"""simple docstring"""





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_     =	logging.get_logger(__name__)

lowerCAmelCase_     =	{
    '''Salesforce/blip-vqa-base''': '''https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json''',
    '''Salesforce/blip-vqa-capfit-large''': (
        '''https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'''
    ),
    '''Salesforce/blip-image-captioning-base''': (
        '''https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'''
    ),
    '''Salesforce/blip-image-captioning-large''': (
        '''https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'''
    ),
    '''Salesforce/blip-itm-base-coco''': '''https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json''',
    '''Salesforce/blip-itm-large-coco''': '''https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json''',
    '''Salesforce/blip-itm-base-flikr''': '''https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json''',
    '''Salesforce/blip-itm-large-flikr''': (
        '''https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'''
    ),
}




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"blip_text_model"





				def __init__(      self							:							Any      ,    _A							:							Optional[int]=3_0_5_2_4      ,    _A							:							str=7_6_8      ,    _A							:							List[Any]=7_6_8      ,    _A							:							Tuple=3_0_7_2      ,    _A							:							Optional[int]=7_6_8      ,    _A							:							Union[str, Any]=1_2      ,    _A							:							int=8      ,    _A							:							Optional[Any]=5_1_2      ,    _A							:							str="gelu"      ,    _A							:							Any=1e-12      ,    _A							:							List[Any]=0.0      ,    _A							:							List[str]=0.0      ,    _A							:							Dict=0.02      ,    _A							:							Any=3_0_5_2_2      ,    _A							:							int=2      ,    _A							:							Union[str, Any]=0      ,    _A							:							str=1_0_2      ,    _A							:							Union[str, Any]=True      ,    _A							:							Union[str, Any]=True      ,    **_A							:							Optional[int]      ,    ):



											"""simple docstring"""







											super().__init__(
											    pad_token_id=_A      ,    bos_token_id=_A      ,    eos_token_id=_A      ,    sep_token_id=_A      ,    **_A      ,    )

											_SCREAMING_SNAKE_CASE		:						Dict			  =    vocab_size
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    hidden_size
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    encoder_hidden_size
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    intermediate_size
											_SCREAMING_SNAKE_CASE		:						Any			  =    projection_dim
											_SCREAMING_SNAKE_CASE		:						int			  =    hidden_dropout_prob
											_SCREAMING_SNAKE_CASE		:						Dict			  =    num_hidden_layers
											_SCREAMING_SNAKE_CASE		:						str			  =    num_attention_heads
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    max_position_embeddings
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    layer_norm_eps
											_SCREAMING_SNAKE_CASE		:						int			  =    hidden_act
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    initializer_range
											_SCREAMING_SNAKE_CASE		:						Any			  =    attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE		:						Any			  =    is_decoder
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    use_cache





				@classmethod
				def 		_lowerCAmelCase		(      cls							:							Tuple      ,    _A							:							Union[str, os.PathLike]      ,    **_A							:							Optional[int]):



											"""simple docstring"""







											cls._set_token_in_kwargs(_A)

											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[Any]			  =    cls.get_config_dict(_A      ,    **_A)

											# get the text config dict if we are loading from BlipConfig
											if config_dict.get("""model_type""") == "blip":
																		_SCREAMING_SNAKE_CASE		:						Any			  =    config_dict["""text_config"""]

											if "model_type" in config_dict and hasattr(cls      ,    """model_type""") and config_dict["model_type"] != cls.model_type:
																		logger.warning(
																		    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																		    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

											return cls.from_dict(_A      ,    **_A)




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"blip_vision_model"





				def __init__(      self							:							int      ,    _A							:							Dict=7_6_8      ,    _A							:							Any=3_0_7_2      ,    _A							:							Union[str, Any]=5_1_2      ,    _A							:							List[str]=1_2      ,    _A							:							List[str]=1_2      ,    _A							:							int=3_8_4      ,    _A							:							Optional[int]=1_6      ,    _A							:							Tuple="gelu"      ,    _A							:							Dict=1e-5      ,    _A							:							Dict=0.0      ,    _A							:							Optional[Any]=1e-10      ,    **_A							:							Dict      ,    ):



