




from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
		import torch

if is_tf_available():
		import tensorflow as tf




class 			UpperCAmelCase_      (   _snake_case       ):




							'''simple docstring'''



							a__						  =     ['''image_processor''']
							a__						  =     '''SamImageProcessor'''





							def __init__(					self       :	List[Any]       ,     a       :	int			)			->					Dict:
										super().__init__(lowerCAmelCase__			)
										SCREAMING_SNAKE_CASE           =					self.image_processor
										SCREAMING_SNAKE_CASE           =					-10
										SCREAMING_SNAKE_CASE           =					self.image_processor.size["""longest_edge"""]





							def __call__(					self       :	Dict       ,     a       :	Optional[Any]=None       ,     a       :	str=None       ,     a       :	str=None       ,     a       :	Union[str, Any]=None       ,     a       :	Optional[Union[str, TensorType]] = None       ,     **a       :	Optional[Any]       ,     )			->					BatchEncoding:
										SCREAMING_SNAKE_CASE           =					self.image_processor(
										    lowerCAmelCase__       ,     return_tensors=lowerCAmelCase__       ,     **lowerCAmelCase__       ,     )

										# pop arguments that are not used in the foward but used nevertheless
										SCREAMING_SNAKE_CASE           =					encoding_image_processor["""original_sizes"""]

										if hasattr(lowerCAmelCase__       ,     """numpy"""			):  # Checks if Torch or TF tensor
													SCREAMING_SNAKE_CASE           =					original_sizes.numpy()

										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					self._check_and_preprocess_points(
										    input_points=lowerCAmelCase__       ,     input_labels=lowerCAmelCase__       ,     input_boxes=lowerCAmelCase__       ,     )

										SCREAMING_SNAKE_CASE           =					self._normalize_and_convert(
										    lowerCAmelCase__       ,     lowerCAmelCase__       ,     input_points=lowerCAmelCase__       ,     input_labels=lowerCAmelCase__       ,     input_boxes=lowerCAmelCase__       ,     return_tensors=lowerCAmelCase__       ,     )

										return encoding_image_processor





							def    _UpperCAmelCase     (					self       :	int       ,     a       :	Tuple       ,     a       :	Optional[int]       ,     a       :	Optional[Any]=None       ,     a       :	Tuple=None       ,     a       :	Optional[int]=None       ,     a       :	Optional[Any]="pt"       ,     )			->					Optional[int]:
										if input_points is not None:
													if len(lowerCAmelCase__			) != len(lowerCAmelCase__			):
																SCREAMING_SNAKE_CASE           =					[
																    self._normalize_coordinates(self.target_size       ,     lowerCAmelCase__       ,     original_sizes[0]			) for point in input_points
																]
													else:
																SCREAMING_SNAKE_CASE           =					[
																    self._normalize_coordinates(self.target_size       ,     lowerCAmelCase__       ,     lowerCAmelCase__			)
																    for point, original_size in zip(lowerCAmelCase__       ,     lowerCAmelCase__			)
																]
													# check that all arrays have the same shape
													if not all(point.shape == input_points[0].shape for point in input_points			):
																if input_labels is not None:
																			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					self._pad_points_and_labels(lowerCAmelCase__       ,     lowerCAmelCase__			)

													SCREAMING_SNAKE_CASE           =					np.array(lowerCAmelCase__			)

										if input_labels is not None:
													SCREAMING_SNAKE_CASE           =					np.array(lowerCAmelCase__			)

										if input_boxes is not None:
													if len(lowerCAmelCase__			) != len(lowerCAmelCase__			):
																SCREAMING_SNAKE_CASE           =					[
																    self._normalize_coordinates(self.target_size       ,     lowerCAmelCase__       ,     original_sizes[0]       ,     is_bounding_box=lowerCAmelCase__			)
																    for box in input_boxes
																]
													else:
																SCREAMING_SNAKE_CASE           =					[
																    self._normalize_coordinates(self.target_size       ,     lowerCAmelCase__       ,     lowerCAmelCase__       ,     is_bounding_box=lowerCAmelCase__			)
																    for box, original_size in zip(lowerCAmelCase__       ,     lowerCAmelCase__			)
																]
													SCREAMING_SNAKE_CASE           =					np.array(lowerCAmelCase__			)

