



'''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


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

transformers_logging.set_verbosity_info()




def lowerCAmelCase    (__A):



     """simple docstring"""



     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    (__A    ,      __A    ,      __A):



     """simple docstring"""



     return max(metric_fn(__A    ,      __A) for gt in ground_truths)




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    [line.strip() for line in open(__A    ,      '''r''').readlines()]
     _a           =    []

     if args.gold_data_mode == "qa":
          _a           =    pd.read_csv(__A    ,      sep='''\t'''    ,      header=__A)
          for answer_list in data[1]:
               _a           =    ast.literal_eval(__A)
               answers.append(__A)
     else:
          _a           =    [line.strip() for line in open(__A    ,      '''r''').readlines()]
          _a           =    [[reference] for reference in references]

     _a           =    _a           =    _a           =    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)

     _a           =    1_00.0 * em / total
     _a           =    1_00.0 * fa / total

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




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    args.k
     _a           =    [line.strip() for line in open(__A    ,      '''r''').readlines()]
     _a           =    [line.strip() for line in open(__A    ,      '''r''').readlines()]

     _a           =    _a           =    0
     for hypo, reference in zip(__A    ,      __A):
          _a           =    set(hypo.split('''\t''')[:k])
          _a           =    set(reference.split('''\t'''))
          total += 1
          em += len(hypo_provenance & ref_provenance) / k

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




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     def strip_title(__A):
          if title.startswith('''"'''):
               _a           =    title[1:]
          if title.endswith('''"'''):
               _a           =    title[:-1]
          return title

     _a           =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
         __A    ,      return_tensors='''pt'''    ,      padding=__A    ,      truncation=__A    ,      )['''input_ids'''].to(args.device)

     _a           =    rag_model.rag.question_encoder(__A)
     _a           =    question_enc_outputs[0]

     _a           =    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'''    ,      )
     _a           =    rag_model.retriever.index.get_doc_dicts(result.doc_ids)
     _a           =    []
     for docs in all_docs:
          _a           =    [strip_title(__A) for title in docs['''title''']]
          provenance_strings.append('''\t'''.join(__A))
     return provenance_strings




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     with torch.no_grad():
          _a           =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
              __A    ,      return_tensors='''pt'''    ,      padding=__A    ,      truncation=__A)

          _a           =    inputs_dict.input_ids.to(args.device)
          _a           =    inputs_dict.attention_mask.to(args.device)
          _a           =    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]]    ,      )
          _a           =    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 lowerCAmelCase    ():



     """simple docstring"""



     _a           =    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.'''    ,      )
     _a           =    parser.parse_args()
     _a           =    torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''')
     return args




def lowerCAmelCase    (__A):



     """simple docstring"""



     _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'''    ,      __A)

     _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(__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'''):
               _a           =    RagRetriever.from_pretrained(__A    ,      **__A)
               _a           =    model_class.from_pretrained(__A    ,      retriever=__A    ,      **__A)
               model.retriever.init_retrieval()
          else:
               _a           =    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:
               _a           =    []
               for line in tqdm(__A):
                    questions.append(line.strip())
                    if len(__A) == args.eval_batch_size:
                         _a           =    evaluate_batch_fn(__A    ,      __A    ,      __A)
                         preds_file.write('''\n'''.join(__A) + '''\n''')
                         preds_file.flush()
                         _a           =    []
               if len(__A) > 0:
                    _a           =    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__":
  lowercase_									=    get_args()
  main(args)








'''simple docstring'''





import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel






class __A     :



 '''simple docstring'''







 def __init__(self					,  A					,  A=13					,  A=7					,  A=True					,  A=True					,  A=False					,  A=True					,  A=99					,  A=32					,  A=5					,  A=4					,  A=37					,  A="gelu"					,  A=0.1					,  A=0.1					,  A=512					,  A=16					,  A=2					,  A=0.02					,  A=3					,  A=4					,  A=None					,  )       ->	str:


      """simple docstring"""



