
import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

					if is_vision_available():
										from transformers import OneFormerImageProcessor
										from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
										from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
					from PIL import Image


def 		UpperCAmelCase     (      a_						,  a_="shi-labs/oneformer_demo"		)						->      Optional[int]:







				"""simple docstring"""



				with open(hf_hub_download(a_						,  a_						,  repo_type="dataset"		)						,  "r"		) as f:
								__A       =					json.load(a_		)
				__A       =					{}
				__A       =					[]
				__A       =					[]
				for key, info in class_info.items():
								__A       =					info["name"]
								class_names.append(info["name"]		)
								if info["isthing"]:
												thing_ids.append(int(a_		)		)
				__A       =					thing_ids
				__A       =					class_names
				return metadata





class 				UpperCAmelCase (    unittest.TestCase    ):




	'''simple docstring'''





	def __init__(			self   :					str				,A   :					Optional[int]				,A   :					List[Any]=7				,A   :					List[str]=3				,A   :					Tuple=30				,A   :					List[Any]=4_00				,A   :					List[str]=None				,A   :					List[str]=True				,A   :					str=True				,A   :					Optional[Any]=[0.5, 0.5, 0.5]				,A   :					Optional[int]=[0.5, 0.5, 0.5]				,A   :					List[Any]=10				,A   :					Dict=False				,A   :					Any=2_55				,A   :					int="shi-labs/oneformer_demo"				,A   :					Tuple="ade20k_panoptic.json"				,A   :					List[Any]=10				,):
					__A       =					parent
					__A       =					batch_size
					__A       =					num_channels
					__A       =					min_resolution
					__A       =					max_resolution
					__A       =					do_resize
					__A       =					{"shortest_edge": 32, "longest_edge": 13_33} if size is None else size
					__A       =					do_normalize
					__A       =					image_mean
					__A       =					image_std
					__A       =					class_info_file
					__A       =					prepare_metadata(A				,A					)
					__A       =					num_text
					__A       =					repo_path

					# for the post_process_functions
					__A       =					2
					__A       =					10
					__A       =					10
					__A       =					3
					__A       =					4
					__A       =					num_labels
					__A       =					do_reduce_labels
					__A       =					ignore_index






	def 							UpperCamelCase_    (			self   :					Dict					):
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "num_labels": self.num_labels,
					    "do_reduce_labels": self.do_reduce_labels,
					    "ignore_index": self.ignore_index,
					    "class_info_file": self.class_info_file,
					    "metadata": self.metadata,
					    "num_text": self.num_text,
					}






	def 							UpperCamelCase_    (			self   :					Dict				,A   :					Optional[int]				,A   :					Tuple=False					):
					if not batched:
									__A       =					image_inputs[0]
									if isinstance(A				,Image.Image					):
													__A     ,				__A       =					image.size
									else:
													__A     ,				__A       =					image.shape[1], image.shape[2]
									if w < h:
													__A       =					int(self.size["shortest_edge"] * h / w					)
													__A       =					self.size["shortest_edge"]
									elif w > h:
													__A       =					self.size["shortest_edge"]
													__A       =					int(self.size["shortest_edge"] * w / h					)
									else:
													__A       =					self.size["shortest_edge"]
													__A       =					self.size["shortest_edge"]

					else:
									__A       =					[]
									for image in image_inputs:
													__A     ,				__A       =					self.get_expected_values([image]					)
													expected_values.append((expected_height, expected_width)					)
									__A       =					max(A				,key=lambda A					: item[0]					)[0]
									__A       =					max(A				,key=lambda A					: item[1]					)[1]

					return expected_height, expected_width







	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					return OneFormerForUniversalSegmentationOutput(
					    # +1 for null class
					    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)					)				,masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)					)				,)





@require_torch
@require_vision
class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE     ,     unittest.TestCase    ):




	'''simple docstring'''





	snake_case_          =	OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
	# only for test_image_processing_common.test_image_proc_to_json_string
	snake_case_          =	image_processing_class






