






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







def 						__magic_name__(			_A       ):
						'''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 						__magic_name__(			):
						'''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()








'''simple docstring'''


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			__a				:				Union[List[PIL.Image.Image], np.ndarray]
			__a				:				Optional[List[bool]]


if is_transformers_available() and is_torch_available():
					from .modeling_roberta_series import RobertaSeriesModelWithTransformation
					from .pipeline_alt_diffusion import AltDiffusionPipeline
					from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
