




import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowerCamelCase	:	List[str]				=   2




class 						__lowercase		:




      """simple docstring"""





      def __init__(				self    ,				*,  # begin keyword-only arguments
          A="<s>"    ,				A="<pad>"    ,				A="</s>"    ,				A="<unk>"    ,				A=None    ,				)		->    List[Any]:
         snake_case				:    List[str]           =	bos, unk, pad, eos
         snake_case				:    Optional[Any]           =	[]
         snake_case				:    List[Any]           =	[]
         snake_case				:    List[str]           =	{}
         snake_case				:    Tuple           =	self.add_symbol(UpperCAmelCase_     )
         snake_case				:    List[str]           =	self.add_symbol(UpperCAmelCase_     )
         snake_case				:    Optional[int]           =	self.add_symbol(UpperCAmelCase_     )
         snake_case				:    Optional[Any]           =	self.add_symbol(UpperCAmelCase_     )
         if extra_special_symbols:
            for s in extra_special_symbols:
               self.add_symbol(UpperCAmelCase_     )
         snake_case				:    List[str]           =	len(self.symbols     )

      def __eq__(				self    ,				A     )		->    List[str]:
         return self.indices == other.indices

      def __getitem__(				self    ,				A     )		->    str:
         if idx < len(self.symbols     ):
            return self.symbols[idx]
         return self.unk_word

      def __len__(				self     )		->    List[str]:
         return len(self.symbols     )

      def __contains__(				self    ,				A     )		->    Tuple:
         return sym in self.indices

      @classmethod
      def      UpperCAmelCase				(				cls    ,				A     )		->    Optional[Any]:
         snake_case				:    Tuple           =	cls()
         d.add_from_file(UpperCAmelCase_     )
         return d

      def      UpperCAmelCase				(				self    ,				A    ,				A=1    ,				A=False     )		->    Dict:
         if word in self.indices and not overwrite:
            snake_case				:    List[str]           =	self.indices[word]
            snake_case				:    Dict           =	self.count[idx] + n
            return idx
         else:
            snake_case				:    List[Any]           =	len(self.symbols     )
            snake_case				:    Any           =	idx
            self.symbols.append(UpperCAmelCase_     )
            self.count.append(UpperCAmelCase_     )
            return idx

      def      UpperCAmelCase				(				self    ,				A     )		->    Union[str, Any]:
         return 0

      def      UpperCAmelCase				(				self    ,				A     )		->    str:
         if isinstance(UpperCAmelCase_    ,				UpperCAmelCase_     ):
            try:
               with open(UpperCAmelCase_    ,				"""r"""    ,				encoding="""utf-8"""     ) as fd:
                  self.add_from_file(UpperCAmelCase_     )
            except FileNotFoundError as fnfe:
               raise fnfe
            except UnicodeError:
               raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(UpperCAmelCase_     )     )
            return

         snake_case				:    List[Any]           =	f.readlines()
         snake_case				:    Optional[Any]           =	self._load_meta(UpperCAmelCase_     )

         for line in lines[indices_start_line:]:
            try:
               snake_case				:    Optional[Any]           =	line.rstrip().rsplit(""" """    ,				1     )
               if field == "#fairseq:overwrite":
                  snake_case				:    Dict           =	True
                  snake_case				:    Optional[int]           =	line.rsplit(""" """    ,				1     )
               else:
                  snake_case				:    Optional[int]           =	False
               snake_case				:    Optional[int]           =	int(UpperCAmelCase_     )
               snake_case				:    Union[str, Any]           =	line
               if word in self and not overwrite:
                  raise RuntimeError(
                      """Duplicate word found when loading Dictionary: '{}'. """
                      """Duplicate words can overwrite earlier ones by adding the """
                      """#fairseq:overwrite flag at the end of the corresponding row """
                      """in the dictionary file. If using the Camembert model, please """
                      """download an updated copy of the model file.""".format(UpperCAmelCase_     )     )
               self.add_symbol(UpperCAmelCase_    ,				n=UpperCAmelCase_    ,				overwrite=UpperCAmelCase_     )
            except ValueError:
               raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'"""     )




