



"""simple docstring"""






import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
		import torch

_lowercase					:					Optional[Any] 		=		logging.get_logger(__name__)






def 	snake_case__  (						):
			"""simple docstring"""
			return importlib.util.find_spec('''deepspeed'''      ) is not None


if is_accelerate_available() and is_deepspeed_available():
		from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
		# Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
		# Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
		from builtins import object as DeepSpeedConfig

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					def __init__(			self				:    List[Any],    lowerCamelCase				:    Union[str, Any]	)->   int:
								set_hf_deepspeed_config(self	)
								dep_version_check('''accelerate'''	)
								dep_version_check('''deepspeed'''	)
								super().__init__(UpperCAmelCase__	)





class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					def __init__(			self				:    Union[str, Any],    lowerCamelCase				:    Optional[Any]	)->   Optional[int]:
								super().__init__(UpperCAmelCase__	)
								lowerCamelCase__						:						int						      =None
								lowerCamelCase__						:						str						      =[]



					def 						snake_case			(			self				:    int	)->   Optional[Any]:







								if self._dtype is None:
											raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype'''	)
								return self._dtype



					def 						snake_case			(			self				:    List[Any],    lowerCamelCase				:    Dict	)->   List[str]:
								lowerCamelCase__						:						Optional[int]						      =self.get_value(UpperCAmelCase__	)
								if val is None:
											return False
								else:
											return val == "auto"



					def 						snake_case			(			self				:    Optional[Any],    lowerCamelCase				:    int,    lowerCamelCase				:    List[str],    lowerCamelCase				:    Optional[Any]=None,    lowerCamelCase				:    Dict=True	)->   Dict:
								lowerCamelCase__						:						int						      =self.find_config_node(UpperCAmelCase__	)
								if config is None:
											return

								if config.get(UpperCAmelCase__	) == "auto":
											lowerCamelCase__						:						List[str]						      =hf_val
											return

								if not must_match:
											return

								lowerCamelCase__						:						int						      =config.get(UpperCAmelCase__	)
								if ds_val is not None and ds_val != hf_val:
											self.mismatches.append(F'''- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}'''	)

					_a       =				partialmethod(lowerCAmelCase_						,  must_match=lowerCAmelCase_  )



					def 						snake_case			(			self				:    Union[str, Any],    lowerCamelCase				:    Tuple	)->   Any:
								lowerCamelCase__						:						Dict						      =args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
								self.fill_match(
								    '''train_micro_batch_size_per_gpu''',    args.per_device_train_batch_size,    '''per_device_train_batch_size'''	)
								self.fill_match('''gradient_accumulation_steps''',    args.gradient_accumulation_steps,    '''gradient_accumulation_steps'''	)
								self.fill_match('''train_batch_size''',    UpperCAmelCase__,    '''train_batch_size (calculated)'''	)
								self.fill_match('''gradient_clipping''',    args.max_grad_norm,    '''max_grad_norm'''	)

								self.fill_match('''optimizer.params.lr''',    args.learning_rate,    '''learning_rate'''	)
								self.fill_match('''optimizer.params.betas''',    [args.adam_betaa, args.adam_betaa],    '''adam_beta1+adam_beta2'''	)
								self.fill_match('''optimizer.params.eps''',    args.adam_epsilon,    '''adam_epsilon'''	)
								self.fill_match('''optimizer.params.weight_decay''',    args.weight_decay,    '''weight_decay'''	)

								self.fill_only('''scheduler.params.warmup_min_lr''',    0	)  # not a trainer arg
								self.fill_match('''scheduler.params.warmup_max_lr''',    args.learning_rate,    '''learning_rate'''	)
								# total_num_steps - will get set in trainer_config_finalize

								# fp16
								if args.fpaa or args.fpaa_full_eval:
											lowerCamelCase__						:						Any						      ="""apex""" if args.fpaa_backend == """apex""" else """amp"""
								else:
											lowerCamelCase__						:						Dict						      =None

								if args.save_on_each_node:
											# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
											lowerCamelCase__						:						List[Any]						      =self.config.get('''checkpoint''',    {}	)
											lowerCamelCase__						:						List[str]						      =args.save_on_each_node

								# amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
								# any here unless the user did the work
								self.fill_match(
								    '''fp16.enabled''',    ((args.fpaa or args.fpaa_full_eval) and fpaa_backend == '''amp'''),    '''fp16|fp16_full_eval+fp16_backend(amp)''',    )

								# apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
								# ZeRO features
								self.fill_match('''amp.enabled''',    fpaa_backend == '''apex''',    '''fp16+fp16_backend(apex)'''	)
								self.fill_match('''amp.opt_level''',    args.fpaa_opt_level,    '''fp16_opt_level'''	)

								self.fill_match('''bf16.enabled''',    (args.bfaa or args.bfaa_full_eval),    '''bf16|bf16_full_eval'''	)

								# deepspeed's default mode is fp16 unless there is a config that says differently
								if self.is_true('''bf16.enabled'''	):
											lowerCamelCase__						:						int						      =torch.bfloataa
								elif self.is_false('''fp16.enabled'''	):
											lowerCamelCase__						:						List[str]						      =torch.floataa
								else:
											lowerCamelCase__						:						int						      =torch.floataa




					def 						snake_case			(			self				:    Optional[Any],    lowerCamelCase				:    Tuple,    lowerCamelCase				:    Optional[int],    lowerCamelCase				:    Optional[Any]	)->   int:
								lowerCamelCase__						:						List[Any]						      =[
								    """zero_optimization.reduce_bucket_size""",
								    """zero_optimization.stage3_prefetch_bucket_size""",
								    """zero_optimization.stage3_param_persistence_threshold""",
								]
								lowerCamelCase__						:						List[Any]						      =[x for x in hidden_size_based_keys if self.is_auto(UpperCAmelCase__	)]

								if len(UpperCAmelCase__	) > 0:
											if hasattr(model.config,    '''hidden_size'''	):
														lowerCamelCase__						:						Union[str, Any]						      =model.config.hidden_size
											elif hasattr(model.config,    '''hidden_sizes'''	):
														# if there are many hidden sizes pick the largest one
														lowerCamelCase__						:						Dict						      =max(model.config.hidden_sizes	)
											else:
														raise ValueError(
														    '''The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '''
														    '''therefore it\'s not possible to automatically fill out the following `auto` entries '''
														    F'''in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing '''
														    '''`auto` values for these keys with an integer value of your choice.'''	)

											self.fill_only('''zero_optimization.reduce_bucket_size''',    hidden_size * hidden_size	)
											if self.is_zeroa():
														# automatically assign the optimal config values based on model config
														self.fill_only('''zero_optimization.stage3_prefetch_bucket_size''',    0.9 * hidden_size * hidden_size	)
														self.fill_only('''zero_optimization.stage3_param_persistence_threshold''',    10 * hidden_size	)

        # scheduler
								self.fill_match('''scheduler.params.total_num_steps''',    UpperCAmelCase__,    '''num_training_steps (calculated)'''	)
								self.fill_match('''scheduler.params.warmup_num_steps''',    args.get_warmup_steps(UpperCAmelCase__	),    '''warmup_steps'''	)

								if len(self.mismatches	) > 0:
											lowerCamelCase__						:						str						      ="""\n""".join(self.mismatches	)
											raise ValueError(
											    '''Please correct the following DeepSpeed config values that mismatch TrainingArguments'''
											    F''' values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to \'auto\'.'''	)





_lowercase					:					Dict 		=		None






def 	snake_case__  (						__lowerCamelCase							:       Any      ):
			"""simple docstring"""
			# this is a special weakref global object to allow us to get to Deepspeed config from APIs
			# that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
			global _hf_deepspeed_config_weak_ref
			# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
			lowerCamelCase__						:						List[Any]						      =weakref.ref(_A      )






def 	snake_case__  (						):
			"""simple docstring"""
			# useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
			global _hf_deepspeed_config_weak_ref
			lowerCamelCase__						:						Tuple						      =None






def 	snake_case__  (						):
			"""simple docstring"""
			if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
						return _hf_deepspeed_config_weak_ref().is_zeroa()
			else:
						return False






def 	snake_case__  (						):
			"""simple docstring"""
			if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
						return _hf_deepspeed_config_weak_ref().config
			else:
						return None






def 	snake_case__  (						__lowerCamelCase							:       Union[str, Any]				,						__lowerCamelCase							:       List[str]				,						__lowerCamelCase							:       Union[str, Any]				,						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			from accelerate.utils import DummyOptim, DummyScheduler

