





import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

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


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


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

transformers_logging.set_verbosity_info()


def 			lowerCamelCase_		(					lowerCAmelCase:		List[Any] )->						Union[str, Any]:
  if "token" in model_name_or_path:
    return "rag_token"
  if "sequence" in model_name_or_path:
    return "rag_sequence"
  if "bart" in model_name_or_path:
    return "bart"
  return None


def 			lowerCamelCase_		(					lowerCAmelCase:		Dict			,							lowerCAmelCase:		Optional[Any]			,							lowerCAmelCase:		Any )->						str:
  return max(metric_fn(lowerCAmelCase			,							lowerCAmelCase ) for gt in ground_truths )


def 			lowerCamelCase_		(					lowerCAmelCase:		Dict			,							lowerCAmelCase:		Optional[int]			,							lowerCAmelCase:		int )->						Tuple:
  _snake_case      : Optional[Any]														=		[line.strip() for line in open(lowerCAmelCase			,							'r' ).readlines()]
  _snake_case      : Union[str, Any]														=		[]

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

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

  _snake_case      : Any														=		1_0_0.0 * em / total
  _snake_case      : Any														=		1_0_0.0 * fa / total

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


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

  _snake_case      : List[str]														=		0
  for hypo, reference in zip(lowerCAmelCase			,							lowerCAmelCase ):
    _snake_case      : List[str]														=		set(hypo.split('\t' )[:k] )
    _snake_case      : Union[str, Any]														=		set(reference.split('\t' ) )
    total += 1
    em += len(hypo_provenance & ref_provenance ) / k

  _snake_case      : str														=		1_0_0.0 * em / total
  logger.info(F"""Precision@{k}: {em: .2f}""" )


def 			lowerCamelCase_		(					lowerCAmelCase:		Any			,							lowerCAmelCase:		List[Any]			,							lowerCAmelCase:		List[str] )->						int:
  def strip_title(lowerCAmelCase:		Dict ):
    if title.startswith('"' ):
      _snake_case      : List[Any]														=		title[1:]
    if title.endswith('"' ):
      _snake_case      : int														=		title[:-1]
    return title

  _snake_case      : Dict														=		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
      lowerCAmelCase			,							return_tensors='pt'			,							padding=lowerCAmelCase			,							truncation=lowerCAmelCase			,							)['input_ids'].to(args.device )

  _snake_case      : List[str]														=		rag_model.rag.question_encoder(lowerCAmelCase )
  _snake_case      : Optional[Any]														=		question_enc_outputs[0]

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


def 			lowerCamelCase_		(					lowerCAmelCase:		int			,							lowerCAmelCase:		Tuple			,							lowerCAmelCase:		List[str] )->						Tuple:
  with torch.no_grad():
    _snake_case      : str														=		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        lowerCAmelCase			,							return_tensors='pt'			,							padding=lowerCAmelCase			,							truncation=lowerCAmelCase )

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

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

    return answers


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


def 			lowerCamelCase_		(					lowerCAmelCase:		Any )->						List[str]:
  _snake_case      : int														=		{}
  if args.model_type is None:
    _snake_case      : int														=		infer_model_type(args.model_name_or_path )
    assert args.model_type is not None
  if args.model_type.startswith('rag' ):
    _snake_case      : int														=		RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
    _snake_case      : Optional[int]														=		args.n_docs
    if args.index_name is not None:
      _snake_case      : int														=		args.index_name
    if args.index_path is not None:
      _snake_case      : Optional[Any]														=		args.index_path
  else:
    _snake_case      : Optional[int]														=		BartForConditionalGeneration

  _snake_case      : List[str]														=		(
      [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'			,							lowerCAmelCase )

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

    logger.info('***** Running evaluation for {} *****'.format(lowerCAmelCase ) )
    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' ):
      _snake_case      : Union[str, Any]														=		RagRetriever.from_pretrained(lowerCAmelCase			,							**lowerCAmelCase )
      _snake_case      : str														=		model_class.from_pretrained(lowerCAmelCase			,							retriever=lowerCAmelCase			,							**lowerCAmelCase )
      model.retriever.init_retrieval()
    else:
      _snake_case      : Optional[Any]														=		model_class.from_pretrained(lowerCAmelCase			,							**lowerCAmelCase )
    model.to(args.device )

