


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     =			2






class   __lowercase				:





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






         """simple docstring"""







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





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






         """simple docstring"""







         return self.indices == other.indices





      def __getitem__(		self						:					Dict					,       __lowerCamelCase						:					List[str]       )    ->   Union[str, Any]:






         """simple docstring"""







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





      def __len__(		self						:					str       )    ->   str:






         """simple docstring"""







         return len(self.symbols       )





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






         """simple docstring"""







         return sym in self.indices





      @classmethod
      def        _lowercase				(		cls						:					List[str]					,       __lowerCamelCase						:					Optional[int]       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     cls()
         d.add_from_file(__lowerCamelCase       )
         return d





      def        _lowercase				(		self						:					Optional[Any]					,       __lowerCamelCase						:					List[Any]					,       __lowerCamelCase						:					Tuple=1					,       __lowerCamelCase						:					Union[str, Any]=False       )    ->   Optional[int]:






         """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(__lowerCamelCase       )
            self.count.append(__lowerCamelCase       )
            return idx





      def        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					Any       )    ->   List[Any]:






         """simple docstring"""







         return 0





      def        _lowercase				(		self						:					Tuple					,       __lowerCamelCase						:					List[str]       )    ->   List[str]:






         """simple docstring"""







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

         UpperCAmelCase							=     f.readlines()
         UpperCAmelCase							=     self._load_meta(__lowerCamelCase       )

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

def   _UpperCamelCase				(						lowerCAmelCase_   )						->Tuple:
   # (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   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->str:
   # 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""": 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(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     =			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     =			parser.parse_args()
 convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)







import numpy






class   __lowercase				:





      def __init__(		self						:					Union[str, Any]					,       __lowerCamelCase						:					numpy.ndarray					,       __lowerCamelCase						:					numpy.ndarray       )    ->   None:






         """simple docstring"""







         UpperCAmelCase							=     input_array

         # Random initial weights are assigned where first argument is the
         # number of nodes in previous layer and second argument is the
         # number of nodes in the next layer.

         # Random initial weights are assigned.
         # self.input_array.shape[1] is used to represent number of nodes in input layer.
         # First hidden layer consists of 4 nodes.
         UpperCAmelCase							=     numpy.random.rand(
             self.input_array.shape[1]					,       4       )

         # Random initial values for the first hidden layer.
         # First hidden layer has 4 nodes.
         # Second hidden layer has 3 nodes.
         UpperCAmelCase							=     numpy.random.rand(
             4					,       3       )

         # Random initial values for the second hidden layer.
         # Second hidden layer has 3 nodes.
         # Output layer has 1 node.
         UpperCAmelCase							=     numpy.random.rand(3					,       1       )

         # Real output values provided.
         UpperCAmelCase							=     output_array

         # Predicted output values by the neural network.
         # Predicted_output array initially consists of zeroes.
         UpperCAmelCase							=     numpy.zeros(output_array.shape       )





      def        _lowercase				(		self						:					List[str]       )    ->   numpy.ndarray:






         """simple docstring"""







         UpperCAmelCase							=     sigmoid(
             numpy.dot(self.input_array					,       self.input_layer_and_first_hidden_layer_weights       )       )

         # layer_between_first_hidden_layer_and_second_hidden_layer is the layer
         # connecting the first hidden set of nodes with the second hidden set of nodes.
         UpperCAmelCase							=     sigmoid(
             numpy.dot(
                 self.layer_between_input_and_first_hidden_layer					,       self.first_hidden_layer_and_second_hidden_layer_weights					,       )       )

         # layer_between_second_hidden_layer_and_output is the layer connecting
         # second hidden layer with the output node.
         UpperCAmelCase							=     sigmoid(
             numpy.dot(
                 self.layer_between_first_hidden_layer_and_second_hidden_layer					,       self.second_hidden_layer_and_output_layer_weights					,       )       )

         return self.layer_between_second_hidden_layer_and_output





      def        _lowercase				(		self						:					Optional[Any]       )    ->   None:






         """simple docstring"""







