import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from ....modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
from ....modeling_utils import ModuleUtilsMixin
from ....utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings


_SCREAMING_SNAKE_CASE     =     logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     =     """MMBTConfig"""







class        UpperCAmelCase_					(				nn.Module     ):




    def __init__(       self	: Tuple   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: List[str]   ,   lowerCAmelCase	: Optional[int] )						->			str:
      super().__init__()
      a__  		=  config
      a__  		=  encoder
      a__  		=  nn.Linear(config.modal_hidden_size   ,   config.hidden_size )
      a__  		=  embeddings.position_embeddings
      a__  		=  embeddings.token_type_embeddings
      a__  		=  embeddings.word_embeddings
      a__  		=  embeddings.LayerNorm
      a__  		=  nn.Dropout(p=config.hidden_dropout_prob )




    def     __lowerCamelCase			(       self	: Tuple   ,   lowerCAmelCase	: Optional[Any]   ,   lowerCAmelCase	: Optional[Any]=None   ,   lowerCAmelCase	: Dict=None   ,   lowerCAmelCase	: Tuple=None   ,   lowerCAmelCase	: Any=None )						->			Union[str, Any]:
      a__  		=  self.proj_embeddings(self.encoder(lowerCAmelCase ) )
      a__  		=  token_embeddings.size(1 )

      if start_token is not None:
        a__  		=  self.word_embeddings(lowerCAmelCase )
        seq_length += 1
        a__  		=  torch.cat([start_token_embeds.unsqueeze(1 ), token_embeddings]   ,   dim=1 )

      if end_token is not None:
        a__  		=  self.word_embeddings(lowerCAmelCase )
        seq_length += 1
        a__  		=  torch.cat([token_embeddings, end_token_embeds.unsqueeze(1 )]   ,   dim=1 )

      if position_ids is None:
        a__  		=  torch.arange(lowerCAmelCase   ,   dtype=torch.long   ,   device=input_modal.device )
        a__  		=  position_ids.unsqueeze(0 ).expand(input_modal.size(0 )   ,   lowerCAmelCase )

      if token_type_ids is None:
        a__  		=  torch.zeros(
            (input_modal.size(0 ), seq_length)   ,   dtype=torch.long   ,   device=input_modal.device )

      a__  		=  self.position_embeddings(lowerCAmelCase )
      a__  		=  self.token_type_embeddings(lowerCAmelCase )
      a__  		=  token_embeddings + position_embeddings + token_type_embeddings
      a__  		=  self.LayerNorm(lowerCAmelCase )
      a__  		=  self.dropout(lowerCAmelCase )
      return embeddings

_SCREAMING_SNAKE_CASE     =     r"""
    MMBT model was proposed in [Supervised Multimodal Bitransformers for Classifying Images and
    Text](https://github.com/facebookresearch/mmbt) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
    It's a supervised multimodal bitransformer model that fuses information from text and other image encoders, and
    obtain state-of-the-art performance on various multimodal classification benchmark tasks.

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`MMBTConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration.
        transformer (`nn.Module`): A text transformer that is used by MMBT.
            It should have embeddings, encoder, and pooler attributes.
        encoder (`nn.Module`): Encoder for the second modality.
            It should take in a batch of modal inputs and return k, n dimension embeddings.
"""

_SCREAMING_SNAKE_CASE     =     r"""
    Args:
        input_modal (`torch.FloatTensor` of shape `(batch_size, ***)`):
            The other modality data. It will be the shape that the encoder for that type expects. e.g. With an Image
            Encoder, the shape would be (batch_size, channels, height, width)
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. It does not expect [CLS] token to be added as it's
            appended to the end of other modality embeddings. Indices can be obtained using [`AutoTokenizer`]. See
            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        modal_start_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Optional start token to be added to Other Modality Embedding. [CLS] Most commonly used for classification
            tasks.
        modal_end_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Optional end token to be added to Other Modality Embedding. [SEP] Most commonly used.
        attention_mask (*optional*) `torch.FloatTensor` of shape `(batch_size, sequence_length)`:
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, sequence_length)`:
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            [What are token type IDs?](../glossary#token-type-ids)
        modal_token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, modal_sequence_length)`:
            Segment token indices to indicate different portions of the non-text modality. The embeddings from these
            tokens will be summed with the respective token embeddings for the non-text modality.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.

