
"""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


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

transformers_logging.set_verbosity_info()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int]		)     ->					Optional[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 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       str		)     ->					List[str]:
  return max(metric_fn(lowerCAmelCase__ ,					lowerCAmelCase__		) for gt in ground_truths		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       Dict		)     ->					int:
  _snake_case					     =       [line.strip() for line in open(lowerCAmelCase__ ,					'''r'''		).readlines()]
  _snake_case					     =       []

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

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

  logger.info(f'''F1: {fa:.2f}'''		)
  logger.info(f'''EM: {em:.2f}'''		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Any		)     ->					Any:
  _snake_case					     =       args.k
  _snake_case					     =       [line.strip() for line in open(lowerCAmelCase__ ,					'''r'''		).readlines()]
  _snake_case					     =       [line.strip() for line in open(lowerCAmelCase__ ,					'''r'''		).readlines()]

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

  _snake_case					     =       1_00.0 * em / total
  logger.info(f'''Precision@{k}: {em: .2f}'''		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       str ,					__lowerCamelCase   :       str		)     ->					Dict:
  def strip_title(__lowerCamelCase   :       Tuple		):
    if title.startswith('''"'''		):
      _snake_case					     =       title[1:]
    if title.endswith('''"'''		):
      _snake_case					     =       title[:-1]
    return title

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

  _snake_case					     =       rag_model.rag.question_encoder(lowerCAmelCase__		)
  _snake_case					     =       question_enc_outputs[0]

  _snake_case					     =       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					     =       rag_model.retriever.index.get_doc_dicts(result.doc_ids		)
  _snake_case					     =       []
  for docs in all_docs:
    _snake_case					     =       [strip_title(lowerCAmelCase__		) for title in docs['''title''']]
    provenance_strings.append('''\t'''.join(lowerCAmelCase__		)		)
  return provenance_strings
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Any		)     ->					Any:
  with torch.no_grad():
    _snake_case					     =       rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        lowerCAmelCase__ ,					return_tensors='''pt''' ,					padding=lowerCAmelCase__ ,					truncation=lowerCAmelCase__		)

    _snake_case					     =       inputs_dict.input_ids.to(args.device		)
    _snake_case					     =       inputs_dict.attention_mask.to(args.device		)
    _snake_case					     =       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					     =       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 					_UpperCAmelCase      ( )     ->					Optional[Any]:
  _snake_case					     =       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					     =       parser.parse_args()
  _snake_case					     =       torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''		)
  return args
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any]		)     ->					str:
  _snake_case					     =       {}
  if args.model_type is None:
    _snake_case					     =       infer_model_type(args.model_name_or_path		)
    assert args.model_type is not None
  if args.model_type.startswith('''rag'''		):
    _snake_case					     =       RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
    _snake_case					     =       args.n_docs
    if args.index_name is not None:
      _snake_case					     =       args.index_name
    if args.index_path is not None:
      _snake_case					     =       args.index_path
  else:
    _snake_case					     =       BartForConditionalGeneration

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







"""simple docstring"""




import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, 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 (
				    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CTRLForSequenceClassification,
				    CTRLLMHeadModel,
				    CTRLModel,
				)
class 					__lowerCAmelCase					:


		'''simple docstring'''



		def __init__(  self							, _a							, _a=14							, _a=7							, _a=True							, _a=True							, _a=True							, _a=True							, _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							, ):
				__a           =			parent
				__a           =			batch_size
				__a           =			seq_length
				__a           =			is_training
				__a           =			use_token_type_ids
				__a           =			use_input_mask
				__a           =			use_labels
				__a           =			use_mc_token_ids
				__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
				__a           =			self.vocab_size - 1

		def 			__UpperCAmelCase       (  self      ):
				__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
				if self.use_mc_token_ids:
						__a           =			ids_tensor([self.batch_size, self.num_choices]							, self.seq_length      )

