
'''simple docstring'''





import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

UpperCAmelCase            = {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
}

UpperCAmelCase            = {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}

@lru_cache()
def   A			(							):
						snake_case							:  Dict			=     (
						    list(range(ord('''!'''							)     ,				ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)     ,				ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)     ,				ord('''ÿ'''							) + 1							)							)
						)
						snake_case							:  Tuple			=     bs[:]
						snake_case							:  int			=     0
						for b in range(2**8							):
												if b not in bs:
																		bs.append(A_							)
																		cs.append(2**8 + n							)
																		n += 1
						snake_case							:  Union[str, Any]			=     [chr(A_							) for n in cs]
						return dict(zip(A_     ,				A_							)							)

def   A			(							A_				:       Any							):
						snake_case							:  List[str]			=     set()
						snake_case							:  int			=     word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)							)
												snake_case							:  List[str]			=     char
						return pairs





class     a (					__magic_name__     ):
				_snake_case		     =			VOCAB_FILES_NAMES
				_snake_case		     =			PRETRAINED_VOCAB_FILES_MAP
				_snake_case		     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		     =			['''input_ids''', '''attention_mask''']




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					Tuple="replace",     SCREAMING_SNAKE_CASE_						:					Dict="<|endoftext|>",     SCREAMING_SNAKE_CASE_						:					str="<|endoftext|>",     SCREAMING_SNAKE_CASE_						:					Any="<|endoftext|>",     SCREAMING_SNAKE_CASE_						:					List[str]=None,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=False,     SCREAMING_SNAKE_CASE_						:					Tuple=False,     **SCREAMING_SNAKE_CASE_						:					str,     ):
										snake_case							:  Dict			=     AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) else bos_token
										snake_case							:  Union[str, Any]			=     AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) else eos_token
										snake_case							:  List[Any]			=     AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) else unk_token
										snake_case							:  Tuple			=     AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) else pad_token
										super().__init__(
										    errors=SCREAMING_SNAKE_CASE_,     unk_token=SCREAMING_SNAKE_CASE_,     bos_token=SCREAMING_SNAKE_CASE_,     eos_token=SCREAMING_SNAKE_CASE_,     pad_token=SCREAMING_SNAKE_CASE_,     add_prefix_space=SCREAMING_SNAKE_CASE_,     add_bos_token=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     )
										snake_case							:  int			=     add_bos_token

										with open(SCREAMING_SNAKE_CASE_,     encoding='''utf-8'''   ) as vocab_handle:
																snake_case							:  Optional[int]			=     json.load(SCREAMING_SNAKE_CASE_   )
										snake_case							:  int			=     {v: k for k, v in self.encoder.items()}
										snake_case							:  Tuple			=     errors  # how to handle errors in decoding
										snake_case							:  List[str]			=     bytes_to_unicode()
										snake_case							:  Optional[int]			=     {v: k for k, v in self.byte_encoder.items()}
										with open(SCREAMING_SNAKE_CASE_,     encoding='''utf-8'''   ) as merges_handle:
																snake_case							:  str			=     merges_handle.read().split('''\n'''   )[1:-1]
										snake_case							:  Optional[Any]			=     [tuple(merge.split()   ) for merge in bpe_merges]
										snake_case							:  Tuple			=     dict(zip(SCREAMING_SNAKE_CASE_,     range(len(SCREAMING_SNAKE_CASE_   )   )   )   )
										snake_case							:  str			=     {}
										snake_case							:  Dict			=     add_prefix_space

										# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
										snake_case							:  Union[str, Any]			=     re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''   )




				@property
				def 						__snake_case				(				self						:					Tuple   ):
										return len(self.encoder   )




				def 						__snake_case				(				self						:					List[Any]   ):
										return dict(self.encoder,     **self.added_tokens_encoder   )




				def 						__snake_case				(				self						:					int,     SCREAMING_SNAKE_CASE_						:					Tuple   ):
										if token in self.cache:
																return self.cache[token]
										snake_case							:  List[Any]			=     tuple(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[Any]			=     get_pairs(SCREAMING_SNAKE_CASE_   )

										if not pairs:
																return token

										while True:
																snake_case							:  Optional[Any]			=     min(SCREAMING_SNAKE_CASE_,     key=lambda SCREAMING_SNAKE_CASE_   : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_,     float('''inf'''   )   )   )
																if bigram not in self.bpe_ranks:
																						break
																snake_case,       snake_case							:  Optional[int]			=     bigram
																snake_case							:  int			=     []
																snake_case							:  str			=     0
																while i < len(SCREAMING_SNAKE_CASE_   ):
																						try:
																												snake_case							:  List[Any]			=     word.index(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
																						except ValueError:
																												new_word.extend(word[i:]   )
																												break
																						else:
																												new_word.extend(word[i:j]   )
																												snake_case							:  Optional[Any]			=     j

																						if word[i] == first and i < len(SCREAMING_SNAKE_CASE_   ) - 1 and word[i + 1] == second:
																												new_word.append(first + second   )
																												i += 2
																						else:
																												new_word.append(word[i]   )
																												i += 1
																snake_case							:  Tuple			=     tuple(SCREAMING_SNAKE_CASE_   )
																snake_case							:  Tuple			=     new_word
																if len(SCREAMING_SNAKE_CASE_   ) == 1:
																						break
																else:
																						snake_case							:  List[str]			=     get_pairs(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Union[str, Any]			=     ''' '''.join(SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[Any]			=     word
										return word




