



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

transformers_logging.set_verbosity_info()


def 	UpperCamelCase			(     snake_case__ :					Optional[int]			)			->  List[str]:
   if "token" in model_name_or_path:
      return "rag_token"
   if "sequence" in model_name_or_path:
      return "rag_sequence"
   if "bart" in model_name_or_path:
      return "bart"
   return None


def 	UpperCamelCase			(     snake_case__ :					List[Any] ,  snake_case__ :					Dict ,  snake_case__ :					Any			)			->  int:
   return max(metric_fn(snake_case__ ,  snake_case__			) for gt in ground_truths			)


def 	UpperCamelCase			(     snake_case__ :					Optional[int] ,  snake_case__ :					List[str] ,  snake_case__ :					Optional[int]			)			->  int:
   UpperCamelCase					:						Optional[Any]					=			[line.strip() for line in open(snake_case__ ,  'r'			).readlines()]
   UpperCamelCase					:						str					=			[]

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

   UpperCamelCase					:						int					=			0
   for prediction, ground_truths in zip(snake_case__ ,  snake_case__			):
      total += 1
      em += metric_max_over_ground_truths(snake_case__ ,  snake_case__ ,  snake_case__			)
      fa += metric_max_over_ground_truths(snake_case__ ,  snake_case__ ,  snake_case__			)

   UpperCamelCase					:						List[Any]					=			100.0 * em / total
   UpperCamelCase					:						Optional[Any]					=			100.0 * fa / total

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


def 	UpperCamelCase			(     snake_case__ :					List[Any] ,  snake_case__ :					List[str] ,  snake_case__ :					List[str]			)			->  Optional[Any]:
   UpperCamelCase					:						List[Any]					=			args.k
   UpperCamelCase					:						Dict					=			[line.strip() for line in open(snake_case__ ,  'r'			).readlines()]
   UpperCamelCase					:						Optional[Any]					=			[line.strip() for line in open(snake_case__ ,  'r'			).readlines()]

   UpperCamelCase					:						Optional[Any]					=			0
   for hypo, reference in zip(snake_case__ ,  snake_case__			):
      UpperCamelCase					:						Dict					=			set(hypo.split('\t'			)[:k]			)
      UpperCamelCase					:						int					=			set(reference.split('\t'			)			)
      total += 1
      em += len(hypo_provenance & ref_provenance			) / k

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


def 	UpperCamelCase			(     snake_case__ :					Dict ,  snake_case__ :					List[str] ,  snake_case__ :					Any			)			->  str:
   def strip_title(snake_case__ :					Optional[int]			):
      if title.startswith('\"'			):
         UpperCamelCase					:						List[str]					=			title[1:]
      if title.endswith('\"'			):
         UpperCamelCase					:						Dict					=			title[:-1]
      return title

   UpperCamelCase					:						int					=			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
       snake_case__ ,  return_tensors='pt' ,  padding=snake_case__ ,  truncation=snake_case__ ,  )["input_ids"].to(args.device			)

   UpperCamelCase					:						Union[str, Any]					=			rag_model.rag.question_encoder(snake_case__			)
   UpperCamelCase					:						Tuple					=			question_enc_outputs[0]

   UpperCamelCase					:						str					=			rag_model.retriever(
       snake_case__ ,  question_enc_pool_output.cpu().detach().to(torch.floataa			).numpy() ,  prefix=rag_model.rag.generator.config.prefix ,  n_docs=rag_model.config.n_docs ,  return_tensors='pt' ,  )
   UpperCamelCase					:						Optional[int]					=			rag_model.retriever.index.get_doc_dicts(result.doc_ids			)
   UpperCamelCase					:						Union[str, Any]					=			[]
   for docs in all_docs:
      UpperCamelCase					:						Dict					=			[strip_title(snake_case__			) for title in docs["title"]]
      provenance_strings.append('\t'.join(snake_case__			)			)
   return provenance_strings


def 	UpperCamelCase			(     snake_case__ :					Any ,  snake_case__ :					Union[str, Any] ,  snake_case__ :					List[str]			)			->  List[Any]:
   with torch.no_grad():
      UpperCamelCase					:						Union[str, Any]					=			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          snake_case__ ,  return_tensors='pt' ,  padding=snake_case__ ,  truncation=snake_case__			)

