






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      :				Optional[Any]							=logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()







def 						SCREAMING_SNAKE_CASE				(   __lowerCAmelCase     )					->      Union[str, Any]:
     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 						SCREAMING_SNAKE_CASE				(   __lowerCAmelCase   ,       __lowerCAmelCase   ,       __lowerCAmelCase     )					->      Optional[Any]:
     return max(metric_fn(UpperCAmelCase_   ,       UpperCAmelCase_     ) for gt in ground_truths     )







def 						SCREAMING_SNAKE_CASE				(   __lowerCAmelCase   ,       __lowerCAmelCase   ,       __lowerCAmelCase     )					->      Dict:
     UpperCamelCase__     :  Tuple										=    [line.strip() for line in open(UpperCAmelCase_   ,       "r"     ).readlines()]
     UpperCamelCase__     :  Optional[Any]										=    []

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

     UpperCamelCase__     :  Tuple										=    0
     for prediction, ground_truths in zip(UpperCAmelCase_   ,       UpperCAmelCase_     ):
          total += 1
          em += metric_max_over_ground_truths(UpperCAmelCase_   ,       UpperCAmelCase_   ,       UpperCAmelCase_     )
          fa += metric_max_over_ground_truths(UpperCAmelCase_   ,       UpperCAmelCase_   ,       UpperCAmelCase_     )

     UpperCamelCase__     :  int										=    1_0_0.0 * em / total
     UpperCamelCase__     :  Optional[int]										=    1_0_0.0 * fa / total

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







def 						SCREAMING_SNAKE_CASE				(   __lowerCAmelCase   ,       __lowerCAmelCase   ,       __lowerCAmelCase     )					->      Optional[int]:
     UpperCamelCase__     :  int										=    args.k
     UpperCamelCase__     :  Dict										=    [line.strip() for line in open(UpperCAmelCase_   ,       "r"     ).readlines()]
     UpperCamelCase__     :  Tuple										=    [line.strip() for line in open(UpperCAmelCase_   ,       "r"     ).readlines()]

     UpperCamelCase__     :  Any										=    0
     for hypo, reference in zip(UpperCAmelCase_   ,       UpperCAmelCase_     ):
          UpperCamelCase__     :  Tuple										=    set(hypo.split("\t"     )[:k]     )
          UpperCamelCase__     :  Dict										=    set(reference.split("\t"     )     )
          total += 1
          em += len(hypo_provenance & ref_provenance     ) / k

     UpperCamelCase__     :  Any										=    1_0_0.0 * em / total
     logger.info(f'Precision@{k}: {em: .2f}'     )







def 						SCREAMING_SNAKE_CASE				(   __lowerCAmelCase   ,       __lowerCAmelCase   ,       __lowerCAmelCase     )					->      int:
     def strip_title(__lowerCAmelCase     ):
          if title.startswith("\""     ):
               UpperCamelCase__     :  Optional[int]										=    title[1:]
          if title.endswith("\""     ):
               UpperCamelCase__     :  Tuple										=    title[:-1]
          return title

     UpperCamelCase__     :  List[Any]										=    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
         UpperCAmelCase_   ,       return_tensors="pt"   ,       padding=UpperCAmelCase_   ,       truncation=UpperCAmelCase_   ,       )['''input_ids'''].to(args.device     )

     UpperCamelCase__     :  List[Any]										=    rag_model.rag.question_encoder(UpperCAmelCase_     )
     UpperCamelCase__     :  Tuple										=    question_enc_outputs[0]

     UpperCamelCase__     :  Tuple										=    rag_model.retriever(
         UpperCAmelCase_   ,       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"   ,       )
     UpperCamelCase__     :  Optional[Any]										=    rag_model.retriever.index.get_doc_dicts(result.doc_ids     )
     UpperCamelCase__     :  Union[str, Any]										=    []
     for docs in all_docs:
          UpperCamelCase__     :  Union[str, Any]										=    [strip_title(UpperCAmelCase_     ) for title in docs['''title''']]
          provenance_strings.append("\t".join(UpperCAmelCase_     )     )
     return provenance_strings







def 						SCREAMING_SNAKE_CASE				(   __lowerCAmelCase   ,       __lowerCAmelCase   ,       __lowerCAmelCase     )					->      List[Any]:
     with torch.no_grad():
          UpperCamelCase__     :  Dict										=    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
              UpperCAmelCase_   ,       return_tensors="pt"   ,       padding=UpperCAmelCase_   ,       truncation=UpperCAmelCase_     )

