


'''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
_SCREAMING_SNAKE_CASE       :   Dict        = {
    'translation': ['bleu'],
    'summarization': ROUGE_KEYS,
}



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



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



def 						_UpperCamelCase							( ):






				"""simple docstring"""



				__magic_name__				:					List[str]						       =       sys.argv[0]

				__magic_name__				:					Tuple						       =       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."
				    )     ,   )
				__magic_name__				:					Optional[int]						       =       parser.parse_known_args()
				# we share some of the args
				args_main.extend(["--task", args.task]		)
				__magic_name__				:					List[Any]						       =       [prog] + args_main

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

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

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

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

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

				__magic_name__				:					Optional[Any]						       =       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]		)		)

				__magic_name__				:					Optional[Any]						       =       results_sorted[0]
				for score in task_score_names[task]:
								del best[score]
				__magic_name__				:					int						       =       [F"""--{k} {v}""" for k, v in best.items()]
				__magic_name__				:					int						       =       ['--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()



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE       :   Any        = {
    "configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config", "DebertaV2OnnxConfig"],
    "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   List[Any]        = ["DebertaV2TokenizerFast"]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   int        = [
		    "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFDebertaV2ForMaskedLM",
		    "TFDebertaV2ForQuestionAnswering",
		    "TFDebertaV2ForSequenceClassification",
		    "TFDebertaV2ForTokenClassification",
		    "TFDebertaV2Model",
		    "TFDebertaV2PreTrainedModel",
		]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   Union[str, Any]        = [
		    "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "DebertaV2ForMaskedLM",
		    "DebertaV2ForMultipleChoice",
		    "DebertaV2ForQuestionAnswering",
		    "DebertaV2ForSequenceClassification",
		    "DebertaV2ForTokenClassification",
		    "DebertaV2Model",
		    "DebertaV2PreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_deberta_va import (
		    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DebertaVaConfig,
		    DebertaVaOnnxConfig,
		)
		from .tokenization_deberta_va import DebertaVaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deberta_va import (
				    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDebertaVaForMaskedLM,
				    TFDebertaVaForQuestionAnswering,
				    TFDebertaVaForSequenceClassification,
				    TFDebertaVaForTokenClassification,
				    TFDebertaVaModel,
				    TFDebertaVaPreTrainedModel,
				)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deberta_va import (
				    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DebertaVaForMaskedLM,
				    DebertaVaForMultipleChoice,
				    DebertaVaForQuestionAnswering,
				    DebertaVaForSequenceClassification,
				    DebertaVaForTokenClassification,
				    DebertaVaModel,
				    DebertaVaPreTrainedModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE       :   Dict        = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)