			lowerCamelCase__						:						int						      =hf_deepspeed_config.config

			# Optimizer + Scheduler
			# Currently supported combos:
			# 1. DS scheduler + DS optimizer: Yes
			# 2. HF scheduler + HF optimizer: Yes
			# 3. DS scheduler + HF optimizer: Yes
			# 4. HF scheduler + DS optimizer: No
			#
			# Unless Offload is enabled in which case it's:
			# 1. DS scheduler + DS optimizer: Yes
			# 2. HF scheduler + HF optimizer: Mostly*
			# 3. DS scheduler + HF optimizer: Mostly*
			# 4. HF scheduler + DS optimizer: No
			#
			# Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

			lowerCamelCase__						:						str						      =None
			if "optimizer" in config:
						if args.adafactor:
									raise ValueError(
									    '''--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '''
									    '''Only one optimizer can be configured.'''      )
						lowerCamelCase__						:						Union[str, Any]						      =DummyOptim(params=_A      )
			else:
						if hf_deepspeed_config.is_offload():
									logger.info(
									    '''Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'''
									    ''' custom optimizer has both CPU and GPU implementation (except LAMB)'''      )

						# ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
						# But trainer uses AdamW by default.
						lowerCamelCase__						:						Dict						      =trainer.create_optimizer()
						# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
						lowerCamelCase__						:						List[Any]						      =True

			lowerCamelCase__						:						Optional[Any]						      =None
			if "scheduler" in config:
						lowerCamelCase__						:						Optional[int]						      =DummyScheduler(_A      )
			else:
						if isinstance(_A				,						_A      ):
									raise ValueError(
									    '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
									    '''Please configure a scheduler in the DeepSpeed config.'''      )
						lowerCamelCase__						:						Optional[int]						      =trainer.create_scheduler(num_training_steps=_A				,						optimizer=_A      )

			return optimizer, lr_scheduler






def 	snake_case__  (						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       Optional[int]=False      ):
			"""simple docstring"""
			from deepspeed.utils import logger as ds_logger

			lowerCamelCase__						:						List[str]						      =trainer.model
			lowerCamelCase__						:						Any						      =trainer.args

			lowerCamelCase__						:						Dict						      =trainer.accelerator.state.deepspeed_plugin.hf_ds_config

			# resume config update - some bits like `model` and `num_training_steps` only become available during train
			hf_deepspeed_config.trainer_config_finalize(_A				,						_A				,						_A      )

			# set the Deepspeed log level consistent with the Trainer
			ds_logger.setLevel(args.get_process_log_level()      )

			if inference:
						# only Z3 makes sense for the inference
						if not hf_deepspeed_config.is_zeroa():
									raise ValueError('''ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'''      )

						# in case the training config is re-used for inference
						hf_deepspeed_config.del_config_sub_tree('''optimizer'''      )
						hf_deepspeed_config.del_config_sub_tree('''lr_scheduler'''      )
						lowerCamelCase__						:						List[Any]						      =None, None
						lowerCamelCase__						:						Any						      =None
			else:
						lowerCamelCase__						:						Optional[Any]						      =None  # important for when deepspeed_init is used as re-init
						lowerCamelCase__						:						Union[str, Any]						      =list(filter(lambda __lowerCamelCase      : p.requires_grad				,						model.parameters()      )      )
						lowerCamelCase__						:						str						      =deepspeed_optim_sched(
						    _A				,						_A				,						_A				,						_A				,						_A      )

			# keep for quick debug:
			# from pprint import pprint; pprint(config)

			return optimizer, lr_scheduler






def 	snake_case__  (						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       Union[str, Any]      ):
			"""simple docstring"""
			# it's possible that the user is trying to resume from model_path, which doesn't necessarily
			# contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
			# a resume from a checkpoint and not just a local pretrained weight. So we check here if the
			# path contains what looks like a deepspeed checkpoint
			import glob

			lowerCamelCase__						:						Any						      =sorted(glob.glob(f'''{checkpoint_path}/global_step*'''      )      )

