


'''simple docstring'''






from typing import Callable, Optional, Union

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


_A      =     logging.get_logger(__name__)

_A      =     {
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}






class      SCREAMING_SNAKE_CASE_	( snake_case		):
			__a					:      Dict					=  '''prophetnet'''
			__a					:      Optional[int]					=  ['''past_key_values''']
			__a					:      Union[str, Any]					=  {
			    '''num_attention_heads''': '''num_encoder_attention_heads''',
			}




			def __init__(      self					,      lowercase = 0.1					,      lowercase = "gelu"					,      lowercase = 3_0_5_2_2					,      lowercase = 1_0_2_4					,      lowercase = 4_0_9_6					,      lowercase = 1_2					,      lowercase = 1_6					,      lowercase = 4_0_9_6					,      lowercase = 1_2					,      lowercase = 1_6					,      lowercase = 0.1					,      lowercase = 0.1					,      lowercase = 5_1_2					,      lowercase = 0.0_2					,      lowercase = True					,      lowercase = True					,      lowercase = 0					,      lowercase = 2					,      lowercase = 3_2					,      lowercase = 1_2_8					,      lowercase = False					,      lowercase = 0.0					,      lowercase = True					,      lowercase = 0					,      lowercase = 1					,      lowercase = 2					,      **lowercase					,      )			->						str:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Dict			   = vocab_size
								__SCREAMING_SNAKE_CASE					:							List[str]			   = hidden_size
								__SCREAMING_SNAKE_CASE					:							str			   = encoder_ffn_dim
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = num_encoder_layers
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = num_encoder_attention_heads
								__SCREAMING_SNAKE_CASE					:							Dict			   = decoder_ffn_dim
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = num_decoder_layers
								__SCREAMING_SNAKE_CASE					:							List[str]			   = num_decoder_attention_heads
								__SCREAMING_SNAKE_CASE					:							int			   = max_position_embeddings
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = init_std  # Normal(0, this parameter)
								__SCREAMING_SNAKE_CASE					:							int			   = activation_function

								# parameters for prophetnet
								__SCREAMING_SNAKE_CASE					:							Tuple			   = ngram
								__SCREAMING_SNAKE_CASE					:							str			   = num_buckets
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = relative_max_distance
								__SCREAMING_SNAKE_CASE					:							Tuple			   = disable_ngram_loss
								__SCREAMING_SNAKE_CASE					:							List[str]			   = eps

								# 3 Types of Dropout
								__SCREAMING_SNAKE_CASE					:							Dict			   = attention_dropout
								__SCREAMING_SNAKE_CASE					:							Tuple			   = activation_dropout
								__SCREAMING_SNAKE_CASE					:							str			   = dropout

								__SCREAMING_SNAKE_CASE					:							int			   = use_cache

								super().__init__(
								    pad_token_id=lowercase					,      bos_token_id=lowercase					,      eos_token_id=lowercase					,      is_encoder_decoder=lowercase					,      add_cross_attention=lowercase					,      decoder_start_token_id=lowercase					,      **lowercase					,      )




			@property
			def 			_snake_case   (      self       )			->						int:


								'''simple docstring'''






								return self.num_encoder_layers + self.num_decoder_layers




			@num_hidden_layers.setter
			def 			_snake_case   (      self					,      lowercase       )			->						int:


								'''simple docstring'''






								raise NotImplementedError(
								    '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
								    ''' `num_decoder_layers`.'''       )






'''simple docstring'''






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
_A      =     {
    """translation""": ["""bleu"""],
    """summarization""": ROUGE_KEYS,
}


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


def A_					(				)     ->  int:
					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = sys.argv[0]

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

					# to support variations like translation_en_to_de"
					__SCREAMING_SNAKE_CASE					:							List[str]			   = '''translation''' if '''translation''' in args.task else '''summarization'''

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

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

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

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

					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = sorted(__SCREAMING_SNAKE_CASE	,   key=operator.itemgetter(*task_score_names[task]       )	,   reverse=__SCREAMING_SNAKE_CASE       )
					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]       )       )

					__SCREAMING_SNAKE_CASE					:							Any			   = results_sorted[0]
					for score in task_score_names[task]:
										del best[score]
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = [f"""--{k} {v}""" for k, v in best.items()]
					__SCREAMING_SNAKE_CASE					:							Tuple			   = ['''--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()


