





'''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__    :       List[Any]              =trt.Logger(trt.Logger.WARNING)
A__    :       int              =absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

A__    :       int              =logging.getLogger(__name__)

A__    :       Optional[int]              =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__    :       Dict              =parser.parse_args()

if args.tokenizer_name:
      A__    :       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__    :       Tuple              =args.per_device_eval_batch_size

A__    :       int              =(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
A__    :       Dict              =True

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

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

A__    :       str              =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__    :       List[Any]              =[network.get_input(i) for i in range(network.num_inputs)]
      A__    :       int              =[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

      with builder.create_builder_config() as config:
            A__    :       Union[str, Any]              =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__    :       Optional[int]              =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__    :       Dict              =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__    :       Tuple              =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__    :       Optional[int]              =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__    :       Any              =raw_datasets['''validation'''].column_names

A__    :       Tuple              ='''question''' if '''question''' in column_names else column_names[0]
A__    :       int              ='''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__    :       Any              =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__    :       Tuple              =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__    :       Optional[Any]              =raw_datasets['''validation''']
# Validation Feature Creation
A__    :       Union[str, 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__    :       List[Any]              =default_data_collator

A__    :       Union[str, Any]              =eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
A__    :       Any              =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__    :       List[Any]              =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__    :       Any              =[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

      # Allocate output buffer
      A__    :       int              =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__    :       Optional[Any]              =cuda.mem_alloc(h_outputa.nbytes)

      # Create a stream in which to copy inputs/outputs and run inference.
      A__    :       Any              =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__    :       Any              =0.0
      A__    :       int              =0
      A__    :       Dict              =timeit.default_timer()

      A__    :       Tuple              =None
      for step, batch in enumerate(eval_dataloader):
            A__       ,						A__    :       str              =model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
            total_time += infer_time
            niter += 1

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

            # necessary to pad predictions and labels for being gathered
            A__    :       List[str]              =accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
            A__    :       Any              =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__    :       Union[str, Any]              =logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)

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

      A__    :       Tuple              =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__    :       int              =post_processing_function(eval_examples, eval_dataset, all_preds)
A__    :       Union[str, Any]              =metric.compute(predictions=prediction.predictions, references=prediction.label_ids)





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













'''simple docstring'''






import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
A__    :       Any              =logging.getLogger()

A__    :       int              =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class 			UpperCAmelCase					(      snake_case_       ):




      def     lowercase__   (      self      :			Optional[Any]      ,      __snake_case      :			Any  )    ->				int:
            os.makedirs(__snake_case      ,      exist_ok=__snake_case  )
            _lowerCAmelCase												=							{"""source""": """What is love ?""", """target""": """life"""}
            _lowerCAmelCase												=							{"""train""": 12, """val""": 2, """test""": 2}
            for split in ["train", "test", "val"]:
                  for field in ["source", "target"]:
                        _lowerCAmelCase												=							"""\n""".join([contents[field]] * n_lines[split]  )
                        with open(os.path.join(__snake_case      ,      f"{split}.{field}"  )      ,      """w"""  ) as f:
                              f.write(__snake_case  )




      def     lowercase__   (      self      :			Tuple      ,      __snake_case      :			int      ,      __snake_case      :			str = "pytorch"  )    ->				int:
            _lowerCAmelCase												=							self.get_auto_remove_tmp_dir()
            _lowerCAmelCase												=							os.path.join(__snake_case      ,      """output"""  )
            _lowerCAmelCase												=							os.path.join(__snake_case      ,      """data"""  )
            self._create_dummy_data(data_dir=__snake_case  )

            _lowerCAmelCase												=							f"\n                --data_dir {data_dir} \\n                --output_dir {output_dir} \\n                --model_name_or_path facebook/rag-sequence-base \\n                --model_type rag_sequence \\n                --do_train \\n                --do_predict \\n                --n_val -1 \\n                --val_check_interval 1.0 \\n                --train_batch_size 2 \\n                --eval_batch_size 1 \\n                --max_source_length 25 \\n                --max_target_length 25 \\n                --val_max_target_length 25 \\n                --test_max_target_length 25 \\n                --label_smoothing 0.1 \\n                --dropout 0.1 \\n                --attention_dropout 0.1 \\n                --weight_decay 0.001 \\n                --adam_epsilon 1e-08 \\n                --max_grad_norm 0.1 \\n                --lr_scheduler polynomial \\n                --learning_rate 3e-04 \\n                --num_train_epochs 1 \\n                --warmup_steps 4 \\n                --gradient_accumulation_steps 1 \\n                --distributed-port 8787 \\n                --use_dummy_dataset 1 \\n                --distributed_retriever {distributed_retriever} \\n            ".split()

            if gpus > 0:
                  testargs.append(f"--gpus={gpus}"  )
                  if is_apex_available():
                        testargs.append("""--fp16"""  )
            else:
                  testargs.append("""--gpus=0"""  )
                  testargs.append("""--distributed_backend=ddp_cpu"""  )
                  testargs.append("""--num_processes=2"""  )

            _lowerCAmelCase												=							[sys.executable, str(Path(finetune_rag.__file__  ).resolve()  )] + testargs
            execute_subprocess_async(__snake_case      ,      env=self.get_env()  )

            _lowerCAmelCase												=							os.path.join(__snake_case      ,      """metrics.json"""  )
            with open(__snake_case  ) as f:
                  _lowerCAmelCase												=							json.load(__snake_case  )
            return result




      @require_torch_gpu
      def     lowercase__   (      self      :			Dict  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							self._run_finetune(gpus=1  )
            self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,      0.2  )




      @require_torch_multi_gpu
      def     lowercase__   (      self      :			int  )    ->				Dict:
            _lowerCAmelCase												=							self._run_finetune(gpus=2  )
            self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,      0.2  )




      @require_torch_gpu
      @require_ray
      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							self._run_finetune(gpus=1      ,      distributed_retriever="""ray"""  )
            self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,      0.2  )




      @require_torch_multi_gpu
      @require_ray
      def     lowercase__   (      self      :			int  )    ->				List[str]:
            _lowerCAmelCase												=							self._run_finetune(gpus=1      ,      distributed_retriever="""ray"""  )
            self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,      0.2  )






