



"""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()

lowerCamelCase  			=		2
class  lowercase__  :







       '''simple docstring'''


       def __init__(				self     :    Union[str, Any]						,							*,  # begin keyword-only arguments
           _UpperCAmelCase     :    Optional[Any]="<s>"						,							_UpperCAmelCase     :    List[Any]="<pad>"						,							_UpperCAmelCase     :    List[Any]="</s>"						,							_UpperCAmelCase     :    Union[str, Any]="<unk>"						,							_UpperCAmelCase     :    Union[str, Any]=None						,							)					->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_     					=				bos, unk, pad, eos
             UpperCAmelCase_     					=				[]
             UpperCAmelCase_     					=				[]
             UpperCAmelCase_     					=				{}
             UpperCAmelCase_     					=				self.add_symbol(_UpperCAmelCase       )
             UpperCAmelCase_     					=				self.add_symbol(_UpperCAmelCase       )
             UpperCAmelCase_     					=				self.add_symbol(_UpperCAmelCase       )
             UpperCAmelCase_     					=				self.add_symbol(_UpperCAmelCase       )
             if extra_special_symbols:
                   for s in extra_special_symbols:
                         self.add_symbol(_UpperCAmelCase       )
             UpperCAmelCase_     					=				len(self.symbols       )



       def __eq__(				self     :    int						,							_UpperCAmelCase     :    int       )					->		Optional[int]:


             '''simple docstring'''




             return self.indices == other.indices



       def __getitem__(				self     :    Optional[int]						,							_UpperCAmelCase     :    str       )					->		Tuple:


             '''simple docstring'''




             if idx < len(self.symbols       ):
                   return self.symbols[idx]
             return self.unk_word



       def __len__(				self     :    Tuple       )					->		str:


             '''simple docstring'''




             return len(self.symbols       )



       def __contains__(				self     :    List[str]						,							_UpperCAmelCase     :    Optional[int]       )					->		Optional[Any]:


             '''simple docstring'''




             return sym in self.indices



       @classmethod
       def 	lowercase__							(				cls     :    Tuple						,							_UpperCAmelCase     :    int       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_     					=				cls()
             d.add_from_file(_UpperCAmelCase       )
             return d



       def 	lowercase__							(				self     :    Optional[Any]						,							_UpperCAmelCase     :    Tuple						,							_UpperCAmelCase     :    Dict=1						,							_UpperCAmelCase     :    List[str]=False       )					->		List[str]:


             '''simple docstring'''




             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(_UpperCAmelCase       )
                   self.count.append(_UpperCAmelCase       )
                   return idx



       def 	lowercase__							(				self     :    Optional[int]						,							_UpperCAmelCase     :    Union[str, Any]       )					->		Optional[int]:


             '''simple docstring'''




             return 0







       def 	lowercase__							(				self     :    Optional[Any]						,							_UpperCAmelCase     :    Dict       )					->		str:


             '''simple docstring'''




             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

             UpperCAmelCase_     					=				f.readlines()
             UpperCAmelCase_     					=				self._load_meta(_UpperCAmelCase       )

             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(_UpperCAmelCase       )
                         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(_UpperCAmelCase       )       )
                         self.add_symbol(_UpperCAmelCase						,							n=_UpperCAmelCase						,							overwrite=_UpperCAmelCase       )
                   except ValueError:
                         raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"       )



def   a__      ( lowerCAmelCase__ ):
      # (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}
      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   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      # prep
      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__":
  lowerCamelCase  			=		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  			=		parser.parse_args()
  convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)











'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


a   	=    logging.get_logger(__name__)

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							b.T
   __SCREAMING_SNAKE_CASE   				=							np.sum(np.square(__UpperCAmelCase   )				,						axis=1   )
   __SCREAMING_SNAKE_CASE   				=							np.sum(np.square(__UpperCAmelCase   )				,						axis=0   )
   __SCREAMING_SNAKE_CASE   				=							np.matmul(__UpperCAmelCase				,						__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							aa[:, None] - 2 * ab + ba[None, :]
   return d

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							x.reshape(-1				,						3   )
   __SCREAMING_SNAKE_CASE   				=							squared_euclidean_distance(__UpperCAmelCase				,						__UpperCAmelCase   )
   return np.argmin(__UpperCAmelCase				,						axis=1   )







class __a			( _snake_case      ):
   __UpperCamelCase :			Any						   =						['pixel_values']





   def __init__(       self					:      Any					,lowerCamelCase					:      Optional[Union[List[List[int]], np.ndarray]] = None					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      Dict[str, int] = None					,lowerCamelCase					:      PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      bool = True					,**lowerCamelCase					:      Optional[Any]					,):

