
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
_lowerCamelCase :       Any     					=    {
    '''translation''': ['''bleu'''],
    '''summarization''': ROUGE_KEYS,
}






def  a_	( __lowercase     :			List[str]				)				->						Any:
			_snake_case						=    search.split()
			_snake_case						=    dict((g.split('='				) for g in groups)				)
			_snake_case						=    list(entries.keys()				)
			_snake_case						=    [[f'''--{k} {v}''' for v in vs.split(':'				)] for k, vs in entries.items()]
			_snake_case						=    [list(__lowercase				) for x in itertools.product(*__lowercase				)]
			return matrix, entry_names






def  a_	( )				->						Dict:
			_snake_case						=    sys.argv[0]

			_snake_case						=    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=__lowercase ,      required=__lowercase ,      help='param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"' ,      )
			parser.add_argument(
			    '--bs' ,      type=__lowercase ,      default=8 ,      required=__lowercase ,      help='initial batch size (may get reduced if it\'s too big)'				)
			parser.add_argument('--task' ,      type=__lowercase ,      help='used for task_specific_params + metrics'				)
			parser.add_argument(
			    '--info' ,      nargs='?' ,      type=__lowercase ,      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.'
			    ) ,      )
			_snake_case			,      _snake_case						=    parser.parse_known_args()
			# we share some of the args
			args_main.extend(['--task', args.task]				)
			_snake_case						=    [prog] + args_main

			# to support variations like translation_en_to_de"
			_snake_case						=    'translation' if 'translation' in args.task else 'summarization'

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

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

						_snake_case						=    run_generate(verbose=__lowercase				)
						# make sure scores are first in the table
						_snake_case						=    OrderedDict()
						for score in task_score_names[task]:
									_snake_case						=    scores[score]
						result.update(__lowercase				)
						results.append(__lowercase				)

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

			_snake_case						=    sorted(__lowercase ,      key=operator.itemgetter(*task_score_names[task]				) ,      reverse=__lowercase				)
			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]				)				)

			_snake_case						=    results_sorted[0]
			for score in task_score_names[task]:
						del best[score]
			_snake_case						=    [f'''--{k} {v}''' for k, v in best.items()]
			_snake_case						=    ['--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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	int					     =      ["image_processor", "tokenizer"]
	_UpperCAmelCase :	Tuple					     =      "EfficientNetImageProcessor"
	_UpperCAmelCase :	Optional[Any]					     =      ("BertTokenizer", "BertTokenizerFast")



	def __init__(						self			:					Tuple    ,						lowercase			:					List[Any]    ,						lowercase			:					Optional[int]			):


				'''simple docstring'''


				super().__init__(lowercase    ,						lowercase			)



	def __call__(						self			:					Union[str, Any]    ,						lowercase			:					Dict=None    ,						lowercase			:					Optional[int]=None    ,						lowercase			:					List[Any]="max_length"    ,						lowercase			:					Optional[int]=64    ,						lowercase			:					int=None    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


				if text is None and images is None:
							raise ValueError('You have to specify either text or images. Both cannot be none.'			)

				if text is not None:
							_snake_case						=    self.tokenizer(
							    lowercase    ,						padding=lowercase    ,						max_length=lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				if images is not None:
							_snake_case						=    self.image_processor(lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				if text is not None and images is not None:
							_snake_case						=    image_features.pixel_values
							return encoding
				elif text is not None:
							return encoding
				else:
							return BatchEncoding(data=dict(**lowercase			)    ,						tensor_type=lowercase			)



	def 				A							(						self			:					Any    ,						*lowercase			:					List[str]    ,						**lowercase			:					Optional[Any]			):


				'''simple docstring'''


				return self.tokenizer.batch_decode(*lowercase    ,						**lowercase			)



	def 				A							(						self			:					List[Any]    ,						*lowercase			:					Union[str, Any]    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


				return self.tokenizer.decode(*lowercase    ,						**lowercase			)





	@property
	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


				_snake_case						=    self.tokenizer.model_input_names
				_snake_case						=    self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)