			if len(_A      ) > 0:
						logger.info(f'''Attempting to resume from {checkpoint_path}'''      )
						# this magically updates self.optimizer and self.lr_scheduler
						lowerCamelCase__						:						Optional[Any]						      =deepspeed_engine.load_checkpoint(
						    _A				,						load_optimizer_states=_A				,						load_lr_scheduler_states=_A      )
						if load_path is None:
									raise ValueError(f'''[deepspeed] failed to resume from checkpoint {checkpoint_path}'''      )
			else:
						raise ValueError(f'''Can\'t find a valid checkpoint at {checkpoint_path}'''      )







import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


UpperCAmelCase__			:  Union[str, Any]					     =     logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()






def        __lowercase   (  _A      )  ->    Optional[int]:
      if "token" in model_name_or_path:
            return "rag_token"
      if "sequence" in model_name_or_path:
            return "rag_sequence"
      if "bart" in model_name_or_path:
            return "bart"
      return None






def        __lowercase   (  _A    ,     _A    ,     _A      )  ->    str:
      return max(metric_fn(_A    ,     _A      ) for gt in ground_truths      )






def        __lowercase   (  _A    ,     _A    ,     _A      )  ->    int:
      SCREAMING_SNAKE_CASE			:   int	  = [line.strip() for line in open(_A    ,     """r"""      ).readlines()]
      SCREAMING_SNAKE_CASE			:   List[Any]	  = []

      if args.gold_data_mode == "qa":
            SCREAMING_SNAKE_CASE			:   List[str]	  = pd.read_csv(_A    ,     sep="""\t"""    ,     header=_A      )
            for answer_list in data[1]:
                  SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = ast.literal_eval(_A      )
                  answers.append(_A      )
      else:
            SCREAMING_SNAKE_CASE			:   List[Any]	  = [line.strip() for line in open(_A    ,     """r"""      ).readlines()]
            SCREAMING_SNAKE_CASE			:   str	  = [[reference] for reference in references]

      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = 0
      for prediction, ground_truths in zip(_A    ,     _A      ):
            total += 1
            em += metric_max_over_ground_truths(_A    ,     _A    ,     _A      )
            fa += metric_max_over_ground_truths(_A    ,     _A    ,     _A      )

      SCREAMING_SNAKE_CASE			:   List[str]	  = 100.0 * em / total
      SCREAMING_SNAKE_CASE			:   Dict	  = 100.0 * fa / total

      logger.info(F"F1: {fa:.2f}"      )
      logger.info(F"EM: {em:.2f}"      )






def        __lowercase   (  _A    ,     _A    ,     _A      )  ->    Any:
      SCREAMING_SNAKE_CASE			:   str	  = args.k
      SCREAMING_SNAKE_CASE			:   int	  = [line.strip() for line in open(_A    ,     """r"""      ).readlines()]
      SCREAMING_SNAKE_CASE			:   List[str]	  = [line.strip() for line in open(_A    ,     """r"""      ).readlines()]

      SCREAMING_SNAKE_CASE			:   Optional[int]	  = 0
      for hypo, reference in zip(_A    ,     _A      ):
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = set(hypo.split("""\t"""      )[:k]      )
            SCREAMING_SNAKE_CASE			:   List[Any]	  = set(reference.split("""\t"""      )      )
            total += 1
            em += len(hypo_provenance & ref_provenance      ) / k

      SCREAMING_SNAKE_CASE			:   int	  = 100.0 * em / total
      logger.info(F"Precision@{k}: {em: .2f}"      )






def        __lowercase   (  _A    ,     _A    ,     _A      )  ->    Optional[int]:
      def strip_title(_A      ):
            if title.startswith("""\""""      ):
                  SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = title[1:]
            if title.endswith("""\""""      ):
                  SCREAMING_SNAKE_CASE			:   List[Any]	  = title[:-1]
            return title

      SCREAMING_SNAKE_CASE			:   Tuple	  = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          _A    ,     return_tensors="""pt"""    ,     padding=_A    ,     truncation=_A    ,     )["""input_ids"""].to(args.device      )

      SCREAMING_SNAKE_CASE			:   str	  = rag_model.rag.question_encoder(_A      )
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = question_enc_outputs[0]