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Optional[Any]=None   ):
										if self.add_bos_token:
																snake_case							:  int			=     [self.bos_token_id]
										else:
																snake_case							:  Dict			=     []

										snake_case							:  List[str]			=     bos_token_ids + token_ids_a

										if token_ids_a is None:
																return output

										return output + bos_token_ids + token_ids_a




				def 						__snake_case				(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					List[int],     SCREAMING_SNAKE_CASE_						:					Optional[List[int]] = None,     SCREAMING_SNAKE_CASE_						:					bool = False   ):
										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=SCREAMING_SNAKE_CASE_,     token_ids_a=SCREAMING_SNAKE_CASE_,     already_has_special_tokens=SCREAMING_SNAKE_CASE_   )

										if not self.add_bos_token:
																return super().get_special_tokens_mask(
																    token_ids_a=SCREAMING_SNAKE_CASE_,     token_ids_a=SCREAMING_SNAKE_CASE_,     already_has_special_tokens=SCREAMING_SNAKE_CASE_   )

										if token_ids_a is None:
																return [1] + ([0] * len(SCREAMING_SNAKE_CASE_   ))
										return [1] + ([0] * len(SCREAMING_SNAKE_CASE_   )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_   ))




				def 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					List[Any]   ):
										snake_case							:  Optional[int]			=     []
										for token in re.findall(self.pat,     SCREAMING_SNAKE_CASE_   ):
																snake_case							:  Dict			=     ''''''.join(
																    self.byte_encoder[b] for b in token.encode('''utf-8'''   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_   ).split(''' '''   )   )
										return bpe_tokens




				def 						__snake_case				(				self						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					List[str]   ):
										return self.encoder.get(SCREAMING_SNAKE_CASE_,     self.encoder.get(self.unk_token   )   )




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					Tuple   ):
										return self.decoder.get(SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Dict   ):
										snake_case							:  Union[str, Any]			=     ''''''.join(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Any			=     bytearray([self.byte_decoder[c] for c in text]   ).decode('''utf-8''',     errors=self.errors   )
										return text




				def 						__snake_case				(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					Optional[str] = None   ):
										if not os.path.isdir(SCREAMING_SNAKE_CASE_   ):
																logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
																return
										snake_case							:  int			=     os.path.join(
										    SCREAMING_SNAKE_CASE_,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
										snake_case							:  List[Any]			=     os.path.join(
										    SCREAMING_SNAKE_CASE_,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']   )

										with open(SCREAMING_SNAKE_CASE_,     '''w''',     encoding='''utf-8'''   ) as f:
																f.write(json.dumps(self.encoder,     indent=2,     sort_keys=SCREAMING_SNAKE_CASE_,     ensure_ascii=SCREAMING_SNAKE_CASE_   ) + '''\n'''   )

										snake_case							:  Optional[int]			=     0
										with open(SCREAMING_SNAKE_CASE_,     '''w''',     encoding='''utf-8'''   ) as writer:
																writer.write('''#version: 0.2\n'''   )
																for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),     key=lambda SCREAMING_SNAKE_CASE_   : kv[1]   ):
																						if index != token_index:
																												logger.warning(
																												    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																												    ''' Please check that the tokenizer is not corrupted!'''   )
																												snake_case							:  Union[str, Any]			=     token_index
																						writer.write(''' '''.join(SCREAMING_SNAKE_CASE_   ) + '''\n'''   )
																						index += 1

										return vocab_file, merge_file




				def 						__snake_case				(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Dict=False,     **SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										snake_case							:  Any			=     kwargs.pop('''add_prefix_space''',     self.add_prefix_space   )
										if is_split_into_words or add_prefix_space:
																snake_case							:  Dict			=     ''' ''' + text
										return (text, kwargs)




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					"Conversation"   ):
										snake_case							:  List[str]			=     []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_,     add_special_tokens=SCREAMING_SNAKE_CASE_   ) + [self.eos_token_id]   )
										if len(SCREAMING_SNAKE_CASE_   ) > self.model_max_length:
																snake_case							:  str			=     input_ids[-self.model_max_length :]
										return input_ids


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


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

transformers_logging.set_verbosity_info()
def 					__lowercase						(    lowerCamelCase__       )					->Any:

