

import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import EfficientNetForImageClassification, EfficientNetModel
  from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor







class 							a__							:







  """simple docstring"""




  def __init__(      self     :			int					,					UpperCAmelCase__     :			Dict					,					UpperCAmelCase__     :			Tuple=1_3					,					UpperCAmelCase__     :			Dict=3_2					,					UpperCAmelCase__     :			Dict=3					,					UpperCAmelCase__     :			Optional[int]=[3, 3, 5]					,					UpperCAmelCase__     :			Any=[3_2, 1_6, 2_4]					,					UpperCAmelCase__     :			Any=[1_6, 2_4, 4_0]					,					UpperCAmelCase__     :			Optional[int]=[1, 1, 2]					,					UpperCAmelCase__     :			Any=[1, 1, 2]					,					UpperCAmelCase__     :			Optional[Any]=[1, 6, 6]					,					UpperCAmelCase__     :			Union[str, Any]=True					,					UpperCAmelCase__     :			int=True					,					UpperCAmelCase__     :			Dict=3_7					,					UpperCAmelCase__     :			int="gelu"					,					UpperCAmelCase__     :			List[Any]=1_0					,					)       ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Any	  = parent
        SCREAMING_SNAKE_CASE			:   str	  = batch_size
        SCREAMING_SNAKE_CASE			:   Any	  = image_size
        SCREAMING_SNAKE_CASE			:   Any	  = num_channels
        SCREAMING_SNAKE_CASE			:   str	  = kernel_sizes
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = in_channels
        SCREAMING_SNAKE_CASE			:   List[str]	  = out_channels
        SCREAMING_SNAKE_CASE			:   Tuple	  = strides
        SCREAMING_SNAKE_CASE			:   Dict	  = num_block_repeats
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = expand_ratios
        SCREAMING_SNAKE_CASE			:   List[Any]	  = is_training
        SCREAMING_SNAKE_CASE			:   str	  = hidden_act
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = num_labels
        SCREAMING_SNAKE_CASE			:   Any	  = use_labels


  def 			_lowercase   (      self     :			Tuple		)       ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   int	  = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        SCREAMING_SNAKE_CASE			:   int	  = None
        if self.use_labels:
              SCREAMING_SNAKE_CASE			:   int	  = ids_tensor([self.batch_size]					,					self.num_labels		)

        SCREAMING_SNAKE_CASE			:   List[Any]	  = self.get_config()
        return config, pixel_values, labels


  def 			_lowercase   (      self     :			Any		)       ->Tuple:
        """simple docstring"""







        return EfficientNetConfig(
            num_channels=self.num_channels					,					kernel_sizes=self.kernel_sizes					,					in_channels=self.in_channels					,					out_channels=self.out_channels					,					strides=self.strides					,					num_block_repeats=self.num_block_repeats					,					expand_ratios=self.expand_ratios					,					hidden_act=self.hidden_act					,					num_labels=self.num_labels					,					)


  def 			_lowercase   (      self     :			List[str]					,					UpperCAmelCase__     :			Any					,					UpperCAmelCase__     :			Any					,					UpperCAmelCase__     :			Optional[Any]		)       ->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Any	  = EfficientNetModel(config=UpperCAmelCase__		)
        model.to(UpperCAmelCase__		)
        model.eval()
        SCREAMING_SNAKE_CASE			:   List[str]	  = model(UpperCAmelCase__		)
        # expected last hidden states: B, C, H // 4, W // 4
        self.parent.assertEqual(
            result.last_hidden_state.shape					,					(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)					,					)


  def 			_lowercase   (      self     :			int					,					UpperCAmelCase__     :			Optional[int]					,					UpperCAmelCase__     :			List[str]					,					UpperCAmelCase__     :			Dict		)       ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Dict	  = EfficientNetForImageClassification(UpperCAmelCase__		)
        model.to(UpperCAmelCase__		)
        model.eval()
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = model(UpperCAmelCase__					,					labels=UpperCAmelCase__		)
        self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.num_labels)		)




  def 			_lowercase   (      self     :			List[Any]		)       ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Optional[int]	  = self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   Any	  = config_and_inputs
        SCREAMING_SNAKE_CASE			:   str	  = {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class 							a__							( UpperCAmelCase						,      UpperCAmelCase						,      unittest.TestCase							):







  """simple docstring"""




  UpperCAmelCase__   :			Optional[int]      							=(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
  UpperCAmelCase__   :			Dict      							=(
      {"""feature-extraction""": EfficientNetModel, """image-classification""": EfficientNetForImageClassification}
      if is_torch_available()
      else {}
  )

  UpperCAmelCase__   :			Dict      							=False
  UpperCAmelCase__   :			str      							=False
  UpperCAmelCase__   :			Dict      							=False
  UpperCAmelCase__   :			List[str]      							=False
  UpperCAmelCase__   :			Dict      							=False


  def 			_lowercase   (      self     :			int		)       ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Any	  = EfficientNetModelTester(self		)
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = ConfigTester(
            self					,					config_class=UpperCAmelCase__					,					has_text_modality=UpperCAmelCase__					,					hidden_size=3_7		)


  def 			_lowercase   (      self     :			Optional[int]		)       ->Optional[Any]:
        """simple docstring"""