										if input_boxes is not None:
													if return_tensors == "pt":
																SCREAMING_SNAKE_CASE           =					torch.from_numpy(lowerCAmelCase__			)
																# boxes batch size of 1 by default
																SCREAMING_SNAKE_CASE           =					input_boxes.unsqueeze(1			) if len(input_boxes.shape			) != 3 else input_boxes
													elif return_tensors == "tf":
																SCREAMING_SNAKE_CASE           =					tf.convert_to_tensor(lowerCAmelCase__			)
																# boxes batch size of 1 by default
																SCREAMING_SNAKE_CASE           =					tf.expand_dims(lowerCAmelCase__       ,     1			) if len(input_boxes.shape			) != 3 else input_boxes
													encoding_image_processor.update({"""input_boxes""": input_boxes}			)
										if input_points is not None:
													if return_tensors == "pt":
																SCREAMING_SNAKE_CASE           =					torch.from_numpy(lowerCAmelCase__			)
																# point batch size of 1 by default
																SCREAMING_SNAKE_CASE           =					input_points.unsqueeze(1			) if len(input_points.shape			) != 4 else input_points
													elif return_tensors == "tf":
																SCREAMING_SNAKE_CASE           =					tf.convert_to_tensor(lowerCAmelCase__			)
																# point batch size of 1 by default
																SCREAMING_SNAKE_CASE           =					tf.expand_dims(lowerCAmelCase__       ,     1			) if len(input_points.shape			) != 4 else input_points
													encoding_image_processor.update({"""input_points""": input_points}			)
										if input_labels is not None:
													if return_tensors == "pt":
																SCREAMING_SNAKE_CASE           =					torch.from_numpy(lowerCAmelCase__			)
																# point batch size of 1 by default
																SCREAMING_SNAKE_CASE           =					input_labels.unsqueeze(1			) if len(input_labels.shape			) != 3 else input_labels
													elif return_tensors == "tf":
																SCREAMING_SNAKE_CASE           =					tf.convert_to_tensor(lowerCAmelCase__			)
																# point batch size of 1 by default
																SCREAMING_SNAKE_CASE           =					tf.expand_dims(lowerCAmelCase__       ,     1			) if len(input_labels.shape			) != 3 else input_labels
													encoding_image_processor.update({"""input_labels""": input_labels}			)

										return encoding_image_processor





							def    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	List[Any]       ,     a       :	Any			)			->					Any:
										SCREAMING_SNAKE_CASE           =					max([point.shape[0] for point in input_points]			)
										SCREAMING_SNAKE_CASE           =					[]
										for i, point in enumerate(lowerCAmelCase__			):
													if point.shape[0] != expected_nb_points:
																SCREAMING_SNAKE_CASE           =					np.concatenate(
																    [point, np.zeros((expected_nb_points - point.shape[0], 2)			) + self.point_pad_value]       ,     axis=0			)
																SCREAMING_SNAKE_CASE           =					np.append(input_labels[i]       ,     [self.point_pad_value]			)
													processed_input_points.append(lowerCAmelCase__			)
										SCREAMING_SNAKE_CASE           =					processed_input_points
										return input_points, input_labels





							def    _UpperCAmelCase     (					self       :	int       ,     a       :	int       ,     a       :	np.ndarray       ,     a       :	Any       ,     a       :	Any=False			)			->					np.ndarray:
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					original_size
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					self.image_processor._get_preprocess_shape(lowerCAmelCase__       ,     longest_edge=lowerCAmelCase__			)
										SCREAMING_SNAKE_CASE           =					deepcopy(lowerCAmelCase__			).astype(lowerCAmelCase__			)

										if is_bounding_box:
													SCREAMING_SNAKE_CASE           =					coords.reshape(-1       ,     2       ,     2			)

										SCREAMING_SNAKE_CASE           =					coords[..., 0] * (new_w / old_w)
										SCREAMING_SNAKE_CASE           =					coords[..., 1] * (new_h / old_h)

										if is_bounding_box:
													SCREAMING_SNAKE_CASE           =					coords.reshape(-1       ,     4			)

										return coords





							def    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	int=None       ,     a       :	Any=None       ,     a       :	Optional[int]=None       ,     )			->					int:
										if input_points is not None:
													if hasattr(lowerCAmelCase__       ,     """numpy"""			):  # Checks for TF or Torch tensor
																SCREAMING_SNAKE_CASE           =					input_points.numpy().tolist()

													if not isinstance(lowerCAmelCase__       ,     lowerCAmelCase__			) or not isinstance(input_points[0]       ,     lowerCAmelCase__			):
																raise ValueError("""Input points must be a list of list of floating points."""			)
													SCREAMING_SNAKE_CASE           =					[np.array(lowerCAmelCase__			) for input_point in input_points]
										else:
													SCREAMING_SNAKE_CASE           =					None

										if input_labels is not None:
													if hasattr(lowerCAmelCase__       ,     """numpy"""			):
																SCREAMING_SNAKE_CASE           =					input_labels.numpy().tolist()

													if not isinstance(lowerCAmelCase__       ,     lowerCAmelCase__			) or not isinstance(input_labels[0]       ,     lowerCAmelCase__			):
																raise ValueError("""Input labels must be a list of list integers."""			)
													SCREAMING_SNAKE_CASE           =					[np.array(lowerCAmelCase__			) for label in input_labels]
										else:
													SCREAMING_SNAKE_CASE           =					None

										if input_boxes is not None:
													if hasattr(lowerCAmelCase__       ,     """numpy"""			):
																SCREAMING_SNAKE_CASE           =					input_boxes.numpy().tolist()