											"""simple docstring"""







											super().__init__(**_A)

											_SCREAMING_SNAKE_CASE		:						Any			  =    hidden_size
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    intermediate_size
											_SCREAMING_SNAKE_CASE		:						int			  =    projection_dim
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    num_hidden_layers
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    num_attention_heads
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    patch_size
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    image_size
											_SCREAMING_SNAKE_CASE		:						str			  =    initializer_range
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    attention_dropout
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    layer_norm_eps
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    hidden_act





				@classmethod
				def 		_lowerCAmelCase		(      cls							:							Optional[int]      ,    _A							:							Union[str, os.PathLike]      ,    **_A							:							int):



											"""simple docstring"""







											cls._set_token_in_kwargs(_A)

											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Any			  =    cls.get_config_dict(_A      ,    **_A)

											# get the vision config dict if we are loading from BlipConfig
											if config_dict.get("""model_type""") == "blip":
																		_SCREAMING_SNAKE_CASE		:						str			  =    config_dict["""vision_config"""]

											if "model_type" in config_dict and hasattr(cls      ,    """model_type""") and config_dict["model_type"] != cls.model_type:
																		logger.warning(
																		    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																		    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

											return cls.from_dict(_A      ,    **_A)




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"blip"
				a			=						True





				def __init__(      self							:							Optional[Any]      ,    _A							:							Any=None      ,    _A							:							List[Any]=None      ,    _A							:							Any=5_1_2      ,    _A							:							str=2.6_592      ,    _A							:							Union[str, Any]=2_5_6      ,    **_A							:							int      ,    ):



											"""simple docstring"""







											super().__init__(**_A)

											if text_config is None:
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    {}
																		logger.info("""`text_config` is `None`. Initializing the `BlipTextConfig` with default values.""")

											if vision_config is None:
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {}
																		logger.info("""`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.""")

											_SCREAMING_SNAKE_CASE		:						str			  =    BlipTextConfig(**_A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    BlipVisionConfig(**_A)

											_SCREAMING_SNAKE_CASE		:						int			  =    self.vision_config.hidden_size

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    projection_dim
											_SCREAMING_SNAKE_CASE		:						Dict			  =    logit_scale_init_value
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    1.0
											_SCREAMING_SNAKE_CASE		:						str			  =    0.02
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    image_text_hidden_size





				@classmethod
				def 		_lowerCAmelCase		(      cls							:							Optional[Any]      ,    _A							:							BlipTextConfig      ,    _A							:							BlipVisionConfig      ,    **_A							:							int):



											"""simple docstring"""







											return cls(text_config=text_config.to_dict()      ,    vision_config=vision_config.to_dict()      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    copy.deepcopy(self.__dict__)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.text_config.to_dict()
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.vision_config.to_dict()
											_SCREAMING_SNAKE_CASE		:						int			  =    self.__class__.model_type
											return output



"""simple docstring"""





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


lowerCAmelCase_     =	logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    List[str]:
							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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Any:
							return max(metric_fn(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) for gt in ground_truths  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[str]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]
							_SCREAMING_SNAKE_CASE		:						Dict			  =    []

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

							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    0
							for prediction, ground_truths in zip(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														total += 1
														em += metric_max_over_ground_truths(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
														fa += metric_max_over_ground_truths(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Dict			  =    1_00.0 * em / total
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    1_00.0 * fa / total

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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    args.k
							_SCREAMING_SNAKE_CASE		:						int			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]
							_SCREAMING_SNAKE_CASE		:						Any			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]

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

							_SCREAMING_SNAKE_CASE		:						int			  =    1_00.0 * em / total
							logger.info(F"""Precision@{k}: {em: .2f}"""  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:
							def strip_title(__SCREAMING_SNAKE_CASE  ):
														if title.startswith("""\""""  ):
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    title[1:]
														if title.endswith("""\""""  ):
																					_SCREAMING_SNAKE_CASE		:						str			  =    title[:-1]
														return title

