

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_	:			Union[str, Any]  	=      logging.get_logger(__name__)

A_	:			str  	=      {
    'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[Any]  							=	'''lxmert'''
					UpperCAmelCase__:       Optional[Any]  							=	{}






					def __init__(			self   ,	A__=3_0522   ,	A__=768   ,	A__=12   ,	A__=9500   ,	A__=1600   ,	A__=400   ,	A__=3072   ,	A__="gelu"   ,	A__=0.1   ,	A__=0.1   ,	A__=512   ,	A__=2   ,	A__=0.0_2   ,	A__=1e-12   ,	A__=9   ,	A__=5   ,	A__=5   ,	A__=2048   ,	A__=4   ,	A__=6.6_7   ,	A__=True   ,	A__=True   ,	A__=True   ,	A__=True   ,	A__=True   ,	A__=True   ,	A__=True   ,	**A__   ,	):
						A__							:   str					       =		vocab_size
						A__							:   int					       =		hidden_size
						A__							:   List[Any]					       =		num_attention_heads
						A__							:   Union[str, Any]					       =		hidden_act
						A__							:   Tuple					       =		intermediate_size
						A__							:   Optional[int]					       =		hidden_dropout_prob
						A__							:   List[str]					       =		attention_probs_dropout_prob
						A__							:   str					       =		max_position_embeddings
						A__							:   int					       =		type_vocab_size
						A__							:   Any					       =		initializer_range
						A__							:   int					       =		layer_norm_eps
						A__							:   Optional[Any]					       =		num_qa_labels
						A__							:   List[Any]					       =		num_object_labels
						A__							:   List[Any]					       =		num_attr_labels
						A__							:   int					       =		l_layers
						A__							:   Union[str, Any]					       =		x_layers
						A__							:   Optional[int]					       =		r_layers
						A__							:   str					       =		visual_feat_dim
						A__							:   Union[str, Any]					       =		visual_pos_dim
						A__							:   Tuple					       =		visual_loss_normalizer
						A__							:   Optional[int]					       =		task_matched
						A__							:   str					       =		task_mask_lm
						A__							:   Optional[int]					       =		task_obj_predict
						A__							:   Optional[Any]					       =		task_qa
						A__							:   int					       =		visual_obj_loss
						A__							:   List[Any]					       =		visual_attr_loss
						A__							:   int					       =		visual_feat_loss
						A__							:   Dict					       =		{"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
						super().__init__(**A__      )






import argparse
from collections import defaultdict

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			Optional[int]   ,				lowercase_:			Optional[Any]   ,				lowercase_:			Union[str, Any]   ,				lowercase_:			Any )					->				int:
	A__							:   Optional[Any]					       =		f"""{file}_{class_name}_{test_name}"""
	done_test[_id] += 1

	with open(lowercase_   ,				"""r""" ) as f:
		A__							:   Union[str, Any]					       =		f.readlines()

	A__							:   str					       =		f"""class {class_name}("""
	A__							:   Optional[Any]					       =		f"""{4 * ' '}def {test_name}("""
	A__							:   Union[str, Any]					       =		f"""{8 * ' '}{correct_line.split()[0]}"""
	A__							:   Optional[int]					       =		f"""{16 * ' '}{correct_line.split()[0]}"""
	A__							:   int					       =		False
	A__							:   str					       =		False
	A__							:   Tuple					       =		False
	A__							:   Optional[int]					       =		False
	A__							:   Optional[Any]					       =		0
	A__							:   Dict					       =		0

	A__							:   List[str]					       =		[]
	for line in lines:
		if line.startswith(lowercase_ ):
			A__							:   Dict					       =		True
		elif in_class and line.startswith(lowercase_ ):
			A__							:   Optional[Any]					       =		True
		elif in_class and in_func and (line.startswith(lowercase_ ) or line.startswith(lowercase_ )):
			A__							:   Tuple					       =		len(line.split(correct_line.split()[0] )[0] )
			count += 1

			if count == done_test[_id]:
				A__							:   Any					       =		True

		if in_class and in_func and in_line:
			if ")" not in line:
				continue
			else:
				A__							:   Dict					       =		True

		if in_class and in_func and in_line and insert_line:
			new_lines.append(f"""{spaces * ' '}{correct_line}""" )
			A__							:   List[str]					       =		False
		else:
			new_lines.append(lowercase_ )

	with open(lowercase_   ,				"""w""" ) as f:
		for line in new_lines:
			f.write(lowercase_ )

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			Optional[Any]=None )					->				Any:
	if fail is not None:
		with open(lowercase_   ,				"""r""" ) as f:
			A__							:   Dict					       =		{l.strip() for l in f.readlines()}
	else:
		A__							:   List[str]					       =		None

	with open(lowercase_   ,				"""r""" ) as f:
		A__							:   int					       =		f.readlines()

	A__							:   Union[str, Any]					       =		defaultdict(lowercase_ )
	for line in correct_lines:
		A__       ,       A__       ,       A__       ,       A__							:   Optional[int]					       =		line.split(""";""" )
		if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
			overwrite_file(lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_ )


if __name__ == "__main__":
	A_	:			Optional[Any]  	=      argparse.ArgumentParser()
	parser.add_argument('--correct_filename', help='filename of tests with expected result')
	parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
	A_	:			Optional[Any]  	=      parser.parse_args()

	main(args.correct_filename, args.fail_filename)