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[Any]:
   # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
   # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
   snake_case				:    List[Any]           =	dict((re.sub(R"""@@$"""						,""""""						,_UpperCAmelCase					), v) if k.endswith("""@@"""					) else (re.sub(R"""$"""						,"""</w>"""						,_UpperCAmelCase					), v) for k, v in d.items()					)
   snake_case				:    Optional[Any]           =	"<s> <pad> </s> <unk>".split()
   # restore the special tokens
   for k in keep_keys:
      del da[f"""{k}</w>"""]
      snake_case				:    Tuple           =	d[k]  # restore
   return da




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Optional[Any]:
   # prep
   if not os.path.exists(_UpperCAmelCase					):
      raise ValueError(f"""path {biogpt_checkpoint_path} does not exist!"""					)
   os.makedirs(_UpperCAmelCase						,exist_ok=_UpperCAmelCase					)
   print(f"""Writing results to {pytorch_dump_folder_path}"""					)

   # handle various types of models

   snake_case				:    str           =	os.path.join(_UpperCAmelCase						,"""checkpoint.pt"""					)
   if not os.path.isfile(_UpperCAmelCase					):
      raise ValueError(f"""path to the file {checkpoint_file} does not exist!"""					)
   snake_case				:    Union[str, Any]           =	torch.load(_UpperCAmelCase						,map_location="""cpu"""					)

   snake_case				:    Dict           =	chkpt["cfg"]["model"]

   # dicts
   snake_case				:    str           =	os.path.join(_UpperCAmelCase						,"""dict.txt"""					)
   if not os.path.isfile(_UpperCAmelCase					):
      raise ValueError(f"""path to the file {dict_file} does not exist!"""					)
   snake_case				:    Optional[Any]           =	Dictionary.load(_UpperCAmelCase					)
   snake_case				:    Dict           =	rewrite_dict_keys(src_dict.indices					)
   snake_case				:    Optional[Any]           =	len(_UpperCAmelCase					)
   snake_case				:    Any           =	os.path.join(_UpperCAmelCase						,VOCAB_FILES_NAMES["""vocab_file"""]					)
   print(f"""Generating {src_vocab_file} of {src_vocab_size} records"""					)
   with open(_UpperCAmelCase						,"""w"""						,encoding="""utf-8"""					) as f:
      f.write(json.dumps(_UpperCAmelCase						,ensure_ascii=_UpperCAmelCase						,indent=_UpperCAmelCase					)					)

   # merges_file (bpecodes)
   snake_case				:    Dict           =	os.path.join(_UpperCAmelCase						,"""bpecodes"""					)
   if not os.path.isfile(_UpperCAmelCase					):
      raise ValueError(f"""path to the file {bpecodes_file} does not exist!"""					)

   snake_case				:    int           =	os.path.join(_UpperCAmelCase						,VOCAB_FILES_NAMES["""merges_file"""]					)
   shutil.copyfile(_UpperCAmelCase						,_UpperCAmelCase					)

   # model config
   snake_case				:    Optional[Any]           =	os.path.join(_UpperCAmelCase						,"""config.json"""					)

   snake_case				:    Union[str, Any]           =	{
       "activation_dropout": args["activation_dropout"],
       "architectures": ["BioGptForCausalLM"],
       "attention_probs_dropout_prob": args["attention_dropout"],
       "bos_token_id": 0,
       "eos_token_id": 2,
       "hidden_act": args["activation_fn"],
       "hidden_dropout_prob": args["dropout"],
       "hidden_size": args["decoder_embed_dim"],
       "initializer_range": 0.02,
       "intermediate_size": args["decoder_ffn_embed_dim"],
       "layer_norm_eps": 1E-12,
       "layerdrop": args["decoder_layerdrop"],
       "max_position_embeddings": args["max_target_positions"],
       "model_type": "biogpt",
       "num_attention_heads": args["decoder_attention_heads"],
       "num_hidden_layers": args["decoder_layers"],
       "pad_token_id": 1,
       "scale_embedding": not args["no_scale_embedding"],
       "tie_word_embeddings": args["share_decoder_input_output_embed"],
       "vocab_size": src_vocab_size,
   }