        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()


  def 			_lowercase   (      self     :			List[str]		)       ->Tuple:
        """simple docstring"""







        return


  @unittest.skip(reason="""EfficientNet does not use inputs_embeds"""		)
  def 			_lowercase   (      self     :			Optional[int]		)       ->List[str]:
        """simple docstring"""







        pass


  @unittest.skip(reason="""EfficientNet does not support input and output embeddings"""		)
  def 			_lowercase   (      self     :			Tuple		)       ->Tuple:
        """simple docstring"""







        pass


  @unittest.skip(reason="""EfficientNet does not use feedforward chunking"""		)
  def 			_lowercase   (      self     :			Union[str, Any]		)       ->Optional[int]:
        """simple docstring"""







        pass


  def 			_lowercase   (      self     :			List[str]		)       ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   Tuple	  = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE			:   str	  = model_class(UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE			:   Any	  = inspect.signature(model.forward		)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE			:   int	  = [*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE			:   Optional[int]	  = ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]					,					UpperCAmelCase__		)


  def 			_lowercase   (      self     :			Optional[Any]		)       ->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[str]	  = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__		)


  def 			_lowercase   (      self     :			List[Any]		)       ->Optional[Any]:
        """simple docstring"""







        def check_hidden_states_output(UpperCAmelCase__     :			Tuple					,					UpperCAmelCase__     :			Union[str, Any]					,					UpperCAmelCase__     :			Dict		):
              SCREAMING_SNAKE_CASE			:   str	  = model_class(UpperCAmelCase__		)
              model.to(UpperCAmelCase__		)
              model.eval()

              with torch.no_grad():
                    SCREAMING_SNAKE_CASE			:   Optional[Any]	  = model(**self._prepare_for_class(UpperCAmelCase__					,					UpperCAmelCase__		)		)

              SCREAMING_SNAKE_CASE			:   int	  = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = sum(config.num_block_repeats		) * 4
              self.assertEqual(len(UpperCAmelCase__		)					,					UpperCAmelCase__		)

              # EfficientNet's feature maps are of shape (batch_size, num_channels, height, width)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]		)					,					[self.model_tester.image_size // 2, self.model_tester.image_size // 2]					,					)

        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   int	  = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE			:   Tuple	  = True
              check_hidden_states_output(UpperCAmelCase__					,					UpperCAmelCase__					,					UpperCAmelCase__		)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = True

              check_hidden_states_output(UpperCAmelCase__					,					UpperCAmelCase__					,					UpperCAmelCase__		)


  def 			_lowercase   (      self     :			List[Any]		)       ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__		)


  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""		)
  def 			_lowercase   (      self     :			Tuple		)       ->Union[str, Any]:
        """simple docstring"""







        pass


  @slow
  def 			_lowercase   (      self     :			Tuple		)       ->str:
        """simple docstring"""







        for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE			:   List[str]	  = EfficientNetModel.from_pretrained(UpperCAmelCase__		)
              self.assertIsNotNone(UpperCAmelCase__		)




  @is_pipeline_test
  @require_vision
  @slow
  def 			_lowercase   (      self     :			Optional[int]		)       ->str:
        """simple docstring"""







        super().test_pipeline_image_classification()






def        __lowercase   (  )  ->    str:
      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
      return image







@require_torch
@require_vision
class 							a__							( unittest.TestCase							):







  """simple docstring"""




  @cached_property
  def 			_lowercase   (      self     :			Any		)       ->Any:
        """simple docstring"""







        return AutoImageProcessor.from_pretrained("""google/efficientnet-b7"""		) if is_vision_available() else None




  @slow
  def 			_lowercase   (      self     :			Any		)       ->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = EfficientNetForImageClassification.from_pretrained("""google/efficientnet-b7"""		).to(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE			:   str	  = self.default_image_processor
        SCREAMING_SNAKE_CASE			:   str	  = prepare_img()
        SCREAMING_SNAKE_CASE			:   int	  = image_processor(images=UpperCAmelCase__					,					return_tensors="""pt"""		).to(UpperCAmelCase__		)

        # forward pass
        with torch.no_grad():
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = model(**UpperCAmelCase__		)

        # verify the logits
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = torch.Size((1, 1_0_0_0)		)
        self.assertEqual(outputs.logits.shape					,					UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = torch.tensor([0.00_01, 0.00_02, 0.00_02]		).to(UpperCAmelCase__		)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]					,					UpperCAmelCase__					,					atol=1e-4		)		)







import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

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


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


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

transformers_logging.set_verbosity_info()






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






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






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

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

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

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

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






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

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

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






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

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

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

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






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

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

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

            return answers






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

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






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

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

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

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

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

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

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

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

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


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