							_SCREAMING_SNAKE_CASE		:						Dict			  =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
							    __SCREAMING_SNAKE_CASE     ,	return_tensors="""pt"""     ,	padding=__SCREAMING_SNAKE_CASE     ,	truncation=__SCREAMING_SNAKE_CASE     ,	)["""input_ids"""].to(args.device  )

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    rag_model.rag.question_encoder(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    question_enc_outputs[0]

							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    rag_model.retriever(
							    __SCREAMING_SNAKE_CASE     ,	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		:						Optional[int]			  =    rag_model.retriever.index.get_doc_dicts(result.doc_ids  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    []
							for docs in all_docs:
														_SCREAMING_SNAKE_CASE		:						str			  =    [strip_title(__SCREAMING_SNAKE_CASE  ) for title in docs["""title"""]]
														provenance_strings.append("""\t""".join(__SCREAMING_SNAKE_CASE  )  )
							return provenance_strings



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[Any]:
							with torch.no_grad():
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
														    __SCREAMING_SNAKE_CASE     ,	return_tensors="""pt"""     ,	padding=__SCREAMING_SNAKE_CASE     ,	truncation=__SCREAMING_SNAKE_CASE  )

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

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

														return answers



def      lowerCamelCase_()->    List[Any]:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    argparse.ArgumentParser()
							parser.add_argument(
							    """--model_type"""     ,	choices=["""rag_sequence""", """rag_token""", """bart"""]     ,	type=__SCREAMING_SNAKE_CASE     ,	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=__SCREAMING_SNAKE_CASE     ,	choices=["""exact""", """compressed""", """legacy"""]     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""RAG model retriever type"""     ,	)
							parser.add_argument(
							    """--index_path"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""Path to the retrieval index"""     ,	)
							parser.add_argument("""--n_docs"""     ,	default=5     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""Number of retrieved docs"""  )
							parser.add_argument(
							    """--model_name_or_path"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""     ,	)
							parser.add_argument(
							    """--eval_mode"""     ,	choices=["""e2e""", """retrieval"""]     ,	default="""e2e"""     ,	type=__SCREAMING_SNAKE_CASE     ,	help=(
							        """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
							        """ precision@k."""
							    )     ,	)
							parser.add_argument("""--k"""     ,	default=1     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""k for the precision@k calculation"""  )
							parser.add_argument(
							    """--evaluation_set"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help="""Path to a file containing evaluation samples"""     ,	)
							parser.add_argument(
							    """--gold_data_path"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help="""Path to a tab-separated file with gold samples"""     ,	)
							parser.add_argument(
							    """--gold_data_mode"""     ,	default="""qa"""     ,	type=__SCREAMING_SNAKE_CASE     ,	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=__SCREAMING_SNAKE_CASE     ,	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=__SCREAMING_SNAKE_CASE     ,	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=__SCREAMING_SNAKE_CASE     ,	help="""Number of beams to be used when generating answers"""     ,	)
							parser.add_argument("""--min_length"""     ,	default=1     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""Min length of the generated answers"""  )
							parser.add_argument("""--max_length"""     ,	default=50     ,	type=__SCREAMING_SNAKE_CASE     ,	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		:						Any			  =    torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""  )
							return args



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

							_SCREAMING_SNAKE_CASE		:						int			  =    (
							    [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"""     ,	__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Tuple			  =    get_scores if args.eval_mode == """e2e""" else get_precision_at_k
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    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(__SCREAMING_SNAKE_CASE     ,	args.predictions_path     ,	args.gold_data_path  )
																					continue

														logger.info("""***** Running evaluation for {} *****""".format(__SCREAMING_SNAKE_CASE  )  )
														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		:						str			  =    RagRetriever.from_pretrained(__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE  )
																					_SCREAMING_SNAKE_CASE		:						Tuple			  =    model_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,	retriever=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE  )
																					model.retriever.init_retrieval()
														else:
																					_SCREAMING_SNAKE_CASE		:						str			  =    model_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE  )
														model.to(args.device  )

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

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


if __name__ == "__main__":
			lowerCAmelCase_     =	get_args()
			main(args)


