





import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
_lowercase		:     int 					={
    """translation""": ["""bleu"""],
    """summarization""": ROUGE_KEYS,
}


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
		lowerCamelCase_  :    Any   	=			search.split()
		lowerCamelCase_  :    List[str]   	=			dict((g.split('='	) for g in groups)	)
		lowerCamelCase_  :    List[Any]   	=			list(entries.keys()	)
		lowerCamelCase_  :    Optional[Any]   	=			[[F"--{k} {v}" for v in vs.split(':'	)] for k, vs in entries.items()]
		lowerCamelCase_  :    Union[str, Any]   	=			[list(lowerCAmelCase__	) for x in itertools.product(*lowerCAmelCase__	)]
		return matrix, entry_names


def 		_SCREAMING_SNAKE_CASE    (   ):
		lowerCamelCase_  :    int   	=			sys.argv[0]

		lowerCamelCase_  :    str   	=			argparse.ArgumentParser(
		    usage=(
		        '\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore'
		        ' refer to `run_eval.py -h` for the complete list.'
		    )	)
		parser.add_argument(
		    '--search'       ,type=lowerCAmelCase__       ,required=lowerCAmelCase__       ,help='param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'       ,)
		parser.add_argument(
		    '--bs'       ,type=lowerCAmelCase__       ,default=8       ,required=lowerCAmelCase__       ,help='initial batch size (may get reduced if it\'s too big)'	)
		parser.add_argument('--task'       ,type=lowerCAmelCase__       ,help='used for task_specific_params + metrics'	)
		parser.add_argument(
		    '--info'       ,nargs='?'       ,type=lowerCAmelCase__       ,const=datetime_now()       ,help=(
		        'add custom notes to be printed before the results table. If no value is passed, the current datetime'
		        ' string will be used.'
		    )       ,)
		lowerCamelCase_	,			lowerCamelCase_  :    int   	=			parser.parse_known_args()
		# we share some of the args
		args_main.extend(['--task', args.task]	)
		lowerCamelCase_  :    List[Any]   	=			[prog] + args_main

		# to support variations like translation_en_to_de"
		lowerCamelCase_  :    Optional[int]   	=			'translation' if 'translation' in args.task else 'summarization'

		lowerCamelCase_	,			lowerCamelCase_  :    Dict   	=			parse_search_arg(args.search	)
		lowerCamelCase_  :    Tuple   	=			task_score_names[task]  # score cols first
		lowerCamelCase_  :    Dict   	=			{col: len(str(lowerCAmelCase__	)	) for col in col_names}
		lowerCamelCase_  :    Union[str, Any]   	=			[]
		for r in matrix:
				lowerCamelCase_  :    int   	=			dict((x.replace('--'       ,''	).split() for x in r)	)
				lowerCamelCase_  :    Optional[int]   	=			' '.join(lowerCAmelCase__	).split()
				args_exp.extend(['--bs', str(args.bs	)]	)  # in case we need to reduce its size due to CUDA OOM
				lowerCamelCase_  :    Dict   	=			args_normal + args_exp

				# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

				lowerCamelCase_  :    str   	=			run_generate(verbose=lowerCAmelCase__	)
				# make sure scores are first in the table
				lowerCamelCase_  :    int   	=			OrderedDict()
				for score in task_score_names[task]:
						lowerCamelCase_  :    Any   	=			scores[score]
				result.update(lowerCAmelCase__	)
				results.append(lowerCAmelCase__	)