	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					__A       =					OneFormerImageProcessorTester(self					)






	@property
	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					return self.image_processing_tester.prepare_image_processor_dict()






	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					self.assertTrue(hasattr(A				,"image_mean"					)					)
					self.assertTrue(hasattr(A				,"image_std"					)					)
					self.assertTrue(hasattr(A				,"do_normalize"					)					)
					self.assertTrue(hasattr(A				,"do_resize"					)					)
					self.assertTrue(hasattr(A				,"size"					)					)
					self.assertTrue(hasattr(A				,"ignore_index"					)					)
					self.assertTrue(hasattr(A				,"class_info_file"					)					)
					self.assertTrue(hasattr(A				,"num_text"					)					)
					self.assertTrue(hasattr(A				,"repo_path"					)					)
					self.assertTrue(hasattr(A				,"metadata"					)					)
					self.assertTrue(hasattr(A				,"do_reduce_labels"					)					)






	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					pass






	def 							UpperCamelCase_    (			self   :					Any					):
					# Initialize image_processor
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					# create random PIL images
					__A       =					prepare_image_inputs(self.image_processing_tester				,equal_resolution=A					)
					for image in image_inputs:
									self.assertIsInstance(A				,Image.Image					)

					# Test not batched input
					__A       =					image_processor(image_inputs[0]				,["semantic"]				,return_tensors="pt"					).pixel_values

					__A     ,				__A       =					self.image_processing_tester.get_expected_values(A					)

					self.assertEqual(
					    encoded_images.shape				,(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,)

					# Test batched
					__A     ,				__A       =					self.image_processing_tester.get_expected_values(A				,batched=A					)

					__A       =					image_processor(
					    A				,["semantic"] * len(A					)				,return_tensors="pt"					).pixel_values
					self.assertEqual(
					    encoded_images.shape				,(
					        self.image_processing_tester.batch_size,
					        self.image_processing_tester.num_channels,
					        expected_height,
					        expected_width,
					    )				,)






	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					# Initialize image_processor
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					# create random numpy tensors
					__A       =					prepare_image_inputs(self.image_processing_tester				,equal_resolution=A				,numpify=A					)
					for image in image_inputs:
									self.assertIsInstance(A				,np.ndarray					)

					# Test not batched input
					__A       =					image_processor(image_inputs[0]				,["semantic"]				,return_tensors="pt"					).pixel_values

					__A     ,				__A       =					self.image_processing_tester.get_expected_values(A					)

					self.assertEqual(
					    encoded_images.shape				,(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,)

					# Test batched
					__A     ,				__A       =					self.image_processing_tester.get_expected_values(A				,batched=A					)

					__A       =					image_processor(
					    A				,["semantic"] * len(A					)				,return_tensors="pt"					).pixel_values
					self.assertEqual(
					    encoded_images.shape				,(
					        self.image_processing_tester.batch_size,
					        self.image_processing_tester.num_channels,
					        expected_height,
					        expected_width,
					    )				,)






	def 							UpperCamelCase_    (			self   :					Tuple					):
					# Initialize image_processor
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					# create random PyTorch tensors
					__A       =					prepare_image_inputs(self.image_processing_tester				,equal_resolution=A				,torchify=A					)
					for image in image_inputs:
									self.assertIsInstance(A				,torch.Tensor					)

					# Test not batched input
					__A       =					image_processor(image_inputs[0]				,["semantic"]				,return_tensors="pt"					).pixel_values

					__A     ,				__A       =					self.image_processing_tester.get_expected_values(A					)

					self.assertEqual(
					    encoded_images.shape				,(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,)