            [What are position IDs?](../glossary#position-ids)
        modal_position_ids (`torch.LongTensor` of shape `(batch_size, modal_sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings for the non-text modality.
            Selected in the range `[0, config.max_position_embeddings - 1]`.

            [What are position IDs?](../glossary#position-ids)
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, embedding_dim)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""







@add_start_docstrings(
    'The bare MMBT Model outputting raw hidden-states without any specific head on top.'  ,  _a  ,  )
class        UpperCAmelCase_					(				nn.Module  ,  _a     ):




    def __init__(       self	: Any   ,   lowerCAmelCase	: int   ,   lowerCAmelCase	: Optional[Any]   ,   lowerCAmelCase	: List[str] )						->			Dict:
      super().__init__()
      a__  		=  config
      a__  		=  transformer
      a__  		=  ModalEmbeddings(lowerCAmelCase   ,   lowerCAmelCase   ,   transformer.embeddings )




    @add_start_docstrings_to_model_forward(lowerCAmelCase )
    @replace_return_docstrings(output_type=lowerCAmelCase   ,   config_class=_CONFIG_FOR_DOC )
    def     __lowerCamelCase			(       self	: Dict   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: Tuple=None   ,   lowerCAmelCase	: int=None   ,   lowerCAmelCase	: List[Any]=None   ,   lowerCAmelCase	: int=None   ,   lowerCAmelCase	: Dict=None   ,   lowerCAmelCase	: Dict=None   ,   lowerCAmelCase	: List[str]=None   ,   lowerCAmelCase	: List[Any]=None   ,   lowerCAmelCase	: Optional[Any]=None   ,   lowerCAmelCase	: Tuple=None   ,   lowerCAmelCase	: Any=None   ,   lowerCAmelCase	: Any=None   ,   lowerCAmelCase	: Optional[int]=None   ,   lowerCAmelCase	: Optional[Any]=None   ,   lowerCAmelCase	: str=None   ,   )						->			Any:
      a__  		=  output_attentions if output_attentions is not None else self.config.output_attentions
      a__  		=  (
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      a__  		=  return_dict if return_dict is not None else self.config.use_return_dict

      if input_ids is not None and inputs_embeds is not None:
        raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time" )
      elif input_ids is not None:
        a__  		=  input_ids.size()
      elif inputs_embeds is not None:
        a__  		=  inputs_embeds.size()[:-1]
      else:
        raise ValueError("You have to specify either input_ids or inputs_embeds" )

      a__  		=  input_ids.device if input_ids is not None else inputs_embeds.device

      a__  		=  self.modal_encoder(
          lowerCAmelCase   ,   start_token=lowerCAmelCase   ,   end_token=lowerCAmelCase   ,   position_ids=lowerCAmelCase   ,   token_type_ids=lowerCAmelCase   ,   )

      a__  		=  modal_embeddings.size()[:-1]

      if token_type_ids is None:
        a__  		=  torch.ones(lowerCAmelCase   ,   dtype=torch.long   ,   device=lowerCAmelCase )

      a__  		=  self.transformer.embeddings(
          input_ids=lowerCAmelCase   ,   position_ids=lowerCAmelCase   ,   token_type_ids=lowerCAmelCase   ,   inputs_embeds=lowerCAmelCase )

      a__  		=  torch.cat([modal_embeddings, txt_embeddings]   ,   1 )

      a__  		=  embedding_output.size()[:-1]

      if attention_mask is None:
        a__  		=  torch.ones(lowerCAmelCase   ,   device=lowerCAmelCase )
      else:
        a__  		=  torch.cat(
            [torch.ones(lowerCAmelCase   ,   device=lowerCAmelCase   ,   dtype=torch.long ), attention_mask]   ,   dim=1 )
      if encoder_attention_mask is None:
        a__  		=  torch.ones(lowerCAmelCase   ,   device=lowerCAmelCase )
      else:
        a__  		=  torch.cat(
            [torch.ones(lowerCAmelCase   ,   device=lowerCAmelCase ), encoder_attention_mask]   ,   dim=1 )

      a__  		=  self.get_extended_attention_mask(lowerCAmelCase   ,   lowerCAmelCase )
      a__  		=  self.invert_attention_mask(lowerCAmelCase )
      a__  		=  self.get_head_mask(lowerCAmelCase   ,   self.config.num_hidden_layers )

