




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			:	Optional[int]							 =     2


class        UpperCamelCase_					:

    '''simple docstring'''






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





           '''simple docstring'''

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


    def __eq__(    self   :			Dict      ,				UpperCAmelCase__   :			Dict)		->Optional[Any]:





           '''simple docstring'''

           return self.indices == other.indices


    def __getitem__(    self   :			int      ,				UpperCAmelCase__   :			Union[str, Any])		->int:





           '''simple docstring'''

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


    def __len__(    self   :			Optional[Any])		->Any:





           '''simple docstring'''

           return len(self.symbols)


    def __contains__(    self   :			List[Any]      ,				UpperCAmelCase__   :			List[str])		->Union[str, Any]:





           '''simple docstring'''

           return sym in self.indices


    @classmethod
    def  SCREAMING_SNAKE_CASE							(    cls   :			Union[str, Any]      ,				UpperCAmelCase__   :			List[Any])		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					cls()
           d.add_from_file(UpperCAmelCase__)
           return d


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			Tuple=1      ,				UpperCAmelCase__   :			str=False)		->Any:





           '''simple docstring'''

           if word in self.indices and not overwrite:
                  A__   				=					self.indices[word]
                  A__   				=					self.count[idx] + n
                  return idx
           else:
                  A__   				=					len(self.symbols)
                  A__   				=					idx
                  self.symbols.append(UpperCAmelCase__)
                  self.count.append(UpperCAmelCase__)
                  return idx


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			str)		->Optional[int]:





           '''simple docstring'''

           return 0





    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			Union[str, Any])		->Dict:





           '''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

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

           for line in lines[indices_start_line:]:
                  try:
                         A__				,							A__   				=					line.rstrip().rsplit(''' '''      ,				1)
                         if field == "#fairseq:overwrite":
                                A__   				=					True
                                A__				,							A__   				=					line.rsplit(''' '''      ,				1)
                         else:
                                A__   				=					False
                         A__   				=					int(UpperCAmelCase__)
                         A__   				=					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_		)     -> Tuple:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

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

       # handle various types of models

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

       A__   				=					chkpt['''cfg''']['''model''']

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

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

       A__   				=					os.path.join(lowercase_				,       VOCAB_FILES_NAMES['''merges_file''']		)
       shutil.copyfile(lowercase_				,       lowercase_		)

       # model config
       A__   				=					os.path.join(lowercase_				,       '''config.json'''		)

       A__   				=					{
           '''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-1_2,
           '''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(lowercase_				,       '''w'''				,       encoding='''utf-8'''		) as f:
              f.write(json.dumps(lowercase_				,       ensure_ascii=lowercase_				,       indent=lowercase_		)		)

       # tokenizer config
       A__   				=					os.path.join(lowercase_				,       lowercase_		)

       A__   				=					{
           '''bos_token''': '''<s>''',
           '''eos_token''': '''</s>''',
           '''model_max_length''': 1_024,
           '''pad_token''': '''<pad>''',
           '''special_tokens_map_file''': None,
           '''tokenizer_class''': '''BioGptTokenizer''',
           '''unk_token''': '''<unk>''',
       }

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

       # model
       A__   				=					chkpt['''model''']

       # remove unneeded keys
       A__   				=					[
           '''decoder.version''',
       ]
       for k in ignore_keys:
              model_state_dict.pop(lowercase_				,       lowercase_		)

       A__   				=					list(model_state_dict.keys()		)
       for layer_name in layer_names:
              if layer_name.endswith('''output_projection.weight'''		):
                     A__   				=					model_state_dict.pop(lowercase_		)
              else:
                     A__   				=					model_state_dict.pop(lowercase_		)

       A__   				=					BioGptConfig.from_pretrained(lowercase_		)
       A__   				=					BioGptForCausalLM(lowercase_		)

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

       # save
       A__   				=					os.path.join(lowercase_				,       lowercase_		)
       print(f"""Generating {pytorch_weights_dump_path}"""		)
       torch.save(lowercase_				,       lowercase_		)

       print('''Conversion is done!'''		)


if __name__ == "__main__":
  _lowerCamelCase			:	Dict							 =     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			:	Optional[Any]							 =     parser.parse_args()
  convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)








from typing import List, Optional, Union

import numpy as np
import PIL.Image

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


_lowerCamelCase			:	str							 =     logging.get_logger(__name__)


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   ['''pixel_values''']


    def __init__(    self   :			List[Any]      ,				UpperCAmelCase__   :			bool = True      ,				UpperCAmelCase__   :			int = 32      ,				UpperCAmelCase__   :			Dict=PILImageResampling.BILINEAR      ,				UpperCAmelCase__   :			bool = True      ,				**UpperCAmelCase__   :			Optional[int]      ,				)		->None:





           '''simple docstring'''

           A__   				=					do_resize
           A__   				=					do_rescale
           A__   				=					size_divisor
           A__   				=					resample
           super().__init__(**UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Optional[ChannelDimension] = None      ,				**UpperCAmelCase__   :			Dict)		->np.ndarray:





           '''simple docstring'''

           A__				,							A__   				=					get_image_size(UpperCAmelCase__)
           # Rounds the height and width down to the closest multiple of size_divisor
           A__   				=					height // size_divisor * size_divisor
           A__   				=					width // size_divisor * size_divisor
           A__   				=					resize(UpperCAmelCase__      ,				(new_h, new_w)      ,				resample=UpperCAmelCase__      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)
           return image


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			float      ,				UpperCAmelCase__   :			Optional[ChannelDimension] = None      ,				**UpperCAmelCase__   :			List[str])		->np.ndarray:





           '''simple docstring'''

           return rescale(image=UpperCAmelCase__      ,				scale=UpperCAmelCase__      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)





    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]      ,				UpperCAmelCase__   :			Optional[bool] = None      ,				UpperCAmelCase__   :			Optional[int] = None      ,				UpperCAmelCase__   :			Dict=None      ,				UpperCAmelCase__   :			Optional[bool] = None      ,				UpperCAmelCase__   :			Optional[Union[TensorType, str]] = None      ,				UpperCAmelCase__   :			ChannelDimension = ChannelDimension.FIRST      ,				**UpperCAmelCase__   :			List[str]      ,				)		->BatchFeature:





           '''simple docstring'''

           A__   				=					do_resize if do_resize is not None else self.do_resize
           A__   				=					do_rescale if do_rescale is not None else self.do_rescale
           A__   				=					size_divisor if size_divisor is not None else self.size_divisor
           A__   				=					resample if resample is not None else self.resample

           if do_resize and size_divisor is None:
                  raise ValueError('''size_divisor is required for resizing''')

           A__   				=					make_list_of_images(UpperCAmelCase__)

           if not valid_images(UpperCAmelCase__):
                  raise ValueError('''Invalid image(s)''')

           # All transformations expect numpy arrays.
           A__   				=					[to_numpy_array(UpperCAmelCase__) for img in images]

           if do_resize:
                  A__   				=					[self.resize(UpperCAmelCase__      ,				size_divisor=UpperCAmelCase__      ,				resample=UpperCAmelCase__) for image in images]

           if do_rescale:
                  A__   				=					[self.rescale(UpperCAmelCase__      ,				scale=1 / 255) for image in images]

           A__   				=					[to_channel_dimension_format(UpperCAmelCase__      ,				UpperCAmelCase__) for image in images]

           A__   				=					{'''pixel_values''': images}
           return BatchFeature(data=UpperCAmelCase__      ,				tensor_type=UpperCAmelCase__)


