






"""simple docstring"""
import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


_lowerCAmelCase       =				datasets.logging.get_logger(__name__)


_lowerCAmelCase       =				"\\n@InProceedings{moosavi2019minimum,\n    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n    year = {2019},\n    booktitle = {Proceedings of the 57th Annual Meeting of\n        the Association for Computational Linguistics (Volume 1: Long Papers)},\n    publisher = {Association for Computational Linguistics},\n    address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n    author = {Amit Bagga and Breck Baldwin},\n    title = {Algorithms for Scoring Coreference Chains},\n    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n    year = {1998},\n    pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n    author = {Xiaoqiang Luo},\n    title = {On coreference resolution performance metrics},\n    booktitle = {In Proc. of HLT/EMNLP},\n    year = {2005},\n    pages = {25--32},\n    publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n    title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",\n    author = \"Moosavi, Nafise Sadat  and\n      Strube, Michael\",\n    booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = aug,\n    year = \"2016\",\n    address = \"Berlin, Germany\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P16-1060\",\n    doi = \"10.18653/v1/P16-1060\",\n    pages = \"632--642\",\n}\n\n"

_lowerCAmelCase       =				"\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn	Type	Description\n1	Document ID	This is a variation on the document filename\n2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3	Word number\n4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.\n5	Part-of-Speech\n6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.\n7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"\n8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.\n9	Word sense	This is the word sense of the word in Column 3.\n10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11	Named Entities	These columns identifies the spans representing various named entities.\n12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN	Coreference	Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n"

_lowerCAmelCase       =				"\nCalculates coreference evaluation metrics.\nArgs:\n    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n        Each prediction is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n        Each reference is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    keep_singletons: After extracting all mentions of key or system files,\n        mentions whose corresponding coreference chain is of size one,\n        are considered as singletons. The default evaluation mode will include\n        singletons in evaluations if they are included in the key or the system files.\n        By setting 'keep_singletons=False', all singletons in the key and system files\n        will be excluded from the evaluation.\n    NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n        leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.\n    min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.\n        Minimum spans are determined using the MINA algorithm.\n\nReturns:\n    'mentions': mentions\n    'muc': MUC metric [Vilain et al, 1995]\n    'bcub': B-cubed [Bagga and Baldwin, 1998]\n    'ceafe': CEAFe [Luo et al., 2005]\n    'lea': LEA [Moosavi and Strube, 2016]\n    'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n    >>> coval = datasets.load_metric('coval')\n    >>> words = ['bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -',\n    ... 'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)',\n    ... 'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)',\n    ... 'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -',\n    ... 'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -',\n    ... 'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -']\n    >>> references = [words]\n    >>> predictions = [words]\n    >>> results = coval.compute(predictions=predictions, references=references)\n    >>> print(results) # doctest:+ELLIPSIS\n    {'mentions/recall': 1.0,[...] 'conll_score': 100.0}\n"






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__=False , snake_case__=False , snake_case__=True , snake_case__=False , snake_case__="dummy_doc"       ):
	A_		:    List[str]						     = {doc: key_lines}
	A_		:    Optional[Any]						     = {doc: sys_lines}

	A_		:    List[Any]						     = {}

	A_		:    Any						     = 0
	A_		:    Optional[Any]						     = 0
	A_		:    Optional[Any]						     = 0
	A_		:    str						     = 0
	A_		:    List[Any]						     = 0
	A_		:    Any						     = 0

	A_     ,  A_		:    Tuple						     = reader.get_doc_mentions(snake_case__ , key_doc_lines[doc] , snake_case__       )
	key_singletons_num += singletons_num

	if NP_only or min_span:
		A_		:    str						     = reader.set_annotated_parse_trees(snake_case__ , key_doc_lines[doc] , snake_case__ , snake_case__       )

	A_     ,  A_		:    Tuple						     = reader.get_doc_mentions(snake_case__ , sys_doc_lines[doc] , snake_case__       )
	sys_singletons_num += singletons_num

	if NP_only or min_span:
		A_		:    Optional[Any]						     = reader.set_annotated_parse_trees(snake_case__ , key_doc_lines[doc] , snake_case__ , snake_case__       )

	if remove_nested:
		A_     ,  A_		:    Dict						     = reader.remove_nested_coref_mentions(snake_case__ , snake_case__       )
		key_nested_coref_num += nested_mentions
		key_removed_nested_clusters += removed_clusters