      a__  		=  self.transformer.encoder(
          lowerCAmelCase   ,   attention_mask=lowerCAmelCase   ,   head_mask=lowerCAmelCase   ,   encoder_hidden_states=lowerCAmelCase   ,   encoder_attention_mask=lowerCAmelCase   ,   output_attentions=lowerCAmelCase   ,   output_hidden_states=lowerCAmelCase   ,   return_dict=lowerCAmelCase   ,   )

      a__  		=  encoder_outputs[0]
      a__  		=  self.transformer.pooler(lowerCAmelCase )

      if not return_dict:
        return (sequence_output, pooled_output) + encoder_outputs[1:]

      return BaseModelOutputWithPooling(
          last_hidden_state=lowerCAmelCase   ,   pooler_output=lowerCAmelCase   ,   hidden_states=encoder_outputs.hidden_states   ,   attentions=encoder_outputs.attentions   ,   )




    def     __lowerCamelCase			(       self	: Any )						->			Union[str, Any]:
      return self.embeddings.word_embeddings




    def     __lowerCamelCase			(       self	: List[str]   ,   lowerCAmelCase	: List[str] )						->			str:
      a__  		=  value







@add_start_docstrings(
    '\n    MMBT Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)\n    '  ,  _a  ,  _a  ,  )
class        UpperCAmelCase_					(				nn.Module     ):




    def __init__(       self	: List[str]   ,   lowerCAmelCase	: Optional[int]   ,   lowerCAmelCase	: Optional[Any]   ,   lowerCAmelCase	: Tuple )						->			List[Any]:
      super().__init__()
      a__  		=  config.num_labels

      a__  		=  MMBTModel(lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase )
      a__  		=  nn.Dropout(config.hidden_dropout_prob )
      a__  		=  nn.Linear(config.hidden_size   ,   config.num_labels )




    def     __lowerCamelCase			(       self	: Dict   ,   lowerCAmelCase	: Optional[Any]   ,   lowerCAmelCase	: str=None   ,   lowerCAmelCase	: Optional[int]=None   ,   lowerCAmelCase	: int=None   ,   lowerCAmelCase	: List[Any]=None   ,   lowerCAmelCase	: int=None   ,   lowerCAmelCase	: str=None   ,   lowerCAmelCase	: List[str]=None   ,   lowerCAmelCase	: Optional[Any]=None   ,   lowerCAmelCase	: List[str]=None   ,   lowerCAmelCase	: Tuple=None   ,   lowerCAmelCase	: List[str]=None   ,   lowerCAmelCase	: Optional[int]=None   ,   )						->			List[Any]:
      a__  		=  return_dict if return_dict is not None else self.config.use_return_dict

      a__  		=  self.mmbt(
          input_modal=lowerCAmelCase   ,   input_ids=lowerCAmelCase   ,   modal_start_tokens=lowerCAmelCase   ,   modal_end_tokens=lowerCAmelCase   ,   attention_mask=lowerCAmelCase   ,   token_type_ids=lowerCAmelCase   ,   modal_token_type_ids=lowerCAmelCase   ,   position_ids=lowerCAmelCase   ,   modal_position_ids=lowerCAmelCase   ,   head_mask=lowerCAmelCase   ,   inputs_embeds=lowerCAmelCase   ,   return_dict=lowerCAmelCase   ,   )

      a__  		=  outputs[1]

      a__  		=  self.dropout(lowerCAmelCase )
      a__  		=  self.classifier(lowerCAmelCase )

      a__  		=  None
      if labels is not None:
        if self.num_labels == 1:
          #  We are doing regression
          a__  		=  MSELoss()
          a__  		=  loss_fct(logits.view(-1 )   ,   labels.view(-1 ) )
        else:
          a__  		=  CrossEntropyLoss()
          a__  		=  loss_fct(logits.view(-1   ,   self.num_labels )   ,   labels.view(-1 ) )

      if not return_dict:
        a__  		=  (logits,) + outputs[2:]
        return ((loss,) + output) if loss is not None else output

      return SequenceClassifierOutput(
          loss=lowerCAmelCase   ,   logits=lowerCAmelCase   ,   hidden_states=outputs.hidden_states   ,   attentions=outputs.attentions   ,   )


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)