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

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

          return answers







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







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

     UpperCamelCase__     :  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"   ,       UpperCAmelCase_     )

     UpperCamelCase__     :  Optional[Any]										=    get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
     UpperCamelCase__     :  Optional[Any]										=    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(UpperCAmelCase_   ,       args.predictions_path   ,       args.gold_data_path     )
               continue

          logger.info("***** Running evaluation for {} *****".format(UpperCAmelCase_     )     )
          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"     ):
               UpperCamelCase__     :  int										=    RagRetriever.from_pretrained(UpperCAmelCase_   ,       **UpperCAmelCase_     )
               UpperCamelCase__     :  str										=    model_class.from_pretrained(UpperCAmelCase_   ,       retriever=UpperCAmelCase_   ,       **UpperCAmelCase_     )
               model.retriever.init_retrieval()
          else:
               UpperCamelCase__     :  Union[str, Any]										=    model_class.from_pretrained(UpperCAmelCase_   ,       **UpperCAmelCase_     )
          model.to(args.device     )

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

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


if __name__ == "__main__":
 lowerCamelCase      :				int							=get_args()
 main(args)

from dataclasses import asdict, dataclass
from typing import Optional

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


_lowercase											=    logging.get_logger(__name__)

# TODO Update this
_lowercase											=    {
    """facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
    # See all ESM models at https://huggingface.co/models?filter=esm
}


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						"esm"


       def __init__(    self						,       __A=None						,       __A=None						,       __A=None						,       __A=768						,       __A=12						,       __A=12						,       __A=3_072						,       __A=0.1						,       __A=0.1						,       __A=1_026						,       __A=0.02						,       __A=1e-1_2						,       __A="absolute"						,       __A=True						,       __A=None						,       __A=False						,       __A=False						,       __A=None						,       __A=None						,       **__A						,       )						->    int:
              super().__init__(pad_token_id=__A						,       mask_token_id=__A						,       **__A				)

              SCREAMING_SNAKE_CASE_							:				Any		=vocab_size
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=hidden_size
              SCREAMING_SNAKE_CASE_							:				int		=num_hidden_layers
              SCREAMING_SNAKE_CASE_							:				int		=num_attention_heads
              SCREAMING_SNAKE_CASE_							:				List[str]		=intermediate_size
              SCREAMING_SNAKE_CASE_							:				int		=hidden_dropout_prob
              SCREAMING_SNAKE_CASE_							:				int		=attention_probs_dropout_prob
              SCREAMING_SNAKE_CASE_							:				List[Any]		=max_position_embeddings
              SCREAMING_SNAKE_CASE_							:				List[Any]		=initializer_range
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=layer_norm_eps
              SCREAMING_SNAKE_CASE_							:				str		=position_embedding_type
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=use_cache
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=emb_layer_norm_before
              SCREAMING_SNAKE_CASE_							:				Tuple		=token_dropout
              SCREAMING_SNAKE_CASE_							:				List[Any]		=is_folding_model
              if is_folding_model:
                     if esmfold_config is None:
                            logger.info('''No esmfold_config supplied for folding model, using default values.'''				)
                            SCREAMING_SNAKE_CASE_							:				Optional[int]		=EsmFoldConfig()
                     elif isinstance(__A						,       __A				):
                            SCREAMING_SNAKE_CASE_							:				List[Any]		=EsmFoldConfig(**__A				)
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=esmfold_config
                     if vocab_list is None:
                            logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'''				)
                            SCREAMING_SNAKE_CASE_							:				Tuple		=get_default_vocab_list()
                     else:
                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=vocab_list
              else:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=None
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=None
              if self.esmfold_config is not None and getattr(self.esmfold_config						,       '''use_esm_attn_map'''						,       __A				):
                     raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'''				)