      SCREAMING_SNAKE_CASE			:   int	  = rag_model.retriever(
          _A    ,     question_enc_pool_output.cpu().detach().to(torch.floataa      ).numpy()    ,     prefix=rag_model.rag.generator.config.prefix    ,     n_docs=rag_model.config.n_docs    ,     return_tensors="""pt"""    ,     )
      SCREAMING_SNAKE_CASE			:   Any	  = rag_model.retriever.index.get_doc_dicts(result.doc_ids      )
      SCREAMING_SNAKE_CASE			:   List[str]	  = []
      for docs in all_docs:
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = [strip_title(_A      ) for title in docs["""title"""]]
            provenance_strings.append("""\t""".join(_A      )      )
      return provenance_strings






def        __lowercase   (  _A    ,     _A    ,     _A      )  ->    Dict:
      with torch.no_grad():
            SCREAMING_SNAKE_CASE			:   Dict	  = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                _A    ,     return_tensors="""pt"""    ,     padding=_A    ,     truncation=_A      )

            SCREAMING_SNAKE_CASE			:   List[Any]	  = inputs_dict.input_ids.to(args.device      )
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = inputs_dict.attention_mask.to(args.device      )
            SCREAMING_SNAKE_CASE			:   Tuple	  = rag_model.generate(  # rag_model overwrites generate
                _A    ,     attention_mask=_A    ,     num_beams=args.num_beams    ,     min_length=args.min_length    ,     max_length=args.max_length    ,     early_stopping=_A    ,     num_return_sequences=1    ,     bad_words_ids=[[0, 0]]    ,     )
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = rag_model.retriever.generator_tokenizer.batch_decode(_A    ,     skip_special_tokens=_A      )

            if args.print_predictions:
                  for q, a in zip(_A    ,     _A      ):
                        logger.info("""Q: {} - A: {}""".format(_A    ,     _A      )      )

            return answers






def        __lowercase   (  )  ->    List[Any]:
      SCREAMING_SNAKE_CASE			:   Tuple	  = argparse.ArgumentParser()
      parser.add_argument(
          """--model_type"""    ,     choices=["""rag_sequence""", """rag_token""", """bart"""]    ,     type=_A    ,     help=(
              """RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
              """ model_name_or_path"""
          )    ,     )
      parser.add_argument(
          """--index_name"""    ,     default=_A    ,     choices=["""exact""", """compressed""", """legacy"""]    ,     type=_A    ,     help="""RAG model retriever type"""    ,     )
      parser.add_argument(
          """--index_path"""    ,     default=_A    ,     type=_A    ,     help="""Path to the retrieval index"""    ,     )
      parser.add_argument("""--n_docs"""    ,     default=5    ,     type=_A    ,     help="""Number of retrieved docs"""      )
      parser.add_argument(
          """--model_name_or_path"""    ,     default=_A    ,     type=_A    ,     required=_A    ,     help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""    ,     )
      parser.add_argument(
          """--eval_mode"""    ,     choices=["""e2e""", """retrieval"""]    ,     default="""e2e"""    ,     type=_A    ,     help=(
              """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
              """ precision@k."""
          )    ,     )
      parser.add_argument("""--k"""    ,     default=1    ,     type=_A    ,     help="""k for the precision@k calculation"""      )
      parser.add_argument(
          """--evaluation_set"""    ,     default=_A    ,     type=_A    ,     required=_A    ,     help="""Path to a file containing evaluation samples"""    ,     )
      parser.add_argument(
          """--gold_data_path"""    ,     default=_A    ,     type=_A    ,     required=_A    ,     help="""Path to a tab-separated file with gold samples"""    ,     )
      parser.add_argument(
          """--gold_data_mode"""    ,     default="""qa"""    ,     type=_A    ,     choices=["""qa""", """ans"""]    ,     help=(
              """Format of the gold data file"""
              """qa - a single line in the following format: question [tab] answer_list"""
              """ans - a single line of the gold file contains the expected answer string"""
          )    ,     )
      parser.add_argument(
          """--predictions_path"""    ,     type=_A    ,     default="""predictions.txt"""    ,     help="""Name of the predictions file, to be stored in the checkpoints directory"""    ,     )
      parser.add_argument(
          """--eval_all_checkpoints"""    ,     action="""store_true"""    ,     help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"""    ,     )
      parser.add_argument(
          """--eval_batch_size"""    ,     default=8    ,     type=_A    ,     help="""Batch size per GPU/CPU for evaluation."""    ,     )
      parser.add_argument(
          """--recalculate"""    ,     help="""Recalculate predictions even if the prediction file exists"""    ,     action="""store_true"""    ,     )
      parser.add_argument(
          """--num_beams"""    ,     default=4    ,     type=_A    ,     help="""Number of beams to be used when generating answers"""    ,     )
      parser.add_argument("""--min_length"""    ,     default=1    ,     type=_A    ,     help="""Min length of the generated answers"""      )
      parser.add_argument("""--max_length"""    ,     default=50    ,     type=_A    ,     help="""Max length of the generated answers"""      )

