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





def  lowerCAmelCase__     (      _UpperCamelCase							:					Any       )       ->					List[str]:





						"""simple docstring"""






						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(_UpperCamelCase       ) for x in itertools.product(*_UpperCamelCase       )]
						return matrix, entry_names





def  lowerCAmelCase__     (      )       ->					str:





						"""simple docstring"""






						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=_UpperCamelCase			,      required=_UpperCamelCase			,      help='param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'			,      )
						parser.add_argument(
						    '--bs'			,      type=_UpperCamelCase			,      default=8			,      required=_UpperCamelCase			,      help='initial batch size (may get reduced if it\'s too big)'       )
						parser.add_argument('--task'			,      type=_UpperCamelCase			,      help='used for task_specific_params + metrics'       )
						parser.add_argument(
						    '--info'			,      nargs='?'			,      type=_UpperCamelCase			,      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(_UpperCamelCase       )       ) for col in col_names}
						snake_case             =      []
						for r in matrix:
												snake_case             =      dict((x.replace('--'			,      ''       ).split() for x in r)       )
												snake_case             =      ' '.join(_UpperCamelCase       ).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=_UpperCamelCase       )
												# make sure scores are first in the table
												snake_case             =      OrderedDict()
												for score in task_score_names[task]:
																		snake_case             =      scores[score]
												result.update(_UpperCamelCase       )
												results.append(_UpperCamelCase       )

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

						snake_case             =      sorted(_UpperCamelCase			,      key=operator.itemgetter(*task_score_names[task]       )			,      reverse=_UpperCamelCase       )
						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()



"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__       =	{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["LlamaTokenizer"]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["LlamaTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	[
					    "OpenLlamaForCausalLM",
					    "OpenLlamaModel",
					    "OpenLlamaPreTrainedModel",
					    "OpenLlamaForSequenceClassification",
					]


if TYPE_CHECKING:
					from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_open_llama import (
										    OpenLlamaForCausalLM,
										    OpenLlamaForSequenceClassification,
										    OpenLlamaModel,
										    OpenLlamaPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