       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				str		=super().to_dict()
              if isinstance(self.esmfold_config						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.esmfold_config.to_dict()
              return output






@dataclass
class 			lowercase_		:
       __lowerCamelCase				       =						None
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						0

       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						False

       __lowerCamelCase				       =						1_2_8
       __lowerCamelCase				       =						None


       def 	_snake_case				(    self				)						->    Optional[Any]:
              if self.trunk is None:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=TrunkConfig()
              elif isinstance(self.trunk						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				List[str]		=TrunkConfig(**self.trunk				)





       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				int		=asdict(self				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.trunk.to_dict()
              return output






@dataclass
class 			lowercase_		:
       __lowerCamelCase				       =						4_8
       __lowerCamelCase				       =						1_0_2_4
       __lowerCamelCase				       =						1_2_8
       __lowerCamelCase				       =						3_2
       __lowerCamelCase				       =						3_2
       __lowerCamelCase				       =						3_2
       __lowerCamelCase				       =						0
       __lowerCamelCase				       =						0
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						4
       __lowerCamelCase				       =						1_2_8
       __lowerCamelCase				       =						None


       def 	_snake_case				(    self				)						->    int:
              if self.structure_module is None:
                     SCREAMING_SNAKE_CASE_							:				Dict		=StructureModuleConfig()
              elif isinstance(self.structure_module						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=StructureModuleConfig(**self.structure_module				)

              if self.max_recycles <= 0:
                     raise ValueError(F'`max_recycles` should be positive, got {self.max_recycles}.'				)
              if self.sequence_state_dim % self.sequence_state_dim != 0:
                     raise ValueError(
                         '''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
                         F' {self.sequence_state_dim} and {self.sequence_state_dim}.'				)
              if self.pairwise_state_dim % self.pairwise_state_dim != 0:
                     raise ValueError(
                         '''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
                         F' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'				)

              SCREAMING_SNAKE_CASE_							:				Tuple		=self.sequence_state_dim // self.sequence_head_width
              SCREAMING_SNAKE_CASE_							:				str		=self.pairwise_state_dim // self.pairwise_head_width

              if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
                     raise ValueError(
                         '''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
                         F' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'				)
              if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
                     raise ValueError(
                         '''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
                         F' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'				)
              if self.pairwise_state_dim % 2 != 0:
                     raise ValueError(F'`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'				)

              if self.dropout >= 0.4:
                     raise ValueError(F'`dropout` should not be greater than 0.4, got {self.dropout}.'				)





       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				str		=asdict(self				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.structure_module.to_dict()
              return output






@dataclass
class 			lowercase_		:
       __lowerCamelCase				       =						3_8_4
       __lowerCamelCase				       =						1_2_8
       __lowerCamelCase				       =						1_6
       __lowerCamelCase				       =						1_2_8
       __lowerCamelCase				       =						1_2
       __lowerCamelCase				       =						4
       __lowerCamelCase				       =						8
       __lowerCamelCase				       =						0.1
       __lowerCamelCase				       =						8
       __lowerCamelCase				       =						1
       __lowerCamelCase				       =						2
       __lowerCamelCase				       =						7
       __lowerCamelCase				       =						1_0
       __lowerCamelCase				       =						1E-8
       __lowerCamelCase				       =						1E5


       def 	_snake_case				(    self				)						->    str:
              return asdict(self				)






def 							SCREAMING_SNAKE_CASE_	(					)			->						int:
       return (
           "<cls>",
           "<pad>",
           "<eos>",
           "<unk>",
           "L",
           "A",
           "G",
           "V",
           "S",
           "E",
           "R",
           "T",
           "I",
           "D",
           "P",
           "K",
           "Q",
           "N",
           "F",
           "Y",
           "M",
           "H",
           "W",
           "C",
           "X",
           "B",
           "U",
           "Z",
           "O",
           ".",
           "-",
           "<null_1>",
           "<mask>",
       )
