






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,
)


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

__magic_name__   :  Optional[int]        =							'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> 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 = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.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)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=8) ->			Union[str, Any]:
      """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





class 					A__     (     __snake_case ):

     '''simple docstring'''






     def __init__( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    UNetaDConditionModel   ,						_SCREAMING_SNAKE_CASE      :    DDPMScheduler   ,						_SCREAMING_SNAKE_CASE      :    VQModel   ,						):






           """simple docstring"""




           super().__init__()

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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    str   ,						_SCREAMING_SNAKE_CASE      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    List[str] ):






           """simple docstring"""




           if latents is None:
                 UpperCamelCase					       =  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					       =  latents.to(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  latents * scheduler.init_noise_sigma
           return latents







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






           """simple docstring"""




           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(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    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.' )

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

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







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






           """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      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    Union[torch.FloatTensor, List[torch.FloatTensor]]   ,						_SCREAMING_SNAKE_CASE      :    Union[torch.FloatTensor, List[torch.FloatTensor]]   ,						_SCREAMING_SNAKE_CASE      :    torch.FloatTensor   ,						_SCREAMING_SNAKE_CASE      :    int = 512   ,						_SCREAMING_SNAKE_CASE      :    int = 512   ,						_SCREAMING_SNAKE_CASE      :    int = 100   ,						_SCREAMING_SNAKE_CASE      :    float = 4.0   ,						_SCREAMING_SNAKE_CASE      :    int = 1   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[torch.FloatTensor] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[str] = "pil"   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						):






           """simple docstring"""




           UpperCamelCase					       =  self._execution_device

           UpperCamelCase					       =  guidance_scale > 1.0

           if isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ):
                 UpperCamelCase					       =  torch.cat(_SCREAMING_SNAKE_CASE   ,						dim=0 )
           if isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ):
                 UpperCamelCase					       =  torch.cat(_SCREAMING_SNAKE_CASE   ,						dim=0 )
           if isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ):
                 UpperCamelCase					       =  torch.cat(_SCREAMING_SNAKE_CASE   ,						dim=0 )

           UpperCamelCase					       =  image_embeds.shape[0] * num_images_per_prompt

           if do_classifier_free_guidance:
                 UpperCamelCase					       =  image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE   ,						dim=0 )
                 UpperCamelCase					       =  negative_image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE   ,						dim=0 )
                 UpperCamelCase					       =  hint.repeat_interleave(_SCREAMING_SNAKE_CASE   ,						dim=0 )

           UpperCamelCase					       =  torch.cat([negative_image_embeds, image_embeds]   ,						dim=0 ).to(dtype=self.unet.dtype   ,						device=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  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					       =  self.scheduler.timesteps

           UpperCamelCase					       =  self.movq.config.latent_channels

           UpperCamelCase       ,  UpperCamelCase					       =  downscale_height_and_width(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						self.movq_scale_factor )

           # create initial latent
           UpperCamelCase					       =  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					       =  torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

                 UpperCamelCase					       =  {'image_embeds': image_embeds, 'hint': hint}
                 UpperCamelCase					       =  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       ,  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(
                     _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						generator=_SCREAMING_SNAKE_CASE   ,						)[0]
           # post-processing
           UpperCamelCase					       =  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					       =  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(_SCREAMING_SNAKE_CASE )

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE )











import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__   :  str        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase) ->			int:
      """simple docstring"""

      UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')
      if "model" in sd.keys():
            UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')['model']

      # pop unnecessary weights
      UpperCamelCase					       =  [
          'decoder.version',
          'decoder.output_projection.weight',
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_UpperCamelCase)

      UpperCamelCase					       =  {
          'decoder.project_in_dim.weight': 'decoder.project_in.weight',
          'decoder.project_out_dim.weight': 'decoder.project_out.weight',
          'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
          'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
      }
      for old_key, new_key in keys_to_rename.items():
            if old_key in sd:
                  UpperCamelCase					       =  sd.pop(_UpperCamelCase)

      UpperCamelCase					       =  list(sd.keys())
      for key in keys:
            if ".qkv_proj." in key:
                  UpperCamelCase					       =  sd[key]
                  # We split QKV in separate Q,K,V

                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.q_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.k_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.v_proj.')

                  UpperCamelCase					       =  value.shape[0]
                  assert depth % 3 == 0
                  # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                  # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  torch.split(_UpperCamelCase       ,							depth // 3       ,							dim=0)

                  UpperCamelCase					       =  q
                  UpperCamelCase					       =  k
                  UpperCamelCase					       =  v
                  del sd[key]

      return sd
@torch.no_grad()
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=None) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  load_checkpoint(_UpperCamelCase)

      if config is not None:
            UpperCamelCase					       =  OPTConfig.from_pretrained(_UpperCamelCase)
      else:
            UpperCamelCase					       =  OPTConfig()

      UpperCamelCase					       =  OPTModel(_UpperCamelCase).half().eval()
      model.load_state_dict(_UpperCamelCase)

      # Check results
      Path(_UpperCamelCase).mkdir(exist_ok=_UpperCamelCase)
      model.save_pretrained(_UpperCamelCase)


if __name__ == "__main__":
       __magic_name__   :  Any        =							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--fairseq_path''',
           type=str,
           help=(
               '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
               ''' https://huggingface.co/models?other=opt_metasq'''
           ),
       )
       parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
       parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
       __magic_name__   :  str        =							parser.parse_args()
       convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



