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

__lowerCamelCase											=  logging.getLogger(__name__)

__lowerCamelCase											=  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",
)

__lowerCamelCase											=  parser.parse_args()

if args.tokenizer_name:
				__lowerCamelCase											=  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											=  args.per_device_eval_batch_size

__lowerCamelCase											=  (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__lowerCamelCase											=  True

__lowerCamelCase											=  '''temp_engine/bert-fp32.engine'''
if args.fpaa:
				__lowerCamelCase											=  '''temp_engine/bert-fp16.engine'''
if args.inta:
				__lowerCamelCase											=  '''temp_engine/bert-int8.engine'''

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

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

				with builder.create_builder_config() as config:
								__lowerCamelCase											=  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)
								__lowerCamelCase											=  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											=  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					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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

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

__lowerCamelCase											=  '''question''' if '''question''' in column_names else column_names[0]
__lowerCamelCase											=  '''context''' if '''context''' in column_names else column_names[1]
__lowerCamelCase											=  '''answers''' if '''answers''' in column_names else column_names[2]

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






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							[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.
							A__	     =							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=_lowercase       ,    stride=args.doc_stride       ,    return_overflowing_tokens=_lowercase       ,    return_offsets_mapping=_lowercase       ,    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.
							A__	     =							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.
							A__	     =							[]

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

							return tokenized_examples


__lowerCamelCase											=  raw_datasets['''validation''']
# Validation Feature Creation
__lowerCamelCase											=  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											=  default_data_collator

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






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__="eval"				):






							"""simple docstring"""





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

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


__lowerCamelCase											=  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					(	UpperCamelCase__				):






											"""simple docstring"""





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

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

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

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

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

								__lowerCamelCase											=  outputs
								__lowerCamelCase											=  torch.tensor(start_logits)
								__lowerCamelCase											=  torch.tensor(end_logits)

								# necessary to pad predictions and labels for being gathered
								__lowerCamelCase											=  accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
								__lowerCamelCase											=  accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

								__lowerCamelCase											=  (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
								__lowerCamelCase											=  logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)

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

				__lowerCamelCase											=  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)

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


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






"""simple docstring"""




import numpy as np
from PIL import Image






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							np.array(UpperCamelCase__				)
							if arr.shape[0] != arr.shape[1]:
														raise ValueError('The input array is not a square matrix'				)
							A__	     =							0
							A__	     =							0
							A__	     =							0
							A__	     =							0

							# compute the shape of the output matrix
							A__	     =							(arr.shape[0] - size) // stride + 1
							# initialize the output matrix with zeros of shape maxpool_shape
							A__	     =							np.zeros((maxpool_shape, maxpool_shape)				)

							while i < arr.shape[0]:
														if i + size > arr.shape[0]:
																					# if the end of the matrix is reached, break
																					break
														while j < arr.shape[1]:
																					# if the end of the matrix is reached, break
																					if j + size > arr.shape[1]:
																												break
																					# compute the maximum of the pooling matrix
																					A__	     =							np.max(arr[i : i + size, j : j + size]				)
																					# shift the pooling matrix by stride of column pixels
																					j += stride
																					mat_j += 1

														# shift the pooling matrix by stride of row pixels
														i += stride
														mat_i += 1

														# reset the column index to 0
														A__	     =							0
														A__	     =							0

							return updated_arr






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							np.array(UpperCamelCase__				)
							if arr.shape[0] != arr.shape[1]:
														raise ValueError('The input array is not a square matrix'				)
							A__	     =							0
							A__	     =							0
							A__	     =							0
							A__	     =							0

							# compute the shape of the output matrix
							A__	     =							(arr.shape[0] - size) // stride + 1
							# initialize the output matrix with zeros of shape avgpool_shape
							A__	     =							np.zeros((avgpool_shape, avgpool_shape)				)

							while i < arr.shape[0]:
														# if the end of the matrix is reached, break
														if i + size > arr.shape[0]:
																					break
														while j < arr.shape[1]:
																					# if the end of the matrix is reached, break
																					if j + size > arr.shape[1]:
																												break
																					# compute the average of the pooling matrix
																					A__	     =							int(np.average(arr[i : i + size, j : j + size]				)				)
																					# shift the pooling matrix by stride of column pixels
																					j += stride
																					mat_j += 1

														# shift the pooling matrix by stride of row pixels
														i += stride
														mat_i += 1
														# reset the column index to 0
														A__	     =							0
														A__	     =							0

							return updated_arr


# Main Function
if __name__ == "__main__":
				from doctest import testmod

				testmod(name="avgpooling", verbose=True)

				# Loading the image
				__lowerCamelCase											=  Image.open("path_to_image")

				# Converting the image to numpy array and maxpooling, displaying the result
				# Ensure that the image is a square matrix

				Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

				# Converting the image to numpy array and averagepooling, displaying the result
				# Ensure that the image is a square matrix

				Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()





