





'''simple docstring'''






import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


A__							:  str    =trt.Logger(trt.Logger.WARNING)
A__							:  int    =absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

A__							:  Optional[int]    =logging.getLogger(__name__)

A__							:  Union[str, Any]    =argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    '''--onnx_model_path''',
    default=None,
    type=str,
    required=True,
    help='''Path to ONNX model: ''',
)

parser.add_argument(
    '''--output_dir''',
    default=None,
    type=str,
    required=True,
    help='''The output directory where the model checkpoints and predictions will be written.''',
)

# Other parameters

parser.add_argument(
    '''--tokenizer_name''',
    default='''''',
    type=str,
    required=True,
    help='''Pretrained tokenizer name or path if not the same as model_name''',
)

parser.add_argument(
    '''--version_2_with_negative''',
    action='''store_true''',
    help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
    '''--null_score_diff_threshold''',
    type=float,
    default=0.0,
    help='''If null_score - best_non_null is greater than the threshold predict null.''',
)

parser.add_argument(
    '''--max_seq_length''',
    default=3_84,
    type=int,
    help=(
        '''The maximum total input sequence length after WordPiece tokenization. Sequences '''
        '''longer than this will be truncated, and sequences shorter than this will be padded.'''
    ),
)
parser.add_argument(
    '''--doc_stride''',
    default=1_28,
    type=int,
    help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)

parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')

parser.add_argument(
    '''--n_best_size''',
    default=20,
    type=int,
    help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
    '''--max_answer_length''',
    default=30,
    type=int,
    help=(
        '''The maximum length of an answer that can be generated. This is needed because the start '''
        '''and end predictions are not conditioned on one another.'''
    ),
)

parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')

parser.add_argument(
    '''--dataset_name''',
    type=str,
    default=None,
    required=True,
    help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
    '''--dataset_config_name''',
    type=str,
    default=None,
    help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
    '''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
    '''--fp16''',
    action='''store_true''',
    help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
    '''--int8''',
    action='''store_true''',
    help='''Whether to use INT8''',
)

A__							:  Tuple    =parser.parse_args()

if args.tokenizer_name:
      A__							:  Optional[int]    =AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
      raise ValueError(
          '''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
          '''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
      )

logger.info('''Training/evaluation parameters %s''', args)

A__							:  Union[str, Any]    =args.per_device_eval_batch_size

A__							:  List[str]    =(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
A__							:  List[str]    =True

A__							:  Union[str, Any]    ='''temp_engine/bert-fp32.engine'''
if args.fpaa:
      A__							:  Any    ='''temp_engine/bert-fp16.engine'''
if args.inta:
      A__							:  Optional[int]    ='''temp_engine/bert-int8.engine'''

# import ONNX file
if not os.path.exists('''temp_engine'''):
      os.makedirs('''temp_engine''')

A__							:  Tuple    =1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
      with open(args.onnx_model_path, '''rb''') as model:
            if not parser.parse(model.read()):
                  for error in range(parser.num_errors):
                        print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
      A__							:  Optional[int]    =[network.get_input(i) for i in range(network.num_inputs)]
      A__							:  List[Any]    =[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

      with builder.create_builder_config() as config:
            A__							:  List[str]    =1 << 50
            if STRICT_TYPES:
                  config.set_flag(trt.BuilderFlag.STRICT_TYPES)
            if args.fpaa:
                  config.set_flag(trt.BuilderFlag.FPaa)
            if args.inta:
                  config.set_flag(trt.BuilderFlag.INTa)
            A__							:  Any    =builder.create_optimization_profile()
            config.add_optimization_profile(profile)
            for i in range(len(input_names)):
                  profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
            A__							:  Optional[int]    =builder.build_engine(network, config)

            # serialize_engine and store in file (can be directly loaded and deserialized):
            with open(engine_name, '''wb''') as f:
                  f.write(engine.serialize())
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				np.asarray(inputs["""input_ids"""]				,      dtype=np.intaa    )
      _lowerCAmelCase					=				np.asarray(inputs["""attention_mask"""]				,      dtype=np.intaa    )
      _lowerCAmelCase					=				np.asarray(inputs["""token_type_ids"""]				,      dtype=np.intaa    )

