





'''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()

__SCREAMING_SNAKE_CASE     :int       =				2







class   A_	:




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




 def __eq__(					self       :    Optional[Any]	,			snake_case_       :    str    ):
      return self.indices == other.indices




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




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




 def __contains__(					self       :    Union[str, Any]	,			snake_case_       :    Tuple    ):
      return sym in self.indices




 @classmethod
 def      lowercase	(					cls       :    Tuple	,			snake_case_       :    Tuple    ):
      _UpperCAmelCase     							=   cls()
      d.add_from_file(snake_case_    )
      return d




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    int	,			snake_case_       :    Dict=1	,			snake_case_       :    List[str]=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(snake_case_    )
           self.count.append(snake_case_    )
           return idx




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    Any    ):
      return 0




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

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

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





def 	UpperCAmelCase_							(					__lowercase							:      Optional[Any]       )		->       str:


     '''simple docstring'''



     _UpperCAmelCase     							=   dict((re.sub(r"@@$"			,     ""			,     __lowercase       ), v) if k.endswith("@@"       ) else (re.sub(r"$"			,     "</w>"			,     __lowercase       ), 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_							(					__lowercase							:      List[str]			,     __lowercase							:      List[str]       )		->       Optional[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

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

     _UpperCAmelCase     							=   chkpt["cfg"]["model"]

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

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

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

     # tokenizer config
     _UpperCAmelCase     							=   os.path.join(__lowercase			,     __lowercase       )

     _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(__lowercase			,     "w"			,     encoding="utf-8"       ) as f:
          f.write(json.dumps(__lowercase			,     ensure_ascii=__lowercase			,     indent=__lowercase       )       )

     # model
     _UpperCAmelCase     							=   chkpt["model"]

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

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

     _UpperCAmelCase     							=   BioGptConfig.from_pretrained(__lowercase       )
     _UpperCAmelCase     							=   BioGptForCausalLM(__lowercase       )

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

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

     print("Conversion is done!"       )


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











'''simple docstring'''




from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


__SCREAMING_SNAKE_CASE     :Dict       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :int       =				{
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""perceiver"""




 def __init__(					self       :    Any	,			snake_case_       :    List[Any]=2_5_6	,			snake_case_       :    str=1_2_8_0	,			snake_case_       :    Optional[int]=7_6_8	,			snake_case_       :    int=1	,			snake_case_       :    List[Any]=2_6	,			snake_case_       :    Dict=8	,			snake_case_       :    List[Any]=8	,			snake_case_       :    Tuple=None	,			snake_case_       :    Tuple=None	,			snake_case_       :    Any="kv"	,			snake_case_       :    Any=1	,			snake_case_       :    List[str]=1	,			snake_case_       :    Optional[int]="gelu"	,			snake_case_       :    List[Any]=0.1	,			snake_case_       :    Dict=0.0_2	,			snake_case_       :    int=1e-12	,			snake_case_       :    List[str]=True	,			snake_case_       :    str=2_6_2	,			snake_case_       :    Optional[Any]=2_0_4_8	,			snake_case_       :    Union[str, Any]=5_6	,			snake_case_       :    Dict=[3_6_8, 4_9_6]	,			snake_case_       :    Tuple=1_6	,			snake_case_       :    Union[str, Any]=1_9_2_0	,			snake_case_       :    List[Any]=1_6	,			snake_case_       :    Tuple=[1, 1_6, 2_2_4, 2_2_4]	,			**snake_case_       :    List[Any]	,			):
      super().__init__(**snake_case_    )

      _UpperCAmelCase     							=   num_latents
      _UpperCAmelCase     							=   d_latents
      _UpperCAmelCase     							=   d_model
      _UpperCAmelCase     							=   num_blocks
      _UpperCAmelCase     							=   num_self_attends_per_block
      _UpperCAmelCase     							=   num_self_attention_heads
      _UpperCAmelCase     							=   num_cross_attention_heads
      _UpperCAmelCase     							=   qk_channels
      _UpperCAmelCase     							=   v_channels
      _UpperCAmelCase     							=   cross_attention_shape_for_attention
      _UpperCAmelCase     							=   self_attention_widening_factor
      _UpperCAmelCase     							=   cross_attention_widening_factor
      _UpperCAmelCase     							=   hidden_act
      _UpperCAmelCase     							=   attention_probs_dropout_prob
      _UpperCAmelCase     							=   initializer_range
      _UpperCAmelCase     							=   layer_norm_eps
      _UpperCAmelCase     							=   use_query_residual
      # masked language modeling attributes
      _UpperCAmelCase     							=   vocab_size
      _UpperCAmelCase     							=   max_position_embeddings
      # image classification attributes
      _UpperCAmelCase     							=   image_size
      # flow attributes
      _UpperCAmelCase     							=   train_size
      # multimodal autoencoding attributes
      _UpperCAmelCase     							=   num_frames
      _UpperCAmelCase     							=   audio_samples_per_frame
      _UpperCAmelCase     							=   samples_per_patch
      _UpperCAmelCase     							=   output_shape







class   A_	(  lowerCAmelCase_			):




 @property
 def      lowercase	(					self       :    int    ):
      if self.task == "multiple-choice":
           _UpperCAmelCase     							=   {0: "batch", 1: "choice", 2: "sequence"}
      else:
           _UpperCAmelCase     							=   {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("inputs", dynamic_axis),
              ("attention_mask", dynamic_axis),
          ]    )




 @property
 def      lowercase	(					self       :    Optional[Any]    ):
      return 1e-4




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]	,			snake_case_       :    int = -1	,			snake_case_       :    int = -1	,			snake_case_       :    int = -1	,			snake_case_       :    bool = False	,			snake_case_       :    Optional[TensorType] = None	,			snake_case_       :    int = 3	,			snake_case_       :    int = 4_0	,			snake_case_       :    int = 4_0	,			):
      # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

      if isinstance(snake_case_	,			snake_case_    ):
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   compute_effective_axis_dimension(
               snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_batch	,			num_token_to_add=0    )
           # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   preprocessor.num_special_tokens_to_add(snake_case_    )
           _UpperCAmelCase     							=   compute_effective_axis_dimension(
               snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_sequence	,			num_token_to_add=snake_case_    )
           # Generate dummy inputs according to compute batch and sequence
           _UpperCAmelCase     							=   [" ".join(["a"]    ) * seq_length] * batch_size
           _UpperCAmelCase     							=   dict(preprocessor(snake_case_	,			return_tensors=snake_case_    )    )
           _UpperCAmelCase     							=   inputs.pop("input_ids"    )
           return inputs
      elif isinstance(snake_case_	,			snake_case_    ) and preprocessor.model_input_names[0] == "pixel_values":
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   compute_effective_axis_dimension(snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_batch    )
           _UpperCAmelCase     							=   self._generate_dummy_images(snake_case_	,			snake_case_	,			snake_case_	,			snake_case_    )
           _UpperCAmelCase     							=   dict(preprocessor(images=snake_case_	,			return_tensors=snake_case_    )    )
           _UpperCAmelCase     							=   inputs.pop("pixel_values"    )
           return inputs
      else:
           raise ValueError(
               "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."    )




