

"""simple docstring"""





from scipy.stats import spearmanr

import datasets


_a       =							'\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'

_a       =							'\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {\'spearmanr\': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results[\'spearmanr\'])\n        -0.7\n        >>> print(round(results[\'spearmanr_pvalue\'], 2))\n        0.19\n'

_a       =							r'\\n@book{kokoska2000crc,\n  title={CRC standard probability and statistics tables and formulae},\n  author={Kokoska, Stephen and Zwillinger, Daniel},\n  year={2000},\n  publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n            Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n            Kern, Robert and Larson, Eric and Carey, C J and\n            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n            Harris, Charles R. and Archibald, Anne M. and\n            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n            Computing in Python}},\n  journal = {Nature Methods},\n  year    = {2020},\n  volume  = {17},\n  pages   = {261--272},\n  adsurl  = {https://rdcu.be/b08Wh},\n  doi     = {10.1038/s41592-019-0686-2},\n}\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,_KWARGS_DESCRIPTION   )
class 					_lowerCAmelCase  (	datasets.Metric   ):







 """simple docstring"""







 def 					_lowercase (			self							:			Dict	):
    return datasets.MetricInfo(
        description=_DESCRIPTION,					citation=_CITATION,					inputs_description=_KWARGS_DESCRIPTION,					features=datasets.Features(
            {
                "predictions": datasets.Value("float"	),
                "references": datasets.Value("float"	),
            }	),					reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"],					)



 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Optional[int]=False	):
    __lowercase								=						spearmanr(UpperCAmelCase__,					UpperCAmelCase__	)
    if return_pvalue:
       return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
    else:
       return {"spearmanr": results[0]}







"""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()

_a       =							2




class 					_lowerCAmelCase  :







 """simple docstring"""







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


 def __eq__(			self							:			List[str],					UpperCAmelCase__							:			Dict	):
    return self.indices == other.indices


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


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


 def __contains__(			self							:			Any,					UpperCAmelCase__							:			Optional[Any]	):
    return sym in self.indices


 @classmethod
 def 					_lowercase (			cls							:			List[Any],					UpperCAmelCase__							:			Optional[Any]	):
    __lowercase								=						cls()
    d.add_from_file(UpperCAmelCase__	)
    return d


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any]=1,					UpperCAmelCase__							:			str=False	):
    if word in self.indices and not overwrite:
       __lowercase								=						self.indices[word]
       __lowercase								=						self.count[idx] + n
       return idx
    else:
       __lowercase								=						len(self.symbols	)
       __lowercase								=						idx
       self.symbols.append(UpperCAmelCase__	)
       self.count.append(UpperCAmelCase__	)
       return idx


 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			str	):
    return 0



 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			List[Any]	):
    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

    __lowercase								=						f.readlines()
    __lowercase								=						self._load_meta(UpperCAmelCase__	)

    for line in lines[indices_start_line:]:
       try:
          __lowercase		,__lowercase								=						line.rstrip().rsplit(" ",					1	)
          if field == "#fairseq:overwrite":
             __lowercase								=						True
             __lowercase		,__lowercase								=						line.rsplit(" ",					1	)
          else:
             __lowercase								=						False
          __lowercase								=						int(UpperCAmelCase__	)
          __lowercase								=						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   _A  (  UpperCamelCase_     :       int)	->						str:
   '''simple docstring'''


   __lowercase								=						dict((re.sub(r"@@$",			"",			UpperCamelCase_), v) if k.endswith("@@") else (re.sub(r"$",			"</w>",			UpperCamelCase_), v) for k, v in d.items())
   __lowercase								=						"<s> <pad> </s> <unk>".split()
   # restore the special tokens
   for k in keep_keys:
      del da[F"""{k}</w>"""]
      __lowercase								=						d[k]  # restore
   return da




def   _A  (  UpperCamelCase_     :       str,			UpperCamelCase_     :       str)	->						List[Any]:
   '''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

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

   __lowercase								=						chkpt["cfg"]["model"]

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

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

   # model config
   __lowercase								=						os.path.join(UpperCamelCase_,			"config.json")

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

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

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

   # model
   __lowercase								=						chkpt["model"]

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

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

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

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

   # save
   __lowercase								=						os.path.join(UpperCamelCase_,			UpperCamelCase_)
   print(F"""Generating {pytorch_weights_dump_path}""")
   torch.save(UpperCamelCase_,			UpperCamelCase_)

   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)




