import faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


__lowerCamelCase			: Optional[Any]							   =		'''\
@inproceedings{pillutla-etal:mauve:neurips2021,
  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
  booktitle = {NeurIPS},
  year      = {2021}
}

'''

__lowerCamelCase			: Any							   =		'''\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.

MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.

For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).

This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
'''

__lowerCamelCase			: Optional[Any]							   =		'''
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
    predictions: list of generated text to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Optional Args:
    num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer
    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
    kmeans_max_iter: maximum number of k-means iterations. Default 500
    featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].
    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU
    max_text_length: maximum number of tokens to consider. Default 1024
    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
    mauve_scaling_factor: "c" from the paper. Default 5.
    verbose: If True (default), print running time updates
    seed: random seed to initialize k-means cluster assignments.
Returns:
    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
    q_hist: same as above, but with q_text.
Examples:

    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
    >>> import datasets
    >>> mauve = datasets.load_metric(\'mauve\')
    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
    >>> print(out.mauve) # doctest: +SKIP
    1.0
'''







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




   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     return datasets.MetricInfo(
         description=_DESCRIPTION       ,	citation=_CITATION       ,	homepage="""https://github.com/krishnap25/mauve"""       ,	inputs_description=_KWARGS_DESCRIPTION       ,	features=datasets.Features(
             {
                 """predictions""": datasets.Value("""string"""       ,	id="""sequence"""      ),
                 """references""": datasets.Value("""string"""       ,	id="""sequence"""      ),
             }      )       ,	codebase_urls=["""https://github.com/krishnap25/mauve"""]       ,	reference_urls=[
             """https://arxiv.org/abs/2102.01454""",
             """https://github.com/krishnap25/mauve""",
         ]       ,	)




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				str       ,	_lowercase			:				Optional[int]       ,	_lowercase			:				Union[str, Any]=None       ,	_lowercase			:				Optional[int]=None       ,	_lowercase			:				List[Any]=None       ,	_lowercase			:				Any=None       ,	_lowercase			:				str="auto"       ,	_lowercase			:				Optional[int]=-1       ,	_lowercase			:				Dict=0.9       ,	_lowercase			:				Optional[int]=5       ,	_lowercase			:				str=5_00       ,	_lowercase			:				Any="gpt2-large"       ,	_lowercase			:				int=-1       ,	_lowercase			:				List[str]=10_24       ,	_lowercase			:				int=25       ,	_lowercase			:				Optional[Any]=5       ,	_lowercase			:				List[Any]=True       ,	_lowercase			:				Optional[int]=25       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      compute_mauve(
         p_text=_lowercase       ,	q_text=_lowercase       ,	p_features=_lowercase       ,	q_features=_lowercase       ,	p_tokens=_lowercase       ,	q_tokens=_lowercase       ,	num_buckets=_lowercase       ,	pca_max_data=_lowercase       ,	kmeans_explained_var=_lowercase       ,	kmeans_num_redo=_lowercase       ,	kmeans_max_iter=_lowercase       ,	featurize_model_name=_lowercase       ,	device_id=_lowercase       ,	max_text_length=_lowercase       ,	divergence_curve_discretization_size=_lowercase       ,	mauve_scaling_factor=_lowercase       ,	verbose=_lowercase       ,	seed=_lowercase       ,	)
     return out








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       ,    *,  # begin keyword-only arguments
	    UpperCAmelCase="<s>"       ,    UpperCAmelCase="<pad>"       ,    UpperCAmelCase="</s>"       ,    UpperCAmelCase="<unk>"       ,    UpperCAmelCase=None       ,    ):
							"""simple docstring"""







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

	def __eq__(							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return self.indices == other.indices

	def __getitem__(							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







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

	def __len__(							self    ):
							"""simple docstring"""







							return len(self.symbols    )

	def __contains__(							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return sym in self.indices

	@classmethod
	def        UpperCamelCase    (							cls       ,    UpperCAmelCase    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=1       ,    UpperCAmelCase=False    ):
							"""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(UpperCAmelCase    )
													self.count.append(UpperCAmelCase    )
													return idx

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return 0

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							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

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

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



def __A    (      __lowerCAmelCase	)->  str:





						"""simple docstring"""



						_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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  str:





						"""simple docstring"""



						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_024,
						    '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)
