


'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  def        _a							(							self :       int ):

        '''simple docstring'''




        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()






  @property
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  Any          =				1
        A_						:  int          =				3
        A_						:  List[Any]          =				(32, 32)

        A_						:  Optional[Any]          =				floats_tensor((batch_size, num_channels) + sizes	,rng=random.Random(0 ) ).to(_a )
        return image






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  int          =				UNetaDConditionModel(
            block_out_channels=(32, 32, 64)	,layers_per_block=2	,sample_size=32	,in_channels=7	,out_channels=4	,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""", """CrossAttnDownBlock2D""")	,up_block_types=("""CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """UpBlock2D""")	,cross_attention_dim=32	,attention_head_dim=8	,use_linear_projection=_a	,only_cross_attention=(True, True, False)	,num_class_embeds=100	,)
        return model






  @property
  def        _a							(							self :       Dict ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  str          =				AutoencoderKL(
            block_out_channels=[32, 32, 64]	,in_channels=3	,out_channels=3	,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D""", """DownEncoderBlock2D"""]	,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]	,latent_channels=4	,)
        return model






  @property
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  Union[str, Any]          =				CLIPTextConfig(
            bos_token_id=0	,eos_token_id=2	,hidden_size=32	,intermediate_size=37	,layer_norm_eps=1e-05	,num_attention_heads=4	,num_hidden_layers=5	,pad_token_id=1	,vocab_size=1000	,hidden_act="""gelu"""	,projection_dim=512	,)
        return CLIPTextModel(_a )






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  Optional[int]          =				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
        A_						:  List[str]          =				self.dummy_cond_unet_upscale
        A_						:  int          =				DDPMScheduler()
        A_						:  Optional[int]          =				DDIMScheduler(prediction_type="""v_prediction""" )
        A_						:  List[str]          =				self.dummy_vae
        A_						:  List[Any]          =				self.dummy_text_encoder
        A_						:  Optional[Any]          =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

        A_						:  List[str]          =				self.dummy_image.cpu().permute(0	,2	,3	,1 )[0]
        A_						:  Dict          =				Image.fromarray(np.uinta(_a ) ).convert("""RGB""" ).resize((64, 64) )

        # make sure here that pndm scheduler skips prk
        A_						:  Optional[int]          =				StableDiffusionUpscalePipeline(
            unet=_a	,low_res_scheduler=_a	,scheduler=_a	,vae=_a	,text_encoder=_a	,tokenizer=_a	,max_noise_level=350	,)
        A_						:  Tuple          =				sd_pipe.to(_a )
        sd_pipe.set_progress_bar_config(disable=_a )

        A_						:  Tuple          =				"""A painting of a squirrel eating a burger"""
        A_						:  Any          =				torch.Generator(device=_a ).manual_seed(0 )
        A_						:  Optional[Any]          =				sd_pipe(
            [prompt]	,image=_a	,generator=_a	,guidance_scale=6.0	,noise_level=20	,num_inference_steps=2	,output_type="""np"""	,)

        A_						:  List[str]          =				output.images

        A_						:  str          =				torch.Generator(device=_a ).manual_seed(0 )
        A_						:  Union[str, Any]          =				sd_pipe(
            [prompt]	,image=_a	,generator=_a	,guidance_scale=6.0	,noise_level=20	,num_inference_steps=2	,output_type="""np"""	,return_dict=_a	,)[0]

        A_						:  Union[str, Any]          =				image[0, -3:, -3:, -1]
        A_						:  Dict          =				image_from_tuple[0, -3:, -3:, -1]

        A_						:  Union[str, Any]          =				low_res_image.size[0] * 4
        assert image.shape == (1, expected_height_width, expected_height_width, 3)
        A_						:  List[str]          =				np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661] )

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  List[str]          =				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
        A_						:  Optional[Any]          =				self.dummy_cond_unet_upscale
        A_						:  Tuple          =				DDPMScheduler()
        A_						:  List[Any]          =				DDIMScheduler(prediction_type="""v_prediction""" )
        A_						:  int          =				self.dummy_vae
        A_						:  str          =				self.dummy_text_encoder
        A_						:  Optional[Any]          =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

        A_						:  List[str]          =				self.dummy_image.cpu().permute(0	,2	,3	,1 )[0]
        A_						:  Optional[Any]          =				Image.fromarray(np.uinta(_a ) ).convert("""RGB""" ).resize((64, 64) )

        # make sure here that pndm scheduler skips prk
        A_						:  List[str]          =				StableDiffusionUpscalePipeline(
            unet=_a	,low_res_scheduler=_a	,scheduler=_a	,vae=_a	,text_encoder=_a	,tokenizer=_a	,max_noise_level=350	,)
        A_						:  Dict          =				sd_pipe.to(_a )
        sd_pipe.set_progress_bar_config(disable=_a )

        A_						:  List[Any]          =				"""A painting of a squirrel eating a burger"""
        A_						:  Union[str, Any]          =				sd_pipe(
            2 * [prompt]	,image=2 * [low_res_image]	,guidance_scale=6.0	,noise_level=20	,num_inference_steps=2	,output_type="""np"""	,)
        A_						:  Tuple          =				output.images
        assert image.shape[0] == 2