      parser.add_argument(
          """--print_predictions"""    ,     action="""store_true"""    ,     help="""If True, prints predictions while evaluating."""    ,     )
      parser.add_argument(
          """--print_docs"""    ,     action="""store_true"""    ,     help="""If True, prints docs retried while generating."""    ,     )
      SCREAMING_SNAKE_CASE			:   Dict	  = parser.parse_args()
      SCREAMING_SNAKE_CASE			:   List[Any]	  = torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""      )
      return args






def        __lowercase   (  _A      )  ->    Union[str, Any]:
      SCREAMING_SNAKE_CASE			:   str	  = {}
      if args.model_type is None:
            SCREAMING_SNAKE_CASE			:   List[Any]	  = infer_model_type(args.model_name_or_path      )
            assert args.model_type is not None
      if args.model_type.startswith("""rag"""      ):
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = args.n_docs
            if args.index_name is not None:
                  SCREAMING_SNAKE_CASE			:   List[str]	  = args.index_name
            if args.index_path is not None:
                  SCREAMING_SNAKE_CASE			:   Tuple	  = args.index_path
      else:
            SCREAMING_SNAKE_CASE			:   Any	  = BartForConditionalGeneration

      SCREAMING_SNAKE_CASE			:   Dict	  = (
          [f.path for f in os.scandir(args.model_name_or_path      ) if f.is_dir()]
          if args.eval_all_checkpoints
          else [args.model_name_or_path]
      )

      logger.info("""Evaluate the following checkpoints: %s"""    ,     _A      )

      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = get_scores if args.eval_mode == """e2e""" else get_precision_at_k
      SCREAMING_SNAKE_CASE			:   List[str]	  = evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval

      for checkpoint in checkpoints:
            if os.path.exists(args.predictions_path      ) and (not args.recalculate):
                  logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path      )      )
                  score_fn(_A    ,     args.predictions_path    ,     args.gold_data_path      )
                  continue

            logger.info("""***** Running evaluation for {} *****""".format(_A      )      )
            logger.info("""  Batch size = %d"""    ,     args.eval_batch_size      )
            logger.info("""  Predictions will be stored under {}""".format(args.predictions_path      )      )

            if args.model_type.startswith("""rag"""      ):
                  SCREAMING_SNAKE_CASE			:   List[Any]	  = RagRetriever.from_pretrained(_A    ,     **_A      )
                  SCREAMING_SNAKE_CASE			:   List[str]	  = model_class.from_pretrained(_A    ,     retriever=_A    ,     **_A      )
                  model.retriever.init_retrieval()
            else:
                  SCREAMING_SNAKE_CASE			:   Any	  = model_class.from_pretrained(_A    ,     **_A      )
            model.to(args.device      )

            with open(args.evaluation_set    ,     """r"""      ) as eval_file, open(args.predictions_path    ,     """w"""      ) as preds_file:
                  SCREAMING_SNAKE_CASE			:   Optional[Any]	  = []
                  for line in tqdm(_A      ):
                        questions.append(line.strip()      )
                        if len(_A      ) == args.eval_batch_size:
                              SCREAMING_SNAKE_CASE			:   List[Any]	  = evaluate_batch_fn(_A    ,     _A    ,     _A      )
                              preds_file.write("""\n""".join(_A      ) + """\n"""      )
                              preds_file.flush()
                              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = []
                  if len(_A      ) > 0:
                        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = evaluate_batch_fn(_A    ,     _A    ,     _A      )
                        preds_file.write("""\n""".join(_A      )      )
                        preds_file.flush()

                  score_fn(_A    ,     args.predictions_path    ,     args.gold_data_path      )


if __name__ == "__main__":
  UpperCAmelCase__			:  str					     =     get_args()
  main(args)