      _a           =    parent
      _a           =    batch_size
      _a           =    seq_length
      _a           =    is_training
      _a           =    use_input_mask
      _a           =    use_token_type_ids
      _a           =    use_labels
      _a           =    vocab_size
      _a           =    hidden_size
      _a           =    num_hidden_layers
      _a           =    num_attention_heads
      _a           =    intermediate_size
      _a           =    hidden_act
      _a           =    hidden_dropout_prob
      _a           =    attention_probs_dropout_prob
      _a           =    max_position_embeddings
      _a           =    type_vocab_size
      _a           =    type_sequence_label_size
      _a           =    initializer_range
      _a           =    num_labels
      _a           =    num_choices
      _a           =    scope



 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



      _a           =    ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size			)

      _a           =    None
      if self.use_input_mask:
           _a           =    random_attention_mask([self.batch_size, self.seq_length]			)

      _a           =    None
      if self.use_token_type_ids:
           _a           =    ids_tensor([self.batch_size, self.seq_length]					,  self.type_vocab_size			)

      _a           =    None
      _a           =    None
      _a           =    None
      if self.use_labels:
           _a           =    ids_tensor([self.batch_size]					,  self.type_sequence_label_size			)
           _a           =    ids_tensor([self.batch_size, self.seq_length]					,  self.num_labels			)
           _a           =    ids_tensor([self.batch_size]					,  self.num_choices			)

      _a           =    self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      return OpenLlamaConfig(
          vocab_size=self.vocab_size					,  hidden_size=self.hidden_size					,  num_hidden_layers=self.num_hidden_layers					,  num_attention_heads=self.num_attention_heads					,  intermediate_size=self.intermediate_size					,  hidden_act=self.hidden_act					,  hidden_dropout_prob=self.hidden_dropout_prob					,  attention_probs_dropout_prob=self.attention_probs_dropout_prob					,  max_position_embeddings=self.max_position_embeddings					,  type_vocab_size=self.type_vocab_size					,  is_decoder=A					,  initializer_range=self.initializer_range					,  use_stable_embedding=A					,  )



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A			)       ->	Any:


      """simple docstring"""



      _a           =    OpenLlamaModel(config=A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  attention_mask=A			)
      _a           =    model(A			)
      self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size)			)



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	Any:


      """simple docstring"""



      _a           =    True
      _a           =    OpenLlamaModel(A			)
      model.to(A			)
      model.eval()
      _a           =    model(
          A					,  attention_mask=A					,  encoder_hidden_states=A					,  encoder_attention_mask=A					,  )
      _a           =    model(
          A					,  attention_mask=A					,  encoder_hidden_states=A					,  )
      _a           =    model(A					,  attention_mask=A			)
      self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size)			)



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	Tuple:


      """simple docstring"""



      _a           =    OpenLlamaForCausalLM(config=A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  attention_mask=A					,  labels=A			)
      self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.vocab_size)			)



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	Dict:


      """simple docstring"""



      _a           =    True
      _a           =    True
      _a           =    OpenLlamaForCausalLM(config=A			)
      model.to(A			)
      model.eval()

      # first forward pass
      _a           =    model(
          A					,  attention_mask=A					,  encoder_hidden_states=A					,  encoder_attention_mask=A					,  use_cache=A					,  )
      _a           =    outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      _a           =    ids_tensor((self.batch_size, 3)					,  config.vocab_size			)
      _a           =    ids_tensor((self.batch_size, 3)					,  vocab_size=2			)

      # append to next input_ids and
      _a           =    torch.cat([input_ids, next_tokens]					,  dim=-1			)
      _a           =    torch.cat([input_mask, next_mask]					,  dim=-1			)

      _a           =    model(
          A					,  attention_mask=A					,  encoder_hidden_states=A					,  encoder_attention_mask=A					,  output_hidden_states=A					,  )['''hidden_states'''][0]
      _a           =    model(
          A					,  attention_mask=A					,  encoder_hidden_states=A					,  encoder_attention_mask=A					,  past_key_values=A					,  output_hidden_states=A					,  )['''hidden_states'''][0]

