layer_norm']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_output']['output_bias']	)
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['predictions_output']['output_bias']	)

    # Seq Relationship Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['classification']['output_kernel']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['classification']['output_bias']	)

    # Load State Dict
    fnet_pretraining_model.load_state_dict(lowerCAmelCase	)

    # Save PreTrained
    print(F"Saving pretrained model to {save_path}"	)
    fnet_pretraining_model.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_         =  argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--fnet_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained FNet model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
   SCREAMING_SNAKE_CASE_         =  parser.parse_args()
   convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)