      # Copy inputs
      cuda.memcpy_htod_async(d_inputs[0]				,      input_ids.ravel()				,      lowerCAmelCase    )
      cuda.memcpy_htod_async(d_inputs[1]				,      attention_mask.ravel()				,      lowerCAmelCase    )
      cuda.memcpy_htod_async(d_inputs[2]				,      token_type_ids.ravel()				,      lowerCAmelCase    )
      # start time
      _lowerCAmelCase					=				time.time()
      # Run inference
      context.execute_async(
          bindings=[int(lowerCAmelCase    ) for d_inp in d_inputs] + [int(lowerCAmelCase    ), int(lowerCAmelCase    )]				,      stream_handle=stream.handle    )
      # Transfer predictions back from GPU
      cuda.memcpy_dtoh_async(lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )
      cuda.memcpy_dtoh_async(lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )
      # Synchronize the stream and take time
      stream.synchronize()
      # end time
      _lowerCAmelCase					=				time.time()
      _lowerCAmelCase					=				end_time - start_time
      _lowerCAmelCase					=				(h_outputa, h_outputa)
      # print(outputs)
      return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
A__							:  Optional[int]    =Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
      datasets.utils.logging.set_verbosity_warning()
      transformers.utils.logging.set_verbosity_info()
else:
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
      set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
      # Downloading and loading a dataset from the hub.
      A__							:  Dict    =load_dataset(args.dataset_name, args.dataset_config_name)
else:
      raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

A__							:  Optional[Any]    =raw_datasets['''validation'''].column_names

A__							:  List[str]    ='''question''' if '''question''' in column_names else column_names[0]
A__							:  str    ='''context''' if '''context''' in column_names else column_names[1]
A__							:  Dict    ='''answers''' if '''answers''' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
A__							:  int    =tokenizer.padding_side == '''right'''

if args.max_seq_length > tokenizer.model_max_length:
      logger.warning(
          F"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
          F"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
      )

A__							:  List[str]    =min(args.max_seq_length, tokenizer.model_max_length)
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				[q.lstrip() for q in examples[question_column_name]]

      # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
      # in one example possible giving several features when a context is long, each of those features having a
      # context that overlaps a bit the context of the previous feature.
      _lowerCAmelCase					=				tokenizer(
          examples[question_column_name if pad_on_right else context_column_name]				,      examples[context_column_name if pad_on_right else question_column_name]				,      truncation="""only_second""" if pad_on_right else """only_first"""				,      max_length=lowerCAmelCase				,      stride=args.doc_stride				,      return_overflowing_tokens=lowerCAmelCase				,      return_offsets_mapping=lowerCAmelCase				,      padding="""max_length"""				,      )

      # Since one example might give us several features if it has a long context, we need a map from a feature to
      # its corresponding example. This key gives us just that.
      _lowerCAmelCase					=				tokenized_examples.pop("""overflow_to_sample_mapping"""    )

      # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
      # corresponding example_id and we will store the offset mappings.
      _lowerCAmelCase					=				[]

      for i in range(len(tokenized_examples["""input_ids"""]    )    ):
            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
            _lowerCAmelCase					=				tokenized_examples.sequence_ids(lowerCAmelCase    )
            _lowerCAmelCase					=				1 if pad_on_right else 0

            # One example can give several spans, this is the index of the example containing this span of text.
            _lowerCAmelCase					=				sample_mapping[i]
            tokenized_examples["example_id"].append(examples["""id"""][sample_index]    )

            # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
            # position is part of the context or not.
            _lowerCAmelCase					=				[
                (o if sequence_ids[k] == context_index else None)
                for k, o in enumerate(tokenized_examples["""offset_mapping"""][i]    )
            ]

      return tokenized_examples


A__							:  List[Any]    =raw_datasets['''validation''']
# Validation Feature Creation
A__							:  Optional[Any]    =eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc='''Running tokenizer on validation dataset''',
)

A__							:  Any    =default_data_collator

A__							:  int    =eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
A__							:  int    =DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase="eval"    ):





      """simple docstring"""


      _lowerCAmelCase					=				postprocess_qa_predictions(
          examples=lowerCAmelCase				,      features=lowerCAmelCase				,      predictions=lowerCAmelCase				,      version_2_with_negative=args.version_2_with_negative				,      n_best_size=args.n_best_size				,      max_answer_length=args.max_answer_length				,      null_score_diff_threshold=args.null_score_diff_threshold				,      output_dir=args.output_dir				,      prefix=lowerCAmelCase				,      )
      # Format the result to the format the metric expects.
      if args.version_2_with_negative:
            _lowerCAmelCase					=				[
                {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
            ]
      else:
            _lowerCAmelCase					=				[{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

      _lowerCAmelCase					=				[{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
      return EvalPrediction(predictions=lowerCAmelCase				,      label_ids=lowerCAmelCase    )


