


'''simple docstring'''







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()

a__  :					str        =       2
class 					lowercase_     :


    def __init__(     self  ,					*,  # begin keyword-only arguments
        a="<s>"  ,					a="<pad>"  ,					a="</s>"  ,					a="<unk>"  ,					a=None  ,					):
          UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__										=				bos, unk, pad, eos
          UpperCamelCase__										=				[]
          UpperCamelCase__										=				[]
          UpperCamelCase__										=				{}
          UpperCamelCase__										=				self.add_symbol(A__    )
          UpperCamelCase__										=				self.add_symbol(A__    )
          UpperCamelCase__										=				self.add_symbol(A__    )
          UpperCamelCase__										=				self.add_symbol(A__    )
          if extra_special_symbols:
                for s in extra_special_symbols:
                      self.add_symbol(A__    )
          UpperCamelCase__										=				len(self.symbols    )


    def __eq__(     self  ,					a    ):
          return self.indices == other.indices


    def __getitem__(     self  ,					a    ):
          if idx < len(self.symbols    ):
                return self.symbols[idx]
          return self.unk_word


    def __len__(     self    ):
          return len(self.symbols    )


    def __contains__(     self  ,					a    ):
          return sym in self.indices


    @classmethod
    def    __a							(     cls  ,					a    ):
          UpperCamelCase__										=				cls()
          d.add_from_file(A__    )
          return d


    def    __a							(     self  ,					a  ,					a=1  ,					a=False    ):
          if word in self.indices and not overwrite:
                UpperCamelCase__										=				self.indices[word]
                UpperCamelCase__										=				self.count[idx] + n
                return idx
          else:
                UpperCamelCase__										=				len(self.symbols    )
                UpperCamelCase__										=				idx
                self.symbols.append(A__    )
                self.count.append(A__    )
                return idx


    def    __a							(     self  ,					a    ):
          return 0


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

          UpperCamelCase__										=				f.readlines()
          UpperCamelCase__										=				self._load_meta(A__    )

          for line in lines[indices_start_line:]:
                try:
                      UpperCamelCase__					,	UpperCamelCase__										=				line.rstrip().rsplit(" "  ,					1    )
                      if field == "#fairseq:overwrite":
                            UpperCamelCase__										=				True
                            UpperCamelCase__					,	UpperCamelCase__										=				line.rsplit(" "  ,					1    )
                      else:
                            UpperCamelCase__										=				False
                      UpperCamelCase__										=				int(A__    )
                      UpperCamelCase__										=				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(A__    )    )
                      self.add_symbol(A__  ,					n=A__  ,					overwrite=A__    )
                except ValueError:
                      raise ValueError("Incorrect dictionary format, expected \'<token> <cnt> [flags]\'"    )







def _UpperCamelCase       (     __A    )							->		List[str]:


      '''simple docstring'''



      UpperCamelCase__										=				dict((re.sub(R"@@$"      ,			""      ,			lowerCAmelCase__    ), v) if k.endswith("@@"    ) else (re.sub(R"$"      ,			"</w>"      ,			lowerCAmelCase__    ), v) for k, v in d.items()    )
      UpperCamelCase__										=				"<s> <pad> </s> <unk>".split()
      # restore the special tokens
      for k in keep_keys:
            del da[F'''{k}</w>''']
            UpperCamelCase__										=				d[k]  # restore
      return da







def _UpperCamelCase       (     __A      ,			__A    )							->		str:


      '''simple docstring'''



      if not os.path.exists(lowerCAmelCase__    ):
            raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!'''    )
      os.makedirs(lowerCAmelCase__      ,			exist_ok=lowerCAmelCase__    )
      print(F'''Writing results to {pytorch_dump_folder_path}'''    )

      # handle various types of models

      UpperCamelCase__										=				os.path.join(lowerCAmelCase__      ,			"checkpoint.pt"    )
      if not os.path.isfile(lowerCAmelCase__    ):
            raise ValueError(F'''path to the file {checkpoint_file} does not exist!'''    )
      UpperCamelCase__										=				torch.load(lowerCAmelCase__      ,			map_location="cpu"    )