					# Test batched
					__A     ,				__A       =					self.image_processing_tester.get_expected_values(A				,batched=A					)

					__A       =					image_processor(
					    A				,["semantic"] * len(A					)				,return_tensors="pt"					).pixel_values
					self.assertEqual(
					    encoded_images.shape				,(
					        self.image_processing_tester.batch_size,
					        self.image_processing_tester.num_channels,
					        expected_height,
					        expected_width,
					    )				,)






	def 							UpperCamelCase_    (			self   :					Union[str, Any]				,A   :					Tuple=False				,A   :					List[str]=False				,A   :					Optional[Any]="np"					):
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					# prepare image and target
					__A       =					self.image_processing_tester.num_labels
					__A       =					None
					__A       =					None
					__A       =					prepare_image_inputs(self.image_processing_tester				,equal_resolution=A					)
					if with_segmentation_maps:
									__A       =					num_labels
									if is_instance_map:
													__A       =					list(range(A					)					) * 2
													__A       =					dict(enumerate(A					)					)
									__A       =					[
									    np.random.randint(0				,high * 2				,(img.size[1], img.size[0])					).astype(np.uinta					) for img in image_inputs
									]
									if segmentation_type == "pil":
													__A       =					[Image.fromarray(A					) for annotation in annotations]

					__A       =					image_processor(
					    A				,["semantic"] * len(A					)				,A				,return_tensors="pt"				,instance_id_to_semantic_id=A				,pad_and_return_pixel_mask=A				,)

					return inputs






	def 							UpperCamelCase_    (			self   :					int					):
					pass






	def 							UpperCamelCase_    (			self   :					Tuple					):
					def common(A   :					Optional[Any]=False				,A   :					str=None					):
									__A       =					self.comm_get_image_processor_inputs(
									    with_segmentation_maps=A				,is_instance_map=A				,segmentation_type=A					)

									__A       =					inputs["mask_labels"]
									__A       =					inputs["class_labels"]
									__A       =					inputs["pixel_values"]
									__A       =					inputs["text_inputs"]

									# check the batch_size
									for mask_label, class_label, text_input in zip(A				,A				,A					):
													self.assertEqual(mask_label.shape[0]				,class_label.shape[0]					)
													# this ensure padding has happened
													self.assertEqual(mask_label.shape[1:]				,pixel_values.shape[2:]					)
													self.assertEqual(len(A					)				,self.image_processing_tester.num_text					)

					common()
					common(is_instance_map=A					)
					common(is_instance_map=A				,segmentation_type="pil"					)
					common(is_instance_map=A				,segmentation_type="pil"					)






	def 							UpperCamelCase_    (			self   :					List[Any]					):
					__A       =					np.zeros((20, 50)					)
					__A       =					1
					__A       =					1
					__A       =					1

					__A       =					binary_mask_to_rle(A					)
					self.assertEqual(len(A					)				,4					)
					self.assertEqual(rle[0]				,21					)
					self.assertEqual(rle[1]				,45					)






	def 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					self.image_processing_class(
					    num_labels=self.image_processing_tester.num_classes				,max_seq_length=77				,task_seq_length=77				,class_info_file="ade20k_panoptic.json"				,num_text=self.image_processing_tester.num_text				,repo_path="shi-labs/oneformer_demo"				,)
					__A       =					self.image_processing_tester.get_fake_oneformer_outputs()

					__A       =					fature_extractor.post_process_semantic_segmentation(A					)

					self.assertEqual(len(A					)				,self.image_processing_tester.batch_size					)
					self.assertEqual(
					    segmentation[0].shape				,(
					        self.image_processing_tester.height,
					        self.image_processing_tester.width,
					    )				,)

