

'''simple docstring'''




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 						UpperCamelCase	(			a		,						a		,						a=8			)				->     Any:






     '''simple docstring'''
     __magic_name__						       =    height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     __magic_name__						       =    width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor






class   _SCREAMING_SNAKE_CASE   (   __a     ):



 def __init__(   self		:   Union[str, Any]			,				a__		:   UNetaDConditionModel			,				a__		:   DDPMScheduler			,				a__		:   VQModel			,				):
      super().__init__()

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



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   List[Any]			,				a__		:   List[str]			,				a__		:   Tuple			,				a__		:   int			,				a__		:   Tuple			,				a__		:   Tuple      ):
      if latents is None:
           __magic_name__						       =    randn_tensor(a__			,				generator=a__			,				device=a__			,				dtype=a__      )
      else:
           if latents.shape != shape:
                raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''      )
           __magic_name__						       =    latents.to(a__      )

      __magic_name__						       =    latents * scheduler.init_noise_sigma
      return latents



 def 			snake_case__  (   self		:   int			,				a__		:   Optional[Any]=0      ):
      if is_accelerate_available():
           from accelerate import cpu_offload
      else:
           raise ImportError('''Please install accelerate via `pip install accelerate`'''      )

      __magic_name__						       =    torch.device(F'''cuda:{gpu_id}'''      )

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



 def 			snake_case__  (   self		:   Dict			,				a__		:   List[Any]=0      ):
      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.'''      )

      __magic_name__						       =    torch.device(F'''cuda:{gpu_id}'''      )

      if self.device.type != "cpu":
           self.to('''cpu'''			,				silence_dtype_warnings=a__      )
           torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

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

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



 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 			snake_case__  (   self		:   Tuple      ):
      if not hasattr(self.unet			,				'''_hf_hook'''      ):
           return self.device
      for module in self.unet.modules():
           if (
               hasattr(a__			,				'''_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(a__      )
 def __call__(   self		:   int			,				a__		:   Union[torch.FloatTensor, List[torch.FloatTensor]]			,				a__		:   Union[torch.FloatTensor, List[torch.FloatTensor]]			,				a__		:   torch.FloatTensor			,				a__		:   int = 512			,				a__		:   int = 512			,				a__		:   int = 100			,				a__		:   float = 4.0			,				a__		:   int = 1			,				a__		:   Optional[Union[torch.Generator, List[torch.Generator]]] = None			,				a__		:   Optional[torch.FloatTensor] = None			,				a__		:   Optional[str] = "pil"			,				a__		:   bool = True			,				):
      __magic_name__						       =    self._execution_device

      __magic_name__						       =    guidance_scale > 1.0

      if isinstance(a__			,				a__      ):
           __magic_name__						       =    torch.cat(a__			,				dim=0      )
      if isinstance(a__			,				a__      ):
           __magic_name__						       =    torch.cat(a__			,				dim=0      )
      if isinstance(a__			,				a__      ):
           __magic_name__						       =    torch.cat(a__			,				dim=0      )

      __magic_name__						       =    image_embeds.shape[0] * num_images_per_prompt

      if do_classifier_free_guidance:
           __magic_name__						       =    image_embeds.repeat_interleave(a__			,				dim=0      )
           __magic_name__						       =    negative_image_embeds.repeat_interleave(a__			,				dim=0      )
           __magic_name__						       =    hint.repeat_interleave(a__			,				dim=0      )

      __magic_name__						       =    torch.cat([negative_image_embeds, image_embeds]			,				dim=0      ).to(dtype=self.unet.dtype			,				device=a__      )
      __magic_name__						       =    torch.cat([hint, hint]			,				dim=0      ).to(dtype=self.unet.dtype			,				device=a__      )
      self.scheduler.set_timesteps(a__			,				device=a__      )
      __magic_name__						       =    self.scheduler.timesteps

      __magic_name__						       =    self.movq.config.latent_channels

      __magic_name__       ,  __magic_name__						       =    downscale_height_and_width(a__			,				a__			,				self.movq_scale_factor      )

      # create initial latent
      __magic_name__						       =    self.prepare_latents(
          (batch_size, num_channels_latents, height, width)			,				image_embeds.dtype			,				a__			,				a__			,				a__			,				self.scheduler			,				)

      for i, t in enumerate(self.progress_bar(a__      )      ):
           # expand the latents if we are doing classifier free guidance
           __magic_name__						       =    torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

