





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




def    __a (     UpperCAmelCase    )      ->int:



	"""simple docstring"""







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




def    __a (     )      ->Union[str, Any]:



	"""simple docstring"""







	A							  =      sys.argv[0]

	A							  =      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."""
	    )       ,			)
	A  ,    A							  =      parser.parse_known_args()
	# we share some of the args
	args_main.extend(["""--task""", args.task]    )
	A							  =      [prog] + args_main

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

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

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

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

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

	A							  =      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]    )    )

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


import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_lowerCamelCase				:	Tuple				    =       logging.getLogger(__name__)



class 							__UpperCAmelCase  :







		'''simple docstring'''







		def __init__(self						:      Union[str, Any]      ):
			A							  =      False







		def        A     (self						:      Any      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      Any      ):
			if not self.initialized:
				A							  =      RagRetriever(
				    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      init_retrieval=_lowerCAmelCase      ,      )
				A							  =      True







		def        A     (self						:      Optional[int]      ):
			self.retriever.index.init_index()







		def        A     (self						:      Optional[int]      ):
			# delete the old self.retriever object before assigning the new index
			del self.retriever
			A							  =      False







		def        A     (self						:      List[str]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      List[str]      ):
			A  ,    A							  =      self.retriever._main_retrieve(_lowerCAmelCase      ,      _lowerCAmelCase      )
			A							  =      self.retriever.index.get_doc_dicts(_lowerCAmelCase      )
			return doc_ids, retrieved_doc_embeds, doc_dicts






class 							__UpperCAmelCase  (       A__						):







		'''simple docstring'''







		def __init__(self						:      Tuple      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      Tuple      ,      _lowerCAmelCase						:      str=None      ):
			if index is not None and index.is_initialized() and len(_lowerCAmelCase      ) > 0:
				raise ValueError(
				    """When using Ray for distributed fine-tuning, """
				    """you'll need to provide the paths instead, """
				    """as the dataset and the index are loaded """
				    """separately. More info in examples/rag/use_own_knowledge_dataset.py """      )

			super().__init__(
			    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      init_retrieval=_lowerCAmelCase      ,      )

			A							  =      retrieval_workers
			A							  =      question_encoder_tokenizer
			A							  =      generator_tokenizer
			if len(self.retrieval_workers      ) > 0:
				ray.get(
				    [
				        worker.create_rag_retriever.remote(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      )
				        for worker in self.retrieval_workers
				    ]      )







		def        A     (self						:      List[Any]      ):
			logger.info("""initializing retrieval"""      )

			if len(self.retrieval_workers      ) > 0:
				ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]      )
			else:
				# Non-distributed training. Load index into this same process.
				self.index.init_index()







		def        A     (self						:      str      ,      _lowerCAmelCase						:      Dict      ,      _lowerCAmelCase						:      Tuple      ):
			if len(self.retrieval_workers      ) > 0:
				# Select a random retrieval actor.
				A							  =      self.retrieval_workers[random.randint(0      ,      len(self.retrieval_workers      ) - 1      )]
				A  ,    A  ,    A							  =      ray.get(
				    random_worker.retrieve.remote(_lowerCAmelCase      ,      _lowerCAmelCase      )      )
			else:
				A  ,    A							  =      self._main_retrieve(_lowerCAmelCase      ,      _lowerCAmelCase      )
				A							  =      self.index.get_doc_dicts(_lowerCAmelCase      )
			return retrieved_doc_embeds, doc_ids, doc_dicts







		@classmethod
		def        A     (cls						:      Union[str, Any]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      Optional[Any]=None      ,      **_lowerCAmelCase						:      Union[str, Any]      ):
			return super(_lowerCAmelCase      ,      cls      ).get_tokenizers(_lowerCAmelCase      ,      _lowerCAmelCase      ,      **_lowerCAmelCase      )







		@classmethod
		def        A     (cls						:      List[Any]      ,      _lowerCAmelCase						:      Optional[int]      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      str=None      ,      **_lowerCAmelCase						:      List[str]      ):
			A							  =      kwargs.pop("""config"""      ,      _lowerCAmelCase      ) or RagConfig.from_pretrained(_lowerCAmelCase      ,      **_lowerCAmelCase      )
			A							  =      RagTokenizer.from_pretrained(_lowerCAmelCase      ,      config=_lowerCAmelCase      )
			A							  =      rag_tokenizer.question_encoder
			A							  =      rag_tokenizer.generator

			if indexed_dataset is not None:
				A							  =      """custom"""
				A							  =      CustomHFIndex(config.retrieval_vector_size      ,      _lowerCAmelCase      )
			else:
				A							  =      cls._build_index(_lowerCAmelCase      )

			return cls(
			    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      retrieval_workers=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      )







		def        A     (self						:      List[Any]      ):
			logger.info("""re-loading the new dataset with embeddings"""      )
			# access from the training loop

			ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]      )

			# build the index object again
			A							  =      self._build_index(self.config      )

			ray.get(
			    [
			        worker.create_rag_retriever.remote(
			            self.config      ,      self.question_encoder_tokenizer      ,      self.generator_tokenizer      ,      _lowerCAmelCase      )
			        for worker in self.retrieval_workers
			    ]      )