		A_     ,  A_		:    str						     = reader.remove_nested_coref_mentions(snake_case__ , snake_case__       )
		sys_nested_coref_num += nested_mentions
		sys_removed_nested_clusters += removed_clusters

	A_		:    Optional[int]						     = reader.get_mention_assignments(snake_case__ , snake_case__       )
	A_		:    Dict						     = reader.get_mention_assignments(snake_case__ , snake_case__       )

	A_		:    int						     = (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

	if remove_nested:
		logger.info(
		    """Number of removed nested coreferring mentions in the key """
		    F"""annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}"""       )
		logger.info(
		    """Number of resulting singleton clusters in the key """
		    F"""annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}"""       )

	if not keep_singletons:
		logger.info(
		    F"""{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system """
		    """files, respectively"""       )

	return doc_coref_infos






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__       ):
	A_		:    Optional[Any]						     = get_coref_infos(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__       )

	A_		:    Union[str, Any]						     = {}
	A_		:    List[Any]						     = 0
	A_		:    str						     = 0

	for name, metric in metrics:
		A_     ,  A_     ,  A_		:    Optional[int]						     = evaluator.evaluate_documents(snake_case__ , snake_case__ , beta=1       )
		if name in ["muc", "bcub", "ceafe"]:
			conll += fa
			conll_subparts_num += 1
		output_scores.update({F"""{name}/recall""": recall, F"""{name}/precision""": precision, F"""{name}/f1""": fa}       )

		logger.info(
		    name.ljust(10       ) , F"""Recall: {recall * 100:.2f}""" , F""" Precision: {precision * 100:.2f}""" , F""" F1: {fa * 100:.2f}""" , )

	if conll_subparts_num == 3:
		A_		:    Tuple						     = (conll / 3) * 100
		logger.info(F"""CoNLL score: {conll:.2f}"""       )
		output_scores.update({"""conll_score""": conll}       )

	return output_scores






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    Tuple						     = False
	for line in key_lines:
		if not line.startswith("""#"""       ):
			if len(line.split()       ) > 6:
				A_		:    str						     = line.split()[5]
				if not parse_col == "-":
					A_		:    Dict						     = True
					break
				else:
					break
	return has_gold_parse




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





	"""simple docstring"""




	def  lowerCamelCase(self				):
		return datasets.MetricInfo(
		    description=_DESCRIPTION  ,  citation=_CITATION  ,  inputs_description=_KWARGS_DESCRIPTION  ,  features=datasets.Features(
		        {
		            """predictions""": datasets.Sequence(datasets.Value("""string"""				)				),
		            """references""": datasets.Sequence(datasets.Value("""string"""				)				),
		        }				)  ,  codebase_urls=["""https://github.com/ns-moosavi/coval"""]  ,  reference_urls=[
		        """https://github.com/ns-moosavi/coval""",
		        """https://www.aclweb.org/anthology/P16-1060""",
		        """http://www.conll.cemantix.org/2012/data.html""",
		    ]  ,  )





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=False  ,  lowerCAmelCase_=False  ,  lowerCAmelCase_=False				):
		A_		:    Optional[Any]						     = [
		    ("""mentions""", evaluator.mentions),
		    ("""muc""", evaluator.muc),
		    ("""bcub""", evaluator.b_cubed),
		    ("""ceafe""", evaluator.ceafe),
		    ("""lea""", evaluator.lea),
		]

		if min_span:
			A_		:    List[str]						     = util.check_gold_parse_annotation(lowerCAmelCase_				)
			if not has_gold_parse:
				raise NotImplementedError("""References should have gold parse annotation to use 'min_span'."""				)
				# util.parse_key_file(key_file)
				# key_file = key_file + ".parsed"

