from sklearn.metrics import fa_score

import datasets


_lowercase							:      int      							='''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


_lowercase							:      List[str]      							='''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


_lowercase							:      str      							='''
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,						_KWARGS_DESCRIPTION			)
class  SCREAMING_SNAKE_CASE_ (					datasets.Metric			):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				List[str]:
         return datasets.MetricInfo(
             description=_DESCRIPTION      ,	citation=_CITATION      ,	inputs_description=_KWARGS_DESCRIPTION      ,	features=datasets.Features(
                 {
                     'predictions': datasets.Sequence(datasets.Value('int32'     )     ),
                     'references': datasets.Sequence(datasets.Value('int32'     )     ),
                 }
                 if self.config_name == 'multilabel'
                 else {
                     'predictions': datasets.Value('int32'     ),
                     'references': datasets.Value('int32'     ),
                 }     )      ,	reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html']      ,	)







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[str]=None      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1      ,	SCREAMING_SNAKE_CASE__    :					Dict="binary"      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=None     )    ->				Optional[Any]:
         A						:     int         =fa_score(
             SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	labels=SCREAMING_SNAKE_CASE__      ,	pos_label=SCREAMING_SNAKE_CASE__      ,	average=SCREAMING_SNAKE_CASE__      ,	sample_weight=SCREAMING_SNAKE_CASE__     )
         return {"f1": float(SCREAMING_SNAKE_CASE__     ) if score.size == 1 else score}







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							:      int      							=2



class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


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







  def __eq__(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				str:
         return self.indices == other.indices







  def __getitem__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				List[Any]:
         if idx < len(self.symbols     ):
                return self.symbols[idx]
         return self.unk_word







  def __len__(				self    :					List[Any]     )    ->				Union[str, Any]:
         return len(self.symbols     )







  def __contains__(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				Tuple:
         return sym in self.indices







  @classmethod
  def 		SCREAMING_SNAKE_CASE_		(				cls    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				Any:
         A						:     Union[str, Any]         =cls()
         d.add_from_file(SCREAMING_SNAKE_CASE__     )
         return d







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					Any=1      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=False     )    ->				Any:
         if word in self.indices and not overwrite:
                A						:     int         =self.indices[word]
                A						:     Union[str, Any]         =self.count[idx] + n
                return idx
         else:
                A						:     Tuple         =len(self.symbols     )
                A						:     str         =idx
                self.symbols.append(SCREAMING_SNAKE_CASE__     )
                self.count.append(SCREAMING_SNAKE_CASE__     )
                return idx







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				Optional[Any]:
         return 0







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

         A						:     str         =f.readlines()
         A						:     int         =self._load_meta(SCREAMING_SNAKE_CASE__     )

         for line in lines[indices_start_line:]:
                try:
                       A  ,  A						:     Optional[int]         =line.rstrip().rsplit(' '      ,	1     )
                       if field == "#fairseq:overwrite":
                              A						:     int         =True
                              A  ,  A						:     Optional[Any]         =line.rsplit(' '      ,	1     )
                       else:
                              A						:     Any         =False
                       A						:     Tuple         =int(SCREAMING_SNAKE_CASE__     )
                       A						:     Optional[int]         =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(SCREAMING_SNAKE_CASE__     )     )
                       self.add_symbol(SCREAMING_SNAKE_CASE__      ,	n=SCREAMING_SNAKE_CASE__      ,	overwrite=SCREAMING_SNAKE_CASE__     )
                except ValueError:
                       raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\''     )







def A__  (			lowercase:     Union[str, Any]					)				->		str:
       # (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}
       A						:     int         =dict((re.sub(r'@@$',		'',		lowercase					), v) if k.endswith('@@'					) else (re.sub(r'$',		'</w>',		lowercase					), v) for k, v in d.items()					)
       A						:     int         ='<s> <pad> </s> <unk>'.split()
       # restore the special tokens
       for k in keep_keys:
              del da[F'{k}</w>']
              A						:     List[Any]         =d[k]  # restore
       return da



def A__  (			lowercase:     Optional[int],		lowercase:     Optional[Any]					)				->		str:
       # prep
       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						:     List[str]         =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						:     Optional[Any]         =torch.load(lowercase,		map_location='cpu'					)

       A						:     Any         =chkpt['cfg']['model']

       # dicts
       A						:     Any         =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						:     Dict         =Dictionary.load(lowercase					)
       A						:     Optional[Any]         =rewrite_dict_keys(src_dict.indices					)
       A						:     Tuple         =len(lowercase					)
       A						:     Any         =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						:     List[str]         =os.path.join(lowercase,		'bpecodes'					)
       if not os.path.isfile(lowercase					):
              raise ValueError(F'path to the file {bpecodes_file} does not exist!'					)

       A						:     List[str]         =os.path.join(lowercase,		VOCAB_FILES_NAMES['merges_file']					)
       shutil.copyfile(lowercase,		lowercase					)

       # model config
       A						:     Tuple         =os.path.join(lowercase,		'config.json'					)

       A						:     Tuple         ={
           '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						:     int         =os.path.join(lowercase,		lowercase					)

       A						:     List[str]         ={
           '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						:     List[Any]         =chkpt['model']

       # remove unneeded keys
       A						:     List[Any]         =[
           'decoder.version',
       ]
       for k in ignore_keys:
              model_state_dict.pop(lowercase,		lowercase					)

       A						:     str         =list(model_state_dict.keys()					)
       for layer_name in layer_names:
              if layer_name.endswith('output_projection.weight'					):
                     A						:     Union[str, Any]         =model_state_dict.pop(lowercase					)
              else:
                     A						:     List[str]         =model_state_dict.pop(lowercase					)

       A						:     Any         =BioGptConfig.from_pretrained(lowercase					)
       A						:     str         =BioGptForCausalLM(lowercase					)

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

       # save
       A						:     Tuple         =os.path.join(lowercase,		lowercase					)
       print(F'Generating {pytorch_weights_dump_path}'					)
       torch.save(lowercase,		lowercase					)

       print('Conversion is done!'					)


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






