





'''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,
)


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

__lowerCAmelCase							:     List[str]		     ="\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 						UpperCamelCase    (							_lowerCamelCase : Tuple						,	_lowerCamelCase : Dict						,	_lowerCamelCase : List[str]=8	):
    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 						UpperCamelCase    (							_lowerCamelCase : Any						,	_lowerCamelCase : List[Any]=5_12						,	_lowerCamelCase : Optional[int]=5_12	):
    A__    =      pil_image.resize((w, h)						,	resample=Image.BICUBIC						,	reducing_gap=1	)
    A__    =      np.array(pil_image.convert("RGB"	)	)
    A__    =      arr.astype(np.floataa	) / 1_2_7.5 - 1
    A__    =      np.transpose(__lowerCAmelCase						,	[2, 0, 1]	)
    A__    =      torch.from_numpy(__lowerCAmelCase	).unsqueeze(0	)
    return image






class        UpperCAmelCase     (	A__    ):
 def __init__(    self				:Tuple						,	lowercase_				:UNetaDConditionModel						,	lowercase_				:DDPMScheduler						,	lowercase_				:VQModel						,	)->							Union[str, Any]:
     super().__init__()

     self.register_modules(
         unet=__snake_case						,	scheduler=__snake_case						,	movq=__snake_case						,	)
     A__    =      2 ** (len(self.movq.config.block_out_channels							) - 1)
 def 					UpperCAmelCase_							(    self				:Dict						,	lowercase_				:Union[str, Any]						,	lowercase_				:List[str]						,	lowercase_				:Tuple							)->							Any:
     # get the original timestep using init_timestep
     A__    =      min(int(num_inference_steps * strength							)						,	__snake_case							)

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

     return timesteps, num_inference_steps - t_start
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:str						,	lowercase_				:List[Any]						,	lowercase_				:Optional[Any]						,	lowercase_				:List[Any]						,	lowercase_				:str						,	lowercase_				:Any						,	lowercase_				:str=None							)->							List[Any]:
     if not isinstance(__snake_case						,	(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(__snake_case							)}"							)

     A__    =      image.to(device=__snake_case						,	dtype=__snake_case							)

     A__    =      batch_size * num_images_per_prompt

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

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

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

         A__    =      self.movq.config.scaling_factor * init_latents

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

     A__    =      init_latents.shape
     A__    =      randn_tensor(__snake_case						,	generator=__snake_case						,	device=__snake_case						,	dtype=__snake_case							)

     # get latents
     A__    =      self.scheduler.add_noise(__snake_case						,	__snake_case						,	__snake_case							)

     A__    =      init_latents

     return latents
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Optional[Any]=0							)->							Optional[int]:
     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(__snake_case						,	__snake_case							)
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:int=0							)->							List[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."							)

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

     if self.device.type != "cpu":
         self.to("cpu"						,	silence_dtype_warnings=__snake_case							)
         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(__snake_case						,	__snake_case						,	prev_module_hook=__snake_case							)

     # We'll offload the last model manually.
     A__    =      hook
 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							List[Any]:
     if not hasattr(self.unet						,	"_hf_hook"							):
         return self.device
     for module in self.unet.modules():
         if (
             hasattr(__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(__snake_case							)
 def __call__(    self				:int						,	lowercase_				:Union[torch.FloatTensor, List[torch.FloatTensor]]						,	lowercase_				:Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]						,	lowercase_				:Union[torch.FloatTensor, List[torch.FloatTensor]]						,	lowercase_				:int = 5_12						,	lowercase_				:int = 5_12						,	lowercase_				:int = 1_00						,	lowercase_				:float = 4.0						,	lowercase_				:float = 0.3						,	lowercase_				:int = 1						,	lowercase_				:Optional[Union[torch.Generator, List[torch.Generator]]] = None						,	lowercase_				:Optional[str] = "pil"						,	lowercase_				:bool = True						,	)->							Union[str, Any]:
     A__    =      self._execution_device

     A__    =      guidance_scale > 1.0

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

     if do_classifier_free_guidance:
         A__    =      image_embeds.repeat_interleave(__snake_case						,	dim=0							)
         A__    =      negative_image_embeds.repeat_interleave(__snake_case						,	dim=0							)

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

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

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

     A__    =      self.movq.encode(__snake_case							)["latents"]
     A__    =      latents.repeat_interleave(__snake_case						,	dim=0							)
     self.scheduler.set_timesteps(__snake_case						,	device=__snake_case							)
     A__,     A__    =      self.get_timesteps(__snake_case						,	__snake_case						,	__snake_case							)
     A__    =      timesteps[:1].repeat(batch_size * num_images_per_prompt							)
     A__,     A__    =      downscale_height_and_width(__snake_case						,	__snake_case						,	self.movq_scale_factor							)
     A__    =      self.prepare_latents(
         __snake_case						,	__snake_case						,	__snake_case						,	__snake_case						,	image_embeds.dtype						,	__snake_case						,	__snake_case							)
     for i, t in enumerate(self.progress_bar(__snake_case							)							):
         # 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=__snake_case						,	timestep=__snake_case						,	encoder_hidden_states=__snake_case						,	added_cond_kwargs=__snake_case						,	return_dict=__snake_case						,	)[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(
             __snake_case						,	__snake_case						,	__snake_case						,	generator=__snake_case						,	)[0]

     # post-processing
     A__    =      self.movq.decode(__snake_case						,	force_not_quantize=__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"]:
         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(__snake_case							)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=__snake_case							)






'''simple docstring'''

import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
__lowerCAmelCase							:     List[str]		     =logging.get_logger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : str	):
    A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)
    if "model" in sd.keys():
        A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)["model"]

    # pop unnecessary weights
    A__    =      [
        "decoder.version",
        "decoder.output_projection.weight",
    ]
    for key in keys_to_delete:
        if key in sd:
            sd.pop(_lowerCamelCase	)

    A__    =      {
        "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:
            A__    =      sd.pop(_lowerCamelCase	)

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

            A__    =      key.replace(".qkv_proj."						,	".q_proj."	)
            A__    =      key.replace(".qkv_proj."						,	".k_proj."	)
            A__    =      key.replace(".qkv_proj."						,	".v_proj."	)

            A__    =      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
            A__,     A__,     A__    =      torch.split(_lowerCamelCase						,	depth // 3						,	dim=0	)

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

    return sd





@torch.no_grad()
def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : Any						,	_lowerCamelCase : Dict=None	):
    A__    =      load_checkpoint(_lowerCamelCase	)

    if config is not None:
        A__    =      OPTConfig.from_pretrained(_lowerCamelCase	)
    else:
        A__    =      OPTConfig()

    A__    =      OPTModel(_lowerCamelCase	).half().eval()
    model.load_state_dict(_lowerCamelCase	)

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


if __name__ == "__main__":
  __lowerCAmelCase							:     List[str]		     =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.")
  __lowerCAmelCase							:     List[Any]		     =parser.parse_args()
  convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



