

from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers.models.layoutlm.modeling_tf_layoutlm import (
					    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFLayoutLMForMaskedLM,
					    TFLayoutLMForQuestionAnswering,
					    TFLayoutLMForSequenceClassification,
					    TFLayoutLMForTokenClassification,
					    TFLayoutLMModel,
					)

class 			a					:


						def __init__(    self  ,   A_  ,   A_=13  ,   A_=7  ,   A_=True  ,   A_=True  ,   A_=True  ,   A_=True  ,   A_=99  ,   A_=32  ,   A_=2  ,   A_=4  ,   A_=37  ,   A_="gelu"  ,   A_=0.1  ,   A_=0.1  ,   A_=512  ,   A_=16  ,   A_=2  ,   A_=0.02  ,   A_=3  ,   A_=4  ,   A_=None  ,   A_=1000  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      parent
										_UpperCAmelCase    :						int							=      batch_size
										_UpperCAmelCase    :						Any							=      seq_length
										_UpperCAmelCase    :						str							=      is_training
										_UpperCAmelCase    :						Optional[Any]							=      use_input_mask
										_UpperCAmelCase    :						Union[str, Any]							=      use_token_type_ids
										_UpperCAmelCase    :						List[Any]							=      use_labels
										_UpperCAmelCase    :						List[Any]							=      vocab_size
										_UpperCAmelCase    :						List[str]							=      hidden_size
										_UpperCAmelCase    :						Union[str, Any]							=      num_hidden_layers
										_UpperCAmelCase    :						Dict							=      num_attention_heads
										_UpperCAmelCase    :						List[str]							=      intermediate_size
										_UpperCAmelCase    :						List[Any]							=      hidden_act
										_UpperCAmelCase    :						List[str]							=      hidden_dropout_prob
										_UpperCAmelCase    :						Optional[int]							=      attention_probs_dropout_prob
										_UpperCAmelCase    :						Optional[Any]							=      max_position_embeddings
										_UpperCAmelCase    :						Union[str, Any]							=      type_vocab_size
										_UpperCAmelCase    :						List[str]							=      type_sequence_label_size
										_UpperCAmelCase    :						Tuple							=      initializer_range
										_UpperCAmelCase    :						Optional[int]							=      num_labels
										_UpperCAmelCase    :						Union[str, Any]							=      num_choices
										_UpperCAmelCase    :						Tuple							=      scope
										_UpperCAmelCase    :						Optional[Any]							=      range_bbox


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      ids_tensor([self.batch_size, self.seq_length]  ,   self.vocab_size		)

										# convert bbox to numpy since TF does not support item assignment
										_UpperCAmelCase    :						List[str]							=      ids_tensor([self.batch_size, self.seq_length, 4]  ,   self.range_bbox		).numpy()
										# Ensure that bbox is legal
										for i in range(bbox.shape[0]		):
														for j in range(bbox.shape[1]		):
																		if bbox[i, j, 3] < bbox[i, j, 1]:
																						_UpperCAmelCase    :						Tuple							=      bbox[i, j, 3]
																						_UpperCAmelCase    :						List[str]							=      bbox[i, j, 1]
																						_UpperCAmelCase    :						int							=      t
																		if bbox[i, j, 2] < bbox[i, j, 0]:
																						_UpperCAmelCase    :						Any							=      bbox[i, j, 2]
																						_UpperCAmelCase    :						List[Any]							=      bbox[i, j, 0]
																						_UpperCAmelCase    :						Tuple							=      t
										_UpperCAmelCase    :						int							=      tf.convert_to_tensor(A_		)

										_UpperCAmelCase    :						Tuple							=      None
										if self.use_input_mask:
														_UpperCAmelCase    :						Union[str, Any]							=      random_attention_mask([self.batch_size, self.seq_length]		)

										_UpperCAmelCase    :						str							=      None
										if self.use_token_type_ids:
														_UpperCAmelCase    :						Dict							=      ids_tensor([self.batch_size, self.seq_length]  ,   self.type_vocab_size		)