      UpperCamelCase__										=				chkpt["cfg"]["model"]

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

      # merges_file (bpecodes)
      UpperCamelCase__										=				os.path.join(lowerCAmelCase__      ,			"bpecodes"    )
      if not os.path.isfile(lowerCAmelCase__    ):
            raise ValueError(F'''path to the file {bpecodes_file} does not exist!'''    )

      UpperCamelCase__										=				os.path.join(lowerCAmelCase__      ,			VOCAB_FILES_NAMES["merges_file"]    )
      shutil.copyfile(lowerCAmelCase__      ,			lowerCAmelCase__    )

      # model config
      UpperCamelCase__										=				os.path.join(lowerCAmelCase__      ,			"config.json"    )

      UpperCamelCase__										=				{
          "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(lowerCAmelCase__      ,			"w"      ,			encoding="utf-8"    ) as f:
            f.write(json.dumps(lowerCAmelCase__      ,			ensure_ascii=lowerCAmelCase__      ,			indent=lowerCAmelCase__    )    )

      # tokenizer config
      UpperCamelCase__										=				os.path.join(lowerCAmelCase__      ,			lowerCAmelCase__    )

      UpperCamelCase__										=				{
          "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(lowerCAmelCase__      ,			"w"      ,			encoding="utf-8"    ) as f:
            f.write(json.dumps(lowerCAmelCase__      ,			ensure_ascii=lowerCAmelCase__      ,			indent=lowerCAmelCase__    )    )

      # model
      UpperCamelCase__										=				chkpt["model"]

      # remove unneeded keys
      UpperCamelCase__										=				[
          "decoder.version",
      ]
      for k in ignore_keys:
            model_state_dict.pop(lowerCAmelCase__      ,			lowerCAmelCase__    )

      UpperCamelCase__										=				list(model_state_dict.keys()    )
      for layer_name in layer_names:
            if layer_name.endswith("output_projection.weight"    ):
                  UpperCamelCase__										=				model_state_dict.pop(lowerCAmelCase__    )
            else:
                  UpperCamelCase__										=				model_state_dict.pop(lowerCAmelCase__    )

      UpperCamelCase__										=				BioGptConfig.from_pretrained(lowerCAmelCase__    )
      UpperCamelCase__										=				BioGptForCausalLM(lowerCAmelCase__    )

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

      # save
      UpperCamelCase__										=				os.path.join(lowerCAmelCase__      ,			lowerCAmelCase__    )
      print(F'''Generating {pytorch_weights_dump_path}'''    )
      torch.save(lowerCAmelCase__      ,			lowerCAmelCase__    )

      print("Conversion is done!"    )


if __name__ == "__main__":
  a__  :					Union[str, Any]        =       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.'
  )
  a__  :					Any        =       parser.parse_args()
  convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__		:str   =    logging.get_logger(__name__)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						'''huggingface/label-files'''
					lowercase							=						'''imagenet-1k-id2label.json'''
					lowercase							=						json.load(open(hf_hub_download(lowerCAmelCase__ ,			lowerCAmelCase__ ,			repo_type='''dataset'''		) ,			'''r'''		)		)
					lowercase							=						{int(lowerCAmelCase__		): v for k, v in idalabel.items()}
					lowercase							=						{v: k for k, v in idalabel.items()}

					lowercase							=						'''std_conv''' if '''bit''' in model_name else False

					# note that when using BiT as backbone for ViT-hybrid checkpoints,
					# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
					# config.conv_layer = "std_conv_same"
					lowercase							=						BitConfig(
					    conv_layer=lowerCAmelCase__ ,			num_labels=1000 ,			idalabel=lowerCAmelCase__ ,			labelaid=lowerCAmelCase__ ,			)

					return config



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					if "stem.conv" in name:
										lowercase							=						name.replace('''stem.conv''' ,			'''bit.embedder.convolution'''		)
					if "blocks" in name:
										lowercase							=						name.replace('''blocks''' ,			'''layers'''		)
					if "head.fc" in name:
										lowercase							=						name.replace('''head.fc''' ,			'''classifier.1'''		)
					if name.startswith('''norm'''		):
										lowercase							=						'''bit.''' + name
					if "bit" not in name and "classifier" not in name:
										lowercase							=						'''bit.encoder.''' + name

					return name



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					lowercase							=						Image.open(requests.get(lowerCAmelCase__ ,			stream=lowerCAmelCase__		).raw		)
					return im