		A_		:    Dict						     = evaluate(
		    key_lines=lowerCAmelCase_  ,  sys_lines=lowerCAmelCase_  ,  metrics=lowerCAmelCase_  ,  NP_only=lowerCAmelCase_  ,  remove_nested=lowerCAmelCase_  ,  keep_singletons=lowerCAmelCase_  ,  min_span=lowerCAmelCase_  ,  )

		return score









"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__=True , snake_case__="pt"       ):
	A_		:    Dict						     = {"""add_prefix_space""": True} if isinstance(snake_case__ , snake_case__       ) and not line.startswith(""" """       ) else {}
	A_		:    int						     = padding_side
	return tokenizer(
	    [line] , max_length=snake_case__ , padding="""max_length""" if pad_to_max_length else None , truncation=snake_case__ , return_tensors=snake_case__ , add_special_tokens=snake_case__ , **snake_case__ , )






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__=None , ):
	A_		:    int						     = input_ids.ne(snake_case__       ).any(dim=0       )
	if attention_mask is None:
		return input_ids[:, keep_column_mask]
	else:
		return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_="train"  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=""  ,  ):
		super().__init__()
		A_		:    str						     = Path(lowerCAmelCase_				).joinpath(type_path + """.source"""				)
		A_		:    Tuple						     = Path(lowerCAmelCase_				).joinpath(type_path + """.target"""				)
		A_		:    Optional[Any]						     = self.get_char_lens(self.src_file				)
		A_		:    Optional[Any]						     = max_source_length
		A_		:    Tuple						     = max_target_length
		assert min(self.src_lens				) > 0, f"""found empty line in {self.src_file}"""
		A_		:    Tuple						     = tokenizer
		A_		:    Optional[int]						     = prefix
		if n_obs is not None:
			A_		:    Union[str, Any]						     = self.src_lens[:n_obs]
		A_		:    Optional[int]						     = src_lang
		A_		:    Union[str, Any]						     = tgt_lang





	def __len__(self				):
		return len(self.src_lens				)





	def __getitem__(self  ,  lowerCAmelCase_				):
		A_		:    Optional[Any]						     = index + 1  # linecache starts at 1
		A_		:    int						     = self.prefix + linecache.getline(str(self.src_file				)  ,  lowerCAmelCase_				).rstrip("""\n"""				)
		A_		:    Any						     = linecache.getline(str(self.tgt_file				)  ,  lowerCAmelCase_				).rstrip("""\n"""				)
		assert source_line, f"""empty source line for index {index}"""
		assert tgt_line, f"""empty tgt line for index {index}"""

		# Need to add eos token manually for T5
		if isinstance(self.tokenizer  ,  lowerCAmelCase_				):
			source_line += self.tokenizer.eos_token
			tgt_line += self.tokenizer.eos_token

		# Pad source and target to the right
		A_		:    Optional[Any]						     = (
		    self.tokenizer.question_encoder if isinstance(self.tokenizer  ,  lowerCAmelCase_				) else self.tokenizer
		)
		A_		:    Optional[int]						     = self.tokenizer.generator if isinstance(self.tokenizer  ,  lowerCAmelCase_				) else self.tokenizer

		A_		:    str						     = encode_line(lowerCAmelCase_  ,  lowerCAmelCase_  ,  self.max_source_length  ,  """right"""				)
		A_		:    Optional[Any]						     = encode_line(lowerCAmelCase_  ,  lowerCAmelCase_  ,  self.max_target_length  ,  """right"""				)

		A_		:    int						     = source_inputs["""input_ids"""].squeeze()
		A_		:    int						     = target_inputs["""input_ids"""].squeeze()
		A_		:    Tuple						     = source_inputs["""attention_mask"""].squeeze()
		return {
		    "input_ids": source_ids,
		    "attention_mask": src_mask,
		    "decoder_input_ids": target_ids,
		}





	@staticmethod
	def  lowerCamelCase(lowerCAmelCase_				):
		return [len(lowerCAmelCase_				) for x in Path(lowerCAmelCase_				).open().readlines()]





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    List[str]						     = torch.stack([x["""input_ids"""] for x in batch]				)
		A_		:    Optional[int]						     = torch.stack([x["""attention_mask"""] for x in batch]				)
		A_		:    Any						     = torch.stack([x["""decoder_input_ids"""] for x in batch]				)
		A_		:    Optional[Any]						     = (
		    self.tokenizer.generator.pad_token_id
		    if isinstance(self.tokenizer  ,  lowerCAmelCase_				)
		    else self.tokenizer.pad_token_id
		)
		A_		:    int						     = (
		    self.tokenizer.question_encoder.pad_token_id
		    if isinstance(self.tokenizer  ,  lowerCAmelCase_				)
		    else self.tokenizer.pad_token_id
		)
		A_		:    List[str]						     = trim_batch(lowerCAmelCase_  ,  lowerCAmelCase_				)
		A_     ,  A_		:    Dict						     = trim_batch(lowerCAmelCase_  ,  lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_				)
		A_		:    Optional[Any]						     = {
		    """input_ids""": source_ids,
		    """attention_mask""": source_mask,
		    """decoder_input_ids""": y,
		}
		return batch