      UpperCamelCase					:						Any					=			inputs_dict.input_ids.to(args.device			)
      UpperCamelCase					:						List[Any]					=			inputs_dict.attention_mask.to(args.device			)
      UpperCamelCase					:						List[str]					=			rag_model.generate(  # rag_model overwrites generate
          snake_case__ ,  attention_mask=snake_case__ ,  num_beams=args.num_beams ,  min_length=args.min_length ,  max_length=args.max_length ,  early_stopping=snake_case__ ,  num_return_sequences=1 ,  bad_words_ids=[[0, 0]] ,  )
      UpperCamelCase					:						Dict					=			rag_model.retriever.generator_tokenizer.batch_decode(snake_case__ ,  skip_special_tokens=snake_case__			)

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

      return answers


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

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


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

   UpperCamelCase					:						Any					=			(
       [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' ,  snake_case__			)

   UpperCamelCase					:						Dict					=			get_scores if args.eval_mode == "e2e" else get_precision_at_k
   UpperCamelCase					:						Optional[int]					=			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(snake_case__ ,  args.predictions_path ,  args.gold_data_path			)
         continue

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

      if args.model_type.startswith('rag'			):
         UpperCamelCase					:						Optional[int]					=			RagRetriever.from_pretrained(snake_case__ ,  **snake_case__			)
         UpperCamelCase					:						Tuple					=			model_class.from_pretrained(snake_case__ ,  retriever=snake_case__ ,  **snake_case__			)
         model.retriever.init_retrieval()
      else:
         UpperCamelCase					:						List[str]					=			model_class.from_pretrained(snake_case__ ,  **snake_case__			)
      model.to(args.device			)

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

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


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








import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ViltImageProcessor



class        lowerCAmelCase_	(						unittest.TestCase							):
       def __init__(    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_=7,    SCREAMING_SNAKE_CASE_=3,    SCREAMING_SNAKE_CASE_=18,    SCREAMING_SNAKE_CASE_=30,    SCREAMING_SNAKE_CASE_=400,    SCREAMING_SNAKE_CASE_=True,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_=2,    SCREAMING_SNAKE_CASE_=True,    SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],    SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],    )  ->	List[Any]:
          UpperCamelCase					:						Optional[int]					=			size if size is not None else {'shortest_edge': 30}
          UpperCamelCase					:						Dict					=			parent
          UpperCamelCase					:						str					=			batch_size
          UpperCamelCase					:						str					=			num_channels
          UpperCamelCase					:						Optional[int]					=			image_size
          UpperCamelCase					:						List[Any]					=			min_resolution
          UpperCamelCase					:						Tuple					=			max_resolution
          UpperCamelCase					:						Tuple					=			do_resize
          UpperCamelCase					:						Any					=			size
          UpperCamelCase					:						int					=			size_divisor
          UpperCamelCase					:						str					=			do_normalize
          UpperCamelCase					:						Optional[int]					=			image_mean
          UpperCamelCase					:						List[str]					=			image_std
       def       snake_case_ (    self )  ->	List[Any]:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "size_divisor": self.size_divisor,
          }






       def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_=False )  ->	Union[str, Any]:
          if not batched:
             UpperCamelCase					:						Optional[Any]					=			self.size['shortest_edge']
             UpperCamelCase					:						int					=			image_inputs[0]
             if isinstance(SCREAMING_SNAKE_CASE_,    Image.Image ):
                UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			image.size
             else:
                UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			image.shape[1], image.shape[2]
             UpperCamelCase					:						Any					=			size / min(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
             if h < w:
                UpperCamelCase			,  UpperCamelCase					:						Optional[int]					=			size, scale * w
             else:
                UpperCamelCase			,  UpperCamelCase					:						List[str]					=			scale * h, size

             UpperCamelCase					:						int					=			int((1333 / 800) * size )
             if max(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ ) > max_size:
                UpperCamelCase					:						Any					=			max_size / max(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
                UpperCamelCase					:						List[Any]					=			newh * scale
                UpperCamelCase					:						Optional[int]					=			neww * scale

             UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			int(newh + 0.5 ), int(neww + 0.5 )
             UpperCamelCase			,  UpperCamelCase					:						int					=			(
                 newh // self.size_divisor * self.size_divisor,
                 neww // self.size_divisor * self.size_divisor,
             )

          else:
             UpperCamelCase					:						Dict					=			[]
             for image in image_inputs:
                UpperCamelCase			,  UpperCamelCase					:						Union[str, Any]					=			self.get_expected_values([image] )
                expected_values.append((expected_height, expected_width) )
             UpperCamelCase					:						List[str]					=			max(SCREAMING_SNAKE_CASE_,    key=lambda SCREAMING_SNAKE_CASE_ : item[0] )[0]
             UpperCamelCase					:						List[str]					=			max(SCREAMING_SNAKE_CASE_,    key=lambda SCREAMING_SNAKE_CASE_ : item[1] )[1]

          return expected_height, expected_width






@require_torch
@require_vision
class        lowerCAmelCase_	(						a__ ,					unittest.TestCase							):
       UpperCAmelCase__       :      List[Any]       =						ViltImageProcessor if is_vision_available() else None
       def       snake_case_ (    self )  ->	Union[str, Any]:
          UpperCamelCase					:						Dict					=			ViltImageProcessingTester(self )
       @property
       def       snake_case_ (    self )  ->	Dict:
          return self.image_processor_tester.prepare_image_processor_dict()
       def       snake_case_ (    self )  ->	Union[str, Any]:
          UpperCamelCase					:						List[str]					=			self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'image_mean' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'image_std' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'do_normalize' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'do_resize' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'size' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'size_divisor' ) )
       def       snake_case_ (    self )  ->	List[Any]:
          UpperCamelCase					:						Union[str, Any]					=			self.image_processing_class.from_dict(self.image_processor_dict )
          self.assertEqual(image_processor.size,    {'shortest_edge': 30} )

          UpperCamelCase					:						Union[str, Any]					=			self.image_processing_class.from_dict(self.image_processor_dict,    size=42 )
          self.assertEqual(image_processor.size,    {'shortest_edge': 42} )
       def       snake_case_ (    self )  ->	Dict:
          pass
       def       snake_case_ (    self )  ->	str:
          # Initialize image_processing
          UpperCamelCase					:						int					=			self.image_processing_class(**self.image_processor_dict )
          # create random PIL images
          UpperCamelCase					:						Any					=			prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_ )
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,    Image.Image )

          # Test not batched input
          UpperCamelCase					:						int					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values

          UpperCamelCase			,  UpperCamelCase					:						Dict					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
          self.assertEqual(
              encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )

          # Test batched
          UpperCamelCase					:						str					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values

          UpperCamelCase			,  UpperCamelCase					:						Tuple					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
          self.assertEqual(
              encoded_images.shape,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ),    )
       def       snake_case_ (    self )  ->	List[str]:
          # Initialize image_processing
          UpperCamelCase					:						Optional[int]					=			self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          UpperCamelCase					:						Optional[Any]					=			prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_,    numpify=SCREAMING_SNAKE_CASE_ )
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,    np.ndarray )

          # Test not batched input
          UpperCamelCase					:						Optional[Any]					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values

          UpperCamelCase			,  UpperCamelCase					:						str					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
          self.assertEqual(
              encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )

          # Test batched
          UpperCamelCase					:						Dict					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values

          UpperCamelCase			,  UpperCamelCase					:						Optional[Any]					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
          self.assertEqual(
              encoded_images.shape,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ),    )






       def       snake_case_ (    self )  ->	Optional[Any]:
          # Initialize image_processing
          UpperCamelCase					:						Optional[int]					=			self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          UpperCamelCase					:						Dict					=			prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_,    torchify=SCREAMING_SNAKE_CASE_ )
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,    torch.Tensor )

          # Test not batched input
          UpperCamelCase					:						List[str]					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values

          UpperCamelCase			,  UpperCamelCase					:						Union[str, Any]					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
          self.assertEqual(
              encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )

          # Test batched
          UpperCamelCase					:						List[Any]					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values

          UpperCamelCase			,  UpperCamelCase					:						Dict					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
          self.assertEqual(
              encoded_images.shape,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ),    )