@torch.no_grad()
def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__=False		):
					'''simple docstring'''
					lowercase							=						get_config(lowerCAmelCase__		)

					# load original model from timm
					lowercase							=						create_model(lowerCAmelCase__ ,			pretrained=lowerCAmelCase__		)
					timm_model.eval()

					# load state_dict of original model
					lowercase							=						timm_model.state_dict()
					for key in state_dict.copy().keys():
										lowercase							=						state_dict.pop(lowerCAmelCase__		)
										lowercase							=						val.squeeze() if '''head''' in key else val

					# load HuggingFace model
					lowercase							=						BitForImageClassification(lowerCAmelCase__		)
					model.eval()
					model.load_state_dict(lowerCAmelCase__		)

					# create image processor
					lowercase							=						create_transform(**resolve_data_config({} ,			model=lowerCAmelCase__		)		)
					lowercase							=						transform.transforms

					lowercase							=						{
					    '''bilinear''': PILImageResampling.BILINEAR,
					    '''bicubic''': PILImageResampling.BICUBIC,
					    '''nearest''': PILImageResampling.NEAREST,
					}

					lowercase							=						BitImageProcessor(
					    do_resize=lowerCAmelCase__ ,			size={'''shortest_edge''': timm_transforms[0].size} ,			resample=pillow_resamplings[timm_transforms[0].interpolation.value] ,			do_center_crop=lowerCAmelCase__ ,			crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]} ,			do_normalize=lowerCAmelCase__ ,			image_mean=timm_transforms[-1].mean.tolist() ,			image_std=timm_transforms[-1].std.tolist() ,			)

					lowercase							=						prepare_img()
					lowercase							=						transform(lowerCAmelCase__		).unsqueeze(0		)
					lowercase							=						processor(lowerCAmelCase__ ,			return_tensors='''pt'''		).pixel_values

					# verify pixel values
					assert torch.allclose(lowerCAmelCase__ ,			lowerCAmelCase__		)

					# verify logits
					with torch.no_grad():
										lowercase							=						model(lowerCAmelCase__		)
										lowercase							=						outputs.logits

					print('''Logits:''' ,			logits[0, :3]		)
					print('''Predicted class:''' ,			model.config.idalabel[logits.argmax(-1		).item()]		)
					lowercase							=						timm_model(lowerCAmelCase__		)
					assert timm_logits.shape == outputs.logits.shape
					assert torch.allclose(lowerCAmelCase__ ,			outputs.logits ,			atol=1E-3		)
					print('''Looks ok!'''		)

					if pytorch_dump_folder_path is not None:
										Path(lowerCAmelCase__		).mkdir(exist_ok=lowerCAmelCase__		)
										print(f'Saving model {model_name} and processor to {pytorch_dump_folder_path}'		)
										model.save_pretrained(lowerCAmelCase__		)
										processor.save_pretrained(lowerCAmelCase__		)

					if push_to_hub:
										print(f'Pushing model {model_name} and processor to the hub'		)
										model.push_to_hub(f'ybelkada/{model_name}'		)
										processor.push_to_hub(f'ybelkada/{model_name}'		)


if __name__ == "__main__":
							lowercase__		:List[Any]   =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--model_name",
							    default="resnetv2_50x1_bitm",
							    type=str,
							    help="Name of the BiT timm model you'd like to convert.",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
							)
							parser.add_argument(
							    "--push_to_hub",
							    action="store_true",
							    help="Whether to push the model to the hub.",
							)

							lowercase__		:List[str]   =    parser.parse_args()
							convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