_lowerCAmelCase       =				getLogger(__name__)






def       __UpperCamelCase						(		snake_case__       ):
	return list(itertools.chain.from_iterable(snake_case__       )       )






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    List[str]						     = get_git_info()
	save_json(snake_case__ , os.path.join(snake_case__ , """git_log.json"""       )       )






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__=4 , **snake_case__       ):
	with open(snake_case__ , """w"""       ) as f:
		json.dump(snake_case__ , snake_case__ , indent=snake_case__ , **snake_case__       )






def       __UpperCamelCase						(		snake_case__       ):
	with open(snake_case__       ) as f:
		return json.load(snake_case__       )






def       __UpperCamelCase						(		):
	A_		:    Optional[int]						     = git.Repo(search_parent_directories=snake_case__       )
	A_		:    Union[str, Any]						     = {
	    """repo_id""": str(snake_case__       ),
	    """repo_sha""": str(repo.head.object.hexsha       ),
	    """repo_branch""": str(repo.active_branch       ),
	    """hostname""": str(socket.gethostname()       ),
	}
	return repo_infos






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	return list(map(snake_case__ , snake_case__       )       )






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	with open(snake_case__ , """wb"""       ) as f:
		return pickle.dump(snake_case__ , snake_case__       )






def       __UpperCamelCase						(		snake_case__       ):

	def remove_articles(snake_case__       ):
		return re.sub(R"""\b(a|an|the)\b""" , """ """ , snake_case__       )

	def white_space_fix(snake_case__       ):
		return " ".join(text.split()       )

	def remove_punc(snake_case__       ):
		A_		:    Optional[Any]						     = set(string.punctuation       )
		return "".join(ch for ch in text if ch not in exclude       )

	def lower(snake_case__       ):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(snake_case__       )       )       )       )






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    Tuple						     = normalize_answer(snake_case__       ).split()
	A_		:    Dict						     = normalize_answer(snake_case__       ).split()
	A_		:    int						     = Counter(snake_case__       ) & Counter(snake_case__       )
	A_		:    Dict						     = sum(common.values()       )
	if num_same == 0:
		return 0
	A_		:    str						     = 1.0 * num_same / len(snake_case__       )
	A_		:    Any						     = 1.0 * num_same / len(snake_case__       )
	A_		:    Union[str, Any]						     = (2 * precision * recall) / (precision + recall)
	return fa






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	return normalize_answer(snake_case__       ) == normalize_answer(snake_case__       )






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	assert len(snake_case__       ) == len(snake_case__       )
	A_		:    Optional[Any]						     = 0
	for hypo, pred in zip(snake_case__ , snake_case__       ):
		em += exact_match_score(snake_case__ , snake_case__       )
	if len(snake_case__       ) > 0:
		em /= len(snake_case__       )
	return {"em": em}






def       __UpperCamelCase						(		snake_case__       ):
	return model_prefix.startswith("""rag"""       )






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__       ):
	A_		:    Any						     = {p: p for p in extra_params}
	# T5 models don't have `dropout` param, they have `dropout_rate` instead
	A_		:    List[Any]						     = """dropout_rate"""
	for p in extra_params:
		if getattr(snake_case__ , snake_case__ , snake_case__       ):
			if not hasattr(snake_case__ , snake_case__       ) and not hasattr(snake_case__ , equivalent_param[p]       ):
				logger.info("""config doesn't have a `{}` attribute""".format(snake_case__       )       )
				delattr(snake_case__ , snake_case__       )
				continue
			A_		:    Dict						     = p if hasattr(snake_case__ , snake_case__       ) else equivalent_param[p]
			setattr(snake_case__ , snake_case__ , getattr(snake_case__ , snake_case__       )       )
			delattr(snake_case__ , snake_case__       )
	return hparams, config

