






"""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()

lowercase_				     =     2


class 					snake_case		:


   '''simple docstring'''

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

     '''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,       _lowerCamelCase :     Dict  ):

     '''simple docstring'''
     return self.indices == other.indices


   def __getitem__(	self :     Tuple,       _lowerCamelCase :     List[Any]  ):

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


   def __len__(	self :     Optional[int]  ):

     '''simple docstring'''
     return len(self.symbols  )


   def __contains__(	self :     Any,       _lowerCamelCase :     Any  ):

     '''simple docstring'''
     return sym in self.indices


   @classmethod
   def 				_SCREAMING_SNAKE_CASE				(	cls :     int,       _lowerCamelCase :     Any  ):

     '''simple docstring'''
     __A									= cls()
     d.add_from_file(__UpperCAmelCase  )
     return d


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     Optional[int],       _lowerCamelCase :     Optional[Any]=1,       _lowerCamelCase :     str=False  ):

     '''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],       _lowerCamelCase :     str  ):

     '''simple docstring'''
     return 0






   def 				_SCREAMING_SNAKE_CASE				(	self :     Any,       _lowerCamelCase :     List[Any]  ):

     '''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 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= dict((re.sub(r'''@@$'''		,       ''''''		,       UpperCamelCase__ ), v) if k.endswith('''@@''' ) else (re.sub(r'''$'''		,       '''</w>'''		,       UpperCamelCase__ ), 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 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  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

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

  __A									= chkpt['''cfg''']['''model''']

  # dicts
  __A									= os.path.join(UpperCamelCase__		,       '''dict.txt''' )
  if not os.path.isfile(UpperCamelCase__ ):
    raise ValueError(f'path to the file {dict_file} does not exist!' )
  __A									= Dictionary.load(UpperCamelCase__ )
  __A									= rewrite_dict_keys(src_dict.indices )
  __A									= len(UpperCamelCase__ )
  __A									= 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)
  __A									= os.path.join(UpperCamelCase__		,       '''bpecodes''' )
  if not os.path.isfile(UpperCamelCase__ ):
    raise ValueError(f'path to the file {bpecodes_file} does not exist!' )

  __A									= os.path.join(UpperCamelCase__		,       VOCAB_FILES_NAMES['''merges_file'''] )
  shutil.copyfile(UpperCamelCase__		,       UpperCamelCase__ )

  # model config
  __A									= os.path.join(UpperCamelCase__		,       '''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(UpperCamelCase__		,       '''w'''		,       encoding='''utf-8''' ) as f:
    f.write(json.dumps(UpperCamelCase__		,       ensure_ascii=UpperCamelCase__		,       indent=UpperCamelCase__ ) )

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

  __A									= {
      '''bos_token''': '''<s>''',
      '''eos_token''': '''</s>''',
      '''model_max_length''': 1_0_2_4,
      '''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
  __A									= chkpt['''model''']

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

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

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

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

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

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


if __name__ == "__main__":
       lowercase_				     =     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.'
       )
       lowercase_				     =     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 transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
				import PIL

__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							if isinstance(UpperCamelCase__				,			(list, tuple) ) and isinstance(videos[0]				,			(list, tuple) ) and is_valid_image(videos[0][0] ):
														return videos

							elif isinstance(UpperCamelCase__				,			(list, tuple) ) and is_valid_image(videos[0] ):
														return [videos]

							elif is_valid_image(UpperCamelCase__ ):
														return [[videos]]

							raise ValueError(F'''Could not make batched video from {videos}''' )


class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  List[Any]  							=						['pixel_values']





						def __init__(				self						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = True						,__UpperCAmelCase = 1 / 2_55						,__UpperCAmelCase = True						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> None:
													super().__init__(**__UpperCAmelCase   )
													A__		   =							size if size is not None else {'shortest_edge': 2_56}
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
													A__		   =							get_size_dict(__UpperCAmelCase						,param_name='crop_size'   )

													A__		   =							do_resize
													A__		   =							size
													A__		   =							do_center_crop
													A__		   =							crop_size
													A__		   =							resample
													A__		   =							do_rescale
													A__		   =							rescale_factor
													A__		   =							offset
													A__		   =							do_normalize
													A__		   =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													A__		   =							image_std if image_std is not None else IMAGENET_STANDARD_STD





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													if "shortest_edge" in size:
																				A__		   =							get_resize_output_image_size(__UpperCAmelCase						,size['shortest_edge']						,default_to_square=__UpperCAmelCase   )
													elif "height" in size and "width" in size:
																				A__		   =							(size['height'], size['width'])
													else:
																				raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''   )
													return resize(__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase   )
													if "height" not in size or "width" not in size:
																				raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''   )
													return center_crop(__UpperCAmelCase						,size=(size['height'], size['width'])						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> Optional[Any]:
													A__		   =							image.astype(np.floataa   )
													if offset:
																				A__		   =							image - (scale / 2)
													return rescale(__UpperCAmelCase						,scale=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													return normalize(__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,)    -> np.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_center_crop and crop_size is None:
																				raise ValueError('Crop size must be specified if do_center_crop is True.'   )

													if do_rescale and rescale_factor is None:
																				raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

													if do_normalize and (image_mean is None or image_std is None):
																				raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

													if offset and not do_rescale:
																				raise ValueError('For offset, do_rescale must also be set to True.'   )

													# All transformations expect numpy arrays.
													A__		   =							to_numpy_array(__UpperCAmelCase   )

													if do_resize:
																				A__		   =							self.resize(image=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase   )

													if do_center_crop:
																				A__		   =							self.center_crop(__UpperCAmelCase						,size=__UpperCAmelCase   )

													if do_rescale:
																				A__		   =							self.rescale(image=__UpperCAmelCase						,scale=__UpperCAmelCase						,offset=__UpperCAmelCase   )

													if do_normalize:
																				A__		   =							self.normalize(image=__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase   )

													A__		   =							to_channel_dimension_format(__UpperCAmelCase						,__UpperCAmelCase   )
													return image





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,**__UpperCAmelCase						,)    -> PIL.Image.Image:
													A__		   =							do_resize if do_resize is not None else self.do_resize
													A__		   =							resample if resample is not None else self.resample
													A__		   =							do_center_crop if do_center_crop is not None else self.do_center_crop
													A__		   =							do_rescale if do_rescale is not None else self.do_rescale
													A__		   =							rescale_factor if rescale_factor is not None else self.rescale_factor
													A__		   =							offset if offset is not None else self.offset
													A__		   =							do_normalize if do_normalize is not None else self.do_normalize
													A__		   =							image_mean if image_mean is not None else self.image_mean
													A__		   =							image_std if image_std is not None else self.image_std

													A__		   =							size if size is not None else self.size
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else self.crop_size
													A__		   =							get_size_dict(__UpperCAmelCase						,param_name='crop_size'   )

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

													A__		   =							make_batched(__UpperCAmelCase   )

													A__		   =							[
													    [
													        self._preprocess_image(
													            image=__UpperCAmelCase						,do_resize=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase						,do_center_crop=__UpperCAmelCase						,crop_size=__UpperCAmelCase						,do_rescale=__UpperCAmelCase						,rescale_factor=__UpperCAmelCase						,offset=__UpperCAmelCase						,do_normalize=__UpperCAmelCase						,image_mean=__UpperCAmelCase						,image_std=__UpperCAmelCase						,data_format=__UpperCAmelCase						,)
													        for img in video
													    ]
													    for video in videos
													]

													A__		   =							{'pixel_values': videos}
													return BatchFeature(data=__UpperCAmelCase						,tensor_type=__UpperCAmelCase   )





