






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      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''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.
__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					(     _lowercase ):

					'''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


__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					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase="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 )


__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					(     _lowercase ):

									'''simple docstring'''







									return trt.volume(engine.get_binding_shape(_lowercase ) ) * engine.get_binding_dtype(_lowercase ).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}')











import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin





class 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					BlenderbotSmallTokenizer
							A_						  =					False

							def       __A (  self:   List[str]   )    ->				int:
												super().setUp()

												_A								= ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
												_A								= dict(zip(__A		,     range(len(__A   )   )   )   )

												_A								= ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
												_A								= {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

												_A								= os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''vocab_file''']   )
												_A								= os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''merges_file''']   )
												with open(self.vocab_file		,     '''w'''		,     encoding='''utf-8'''   ) as fp:
																	fp.write(json.dumps(__A   ) + '''\n'''   )
												with open(self.merges_file		,     '''w'''		,     encoding='''utf-8'''   ) as fp:
																	fp.write('''\n'''.join(__A   )   )

							def       __A (  self:   str		,     **__A:   Optional[Any]   )    ->				Dict:
												kwargs.update(self.special_tokens_map   )
												return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname		,     **__A   )

							def       __A (  self:   str		,     __A:   List[str]   )    ->				int:
												_A								= '''adapt act apte'''
												_A								= '''adapt act apte'''
												return input_text, output_text

							def       __A (  self:   Union[str, Any]   )    ->				Any:
												_A								= BlenderbotSmallTokenizer(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map   )
												_A								= '''adapt act apte'''
												_A								= ['''adapt''', '''act''', '''ap@@''', '''te''']
												_A								= tokenizer.tokenize(__A   )
												self.assertListEqual(__A		,     __A   )

												_A								= [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

												_A								= [0, 1, 2, 3, 4, 5]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__A   )		,     __A   )

							def       __A (  self:   Any   )    ->				List[str]:
												_A								= BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
												assert tok('''sam'''   ).input_ids == [13_84]
												_A								= '''I am a small frog.'''
												_A								= tok([src_text]		,     padding=__A		,     truncation=__A   )['''input_ids''']
												_A								= tok.batch_decode(__A		,     skip_special_tokens=__A		,     clean_up_tokenization_spaces=__A   )[0]
												assert src_text != decoded  # I wish it did!
												assert decoded == "i am a small frog ."



							def       __A (  self:   Any   )    ->				int:
												_A								= BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
												_A								= '''I am a small frog .'''
												_A								= '''.'''
												_A								= tok(__A   )['''input_ids''']
												_A								= tok(__A   )['''input_ids''']

												assert encoded[-1] == encoded_dot[0]



