

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		=trt.Logger(trt.Logger.WARNING)
a		=absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

a		=logging.getLogger(__name__)

a		=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=384,
    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=128,
    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		=parser.parse_args()

if args.tokenizer_name:
					a		=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		=args.per_device_eval_batch_size

a		=(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
a		=True

a		="""temp_engine/bert-fp32.engine"""
if args.fpaa:
					a		="""temp_engine/bert-fp16.engine"""
if args.inta:
					a		="""temp_engine/bert-int8.engine"""

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

a		=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		=[network.get_input(i) for i in range(network.num_inputs)]
					a		=[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

					with builder.create_builder_config() as config:
										a		=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		=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		=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    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)  ->     Optional[int]:
	__lowerCamelCase				:				int					       = np.asarray(inputs['input_ids']	, dtype=np.intaa			)
	__lowerCamelCase				:				List[str]					       = np.asarray(inputs['attention_mask']	, dtype=np.intaa			)
	__lowerCamelCase				:				List[Any]					       = np.asarray(inputs['token_type_ids']	, dtype=np.intaa			)

	# Copy inputs
	cuda.memcpy_htod_async(d_inputs[0]	, input_ids.ravel()	, snake_case__			)
	cuda.memcpy_htod_async(d_inputs[1]	, attention_mask.ravel()	, snake_case__			)
	cuda.memcpy_htod_async(d_inputs[2]	, token_type_ids.ravel()	, snake_case__			)
	# start time
	__lowerCamelCase				:				Union[str, Any]					       = time.time()
	# Run inference
	context.execute_async(
	    bindings=[int(snake_case__			) for d_inp in d_inputs] + [int(snake_case__			), int(snake_case__			)]	, stream_handle=stream.handle			)
	# Transfer predictions back from GPU
	cuda.memcpy_dtoh_async(snake_case__	, snake_case__	, snake_case__			)
	cuda.memcpy_dtoh_async(snake_case__	, snake_case__	, snake_case__			)
	# Synchronize the stream and take time
	stream.synchronize()
	# end time
	__lowerCamelCase				:				int					       = time.time()
	__lowerCamelCase				:				int					       = end_time - start_time
	__lowerCamelCase				:				Tuple					       = (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		=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		=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		=raw_datasets["""validation"""].column_names

a		="""question""" if """question""" in column_names else column_names[0]
a		="""context""" if """context""" in column_names else column_names[1]
a		="""answers""" if """answers""" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
a		=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		=min(args.max_seq_length, tokenizer.model_max_length)






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     Optional[Any]:
	# Some of the questions have lots of whitespace on the left, which is not useful and will make the
	# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
	# left whitespace
	__lowerCamelCase				:				Optional[int]					       = [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				:				Dict					       = 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=snake_case__	, stride=args.doc_stride	, return_overflowing_tokens=snake_case__	, return_offsets_mapping=snake_case__	, 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				:				List[str]					       = 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				:				str					       = []

	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				:				str					       = tokenized_examples.sequence_ids(snake_case__			)
		__lowerCamelCase				:				Optional[Any]					       = 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				:				Any					       = 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				:				Optional[int]					       = [
		    (o if sequence_ids[k] == context_index else None)
		    for k, o in enumerate(tokenized_examples['offset_mapping'][i]			)
		]

	return tokenized_examples


a		=raw_datasets["""validation"""]
# Validation Feature Creation
a		=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		=default_data_collator

a		=eval_dataset.remove_columns(["""example_id""", """offset_mapping"""])
a		=DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__="eval"			)  ->     Dict:
	# Post-processing: we match the start logits and end logits to answers in the original context.
	__lowerCamelCase				:				Dict					       = postprocess_qa_predictions(
	    examples=snake_case__	, features=snake_case__	, predictions=snake_case__	, 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=snake_case__	, )
	# Format the result to the format the metric expects.
	if args.version_2_with_negative:
		__lowerCamelCase				:				Optional[int]					       = [
		    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
		]
	else:
		__lowerCamelCase				:				Union[str, Any]					       = [{'id': k, 'prediction_text': v} for k, v in predictions.items()]

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


a		=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    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     Union[str, Any]:
						return trt.volume(engine.get_binding_shape(snake_case__			)			) * engine.get_binding_dtype(snake_case__			).itemsize

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

					# Allocate output buffer
					a		=cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
					a		=cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
					a		=cuda.mem_alloc(h_outputa.nbytes)
					a		=cuda.mem_alloc(h_outputa.nbytes)

					# Create a stream in which to copy inputs/outputs and run inference.
					a		=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		=0.0
					a		=0
					a		=timeit.default_timer()

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

										a		,       a		=outputs
										a		=torch.tensor(start_logits)
										a		=torch.tensor(end_logits)

										# necessary to pad predictions and labels for being gathered
										a		=accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
										a		=accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

										a		=(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
										a		=logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

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

					a		=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 * 1000 / niter))
					logger.info("""Total Inference Time =  {:.3f} ms""".format(total_time * 1000))

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

a		=post_processing_function(eval_examples, eval_dataset, all_preds)
a		=metric.compute(predictions=prediction.predictions, references=prediction.label_ids)

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









from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Any        =  "canine"
       def __init__(			self,			SCREAMING_SNAKE_CASE_=768,			SCREAMING_SNAKE_CASE_=12,			SCREAMING_SNAKE_CASE_=12,			SCREAMING_SNAKE_CASE_=3072,			SCREAMING_SNAKE_CASE_="gelu",			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=1_6384,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=1e-12,			SCREAMING_SNAKE_CASE_=0,			SCREAMING_SNAKE_CASE_=0XE000,			SCREAMING_SNAKE_CASE_=0XE001,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=8,			SCREAMING_SNAKE_CASE_=1_6384,			SCREAMING_SNAKE_CASE_=128,			**SCREAMING_SNAKE_CASE_,			)     ->		int:
          super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,			bos_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	max_position_embeddings
          UpperCamelCase    :  Tuple							=	hidden_size
          UpperCamelCase    :  Union[str, Any]							=	num_hidden_layers
          UpperCamelCase    :  Optional[int]							=	num_attention_heads
          UpperCamelCase    :  Tuple							=	intermediate_size
          UpperCamelCase    :  List[str]							=	hidden_act
          UpperCamelCase    :  Union[str, Any]							=	hidden_dropout_prob
          UpperCamelCase    :  Optional[int]							=	attention_probs_dropout_prob
          UpperCamelCase    :  Optional[Any]							=	initializer_range
          UpperCamelCase    :  Tuple							=	type_vocab_size
          UpperCamelCase    :  Any							=	layer_norm_eps

          # Character config:
          UpperCamelCase    :  List[Any]							=	downsampling_rate
          UpperCamelCase    :  Optional[int]							=	upsampling_kernel_size
          UpperCamelCase    :  Tuple							=	num_hash_functions
          UpperCamelCase    :  Union[str, Any]							=	num_hash_buckets
          UpperCamelCase    :  str							=	local_transformer_stride