				__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()

				__a           =			ids_tensor([self.num_hidden_layers, self.num_attention_heads]							, 2      )

				return (
				    config,
				    input_ids,
				    input_mask,
				    head_mask,
				    token_type_ids,
				    mc_token_ids,
				    sequence_labels,
				    token_labels,
				    choice_labels,
				)

		def 			__UpperCAmelCase       (  self      ):
				return CTRLConfig(
				    vocab_size=self.vocab_size							, n_embd=self.hidden_size							, n_layer=self.num_hidden_layers							, n_head=self.num_attention_heads							, n_positions=self.max_position_embeddings							, pad_token_id=self.pad_token_id							, )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, *_a      ):
				__a           =			CTRLModel(config=_a      )
				model.to(_a      )
				model.eval()

				model(_a							, token_type_ids=_a							, head_mask=_a      )
				model(_a							, token_type_ids=_a      )
				__a           =			model(_a      )
				self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)      )
				self.parent.assertEqual(len(result.past_key_values      )							, config.n_layer      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, *_a      ):
				__a           =			CTRLLMHeadModel(_a      )
				model.to(_a      )
				model.eval()

				__a           =			model(_a							, token_type_ids=_a							, labels=_a      )
				self.parent.assertEqual(result.loss.shape							, ()      )
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.prepare_config_and_inputs()

				(
				    (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     
				)           =			config_and_inputs

				__a           =			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

				return config, inputs_dict





		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, *_a      ):
				__a           =			self.num_labels
				__a           =			CTRLForSequenceClassification(_a      )
				model.to(_a      )
				model.eval()
				__a           =			ids_tensor([self.batch_size]							, self.type_sequence_label_size      )
				__a           =			model(_a							, token_type_ids=_a							, labels=_a      )
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.num_labels)      )







@require_torch
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			str										=				(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
		__UpperCAmelCase		:			Union[str, Any]										=				(CTRLLMHeadModel,) if is_torch_available() else ()
		__UpperCAmelCase		:			Union[str, Any]										=				(
		    {
		        'feature-extraction': CTRLModel,
		        'text-classification': CTRLForSequenceClassification,
		        'text-generation': CTRLLMHeadModel,
		        'zero-shot': CTRLForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		__UpperCAmelCase		:			Optional[Any]										=				True
		__UpperCAmelCase		:			List[Any]										=				False
		__UpperCAmelCase		:			str										=				False

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a      ):
				if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
						# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
						# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
						# config could not be created.
						return True

				return False

		def 			__UpperCAmelCase       (  self      ):
				__a           =			CTRLModelTester(self      )
				__a           =			ConfigTester(self							, config_class=_a							, n_embd=37      )

		def 			__UpperCAmelCase       (  self      ):
				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()

		def 			__UpperCAmelCase       (  self      ):
				self.config_tester.run_common_tests()

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_ctrl_model(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_lm_head_model(*_a      )

		@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''      )
		def 			__UpperCAmelCase       (  self      ):
				pass

		@slow
		def 			__UpperCAmelCase       (  self      ):
				for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__a           =			CTRLModel.from_pretrained(_a      )
						self.assertIsNotNone(_a      )





		@unittest.skip('''The model doesn\'t support left padding'''      )  # and it's not used enough to be worth fixing :)
		def 			__UpperCAmelCase       (  self      ):
				pass







@require_torch
class 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		def 			__UpperCAmelCase       (  self      ):
				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()





		@slow
		def 			__UpperCAmelCase       (  self      ):
				__a           =			CTRLLMHeadModel.from_pretrained('''ctrl'''      )
				model.to(_a      )
				__a           =			torch.tensor(
				    [[11_859, 0, 1_611, 8]]							, dtype=torch.long							, device=_a      )  # Legal the president is
				__a           =			[
				    11_859,
				    0,
				    1_611,
				    8,
				    5,
				    150,
				    26_449,
				    2,
				    19,
				    348,
				    469,
				    3,
				    2_595,
				    48,
				    20_740,
				    246_533,
				    246_533,
				    19,
				    30,
				    5,
				]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

				__a           =			model.generate(_a							, do_sample=_a      )
				self.assertListEqual(output_ids[0].tolist()							, _a      )