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

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


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











import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


lowerCAmelCase_        =			logging.get_logger(__name__)

lowerCAmelCase_        =			OrderedDict(
    [
        ("""align""", """AlignProcessor"""),
        ("""altclip""", """AltCLIPProcessor"""),
        ("""bark""", """BarkProcessor"""),
        ("""blip""", """BlipProcessor"""),
        ("""blip-2""", """Blip2Processor"""),
        ("""bridgetower""", """BridgeTowerProcessor"""),
        ("""chinese_clip""", """ChineseCLIPProcessor"""),
        ("""clap""", """ClapProcessor"""),
        ("""clip""", """CLIPProcessor"""),
        ("""clipseg""", """CLIPSegProcessor"""),
        ("""flava""", """FlavaProcessor"""),
        ("""git""", """GitProcessor"""),
        ("""groupvit""", """CLIPProcessor"""),
        ("""hubert""", """Wav2Vec2Processor"""),
        ("""instructblip""", """InstructBlipProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2Processor"""),
        ("""layoutlmv3""", """LayoutLMv3Processor"""),
        ("""markuplm""", """MarkupLMProcessor"""),
        ("""mctct""", """MCTCTProcessor"""),
        ("""mgp-str""", """MgpstrProcessor"""),
        ("""oneformer""", """OneFormerProcessor"""),
        ("""owlvit""", """OwlViTProcessor"""),
        ("""pix2struct""", """Pix2StructProcessor"""),
        ("""sam""", """SamProcessor"""),
        ("""sew""", """Wav2Vec2Processor"""),
        ("""sew-d""", """Wav2Vec2Processor"""),
        ("""speech_to_text""", """Speech2TextProcessor"""),
        ("""speech_to_text_2""", """Speech2Text2Processor"""),
        ("""speecht5""", """SpeechT5Processor"""),
        ("""trocr""", """TrOCRProcessor"""),
        ("""tvlt""", """TvltProcessor"""),
        ("""unispeech""", """Wav2Vec2Processor"""),
        ("""unispeech-sat""", """Wav2Vec2Processor"""),
        ("""vilt""", """ViltProcessor"""),
        ("""vision-text-dual-encoder""", """VisionTextDualEncoderProcessor"""),
        ("""wav2vec2""", """Wav2Vec2Processor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2Processor"""),
        ("""wavlm""", """Wav2Vec2Processor"""),
        ("""whisper""", """WhisperProcessor"""),
        ("""xclip""", """XCLIPProcessor"""),
    ]
)

lowerCAmelCase_        =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)


def 			lowerCamelCase_		(					lowerCAmelCase:		str )->						Tuple:
  for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
    if class_name in processors:
      _snake_case      : Optional[Any]														=		model_type_to_module_name(lowerCAmelCase )

      _snake_case      : Tuple														=		importlib.import_module(F""".{module_name}"""			,							'transformers.models' )
      try:
        return getattr(lowerCAmelCase			,							lowerCAmelCase )
      except AttributeError:
        continue

  for processor in PROCESSOR_MAPPING._extra_content.values():
    if getattr(lowerCAmelCase			,							'__name__'			,							lowerCAmelCase ) == class_name:
      return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
  _snake_case      : Union[str, Any]														=		importlib.import_module('transformers' )
  if hasattr(lowerCAmelCase			,							lowerCAmelCase ):
    return getattr(lowerCAmelCase			,							lowerCAmelCase )

  return None







class 				_lowerCAmelCase			:


  '''simple docstring'''







  def __init__(					self					:    Optional[Any]							):


    '''simple docstring'''





    raise EnvironmentError(
        'AutoProcessor is designed to be instantiated '
        'using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'							)







  @classmethod
  @replace_list_option_in_docstrings(UpperCamelCase							)
  def UpperCamelCase_       (					cls					:    Optional[Any]					, UpperCamelCase					:    Optional[Any]					, **UpperCamelCase					:    Dict							):


    '''simple docstring'''





    _snake_case      : Optional[int]														=		kwargs.pop('config'					, UpperCamelCase							)
    _snake_case      : str														=		kwargs.pop('trust_remote_code'					, UpperCamelCase							)
    _snake_case      : List[Any]														=		True

    _snake_case      : str														=		None
    _snake_case      : str														=		None

    # First, let's see if we have a preprocessor config.
    # Filter the kwargs for `get_file_from_repo`.
    _snake_case      : Dict														=		{
        key: kwargs[key] for key in inspect.signature(UpperCamelCase							).parameters.keys() if key in kwargs
    }
    # Let's start by checking whether the processor class is saved in an image processor
    _snake_case      : List[Any]														=		get_file_from_repo(
        UpperCamelCase					, UpperCamelCase					, **UpperCamelCase							)
    if preprocessor_config_file is not None:
      _snake_case							,    _snake_case      : Optional[int]														=		ImageProcessingMixin.get_image_processor_dict(UpperCamelCase					, **UpperCamelCase							)
      _snake_case      : List[str]														=		config_dict.get('processor_class'					, UpperCamelCase							)
      if "AutoProcessor" in config_dict.get('auto_map'					, {}							):
        _snake_case      : Optional[Any]														=		config_dict['auto_map']['AutoProcessor']