      # select random slice
      _a           =    ids_tensor((1,)					,  output_from_past.shape[-1]			).item()
      _a           =    output_from_no_past[:, -3:, random_slice_idx].detach()
      _a           =    output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]			)

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(A					,  A					,  atol=1E-3			)			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    self.prepare_config_and_inputs()
      (
          (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       
      )           =    config_and_inputs
      _a           =    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict






@require_torch
class __A     (    A  ,  A  ,  A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[int]		=			(
     (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
 )
 __lowerCamelCase       :			Any		=			(OpenLlamaForCausalLM,) if is_torch_available() else ()
 __lowerCamelCase       :			List[Any]		=			(
     {
         'feature-extraction': OpenLlamaModel,
         'text-classification': OpenLlamaForSequenceClassification,
         'text-generation': OpenLlamaForCausalLM,
         'zero-shot': OpenLlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowerCamelCase       :			List[str]		=			False
 __lowerCamelCase       :			List[str]		=			False



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    OpenLlamaModelTester(self			)
      _a           =    ConfigTester(self					,  config_class=A					,  hidden_size=37			)



 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



      self.config_tester.run_common_tests()



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


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           _a           =    type
           self.model_tester.create_and_check_model(*A			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      _a           =    3
      _a           =    input_dict['''input_ids''']
      _a           =    input_ids.ne(1			).to(A			)
      _a           =    ids_tensor([self.model_tester.batch_size]					,  self.model_tester.type_sequence_label_size			)
      _a           =    OpenLlamaForSequenceClassification(A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  attention_mask=A					,  labels=A			)
      self.assertEqual(result.logits.shape					,  (self.model_tester.batch_size, self.model_tester.num_labels)			)



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      _a           =    3
      _a           =    '''single_label_classification'''
      _a           =    input_dict['''input_ids''']
      _a           =    input_ids.ne(1			).to(A			)
      _a           =    ids_tensor([self.model_tester.batch_size]					,  self.model_tester.type_sequence_label_size			)
      _a           =    OpenLlamaForSequenceClassification(A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  attention_mask=A					,  labels=A			)
      self.assertEqual(result.logits.shape					,  (self.model_tester.batch_size, self.model_tester.num_labels)			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      _a           =    3
      _a           =    '''multi_label_classification'''
      _a           =    input_dict['''input_ids''']
      _a           =    input_ids.ne(1			).to(A			)
      _a           =    ids_tensor(
          [self.model_tester.batch_size, config.num_labels]					,  self.model_tester.type_sequence_label_size			).to(torch.float			)
      _a           =    OpenLlamaForSequenceClassification(A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  attention_mask=A					,  labels=A			)
      self.assertEqual(result.logits.shape					,  (self.model_tester.batch_size, self.model_tester.num_labels)			)



 @unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test'''			)
 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      pass



 @parameterized.expand([('''linear''',), ('''dynamic''',)]			)
 def    a__    (self					,  A			)       ->	Optional[int]:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      _a           =    ids_tensor([1, 10]					,  config.vocab_size			)
      _a           =    ids_tensor([1, int(config.max_position_embeddings * 1.5			)]					,  config.vocab_size			)

      set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
      _a           =    OpenLlamaModel(A			)
      original_model.to(A			)
      original_model.eval()
      _a           =    original_model(A			).last_hidden_state
      _a           =    original_model(A			).last_hidden_state

      set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
      _a           =    {'''type''': scaling_type, '''factor''': 10.0}
      _a           =    OpenLlamaModel(A			)
      scaled_model.to(A			)
      scaled_model.eval()
      _a           =    scaled_model(A			).last_hidden_state
      _a           =    scaled_model(A			).last_hidden_state

      # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
      # maximum sequence length, so the outputs for the short input should match.
      if scaling_type == "dynamic":
           self.assertTrue(torch.allclose(A					,  A					,  atol=1E-5			)			)
      else:
           self.assertFalse(torch.allclose(A					,  A					,  atol=1E-5			)			)

      # The output should be different for long inputs
      self.assertFalse(torch.allclose(A					,  A					,  atol=1E-5			)			)