					__A       =					[(1, 4) for i in range(self.image_processing_tester.batch_size					)]
					__A       =					fature_extractor.post_process_semantic_segmentation(A				,target_sizes=A					)

					self.assertEqual(segmentation[0].shape				,target_sizes[0]					)






	def 							UpperCamelCase_    (			self   :					int					):
					__A       =					self.image_processing_class(
					    num_labels=self.image_processing_tester.num_classes				,max_seq_length=77				,task_seq_length=77				,class_info_file="ade20k_panoptic.json"				,num_text=self.image_processing_tester.num_text				,repo_path="shi-labs/oneformer_demo"				,)
					__A       =					self.image_processing_tester.get_fake_oneformer_outputs()
					__A       =					image_processor.post_process_instance_segmentation(A				,threshold=0					)

					self.assertTrue(len(A					) == self.image_processing_tester.batch_size					)
					for el in segmentation:
									self.assertTrue("segmentation" in el					)
									self.assertTrue("segments_info" in el					)
									self.assertEqual(type(el["segments_info"]					)				,A					)
									self.assertEqual(
									    el["segmentation"].shape				,(self.image_processing_tester.height, self.image_processing_tester.width)					)







	def 							UpperCamelCase_    (			self   :					Tuple					):
					__A       =					self.image_processing_class(
					    num_labels=self.image_processing_tester.num_classes				,max_seq_length=77				,task_seq_length=77				,class_info_file="ade20k_panoptic.json"				,num_text=self.image_processing_tester.num_text				,repo_path="shi-labs/oneformer_demo"				,)
					__A       =					self.image_processing_tester.get_fake_oneformer_outputs()
					__A       =					image_processor.post_process_panoptic_segmentation(A				,threshold=0					)

					self.assertTrue(len(A					) == self.image_processing_tester.batch_size					)
					for el in segmentation:
									self.assertTrue("segmentation" in el					)
									self.assertTrue("segments_info" in el					)
									self.assertEqual(type(el["segments_info"]					)				,A					)
									self.assertEqual(
									    el["segmentation"].shape				,(self.image_processing_tester.height, self.image_processing_tester.width)					)




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 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=True							,      _UpperCamelCase="pt"	):


			'''simple docstring'''







			__lowercase			       = {'''add_prefix_space''': True} if isinstance(_UpperCamelCase							,      _UpperCamelCase	) and not line.startswith(''' '''	) else {}
			__lowercase			       = padding_side
			return tokenizer(
			    [line]							,      max_length=_UpperCamelCase							,      padding='''max_length''' if pad_to_max_length else None							,      truncation=_UpperCamelCase							,      return_tensors=_UpperCamelCase							,      add_special_tokens=_UpperCamelCase							,      **_UpperCamelCase							,      )


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=None							,      ):


			'''simple docstring'''







			__lowercase			       = input_ids.ne(_UpperCamelCase	).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 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_="train"			,							snake_case_=None			,							snake_case_=None			,							snake_case_=None			,							snake_case_=""			,							) ->      Tuple:







								'''simple docstring'''

								super().__init__()
								__lowercase			       = Path(snake_case_    ).joinpath(type_path + '''.source'''    )
								__lowercase			       = Path(snake_case_    ).joinpath(type_path + '''.target'''    )
								__lowercase			       = self.get_char_lens(self.src_file    )
								__lowercase			       = max_source_length
								__lowercase			       = max_target_length
								assert min(self.src_lens    ) > 0, F'found empty line in {self.src_file}'
								__lowercase			       = tokenizer
								__lowercase			       = prefix
								if n_obs is not None:
											__lowercase			       = self.src_lens[:n_obs]
								__lowercase			       = src_lang
								__lowercase			       = tgt_lang






					def __len__(					self    ) ->      List[Any]:







								'''simple docstring'''

								return len(self.src_lens    )






					def __getitem__(					self			,							snake_case_    ) ->      Dict[str, torch.Tensor]:







								'''simple docstring'''

								__lowercase			       = index + 1  # linecache starts at 1
								__lowercase			       = self.prefix + linecache.getline(str(self.src_file    )			,							snake_case_    ).rstrip('''\n'''    )
								__lowercase			       = linecache.getline(str(self.tgt_file    )			,							snake_case_    ).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			,							snake_case_    ):
											source_line += self.tokenizer.eos_token
											tgt_line += self.tokenizer.eos_token