										_UpperCAmelCase    :						Optional[Any]							=      None
										_UpperCAmelCase    :						Tuple							=      None
										_UpperCAmelCase    :						Dict							=      None
										if self.use_labels:
														_UpperCAmelCase    :						List[Any]							=      ids_tensor([self.batch_size]  ,   self.type_sequence_label_size		)
														_UpperCAmelCase    :						Dict							=      ids_tensor([self.batch_size, self.seq_length]  ,   self.num_labels		)
														_UpperCAmelCase    :						Optional[int]							=      ids_tensor([self.batch_size]  ,   self.num_choices		)

										_UpperCAmelCase    :						Optional[int]							=      LayoutLMConfig(
										    vocab_size=self.vocab_size  ,   hidden_size=self.hidden_size  ,   num_hidden_layers=self.num_hidden_layers  ,   num_attention_heads=self.num_attention_heads  ,   intermediate_size=self.intermediate_size  ,   hidden_act=self.hidden_act  ,   hidden_dropout_prob=self.hidden_dropout_prob  ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,   max_position_embeddings=self.max_position_embeddings  ,   type_vocab_size=self.type_vocab_size  ,   initializer_range=self.initializer_range  ,   )

										return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      TFLayoutLMModel(config=A_		)

										_UpperCAmelCase    :						Any							=      model(A_  ,   A_  ,   attention_mask=A_  ,   token_type_ids=A_		)
										_UpperCAmelCase    :						Any							=      model(A_  ,   A_  ,   token_type_ids=A_		)
										_UpperCAmelCase    :						Dict							=      model(A_  ,   A_		)
										self.parent.assertEqual(result.last_hidden_state.shape  ,   (self.batch_size, self.seq_length, self.hidden_size)		)
										self.parent.assertEqual(result.pooler_output.shape  ,   (self.batch_size, self.hidden_size)		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      TFLayoutLMForMaskedLM(config=A_		)

										_UpperCAmelCase    :						List[Any]							=      model(A_  ,   A_  ,   attention_mask=A_  ,   token_type_ids=A_  ,   labels=A_		)
										self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.seq_length, self.vocab_size)		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.num_labels
										_UpperCAmelCase    :						Any							=      TFLayoutLMForSequenceClassification(config=A_		)

										_UpperCAmelCase    :						Tuple							=      model(A_  ,   A_  ,   attention_mask=A_  ,   token_type_ids=A_		)
										self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.num_labels)		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self.num_labels
										_UpperCAmelCase    :						str							=      TFLayoutLMForTokenClassification(config=A_		)

										_UpperCAmelCase    :						Any							=      model(A_  ,   A_  ,   attention_mask=A_  ,   token_type_ids=A_  ,   labels=A_		)
										self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.seq_length, self.num_labels)		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      TFLayoutLMForQuestionAnswering(config=A_		)

										_UpperCAmelCase    :						Dict							=      model(A_  ,   A_  ,   attention_mask=A_  ,   token_type_ids=A_		)
										self.parent.assertEqual(result.start_logits.shape  ,   (self.batch_size, self.seq_length)		)
										self.parent.assertEqual(result.end_logits.shape  ,   (self.batch_size, self.seq_length)		)





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self.prepare_config_and_inputs()
										(
										    (
										    _UpperCAmelCase
										)						,							(
										    _UpperCAmelCase
										)						,							(
										    _UpperCAmelCase
										)						,							(
										    _UpperCAmelCase
										)						,							(
										    _UpperCAmelCase
										)						,							(
										    _UpperCAmelCase
										)						,							(
										    _UpperCAmelCase
										)						,							(
										    _UpperCAmelCase
										)						,							
										)    :						List[Any]							=      config_and_inputs
										_UpperCAmelCase    :						Optional[Any]							=      {
										    "input_ids": input_ids,
										    "bbox": bbox,
										    "token_type_ids": token_type_ids,
										    "attention_mask": input_mask,
										}
										return config, inputs_dict