A__							:  Dict    =load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')

# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
      # setup for TRT inferrence
      for i in range(len(input_names)):
            context.set_binding_shape(i, INPUT_SHAPE)
      assert context.all_binding_shapes_specified
      def   UpperCamelCase__ (						lowerCAmelCase    ):





            """simple docstring"""


            return trt.volume(engine.get_binding_shape(lowerCAmelCase    )    ) * engine.get_binding_dtype(lowerCAmelCase    ).itemsize

      # Allocate device memory for inputs and outputs.
      A__							:  str    =[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

      # Allocate output buffer
      A__							:  str    =cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
      A__							:  List[Any]    =cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
      A__							:  List[Any]    =cuda.mem_alloc(h_outputa.nbytes)
      A__							:  Union[str, Any]    =cuda.mem_alloc(h_outputa.nbytes)

      # Create a stream in which to copy inputs/outputs and run inference.
      A__							:  List[str]    =cuda.Stream()

      # Evaluation
      logger.info('''***** Running Evaluation *****''')
      logger.info(F"""  Num examples = {len(eval_dataset)}""")
      logger.info(F"""  Batch size = {args.per_device_eval_batch_size}""")

      A__							:  int    =0.0
      A__							:  List[str]    =0
      A__							:  Optional[Any]    =timeit.default_timer()

      A__							:  Any    =None
      for step, batch in enumerate(eval_dataloader):
            A__  ,		A__							:  Optional[int]    =model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
            total_time += infer_time
            niter += 1

            A__  ,		A__							:  List[str]    =outputs
            A__							:  int    =torch.tensor(start_logits)
            A__							:  Any    =torch.tensor(end_logits)

            # necessary to pad predictions and labels for being gathered
            A__							:  str    =accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
            A__							:  Optional[int]    =accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

            A__							:  str    =(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
            A__							:  Optional[int]    =logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)

      if all_preds is not None:
            A__							:  List[Any]    =nested_truncate(all_preds, len(eval_dataset))

      A__							:  Dict    =timeit.default_timer() - start_time
      logger.info('''  Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
      # Inference time from TRT
      logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 10_00 / niter))
      logger.info('''Total Inference Time =  {:.3f} ms'''.format(total_time * 10_00))





      logger.info('''Total Number of Inference =  %d''', niter)

A__							:  Dict    =post_processing_function(eval_examples, eval_dataset, all_preds)
A__							:  Tuple    =metric.compute(predictions=prediction.predictions, references=prediction.label_ids)





logger.info(F"""Evaluation metrics: {eval_metric}""")













'''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


A__							:  Union[str, Any]    =logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """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   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      return max(metric_fn(lowerCAmelCase				,      lowerCAmelCase    ) for gt in ground_truths    )
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				[line.strip() for line in open(lowerCAmelCase				,      """r"""    ).readlines()]
      _lowerCAmelCase					=				[]

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

      _lowerCAmelCase					=				_lowerCAmelCase					=				_lowerCAmelCase					=				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    )

      _lowerCAmelCase					=				100.0 * em / total
      _lowerCAmelCase					=				100.0 * fa / total

      logger.info(f"F1: {fa:.2f}"    )
      logger.info(f"EM: {em:.2f}"    )
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


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

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

      _lowerCAmelCase					=				100.0 * em / total
      logger.info(f"Precision@{k}: {em: .2f}"    )
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


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

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

      _lowerCAmelCase					=				rag_model.rag.question_encoder(lowerCAmelCase    )
      _lowerCAmelCase					=				question_enc_outputs[0]

      _lowerCAmelCase					=				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"""				,      )
      _lowerCAmelCase					=				rag_model.retriever.index.get_doc_dicts(result.doc_ids    )
      _lowerCAmelCase					=				[]
      for docs in all_docs:
            _lowerCAmelCase					=				[strip_title(lowerCAmelCase    ) for title in docs["""title"""]]
            provenance_strings.append("""\t""".join(lowerCAmelCase    )    )
      return provenance_strings
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


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

            _lowerCAmelCase					=				inputs_dict.input_ids.to(args.device    )
            _lowerCAmelCase					=				inputs_dict.attention_mask.to(args.device    )
            _lowerCAmelCase					=				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]]				,      )
            _lowerCAmelCase					=				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__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				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."""				,      )
      _lowerCAmelCase					=				parser.parse_args()
      _lowerCAmelCase					=				torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""    )
      return args
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


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

      _lowerCAmelCase					=				(
          [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    )

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