  '''simple docstring'''




  if "token" in model_name_or_path:
    return "rag_token"
  if "sequence" in model_name_or_path:
    return "rag_sequence"
  if "bart" in model_name_or_path:
    return "bart"
  return None
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->List[str]:

  '''simple docstring'''




  return max(metric_fn(lowerCamelCase__			,					lowerCamelCase__       ) for gt in ground_truths       )
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->Optional[Any]:

  '''simple docstring'''




  a__  		=  [line.strip() for line in open(lowerCamelCase__			,					"r"       ).readlines()]
  a__  		=  []

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

  a__  		=  a__  		=  a__  		=  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__       )

  a__  		=  100.0 * em / total
  a__  		=  100.0 * fa / total

  logger.info(f"F1: {fa:.2f}"       )
  logger.info(f"EM: {em:.2f}"       )
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->Tuple:

  '''simple docstring'''




  a__  		=  args.k
  a__  		=  [line.strip() for line in open(lowerCamelCase__			,					"r"       ).readlines()]
  a__  		=  [line.strip() for line in open(lowerCamelCase__			,					"r"       ).readlines()]

  a__  		=  a__  		=  0
  for hypo, reference in zip(lowerCamelCase__			,					lowerCamelCase__       ):
    a__  		=  set(hypo.split("\t"       )[:k]       )
    a__  		=  set(reference.split("\t"       )       )
    total += 1
    em += len(hypo_provenance & ref_provenance       ) / k

  a__  		=  100.0 * em / total
  logger.info(f"Precision@{k}: {em: .2f}"       )
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->str:

  '''simple docstring'''




  def strip_title(lowerCamelCase__       ):
    if title.startswith("\""       ):
      a__  		=  title[1:]
    if title.endswith("\""       ):
      a__  		=  title[:-1]
    return title

  a__  		=  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
      lowerCamelCase__			,					return_tensors="pt"			,					padding=lowerCamelCase__			,					truncation=lowerCamelCase__			,					)["input_ids"].to(args.device       )

  a__  		=  rag_model.rag.question_encoder(lowerCamelCase__       )
  a__  		=  question_enc_outputs[0]

  a__  		=  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"			,					)
  a__  		=  rag_model.retriever.index.get_doc_dicts(result.doc_ids       )
  a__  		=  []
  for docs in all_docs:
    a__  		=  [strip_title(lowerCamelCase__       ) for title in docs["title"]]
    provenance_strings.append("\t".join(lowerCamelCase__       )       )
  return provenance_strings
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->List[str]:

  '''simple docstring'''




  with torch.no_grad():
    a__  		=  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        lowerCamelCase__			,					return_tensors="pt"			,					padding=lowerCamelCase__			,					truncation=lowerCamelCase__       )

    a__  		=  inputs_dict.input_ids.to(args.device       )
    a__  		=  inputs_dict.attention_mask.to(args.device       )
    a__  		=  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]]			,					)
    a__  		=  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 					__lowercase						(    )					->str:

  '''simple docstring'''




  a__  		=  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."			,					)
  a__  		=  parser.parse_args()
  a__  		=  torch.device("cuda" if torch.cuda.is_available() else "cpu"       )
  return args
def 					__lowercase						(    lowerCamelCase__       )					->Dict:

  '''simple docstring'''




  a__  		=  {}
  if args.model_type is None:
    a__  		=  infer_model_type(args.model_name_or_path       )
    assert args.model_type is not None
  if args.model_type.startswith("rag"       ):
    a__  		=  RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
    a__  		=  args.n_docs
    if args.index_name is not None:
      a__  		=  args.index_name
    if args.index_path is not None:
      a__  		=  args.index_path
  else:
    a__  		=  BartForConditionalGeneration

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

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

  a__  		=  get_scores if args.eval_mode == "e2e" else get_precision_at_k
  a__  		=  evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

  for checkpoint in checkpoints:
    if os.path.exists(args.predictions_path       ) and (not args.recalculate):
      logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path       )       )
      score_fn(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"       ):
      a__  		=  RagRetriever.from_pretrained(lowerCamelCase__			,					**lowerCamelCase__       )
      a__  		=  model_class.from_pretrained(lowerCamelCase__			,					retriever=lowerCamelCase__			,					**lowerCamelCase__       )
      model.retriever.init_retrieval()
    else:
      a__  		=  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:
      a__  		=  []
      for line in tqdm(lowerCamelCase__       ):
        questions.append(line.strip()       )
        if len(lowerCamelCase__       ) == args.eval_batch_size:
          a__  		=  evaluate_batch_fn(lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )
          preds_file.write("\n".join(lowerCamelCase__       ) + "\n"       )
          preds_file.flush()
          a__  		=  []
      if len(lowerCamelCase__       ) > 0:
        a__  		=  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__":
     _SCREAMING_SNAKE_CASE     =     get_args()
     main(args)