@require_tf
class 			a					(		UpperCAmelCase							,	UpperCAmelCase							,	unittest.TestCase			):
						_lowercase        =     (
						    (
						        TFLayoutLMModel,
						        TFLayoutLMForMaskedLM,
						        TFLayoutLMForTokenClassification,
						        TFLayoutLMForSequenceClassification,
						        TFLayoutLMForQuestionAnswering,
						    )
						    if is_tf_available()
						    else ()
						)
						_lowercase        =     (
						    {
						        "feature-extraction": TFLayoutLMModel,
						        "fill-mask": TFLayoutLMForMaskedLM,
						        "text-classification": TFLayoutLMForSequenceClassification,
						        "token-classification": TFLayoutLMForTokenClassification,
						        "zero-shot": TFLayoutLMForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						_lowercase        =     False
						_lowercase        =     True
						_lowercase        =     1_0


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      TFLayoutLMModelTester(self		)
										_UpperCAmelCase    :						Union[str, Any]							=      ConfigTester(self  ,   config_class=A_  ,   hidden_size=37		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*A_		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase    :						Optional[Any]							=      TFLayoutLMModel.from_pretrained(A_		)
														self.assertIsNotNone(A_		)





						@unittest.skip("Onnx compliancy broke with TF 2.10"		)
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass







def 							__SCREAMING_SNAKE_CASE							(			)				->    str:
				# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
				# fmt: off
				_UpperCAmelCase    :						int							=      tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]	)  # noqa: E231
				_UpperCAmelCase    :						Tuple							=      tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]	)  # noqa: E231
				_UpperCAmelCase    :						Optional[int]							=      tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]	)  # noqa: E231
				_UpperCAmelCase    :						List[str]							=      tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]	)  # noqa: E231
				# these are sequence labels (i.e. at the token level)
				_UpperCAmelCase    :						Union[str, Any]							=      tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]	)  # noqa: E231
				# fmt: on

				return input_ids, attention_mask, bbox, token_type_ids, labels





@require_tf
class 			a					(		unittest.TestCase			):


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"		)

										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						List[Any]							=      prepare_layoutlm_batch_inputs()

										# forward pass
										_UpperCAmelCase    :						List[Any]							=      model(input_ids=A_  ,   bbox=A_  ,   attention_mask=A_  ,   token_type_ids=A_		)

										# test the sequence output on [0, :3, :3]
										_UpperCAmelCase    :						Union[str, Any]							=      tf.convert_to_tensor(
										    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]  ,   )

										self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]  ,   A_  ,   atol=1e-3		)		)

										# test the pooled output on [1, :3]
										_UpperCAmelCase    :						Dict							=      tf.convert_to_tensor([-0.65_80, -0.02_14, 0.85_52]		)

										self.assertTrue(np.allclose(outputs.pooler_output[1, :3]  ,   A_  ,   atol=1e-3		)		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"  ,   num_labels=2		)

										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[Any]							=      prepare_layoutlm_batch_inputs()

										# forward pass
										_UpperCAmelCase    :						List[Any]							=      model(
										    input_ids=A_  ,   bbox=A_  ,   attention_mask=A_  ,   token_type_ids=A_  ,   labels=tf.convert_to_tensor([1, 1]		)  ,   )

										# test whether we get a loss as a scalar
										_UpperCAmelCase    :						Tuple							=      outputs.loss
										_UpperCAmelCase    :						Dict							=      (2,)
										self.assertEqual(loss.shape  ,   A_		)

										# test the shape of the logits
										_UpperCAmelCase    :						Tuple							=      outputs.logits
										_UpperCAmelCase    :						Optional[Any]							=      (2, 2)
										self.assertEqual(logits.shape  ,   A_		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"  ,   num_labels=13		)

										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						Union[str, Any]							=      prepare_layoutlm_batch_inputs()

										# forward pass
										_UpperCAmelCase    :						List[Any]							=      model(
										    input_ids=A_  ,   bbox=A_  ,   attention_mask=A_  ,   token_type_ids=A_  ,   labels=A_		)

										# test the shape of the logits
										_UpperCAmelCase    :						Any							=      outputs.logits
										_UpperCAmelCase    :						str							=      tf.convert_to_tensor((2, 25, 13)		)
										self.assertEqual(logits.shape  ,   A_		)





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"		)

										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						int							=      prepare_layoutlm_batch_inputs()