   # good hparam defaults to start with

   print(f"""Generating {biogpt_model_config_file}"""					)
   with open(_UpperCAmelCase						,"""w"""						,encoding="""utf-8"""					) as f:
      f.write(json.dumps(_UpperCAmelCase						,ensure_ascii=_UpperCAmelCase						,indent=_UpperCAmelCase					)					)

   # tokenizer config
   snake_case				:    List[str]           =	os.path.join(_UpperCAmelCase						,_UpperCAmelCase					)

   snake_case				:    Union[str, Any]           =	{
       "bos_token": "<s>",
       "eos_token": "</s>",
       "model_max_length": 1024,
       "pad_token": "<pad>",
       "special_tokens_map_file": None,
       "tokenizer_class": "BioGptTokenizer",
       "unk_token": "<unk>",
   }

   print(f"""Generating {biogpt_tokenizer_config_file}"""					)
   with open(_UpperCAmelCase						,"""w"""						,encoding="""utf-8"""					) as f:
      f.write(json.dumps(_UpperCAmelCase						,ensure_ascii=_UpperCAmelCase						,indent=_UpperCAmelCase					)					)

   # model
   snake_case				:    Optional[int]           =	chkpt["model"]

   # remove unneeded keys
   snake_case				:    List[str]           =	[
       "decoder.version",
   ]
   for k in ignore_keys:
      model_state_dict.pop(_UpperCAmelCase						,_UpperCAmelCase					)

   snake_case				:    Union[str, Any]           =	list(model_state_dict.keys()					)
   for layer_name in layer_names:
      if layer_name.endswith("""output_projection.weight"""					):
         snake_case				:    int           =	model_state_dict.pop(_UpperCAmelCase					)
      else:
         snake_case				:    Union[str, Any]           =	model_state_dict.pop(_UpperCAmelCase					)

   snake_case				:    int           =	BioGptConfig.from_pretrained(_UpperCAmelCase					)
   snake_case				:    Optional[Any]           =	BioGptForCausalLM(_UpperCAmelCase					)

   # check that it loads ok
   model_new.load_state_dict(_UpperCAmelCase					)

   # save
   snake_case				:    Optional[Any]           =	os.path.join(_UpperCAmelCase						,_UpperCAmelCase					)
   print(f"""Generating {pytorch_weights_dump_path}"""					)
   torch.save(_UpperCAmelCase						,_UpperCAmelCase					)

   print("""Conversion is done!"""					)


if __name__ == "__main__":
       lowerCamelCase	:	str				=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--biogpt_checkpoint_path',
           default=None,
           type=str,
           required=True,
           help=(
               'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
               ' bpecodes, etc.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       lowerCamelCase	:	Dict				=   parser.parse_args()
       convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)











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

lowerCamelCase	:	Dict				=   '\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__ (   lowercase						,lowercase						,lowercase=8					)  ->       Optional[int]:
   snake_case				:    Dict           =	height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   snake_case				:    int           =	width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      def __init__(				self    ,				A    ,				A    ,				A    ,				)		->    int:
         super().__init__()

         self.register_modules(
             unet=A    ,				scheduler=A    ,				movq=A    ,				)
         snake_case				:    Tuple           =	2 ** (len(self.movq.config.block_out_channels     ) - 1)

