
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=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',
)

__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 << 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)
												__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 							a		(				snake_case__:   str		,  snake_case__:   List[str]		,  snake_case__:   List[Any]		,  snake_case__:   List[str]		,  snake_case__:   Optional[int]		,  snake_case__:   Optional[Any]		,  snake_case__:   List[str]		,  snake_case__:   List[Any]   ):


				'''simple docstring'''
				lowercase_	    =   np.asarray(inputs['''input_ids''']		,  dtype=np.intaa   )
				lowercase_	    =   np.asarray(inputs['''attention_mask''']		,  dtype=np.intaa   )
				lowercase_	    =   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
				lowercase_	    =   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
				lowercase_	    =   time.time()
				lowercase_	    =   end_time - start_time
				lowercase_	    =   (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 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				# 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
				lowercase_	    =   [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.
				lowercase_	    =   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.
				lowercase_	    =   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.
				lowercase_	    =   []

				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).
								lowercase_	    =   tokenized_examples.sequence_ids(snake_case__   )
								lowercase_	    =   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.
								lowercase_	    =   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.
								lowercase_	    =   [
								    (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 							a		(				snake_case__:   Tuple		,  snake_case__:   int		,  snake_case__:   Union[str, Any]		,  snake_case__:   Optional[int]="eval"   ):


				'''simple docstring'''
				# Post-processing: we match the start logits and end logits to answers in the original context.
				lowercase_	    =   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:
								lowercase_	    =   [
								    {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
								]
				else:
								lowercase_	    =   [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

				lowercase_	    =   [{'''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 							a		(				snake_case__:   Dict   ):


										'''simple docstring'''
										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=-1_0_0)
												__a         =   accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

												__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=-1_0_0)

						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 * 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)

__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}")










# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):
						UpperCamelCase      =       1

						@register_to_config
						def __init__(		self :Any     ,			__snake_case :Tuple=20_00     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :Any=20     ,			__snake_case :Optional[int]=1E-3			):
										'''simple docstring'''

										__magic_name__						:						Dict										=None
										__magic_name__						:						List[str]										=None
										__magic_name__						:						str										=None

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :Union[str, torch.device] = None			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=torch.linspace(1     ,			self.config.sampling_eps     ,			__snake_case     ,			device=__snake_case			)

						def      A__    (		self :List[str]     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :int     ,			__snake_case :List[str]=None			):
										'''simple docstring'''

										if self.timesteps is None:
														raise ValueError(
														    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""			)

										# TODO(Patrick) better comments + non-PyTorch
										# postprocess model score
										__magic_name__						:						int										=(
										    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										__magic_name__						:						Optional[int]										=torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
										__magic_name__						:						str										=std.flatten()
										while len(std.shape			) < len(score.shape			):
														__magic_name__						:						List[str]										=std.unsqueeze(-1			)
										__magic_name__						:						Union[str, Any]										=-score / std

										# compute
										__magic_name__						:						Tuple										=-1.0 / len(self.timesteps			)

										__magic_name__						:						int										=self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
										__magic_name__						:						Dict										=beta_t.flatten()
										while len(beta_t.shape			) < len(x.shape			):
														__magic_name__						:						Any										=beta_t.unsqueeze(-1			)
										__magic_name__						:						Dict										=-0.5 * beta_t * x

										__magic_name__						:						Optional[int]										=torch.sqrt(__snake_case			)
										__magic_name__						:						int										=drift - diffusion**2 * score
										__magic_name__						:						List[str]										=x + drift * dt

										# add noise
										__magic_name__						:						Optional[int]										=randn_tensor(x.shape     ,			layout=x.layout     ,			generator=__snake_case     ,			device=x.device     ,			dtype=x.dtype			)
										__magic_name__						:						Optional[Any]										=x_mean + diffusion * math.sqrt(-dt			) * noise

										return x, x_mean


						def __len__(		self :List[Any]			):
										'''simple docstring'''

										return self.config.num_train_timesteps