           __magic_name__						       =    {'''image_embeds''': image_embeds, '''hint''': hint}
           __magic_name__						       =    self.unet(
               sample=a__			,				timestep=a__			,				encoder_hidden_states=a__			,				added_cond_kwargs=a__			,				return_dict=a__			,				)[0]

           if do_classifier_free_guidance:
                __magic_name__       ,  __magic_name__						       =    noise_pred.split(latents.shape[1]			,				dim=1      )
                __magic_name__       ,  __magic_name__						       =    noise_pred.chunk(2      )
                __magic_name__       ,  __magic_name__						       =    variance_pred.chunk(2      )
                __magic_name__						       =    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                __magic_name__						       =    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"]
           ):
                __magic_name__       ,  __magic_name__						       =    noise_pred.split(latents.shape[1]			,				dim=1      )

           # compute the previous noisy sample x_t -> x_t-1
           __magic_name__						       =    self.scheduler.step(
               a__			,				a__			,				a__			,				generator=a__			,				)[0]
      # post-processing
      __magic_name__						       =    self.movq.decode(a__			,				force_not_quantize=a__      )['''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"]:
           __magic_name__						       =    image * 0.5 + 0.5
           __magic_name__						       =    image.clamp(0			,				1      )
           __magic_name__						       =    image.cpu().permute(0			,				2			,				3			,				1      ).float().numpy()

      if output_type == "pil":
           __magic_name__						       =    self.numpy_to_pil(a__      )

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=a__      )









'''simple docstring'''




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer




def 						UpperCamelCase	(			a		,						a		,						a		,						a=1024			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    [], []

     __magic_name__						       =    list(zip(a		,						a			)			)
     __magic_name__       ,  __magic_name__						       =    sorted_examples[0]

     def is_too_big(a			):
          return tok(a		,						return_tensors='''pt'''			).input_ids.shape[1] > max_tokens

     for src, tgt in tqdm(sorted_examples[1:]			):
          __magic_name__						       =    new_src + ''' ''' + src
          __magic_name__						       =    new_tgt + ''' ''' + tgt
          if is_too_big(a			) or is_too_big(a			):  # cant fit, finalize example
               finished_src.append(a			)
               finished_tgt.append(a			)
               __magic_name__       ,  __magic_name__						       =    src, tgt
          else:  # can fit, keep adding
               __magic_name__       ,  __magic_name__						       =    cand_src, cand_tgt

    # cleanup
     if new_src:
          assert new_tgt
          finished_src.append(a			)
          finished_tgt.append(a			)
     return finished_src, finished_tgt




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     Any:






     '''simple docstring'''
     __magic_name__						       =    Path(a			)
     save_path.mkdir(exist_ok=a			)
     for split in ["train"]:
          __magic_name__       ,  __magic_name__						       =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__       ,  __magic_name__						       =    pack_examples(a		,						a		,						a		,						a			)
          print(F'''packed {split} split from {len(a			)} examples -> {len(a			)}.'''			)
          Path(save_path / F'''{split}.source'''			).open('''w'''			).write('''\n'''.join(a			)			)
          Path(save_path / F'''{split}.target'''			).open('''w'''			).write('''\n'''.join(a			)			)
     for split in ["val", "test"]:
          __magic_name__       ,  __magic_name__						       =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
          shutil.copyfile(a		,						save_path / F'''{split}.source'''			)
          shutil.copyfile(a		,						save_path / F'''{split}.target'''			)




def 						UpperCamelCase	(			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    argparse.ArgumentParser()
     parser.add_argument('''--tok_name'''		,						type=a		,						help='''like facebook/bart-large-cnn,t5-base, etc.'''			)
     parser.add_argument('''--max_seq_len'''		,						type=a		,						default=128			)
     parser.add_argument('''--data_dir'''		,						type=a			)
     parser.add_argument('''--save_path'''		,						type=a			)
     __magic_name__						       =    parser.parse_args()
     __magic_name__						       =    AutoTokenizer.from_pretrained(args.tok_name			)
     return pack_data_dir(a		,						Path(args.data_dir			)		,						args.max_seq_len		,						args.save_path			)


if __name__ == "__main__":
      packer_cli()