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A     )		->    str:
         if latents is None:
            snake_case				:    List[str]           =	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}"""     )
            snake_case				:    Union[str, Any]           =	latents.to(A     )

         snake_case				:    Optional[Any]           =	latents * scheduler.init_noise_sigma
         return latents

      def      UpperCAmelCase				(				self    ,				A=0     )		->    List[str]:
         if is_accelerate_available():
            from accelerate import cpu_offload
         else:
            raise ImportError("""Please install accelerate via `pip install accelerate`"""     )

         snake_case				:    Dict           =	torch.device(f"""cuda:{gpu_id}"""     )

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

      def      UpperCAmelCase				(				self    ,				A=0     )		->    Optional[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."""     )

         snake_case				:    int           =	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)

         snake_case				:    Tuple           =	None
         for cpu_offloaded_model in [self.unet, self.movq]:
            snake_case     , snake_case				:    Tuple           =	cpu_offload_with_hook(A    ,				A    ,				prev_module_hook=A     )

         # We'll offload the last model manually.
         snake_case				:    Tuple           =	hook

      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def      UpperCAmelCase				(				self     )		->    str:
         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    ,				A    ,				A    ,				A    ,				A = 5_1_2    ,				A = 5_1_2    ,				A = 1_0_0    ,				A = 4.0    ,				A = 1    ,				A = None    ,				A = None    ,				A = "pil"    ,				A = True    ,				)		->    Any:
         snake_case				:    int           =	self._execution_device

         snake_case				:    Tuple           =	guidance_scale > 1.0

         if isinstance(A    ,				A     ):
            snake_case				:    str           =	torch.cat(A    ,				dim=0     )
         if isinstance(A    ,				A     ):
            snake_case				:    int           =	torch.cat(A    ,				dim=0     )
         if isinstance(A    ,				A     ):
            snake_case				:    Tuple           =	torch.cat(A    ,				dim=0     )

         snake_case				:    List[Any]           =	image_embeds.shape[0] * num_images_per_prompt

         if do_classifier_free_guidance:
            snake_case				:    Optional[int]           =	image_embeds.repeat_interleave(A    ,				dim=0     )
            snake_case				:    Dict           =	negative_image_embeds.repeat_interleave(A    ,				dim=0     )
            snake_case				:    Optional[Any]           =	hint.repeat_interleave(A    ,				dim=0     )

         snake_case				:    List[str]           =	torch.cat([negative_image_embeds, image_embeds]    ,				dim=0     ).to(dtype=self.unet.dtype    ,				device=A     )
         snake_case				:    Optional[Any]           =	torch.cat([hint, hint]    ,				dim=0     ).to(dtype=self.unet.dtype    ,				device=A     )
         self.scheduler.set_timesteps(A    ,				device=A     )
         snake_case				:    Tuple           =	self.scheduler.timesteps

         snake_case				:    Tuple           =	self.movq.config.latent_channels

         snake_case     , snake_case				:    Optional[int]           =	downscale_height_and_width(A    ,				A    ,				self.movq_scale_factor     )

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

            snake_case				:    Any           =	{"""image_embeds""": image_embeds, """hint""": hint}
            snake_case				:    List[Any]           =	self.unet(
                sample=A    ,				timestep=A    ,				encoder_hidden_states=A    ,				added_cond_kwargs=A    ,				return_dict=A    ,				)[0]

            if do_classifier_free_guidance:
               snake_case     , snake_case				:    Union[str, Any]           =	noise_pred.split(latents.shape[1]    ,				dim=1     )
               snake_case     , snake_case				:    Tuple           =	noise_pred.chunk(2     )
               snake_case     , snake_case				:    Optional[Any]           =	variance_pred.chunk(2     )
               snake_case				:    Optional[Any]           =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
               snake_case				:    Dict           =	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"]
            ):
               snake_case     , snake_case				:    int           =	noise_pred.split(latents.shape[1]    ,				dim=1     )

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

         if output_type == "pil":
            snake_case				:    Tuple           =	self.numpy_to_pil(A     )

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=A     )





