




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


lowercase__ :     int			       =   logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()

def    SCREAMING_SNAKE_CASE				(  __UpperCamelCase)     ->	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    SCREAMING_SNAKE_CASE				(  __UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase)     ->	List[Any]:
     return max(metric_fn(UpperCAmelCase__						,   UpperCAmelCase__) for gt in ground_truths)

def    SCREAMING_SNAKE_CASE				(  __UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase)     ->	List[str]:
     a           = [line.strip() for line in open(UpperCAmelCase__						,   "r").readlines()]
     a           = []

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

     a           = a           = a           = 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__)

     a           = 100.0 * em / total
     a           = 100.0 * fa / total

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

def    SCREAMING_SNAKE_CASE				(  __UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase)     ->	str:
     a           = args.k
     a           = [line.strip() for line in open(UpperCAmelCase__						,   "r").readlines()]
     a           = [line.strip() for line in open(UpperCAmelCase__						,   "r").readlines()]

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

     a           = 100.0 * em / total
     logger.info(f'''Precision@{k}: {em: .2f}''')

def    SCREAMING_SNAKE_CASE				(  __UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase)     ->	int:
     def strip_title(__UpperCamelCase):
          if title.startswith("\""):
               a           = title[1:]
          if title.endswith("\""):
               a           = title[:-1]
          return title

     a           = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
         UpperCAmelCase__						,   return_tensors="pt"						,   padding=UpperCAmelCase__						,   truncation=UpperCAmelCase__						,   )["input_ids"].to(args.device)

     a           = rag_model.rag.question_encoder(UpperCAmelCase__)
     a           = question_enc_outputs[0]

     a           = 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"						,   )
     a           = rag_model.retriever.index.get_doc_dicts(result.doc_ids)
     a           = []
     for docs in all_docs:
          a           = [strip_title(UpperCAmelCase__) for title in docs["title"]]
          provenance_strings.append("\t".join(UpperCAmelCase__))
     return provenance_strings

def    SCREAMING_SNAKE_CASE				(  __UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase)     ->	List[str]:
     with torch.no_grad():
          a           = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
              UpperCAmelCase__						,   return_tensors="pt"						,   padding=UpperCAmelCase__						,   truncation=UpperCAmelCase__)

          a           = inputs_dict.input_ids.to(args.device)
          a           = inputs_dict.attention_mask.to(args.device)
          a           = 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]]						,   )
          a           = 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				(  )     ->	Union[str, Any]:
     a           = 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."						,   )
     a           = parser.parse_args()
     a           = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     return args

def    SCREAMING_SNAKE_CASE				(  __UpperCamelCase)     ->	List[Any]:
     a           = {}
     if args.model_type is None:
          a           = infer_model_type(args.model_name_or_path)
          assert args.model_type is not None
     if args.model_type.startswith("rag"):
          a           = RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
          a           = args.n_docs
          if args.index_name is not None:
               a           = args.index_name
          if args.index_path is not None:
               a           = args.index_path
     else:
          a           = BartForConditionalGeneration

     a           = (
         [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__)

     a           = get_scores if args.eval_mode == "e2e" else get_precision_at_k
     a           = 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"):
               a           = RagRetriever.from_pretrained(UpperCAmelCase__						,   **UpperCAmelCase__)
               a           = model_class.from_pretrained(UpperCAmelCase__						,   retriever=UpperCAmelCase__						,   **UpperCAmelCase__)
               model.retriever.init_retrieval()
          else:
               a           = 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:
               a           = []
               for line in tqdm(UpperCAmelCase__):
                    questions.append(line.strip())
                    if len(UpperCAmelCase__) == args.eval_batch_size:
                         a           = evaluate_batch_fn(UpperCAmelCase__						,   UpperCAmelCase__						,   UpperCAmelCase__)
                         preds_file.write("\n".join(UpperCAmelCase__) + "\n")
                         preds_file.flush()
                         a           = []
               if len(UpperCAmelCase__) > 0:
                    a           = 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__":
   lowercase__ :     Any			       =   get_args()
   main(args)




'''simple docstring'''







import os
import sys
import unittest


__lowerCamelCase									=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__lowerCamelCase									=     os.path.join(git_repo_path, '''src''', '''transformers''')

__lowerCamelCase									=     '''
{0} = None
'''

__lowerCamelCase									=     '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


__lowerCamelCase									=     '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''


class        A__   (   unittest.TestCase      ):



    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           A_						       =   find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""							)
           self.assertIsNone(UpperCamelCase__							)

           A_						       =   find_backend("""    if not is_tokenizers_available():"""							)
           self.assertEqual(UpperCamelCase__		,					"""tokenizers"""							)

           A_						       =   find_backend("""    if not is_tensorflow_text_available():"""							)
           self.assertEqual(UpperCamelCase__		,					"""tensorflow_text"""							)

           A_						       =   find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""							)
           self.assertEqual(UpperCamelCase__		,					"""sentencepiece_and_tokenizers"""							)

           A_						       =   find_backend(
               """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""							)
           self.assertEqual(UpperCamelCase__		,					"""sentencepiece_and_tensorflow_text"""							)

           A_						       =   find_backend(
               """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""							)
           self.assertEqual(UpperCamelCase__		,					"""sentencepiece_and_tokenizers_and_vision"""							)



    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           A_						       =   read_init()
           # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
           self.assertIn("""torch"""		,					UpperCamelCase__							)
           self.assertIn("""tensorflow_text"""		,					UpperCamelCase__							)
           self.assertIn("""sentencepiece_and_tokenizers"""		,					UpperCamelCase__							)

           # Likewise, we can't assert on the exact content of a key
           self.assertIn("""BertModel"""		,					objects["""torch"""]							)
           self.assertIn("""TFBertModel"""		,					objects["""tf"""]							)
           self.assertIn("""FlaxBertModel"""		,					objects["""flax"""]							)
           self.assertIn("""BertModel"""		,					objects["""torch"""]							)
           self.assertIn("""TFBertTokenizer"""		,					objects["""tensorflow_text"""]							)
           self.assertIn("""convert_slow_tokenizer"""		,					objects["""sentencepiece_and_tokenizers"""]							)



    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   create_dummy_object("""CONSTANT"""		,					"""'torch'"""							)
           self.assertEqual(UpperCamelCase__		,					"""\nCONSTANT = None\n"""							)

           A_						       =   create_dummy_object("""function"""		,					"""'torch'"""							)
           self.assertEqual(
               UpperCamelCase__		,					"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""							)

           A_						       =   """
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
           A_						       =   create_dummy_object("""FakeClass"""		,					"""'torch'"""							)
           self.assertEqual(UpperCamelCase__		,					UpperCamelCase__							)







    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
           A_						       =   create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}							)
           self.assertEqual(dummy_files["""torch"""]		,					UpperCamelCase__							)