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

								__lowercase			       = encode_line(snake_case_			,							snake_case_			,							self.max_source_length			,							'''right'''    )
								__lowercase			       = encode_line(snake_case_			,							snake_case_			,							self.max_target_length			,							'''right'''    )

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






					@staticmethod
					def A   (					snake_case_    ) ->      Any:







								'''simple docstring'''

								return [len(snake_case_    ) for x in Path(snake_case_    ).open().readlines()]






					def A   (					self			,							snake_case_    ) ->      Dict[str, torch.Tensor]:







								'''simple docstring'''

								__lowercase			       = torch.stack([x['''input_ids'''] for x in batch]    )
								__lowercase			       = torch.stack([x['''attention_mask'''] for x in batch]    )
								__lowercase			       = torch.stack([x['''decoder_input_ids'''] for x in batch]    )
								__lowercase			       = (
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer			,							snake_case_    )
								    else self.tokenizer.pad_token_id
								)
								__lowercase			       = (
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer			,							snake_case_    )
								    else self.tokenizer.pad_token_id
								)
								__lowercase			       = trim_batch(snake_case_			,							snake_case_    )
								__lowercase       ,			__lowercase			       = trim_batch(snake_case_			,							snake_case_			,							attention_mask=snake_case_    )
								__lowercase			       = {
								    '''input_ids''': source_ids,
								    '''attention_mask''': source_mask,
								    '''decoder_input_ids''': y,
								}
								return batch




a			:			int												=					getLogger(__name__)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			return list(itertools.chain.from_iterable(_UpperCamelCase	)	)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = get_git_info()
			save_json(_UpperCamelCase							,      os.path.join(_UpperCamelCase							,      '''git_log.json'''	)	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=4							,      **_UpperCamelCase	):


			'''simple docstring'''







			with open(_UpperCamelCase							,      '''w'''	) as f:
						json.dump(_UpperCamelCase							,      _UpperCamelCase							,      indent=_UpperCamelCase							,      **_UpperCamelCase	)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			with open(_UpperCamelCase	) as f:
						return json.load(_UpperCamelCase	)


def 		lowercase_	(  ):


			'''simple docstring'''







			__lowercase			       = git.Repo(search_parent_directories=_UpperCamelCase	)
			__lowercase			       = {
			    '''repo_id''': str(_UpperCamelCase	),
			    '''repo_sha''': str(repo.head.object.hexsha	),
			    '''repo_branch''': str(repo.active_branch	),
			    '''hostname''': str(socket.gethostname()	),
			}
			return repo_infos


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			return list(map(_UpperCamelCase							,      _UpperCamelCase	)	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			with open(_UpperCamelCase							,      '''wb'''	) as f:
						return pickle.dump(_UpperCamelCase							,      _UpperCamelCase	)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







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

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

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

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

			return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase	)	)	)	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = normalize_answer(_UpperCamelCase	).split()
			__lowercase			       = normalize_answer(_UpperCamelCase	).split()
			__lowercase			       = Counter(_UpperCamelCase	) & Counter(_UpperCamelCase	)
			__lowercase			       = sum(common.values()	)
			if num_same == 0:
						return 0
			__lowercase			       = 1.0 * num_same / len(_UpperCamelCase	)
			__lowercase			       = 1.0 * num_same / len(_UpperCamelCase	)
			__lowercase			       = (2 * precision * recall) / (precision + recall)
			return fa


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			return normalize_answer(_UpperCamelCase	) == normalize_answer(_UpperCamelCase	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			assert len(_UpperCamelCase	) == len(_UpperCamelCase	)
			__lowercase			       = 0
			for hypo, pred in zip(_UpperCamelCase							,      _UpperCamelCase	):
						em += exact_match_score(_UpperCamelCase							,      _UpperCamelCase	)
			if len(_UpperCamelCase	) > 0:
						em /= len(_UpperCamelCase	)
			return {"em": em}


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			return model_prefix.startswith('''rag'''	)





def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







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


