




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_			:       str	       =				{
    'translation': ['bleu'],
    'summarization': ROUGE_KEYS,
}


def 							__SCREAMING_SNAKE_CASE						(    snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase									=  search.split()
						_UpperCAmelCase									=  dict((g.split("="		) for g in groups)		)
						_UpperCAmelCase									=  list(entries.keys()		)
						_UpperCAmelCase									=  [[f"""--{k} {v}""" for v in vs.split(":"		)] for k, vs in entries.items()]
						_UpperCAmelCase									=  [list(__A		) for x in itertools.product(*__A		)]
						return matrix, entry_names


def 							__SCREAMING_SNAKE_CASE						(    ):

						'''simple docstring'''


						_UpperCAmelCase									=  sys.argv[0]

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

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

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

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

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

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

						_UpperCAmelCase									=  sorted(__A			,					key=operator.itemgetter(*task_score_names[task]		)			,					reverse=__A		)
						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]		)		)

						_UpperCAmelCase									=  results_sorted[0]
						for score in task_score_names[task]:
												del best[score]
						_UpperCAmelCase									=  [f"""--{k} {v}""" for k, v in best.items()]
						_UpperCAmelCase									=  ["--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 typing import TYPE_CHECKING

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


lowercase_			:       List[str]						       =				{
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			:       Any						       =				[
				    'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'RwkvForCausalLM',
				    'RwkvModel',
				    'RwkvPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rwkv import (
								    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RwkvForCausalLM,
								    RwkvModel,
								    RwkvPreTrainedModel,
								)
else:
				import sys

				lowercase_			:       Union[str, Any]						       =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