        # If not found, let's check whether the processor class is saved in a feature extractor config
    if preprocessor_config_file is not None and processor_class is None:
      _snake_case							,    _snake_case      : Union[str, Any]														=		FeatureExtractionMixin.get_feature_extractor_dict(UpperCamelCase					, **UpperCamelCase							)
      _snake_case      : int														=		config_dict.get('processor_class'					, UpperCamelCase							)
      if "AutoProcessor" in config_dict.get('auto_map'					, {}							):
        _snake_case      : str														=		config_dict['auto_map']['AutoProcessor']

    if processor_class is None:
      # Next, let's check whether the processor class is saved in a tokenizer
      _snake_case      : Union[str, Any]														=		get_file_from_repo(
          UpperCamelCase					, UpperCamelCase					, **UpperCamelCase							)
      if tokenizer_config_file is not None:
        with open(UpperCamelCase					, encoding='utf-8'							) as reader:
          _snake_case      : Optional[int]														=		json.load(UpperCamelCase							)

        _snake_case      : int														=		config_dict.get('processor_class'					, UpperCamelCase							)
        if "AutoProcessor" in config_dict.get('auto_map'					, {}							):
          _snake_case      : str														=		config_dict['auto_map']['AutoProcessor']

    if processor_class is None:
      # Otherwise, load config, if it can be loaded.
      if not isinstance(UpperCamelCase					, UpperCamelCase							):
        _snake_case      : Dict														=		AutoConfig.from_pretrained(
            UpperCamelCase					, trust_remote_code=UpperCamelCase					, **UpperCamelCase							)

      # And check if the config contains the processor class.
      _snake_case      : str														=		getattr(UpperCamelCase					, 'processor_class'					, UpperCamelCase							)
      if hasattr(UpperCamelCase					, 'auto_map'							) and "AutoProcessor" in config.auto_map:
        _snake_case      : Any														=		config.auto_map['AutoProcessor']

    if processor_class is not None:
      _snake_case      : Tuple														=		processor_class_from_name(UpperCamelCase							)

    _snake_case      : int														=		processor_auto_map is not None
    _snake_case      : Optional[int]														=		processor_class is not None or type(UpperCamelCase							) in PROCESSOR_MAPPING
    _snake_case      : Union[str, Any]														=		resolve_trust_remote_code(
        UpperCamelCase					, UpperCamelCase					, UpperCamelCase					, UpperCamelCase							)

    if has_remote_code and trust_remote_code:
      _snake_case      : List[str]														=		get_class_from_dynamic_module(
          UpperCamelCase					, UpperCamelCase					, **UpperCamelCase							)
      _snake_case      : Optional[Any]														=		kwargs.pop('code_revision'					, UpperCamelCase							)
      if os.path.isdir(UpperCamelCase							):
        processor_class.register_for_auto_class()
      return processor_class.from_pretrained(
          UpperCamelCase					, trust_remote_code=UpperCamelCase					, **UpperCamelCase							)
    elif processor_class is not None:
      return processor_class.from_pretrained(
          UpperCamelCase					, trust_remote_code=UpperCamelCase					, **UpperCamelCase							)
    # Last try: we use the PROCESSOR_MAPPING.
    elif type(UpperCamelCase							) in PROCESSOR_MAPPING:
      return PROCESSOR_MAPPING[type(UpperCamelCase							)].from_pretrained(UpperCamelCase					, **UpperCamelCase							)

    # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
    # tokenizer.
    try:
      return AutoTokenizer.from_pretrained(
          UpperCamelCase					, trust_remote_code=UpperCamelCase					, **UpperCamelCase							)
    except Exception:
      try:
        return AutoImageProcessor.from_pretrained(
            UpperCamelCase					, trust_remote_code=UpperCamelCase					, **UpperCamelCase							)
      except Exception:
        pass

      try:
        return AutoFeatureExtractor.from_pretrained(
            UpperCamelCase					, trust_remote_code=UpperCamelCase					, **UpperCamelCase							)
      except Exception:
        pass

    raise ValueError(
        f"""Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a """
        'tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'
        'the files of at least one of those processing classes.'							)







  @staticmethod
  def UpperCamelCase_       (					UpperCamelCase					:    int					, UpperCamelCase					:    str							):


    '''simple docstring'''





    PROCESSOR_MAPPING.register(UpperCamelCase					, UpperCamelCase							)




