

'''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


lowerCamelCase			:				List[str] 			=					trt.Logger(trt.Logger.WARNING)
lowerCamelCase			:				List[str] 			=					absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

lowerCamelCase			:				Tuple 			=					logging.getLogger(__name__)

lowerCamelCase			:				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_8_4,
    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_2_8,
    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=2_0,
    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=3_0,
    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=4_2, 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",
)

lowerCamelCase			:				List[str] 			=					parser.parse_args()

if args.tokenizer_name:
     lowerCamelCase			:				Optional[Any] 			=					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)

lowerCamelCase			:				Tuple 			=					args.per_device_eval_batch_size

lowerCamelCase			:				Dict 			=					(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
lowerCamelCase			:				List[Any] 			=					True

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

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

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

     with builder.create_builder_config() as config:
          lowerCamelCase			:				str 			=					1 << 5_0
          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)
          lowerCamelCase			:				Dict 			=					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)
          lowerCamelCase			:				List[str] 			=					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 	_lowerCAmelCase (						_UpperCamelCase						:					Any							,			_UpperCamelCase						:					Tuple							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					Any							,			_UpperCamelCase						:					Any							,			_UpperCamelCase						:					Dict							,			_UpperCamelCase						:					Optional[int]    )				->      Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =np.asarray(inputs['input_ids']							,			dtype=np.intaa    )
     _SCREAMING_SNAKE_CASE							   =np.asarray(inputs['attention_mask']							,			dtype=np.intaa    )
     _SCREAMING_SNAKE_CASE							   =np.asarray(inputs['token_type_ids']							,			dtype=np.intaa    )

     # Copy inputs
     cuda.memcpy_htod_async(d_inputs[0]							,			input_ids.ravel()							,			_UpperCamelCase    )
     cuda.memcpy_htod_async(d_inputs[1]							,			attention_mask.ravel()							,			_UpperCamelCase    )
     cuda.memcpy_htod_async(d_inputs[2]							,			token_type_ids.ravel()							,			_UpperCamelCase    )
     # start time
     _SCREAMING_SNAKE_CASE							   =time.time()
     # Run inference
     context.execute_async(
         bindings=[int(_UpperCamelCase    ) for d_inp in d_inputs] + [int(_UpperCamelCase    ), int(_UpperCamelCase    )]							,			stream_handle=stream.handle    )
     # Transfer predictions back from GPU
     cuda.memcpy_dtoh_async(_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase    )
     cuda.memcpy_dtoh_async(_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase    )
     # Synchronize the stream and take time
     stream.synchronize()
     # end time
     _SCREAMING_SNAKE_CASE							   =time.time()
     _SCREAMING_SNAKE_CASE							   =end_time - start_time
     _SCREAMING_SNAKE_CASE							   =(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.
lowerCamelCase			:				Dict 			=					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.
     lowerCamelCase			:				List[str] 			=					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.

lowerCamelCase			:				Any 			=					raw_datasets["validation"].column_names

lowerCamelCase			:				Tuple 			=					"question" if "question" in column_names else column_names[0]
lowerCamelCase			:				Any 			=					"context" if "context" in column_names else column_names[1]
lowerCamelCase			:				List[str] 			=					"answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
lowerCamelCase			:				Optional[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}.'''
     )

lowerCamelCase			:				List[str] 			=					min(args.max_seq_length, tokenizer.model_max_length)


def 	_lowerCAmelCase (						_UpperCamelCase						:					Dict    )				->      int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =[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.
     _SCREAMING_SNAKE_CASE							   =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=_UpperCamelCase							,			stride=args.doc_stride							,			return_overflowing_tokens=_UpperCamelCase							,			return_offsets_mapping=_UpperCamelCase							,			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.
     _SCREAMING_SNAKE_CASE							   =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.
     _SCREAMING_SNAKE_CASE							   =[]

     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).
          _SCREAMING_SNAKE_CASE							   =tokenized_examples.sequence_ids(_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   =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.
          _SCREAMING_SNAKE_CASE							   =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.
          _SCREAMING_SNAKE_CASE							   =[
              (o if sequence_ids[k] == context_index else None)
              for k, o in enumerate(tokenized_examples['offset_mapping'][i]    )
          ]

     return tokenized_examples


lowerCamelCase			:				Union[str, Any] 			=					raw_datasets["validation"]
# Validation Feature Creation
lowerCamelCase			:				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",
)

lowerCamelCase			:				List[Any] 			=					default_data_collator

lowerCamelCase			:				Optional[Any] 			=					eval_dataset.remove_columns(["example_id", "offset_mapping"])
lowerCamelCase			:				Optional[Any] 			=					DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)


def 	_lowerCAmelCase (						_UpperCamelCase						:					Union[str, Any]							,			_UpperCamelCase						:					Tuple							,			_UpperCamelCase						:					Dict							,			_UpperCamelCase						:					Tuple="eval"    )				->      Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =postprocess_qa_predictions(
         examples=_UpperCamelCase							,			features=_UpperCamelCase							,			predictions=_UpperCamelCase							,			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=_UpperCamelCase							,			)
     # Format the result to the format the metric expects.
     if args.version_2_with_negative:
          _SCREAMING_SNAKE_CASE							   =[
              {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
          ]
     else:
          _SCREAMING_SNAKE_CASE							   =[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

     _SCREAMING_SNAKE_CASE							   =[{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
     return EvalPrediction(predictions=_UpperCamelCase							,			label_ids=_UpperCamelCase    )


lowerCamelCase			:				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 	_lowerCAmelCase (						_UpperCamelCase						:					str    )				->      Any:
          """simple docstring"""




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

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

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

     # Create a stream in which to copy inputs/outputs and run inference.
     lowerCamelCase			:				List[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}''')

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

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

          lowerCamelCase			,   lowerCamelCase			:				int 			=					outputs
          lowerCamelCase			:				Optional[Any] 			=					torch.tensor(start_logits)
          lowerCamelCase			:				Optional[Any] 			=					torch.tensor(end_logits)

          # necessary to pad predictions and labels for being gathered
          lowerCamelCase			:				Any 			=					accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_0_0)
          lowerCamelCase			:				Optional[int] 			=					accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

          lowerCamelCase			:				Union[str, Any] 			=					(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
          lowerCamelCase			:				Union[str, Any] 			=					logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_0_0)

     if all_preds is not None:
          lowerCamelCase			:				Dict 			=					nested_truncate(all_preds, len(eval_dataset))

     lowerCamelCase			:				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 * 1_0_0_0 / niter))
     logger.info("Total Inference Time =  {:.3f} ms".format(total_time * 1_0_0_0))







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

lowerCamelCase			:				List[Any] 			=					post_processing_function(eval_examples, eval_dataset, all_preds)
lowerCamelCase			:				Optional[Any] 			=					metric.compute(predictions=prediction.predictions, references=prediction.label_ids)







logger.info(f'''Evaluation metrics: {eval_metric}''')



'''simple docstring'''
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


lowerCamelCase			:				Optional[Any] 			=					logging.get_logger(__name__)

lowerCamelCase			:				Optional[Any] 			=					"T5Config"







class 							A__   (	A__  ):
  A__					=    'mt5'
  A__					=    MTaConfig







class 							A__   (	A__  ):
  A__					=    'mt5'
  A__					=    MTaConfig







class 							A__   (	A__  ):
  A__					=    'mt5'
  A__					=    MTaConfig