				# find widest entries
				for k, v in result.items():
						lowerCamelCase_  :    int   	=			len(str(lowerCAmelCase__	)	)
						if l > col_widths[k]:
								lowerCamelCase_  :    Tuple   	=			l

		lowerCamelCase_  :    Any   	=			sorted(lowerCAmelCase__       ,key=operator.itemgetter(*task_score_names[task]	)       ,reverse=lowerCAmelCase__	)
		print(' | '.join([F"{col:{col_widths[col]}}" for col in col_names]	)	)
		print(' | '.join([F"{'-'*col_widths[col]}" for col in col_names]	)	)
		for row in results_sorted:
				print(' | '.join([F"{row[col]:{col_widths[col]}}" for col in col_names]	)	)

		lowerCamelCase_  :    Union[str, Any]   	=			results_sorted[0]
		for score in task_score_names[task]:
				del best[score]
		lowerCamelCase_  :    Optional[Any]   	=			[F"--{k} {v}" for k, v in best.items()]
		lowerCamelCase_  :    Dict   	=			['--bs', str(args.bs	)]
		if args.info:
				print(F"\nInfo: {args.info}"	)
		print('\nBest score args:'	)
		print(' '.join(args_main + best_args + dyn_args	)	)

		return results_sorted


if __name__ == "__main__":
		# Usage:
		# [normal-run_eval_search.py cmd plus] \
		# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
		#
		# Example:
		# PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
		# $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
		# --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
		# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
		run_search()







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


_lowercase		:     Dict 					=logging.get_logger(__name__)

_lowercase		:     Any 					={
    """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
}






class 	UpperCamelCase_     (     snake_case__       ):
					_a			:	List[str]							=				'vilt'



					def __init__(		self    :    int					,			lowerCamelCase    :    Any=3_05_22					,			lowerCamelCase    :    Union[str, Any]=2					,			lowerCamelCase    :    Optional[Any]=2					,			lowerCamelCase    :    str=40					,			lowerCamelCase    :    List[str]=7_68					,			lowerCamelCase    :    Optional[int]=12					,			lowerCamelCase    :    List[Any]=12					,			lowerCamelCase    :    List[str]=30_72					,			lowerCamelCase    :    Union[str, Any]="gelu"					,			lowerCamelCase    :    int=0.0					,			lowerCamelCase    :    str=0.0					,			lowerCamelCase    :    int=0.02					,			lowerCamelCase    :    Union[str, Any]=1E-12					,			lowerCamelCase    :    Dict=3_84					,			lowerCamelCase    :    List[str]=32					,			lowerCamelCase    :    Dict=3					,			lowerCamelCase    :    List[str]=True					,			lowerCamelCase    :    int=-1					,			lowerCamelCase    :    List[Any]=False					,			lowerCamelCase    :    Any=-1					,			**lowerCamelCase    :    str					,			):
							super().__init__(tie_word_embeddings=lowerCamelCase					,			**lowerCamelCase	)

							lowerCamelCase_  :    str   	=			vocab_size
							lowerCamelCase_  :    Dict   	=			type_vocab_size
							lowerCamelCase_  :    str   	=			modality_type_vocab_size
							lowerCamelCase_  :    Optional[Any]   	=			max_position_embeddings

							lowerCamelCase_  :    List[Any]   	=			hidden_size
							lowerCamelCase_  :    Union[str, Any]   	=			num_hidden_layers
							lowerCamelCase_  :    Any   	=			num_attention_heads
							lowerCamelCase_  :    List[Any]   	=			intermediate_size
							lowerCamelCase_  :    Union[str, Any]   	=			hidden_act
							lowerCamelCase_  :    Any   	=			hidden_dropout_prob
							lowerCamelCase_  :    Optional[int]   	=			attention_probs_dropout_prob
							lowerCamelCase_  :    Any   	=			initializer_range
							lowerCamelCase_  :    str   	=			layer_norm_eps

							lowerCamelCase_  :    Optional[Any]   	=			image_size
							lowerCamelCase_  :    List[Any]   	=			patch_size
							lowerCamelCase_  :    Tuple   	=			num_channels
							lowerCamelCase_  :    Optional[Any]   	=			qkv_bias
							lowerCamelCase_  :    Optional[int]   	=			max_image_length
							lowerCamelCase_  :    Optional[Any]   	=			num_images