													if (
													    not isinstance(lowerCAmelCase__       ,     lowerCAmelCase__			)
													    or not isinstance(input_boxes[0]       ,     lowerCAmelCase__			)
													    or not isinstance(input_boxes[0][0]       ,     lowerCAmelCase__			)
													):
																raise ValueError("""Input boxes must be a list of list of list of floating points."""			)
													SCREAMING_SNAKE_CASE           =					[np.array(lowerCAmelCase__			).astype(np.floataa			) for box in input_boxes]
										else:
													SCREAMING_SNAKE_CASE           =					None

										return input_points, input_labels, input_boxes





							@property
							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					int:
										SCREAMING_SNAKE_CASE           =					self.image_processor.model_input_names
										return list(dict.fromkeys(lowerCAmelCase__			)			)







							def    _UpperCAmelCase     (					self       :	List[str]       ,     *a       :	Optional[int]       ,     **a       :	Tuple			)			->					List[Any]:
										return self.image_processor.post_process_masks(*lowerCAmelCase__       ,     **lowerCAmelCase__			)












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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=True					,	SCREAMING_SNAKE_CASE="pt"   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					{"""add_prefix_space""": True} if isinstance(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ) and not line.startswith(""" """   ) else {}
			SCREAMING_SNAKE_CASE           =					padding_side
			return tokenizer(
			    [line]					,	max_length=SCREAMING_SNAKE_CASE					,	padding="""max_length""" if pad_to_max_length else None					,	truncation=SCREAMING_SNAKE_CASE					,	return_tensors=SCREAMING_SNAKE_CASE					,	add_special_tokens=SCREAMING_SNAKE_CASE					,	**SCREAMING_SNAKE_CASE					,	)
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=None					,	):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					input_ids.ne(SCREAMING_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 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



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





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





							def __getitem__(					self       :	Any       ,     a       :	Optional[int]			)			->					Dict[str, torch.Tensor]:
										SCREAMING_SNAKE_CASE           =					index + 1  # linecache starts at 1
										SCREAMING_SNAKE_CASE           =					self.prefix + linecache.getline(str(self.src_file			)       ,     a			).rstrip("""\n"""			)
										SCREAMING_SNAKE_CASE           =					linecache.getline(str(self.tgt_file			)       ,     a			).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       ,     a			):
													source_line += self.tokenizer.eos_token
													tgt_line += self.tokenizer.eos_token

										# Pad source and target to the right
										SCREAMING_SNAKE_CASE           =					(
										    self.tokenizer.question_encoder if isinstance(self.tokenizer       ,     a			) else self.tokenizer
										)
										SCREAMING_SNAKE_CASE           =					self.tokenizer.generator if isinstance(self.tokenizer       ,     a			) else self.tokenizer

										SCREAMING_SNAKE_CASE           =					encode_line(a       ,     a       ,     self.max_source_length       ,     """right"""			)
										SCREAMING_SNAKE_CASE           =					encode_line(a       ,     a       ,     self.max_target_length       ,     """right"""			)

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





							@staticmethod
							def    _UpperCAmelCase     (					a       :	Optional[int]			)			->					List[Any]:
										return [len(a			) for x in Path(a			).open().readlines()]







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





__A						:							Tuple							=  getLogger(__name__)
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return list(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					get_git_info()
			save_json(SCREAMING_SNAKE_CASE					,	os.path.join(SCREAMING_SNAKE_CASE					,	"""git_log.json"""   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=4					,	**SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			with open(SCREAMING_SNAKE_CASE					,	"""w"""   ) as f:
						json.dump(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	indent=SCREAMING_SNAKE_CASE					,	**SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			with open(SCREAMING_SNAKE_CASE   ) as f:
						return json.load(SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					{
			    """repo_id""": str(SCREAMING_SNAKE_CASE   ),
			    """repo_sha""": str(repo.head.object.hexsha   ),
			    """repo_branch""": str(repo.active_branch   ),
			    """hostname""": str(socket.gethostname()   ),
			}
			return repo_infos
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return list(map(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			with open(SCREAMING_SNAKE_CASE					,	"""wb"""   ) as f:
						return pickle.dump(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''

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

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

			def remove_punc(SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE           =					set(string.punctuation   )
						return "".join(ch for ch in text if ch not in exclude   )

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

			return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE   )   )   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					normalize_answer(SCREAMING_SNAKE_CASE   ).split()
			SCREAMING_SNAKE_CASE           =					normalize_answer(SCREAMING_SNAKE_CASE   ).split()
			SCREAMING_SNAKE_CASE           =					Counter(SCREAMING_SNAKE_CASE   ) & Counter(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					sum(common.values()   )
			if num_same == 0:
						return 0
			SCREAMING_SNAKE_CASE           =					1.0 * num_same / len(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					1.0 * num_same / len(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					(2 * precision * recall) / (precision + recall)
			return fa
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return normalize_answer(SCREAMING_SNAKE_CASE   ) == normalize_answer(SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			assert len(SCREAMING_SNAKE_CASE   ) == len(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					0
			for hypo, pred in zip(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
						em += exact_match_score(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			if len(SCREAMING_SNAKE_CASE   ) > 0:
						em /= len(SCREAMING_SNAKE_CASE   )
			return {"em": em}
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return model_prefix.startswith("""rag"""   )




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






