import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


_lowercase							:      Any      							='''\
@inproceedings{snover-etal-2006-study,
    title = "A Study of Translation Edit Rate with Targeted Human Annotation",
    author = "Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John",
    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",
    month = aug # " 8-12",
    year = "2006",
    address = "Cambridge, Massachusetts, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://aclanthology.org/2006.amta-papers.25",
    pages = "223--231",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

_lowercase							:      Any      							='''\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
'''

_lowercase							:      int      							='''
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)
    \'num_edits\' (int): The cumulative number of edits
    \'ref_length\' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?",
        ...                     "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}

    Example 2:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}

    Example 3:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}

    Example 4:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}

    Example 5:
        >>> predictions = ["does this sentence match??",
        ...                    "what about this sentence?",
        ...                    "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}
'''



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


  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				str:
         if version.parse(scb.__version__     ) < version.parse('1.4.12'     ):
                raise ImportWarning(
                    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
                    'You can install it with `pip install "sacrebleu>=1.4.12"`.'     )
         return datasets.MetricInfo(
             description=_DESCRIPTION      ,	citation=_CITATION      ,	homepage='http://www.cs.umd.edu/~snover/tercom/'      ,	inputs_description=_KWARGS_DESCRIPTION      ,	features=datasets.Features(
                 {
                     'predictions': datasets.Value('string'      ,	id='sequence'     ),
                     'references': datasets.Sequence(datasets.Value('string'      ,	id='sequence'     )      ,	id='references'     ),
                 }     )      ,	codebase_urls=['https://github.com/mjpost/sacreBLEU#ter']      ,	reference_urls=[
                 'https://github.com/jhclark/tercom',
             ]      ,	)







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					bool = False      ,	SCREAMING_SNAKE_CASE__    :					bool = False      ,	SCREAMING_SNAKE_CASE__    :					bool = False      ,	SCREAMING_SNAKE_CASE__    :					bool = False      ,	)    ->				Any:
         A						:     Any         =len(references[0]     )
         if any(len(SCREAMING_SNAKE_CASE__     ) != references_per_prediction for refs in references     ):
                raise ValueError('Sacrebleu requires the same number of references for each prediction'     )
         A						:     Union[str, Any]         =[[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE__     )]

         A						:     List[Any]         =TER(
             normalized=SCREAMING_SNAKE_CASE__      ,	no_punct=SCREAMING_SNAKE_CASE__      ,	asian_support=SCREAMING_SNAKE_CASE__      ,	case_sensitive=SCREAMING_SNAKE_CASE__      ,	)
         A						:     str         =sb_ter.corpus_score(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}







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)






