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


def 					__lowerCAmelCase			(  _A					):



							"""simple docstring"""


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


def 					__lowerCAmelCase			(  ):



							"""simple docstring"""


							_lowercase				=	sys.argv[0]

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

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

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

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

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

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

							_lowercase				=	sorted(lowerCAmelCase__ ,key=operator.itemgetter(*task_score_names[task]					) ,reverse=lowerCAmelCase__					)
							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]					)					)

							_lowercase				=	results_sorted[0]
							for score in task_score_names[task]:
														del best[score]
							_lowercase				=	[f'''--{k} {v}''' for k, v in best.items()]
							_lowercase				=	['--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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A_:      int        =			{
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_:      List[str]        =			['BartTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_:      int        =			[
				    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BartForCausalLM',
				    'BartForConditionalGeneration',
				    'BartForQuestionAnswering',
				    'BartForSequenceClassification',
				    'BartModel',
				    'BartPretrainedModel',
				    'PretrainedBartModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_:      Any        =			[
				    'TFBartForConditionalGeneration',
				    'TFBartForSequenceClassification',
				    'TFBartModel',
				    'TFBartPretrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_:      str        =			[
				    'FlaxBartDecoderPreTrainedModel',
				    'FlaxBartForCausalLM',
				    'FlaxBartForConditionalGeneration',
				    'FlaxBartForQuestionAnswering',
				    'FlaxBartForSequenceClassification',
				    'FlaxBartModel',
				    'FlaxBartPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
				from .tokenization_bart import BartTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bart_fast import BartTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bart import (
								    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BartForCausalLM,
								    BartForConditionalGeneration,
								    BartForQuestionAnswering,
								    BartForSequenceClassification,
								    BartModel,
								    BartPretrainedModel,
								    PretrainedBartModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bart import (
								    TFBartForConditionalGeneration,
								    TFBartForSequenceClassification,
								    TFBartModel,
								    TFBartPretrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bart import (
								    FlaxBartDecoderPreTrainedModel,
								    FlaxBartForCausalLM,
								    FlaxBartForConditionalGeneration,
								    FlaxBartForQuestionAnswering,
								    FlaxBartForSequenceClassification,
								    FlaxBartModel,
								    FlaxBartPreTrainedModel,
								)

else:
				import sys

				A_:      Optional[Any]        =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