         UpperCAmelCase							=     numpy.dot(
             self.layer_between_first_hidden_layer_and_second_hidden_layer.T					,       2
             * (self.output_array - self.predicted_output)
             * sigmoid_derivative(self.predicted_output       )					,       )
         UpperCAmelCase							=     numpy.dot(
             self.layer_between_input_and_first_hidden_layer.T					,       numpy.dot(
                 2
                 * (self.output_array - self.predicted_output)
                 * sigmoid_derivative(self.predicted_output       )					,       self.second_hidden_layer_and_output_layer_weights.T					,       )
             * sigmoid_derivative(
                 self.layer_between_first_hidden_layer_and_second_hidden_layer       )					,       )
         UpperCAmelCase							=     numpy.dot(
             self.input_array.T					,       numpy.dot(
                 numpy.dot(
                     2
                     * (self.output_array - self.predicted_output)
                     * sigmoid_derivative(self.predicted_output       )					,       self.second_hidden_layer_and_output_layer_weights.T					,       )
                 * sigmoid_derivative(
                     self.layer_between_first_hidden_layer_and_second_hidden_layer       )					,       self.first_hidden_layer_and_second_hidden_layer_weights.T					,       )
             * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer       )					,       )

         self.input_layer_and_first_hidden_layer_weights += (
             updated_input_layer_and_first_hidden_layer_weights
         )
         self.first_hidden_layer_and_second_hidden_layer_weights += (
             updated_first_hidden_layer_and_second_hidden_layer_weights
         )
         self.second_hidden_layer_and_output_layer_weights += (
             updated_second_hidden_layer_and_output_layer_weights
         )





      def        _lowercase				(		self						:					Any					,       __lowerCamelCase						:					numpy.ndarray					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					bool       )    ->   None:






         """simple docstring"""







         for iteration in range(1					,       iterations + 1       ):
            UpperCAmelCase							=     self.feedforward()
            self.back_propagation()
            if give_loss:
               UpperCAmelCase							=     numpy.mean(numpy.square(output - self.feedforward()       )       )
               print(F"""Iteration {iteration} Loss: {loss}"""       )





      def        _lowercase				(		self						:					List[str]					,       __lowerCamelCase						:					numpy.ndarray       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     input_arr

         UpperCAmelCase							=     sigmoid(
             numpy.dot(self.array					,       self.input_layer_and_first_hidden_layer_weights       )       )

         UpperCAmelCase							=     sigmoid(
             numpy.dot(
                 self.layer_between_input_and_first_hidden_layer					,       self.first_hidden_layer_and_second_hidden_layer_weights					,       )       )

         UpperCAmelCase							=     sigmoid(
             numpy.dot(
                 self.layer_between_first_hidden_layer_and_second_hidden_layer					,       self.second_hidden_layer_and_output_layer_weights					,       )       )

         return int(self.layer_between_second_hidden_layer_and_output > 0.6       )

def   _UpperCamelCase				(						lowerCAmelCase_   )						->numpy.ndarray:
   return 1 / (1 + numpy.exp(-value   ))
def   _UpperCamelCase				(						lowerCAmelCase_   )						->numpy.ndarray:
   return (value) * (1 - (value))
def   _UpperCamelCase				(						)						->int:
   UpperCAmelCase							=     numpy.array(
       (
           [0, 0, 0],
           [0, 0, 1],
           [0, 1, 0],
           [0, 1, 1],
           [1, 0, 0],
           [1, 0, 1],
           [1, 1, 0],
           [1, 1, 1],
       )  ,						dtype=numpy.floataa  ,						)

   # True output values for the given input values.
   UpperCAmelCase							=     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])  ,						dtype=numpy.floataa   )

   # Calling neural network class.
   UpperCAmelCase							=     TwoHiddenLayerNeuralNetwork(
       input_array=lowerCAmelCase_  ,						output_array=lowerCAmelCase_   )

   # Calling training function.
   # Set give_loss to True if you want to see loss in every iteration.
   neural_network.train(output=lowerCAmelCase_  ,						iterations=1_0  ,						give_loss=lowerCAmelCase_   )

   return neural_network.predict(numpy.array(([1, 1, 1])  ,						dtype=numpy.floataa   )   )


if __name__ == "__main__":
 example()