      '''simple docstring'''

      super().__init__(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							size if size is not None else {"""height""": 256, """width""": 256}
      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							np.array(lowerCamelCase     ) if clusters is not None else None
      __SCREAMING_SNAKE_CASE   				=							do_resize
      __SCREAMING_SNAKE_CASE   				=							size
      __SCREAMING_SNAKE_CASE   				=							resample
      __SCREAMING_SNAKE_CASE   				=							do_normalize
      __SCREAMING_SNAKE_CASE   				=							do_color_quantize





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      np.ndarray					,lowerCamelCase					:      Dict[str, int]					,lowerCamelCase					:      PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase					:      int					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase     )
      if "height" not in size or "width" not in size:
         raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""     )
      return resize(
          lowerCamelCase					,size=(size["""height"""], size["""width"""])					,resample=lowerCamelCase					,data_format=lowerCamelCase					,**lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      np.ndarray					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = None					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							rescale(image=lowerCamelCase					,scale=1 / 127.5					,data_format=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							image - 1
      return image





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      ImageInput					,lowerCamelCase					:      bool = None					,lowerCamelCase					:      Dict[str, int] = None					,lowerCamelCase					:      PILImageResampling = None					,lowerCamelCase					:      bool = None					,lowerCamelCase					:      Optional[bool] = None					,lowerCamelCase					:      Optional[Union[List[List[int]], np.ndarray]] = None					,lowerCamelCase					:      Optional[Union[str, TensorType]] = None					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					,**lowerCamelCase					:      Any					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							do_resize if do_resize is not None else self.do_resize
      __SCREAMING_SNAKE_CASE   				=							size if size is not None else self.size
      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							resample if resample is not None else self.resample
      __SCREAMING_SNAKE_CASE   				=							do_normalize if do_normalize is not None else self.do_normalize
      __SCREAMING_SNAKE_CASE   				=							do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      __SCREAMING_SNAKE_CASE   				=							clusters if clusters is not None else self.clusters
      __SCREAMING_SNAKE_CASE   				=							np.array(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							make_list_of_images(lowerCamelCase     )

      if not valid_images(lowerCamelCase     ):
         raise ValueError(
             """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
             """torch.Tensor, tf.Tensor or jax.ndarray."""     )

      if do_resize and size is None or resample is None:
         raise ValueError("""Size and resample must be specified if do_resize is True."""     )

      if do_color_quantize and clusters is None:
         raise ValueError("""Clusters must be specified if do_color_quantize is True."""     )

      # All transformations expect numpy arrays.
      __SCREAMING_SNAKE_CASE   				=							[to_numpy_array(lowerCamelCase     ) for image in images]

      if do_resize:
         __SCREAMING_SNAKE_CASE   				=							[self.resize(image=lowerCamelCase					,size=lowerCamelCase					,resample=lowerCamelCase     ) for image in images]

      if do_normalize:
         __SCREAMING_SNAKE_CASE   				=							[self.normalize(image=lowerCamelCase     ) for image in images]

      if do_color_quantize:
         __SCREAMING_SNAKE_CASE   				=							[to_channel_dimension_format(lowerCamelCase					,ChannelDimension.LAST     ) for image in images]
         # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
         __SCREAMING_SNAKE_CASE   				=							np.array(lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							color_quantize(lowerCamelCase					,lowerCamelCase     ).reshape(images.shape[:-1]     )

         # flatten to (batch_size, height*width)
         __SCREAMING_SNAKE_CASE   				=							images.shape[0]
         __SCREAMING_SNAKE_CASE   				=							images.reshape(lowerCamelCase					,-1     )

         # We need to convert back to a list of images to keep consistent behaviour across processors.
         __SCREAMING_SNAKE_CASE   				=							list(lowerCamelCase     )
      else:
         __SCREAMING_SNAKE_CASE   				=							[to_channel_dimension_format(lowerCamelCase					,lowerCamelCase     ) for image in images]

      __SCREAMING_SNAKE_CASE   				=							{"""input_ids""": images}
      return BatchFeature(data=lowerCamelCase					,tensor_type=lowerCamelCase     )