										# forward pass
										_UpperCAmelCase    :						Tuple							=      model(input_ids=A_  ,   bbox=A_  ,   attention_mask=A_  ,   token_type_ids=A_		)

										# test the shape of the logits
										_UpperCAmelCase    :						Union[str, Any]							=      tf.convert_to_tensor((2, 25)		)
										self.assertEqual(outputs.start_logits.shape  ,   A_		)
										self.assertEqual(outputs.end_logits.shape  ,   A_		)





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


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

SCREAMING_SNAKE_CASE_            = logging.getLogger(__name__)

SCREAMING_SNAKE_CASE_            = 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',
)

SCREAMING_SNAKE_CASE_            = parser.parse_args()

if args.tokenizer_name:
					SCREAMING_SNAKE_CASE_            = 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)

SCREAMING_SNAKE_CASE_            = args.per_device_eval_batch_size

SCREAMING_SNAKE_CASE_            = (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
SCREAMING_SNAKE_CASE_            = True

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

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

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

					with builder.create_builder_config() as config:
										SCREAMING_SNAKE_CASE_            = 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)
										SCREAMING_SNAKE_CASE_            = 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)
										SCREAMING_SNAKE_CASE_            = 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:					Optional[Any]					, lowerCAmelCase:					List[str]					, lowerCAmelCase:					Any					, lowerCAmelCase:					str					, lowerCAmelCase:					str					, lowerCAmelCase:					List[str]					, lowerCAmelCase:					Dict					, lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				_UpperCAmelCase    :						Dict							=      np.asarray(inputs["input_ids"]					, dtype=np.intaa	)
				_UpperCAmelCase    :						List[str]							=      np.asarray(inputs["attention_mask"]					, dtype=np.intaa	)
				_UpperCAmelCase    :						Union[str, Any]							=      np.asarray(inputs["token_type_ids"]					, dtype=np.intaa	)

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

SCREAMING_SNAKE_CASE_            = raw_datasets['validation'].column_names

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

# Padding side determines if we do (question|context) or (context|question).
SCREAMING_SNAKE_CASE_            = 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}.'''
					)

SCREAMING_SNAKE_CASE_            = min(args.max_seq_length, tokenizer.model_max_length)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str	)				->    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
				_UpperCAmelCase    :						Optional[Any]							=      [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.
				_UpperCAmelCase    :						List[str]							=      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=lowerCAmelCase					, stride=args.doc_stride					, return_overflowing_tokens=lowerCAmelCase					, return_offsets_mapping=lowerCAmelCase					, 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.
				_UpperCAmelCase    :						List[Any]							=      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.
				_UpperCAmelCase    :						Tuple							=      []

				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).
								_UpperCAmelCase    :						Tuple							=      tokenized_examples.sequence_ids(lowerCAmelCase	)
								_UpperCAmelCase    :						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.
								_UpperCAmelCase    :						List[str]							=      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.
								_UpperCAmelCase    :						Any							=      [
								    (o if sequence_ids[k] == context_index else None)
								    for k, o in enumerate(tokenized_examples["offset_mapping"][i]	)
								]

				return tokenized_examples


SCREAMING_SNAKE_CASE_            = raw_datasets['validation']
# Validation Feature Creation
SCREAMING_SNAKE_CASE_            = 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',
)

SCREAMING_SNAKE_CASE_            = default_data_collator

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

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

				_UpperCAmelCase    :						Optional[int]							=      [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
				return EvalPrediction(predictions=lowerCAmelCase					, label_ids=lowerCAmelCase	)


SCREAMING_SNAKE_CASE_            = 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:					str	)				->    List[str]:
									return trt.volume(engine.get_binding_shape(lowerCAmelCase	)	) * engine.get_binding_dtype(lowerCAmelCase	).itemsize

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

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

					# Create a stream in which to copy inputs/outputs and run inference.
					SCREAMING_SNAKE_CASE_            = 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}''')

					SCREAMING_SNAKE_CASE_            = 0.0
					SCREAMING_SNAKE_CASE_            = 0
					SCREAMING_SNAKE_CASE_            = timeit.default_timer()

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

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

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

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

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

					SCREAMING_SNAKE_CASE_            = 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)

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





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