        A_						:  Tuple          =				torch.Generator(device=_a ).manual_seed(0 )
        A_						:  List[str]          =				sd_pipe(
            [prompt]	,image=_a	,generator=_a	,num_images_per_prompt=2	,guidance_scale=6.0	,noise_level=20	,num_inference_steps=2	,output_type="""np"""	,)
        A_						:  Any          =				output.images
        assert image.shape[0] == 2






  @unittest.skipIf(torch_device != """cuda"""	,"""This test requires a GPU""" )
  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  Any          =				self.dummy_cond_unet_upscale
        A_						:  Tuple          =				DDPMScheduler()
        A_						:  Tuple          =				DDIMScheduler(prediction_type="""v_prediction""" )
        A_						:  Optional[Any]          =				self.dummy_vae
        A_						:  List[str]          =				self.dummy_text_encoder
        A_						:  Tuple          =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

        A_						:  Tuple          =				self.dummy_image.cpu().permute(0	,2	,3	,1 )[0]
        A_						:  Tuple          =				Image.fromarray(np.uinta(_a ) ).convert("""RGB""" ).resize((64, 64) )

        # put models in fp16, except vae as it overflows in fp16
        A_						:  Optional[Any]          =				unet.half()
        A_						:  Tuple          =				text_encoder.half()

        # make sure here that pndm scheduler skips prk
        A_						:  List[Any]          =				StableDiffusionUpscalePipeline(
            unet=_a	,low_res_scheduler=_a	,scheduler=_a	,vae=_a	,text_encoder=_a	,tokenizer=_a	,max_noise_level=350	,)
        A_						:  str          =				sd_pipe.to(_a )
        sd_pipe.set_progress_bar_config(disable=_a )

        A_						:  Optional[int]          =				"""A painting of a squirrel eating a burger"""
        A_						:  List[Any]          =				torch.manual_seed(0 )
        A_						:  str          =				sd_pipe(
            [prompt]	,image=_a	,generator=_a	,num_inference_steps=2	,output_type="""np"""	,).images

        A_						:  str          =				low_res_image.size[0] * 4
        assert image.shape == (1, expected_height_width, expected_height_width, 3)







@slow
@require_torch_gpu
class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  def        _a							(							self :       str ):

        '''simple docstring'''




        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()






  def        _a							(							self :       Dict ):

        '''simple docstring'''




        A_						:  Any          =				load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/sd2-upscale/low_res_cat.png""" )
        A_						:  List[Any]          =				load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
            """/upsampled_cat.npy""" )

        A_						:  List[str]          =				"""stabilityai/stable-diffusion-x4-upscaler"""
        A_						:  List[str]          =				StableDiffusionUpscalePipeline.from_pretrained(_a )
        pipe.to(_a )
        pipe.set_progress_bar_config(disable=_a )
        pipe.enable_attention_slicing()

        A_						:  Tuple          =				"""a cat sitting on a park bench"""

        A_						:  int          =				torch.manual_seed(0 )
        A_						:  int          =				pipe(
            prompt=_a	,image=_a	,generator=_a	,output_type="""np"""	,)
        A_						:  Any          =				output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image ).max() < 1e-3






  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        A_						:  Optional[int]          =				load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/sd2-upscale/low_res_cat.png""" )
        A_						:  List[Any]          =				load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
            """/upsampled_cat_fp16.npy""" )

        A_						:  Tuple          =				"""stabilityai/stable-diffusion-x4-upscaler"""
        A_						:  int          =				StableDiffusionUpscalePipeline.from_pretrained(
            _a	,torch_dtype=torch.floataa	,)
        pipe.to(_a )
        pipe.set_progress_bar_config(disable=_a )
        pipe.enable_attention_slicing()

        A_						:  Any          =				"""a cat sitting on a park bench"""

        A_						:  Union[str, Any]          =				torch.manual_seed(0 )
        A_						:  List[str]          =				pipe(
            prompt=_a	,image=_a	,generator=_a	,output_type="""np"""	,)
        A_						:  Optional[int]          =				output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image ).max() < 5e-1






  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        A_						:  Dict          =				load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/sd2-upscale/low_res_cat.png""" )

        A_						:  Any          =				"""stabilityai/stable-diffusion-x4-upscaler"""
        A_						:  Tuple          =				StableDiffusionUpscalePipeline.from_pretrained(
            _a	,torch_dtype=torch.floataa	,)
        pipe.to(_a )
        pipe.set_progress_bar_config(disable=_a )
        pipe.enable_attention_slicing(1 )
        pipe.enable_sequential_cpu_offload()

        A_						:  Tuple          =				"""a cat sitting on a park bench"""

        A_						:  Any          =				torch.manual_seed(0 )
        A_						:  Optional[Any]          =				pipe(
            prompt=_a	,image=_a	,generator=_a	,num_inference_steps=5	,output_type="""np"""	,)

        A_						:  List[Any]          =				torch.cuda.max_memory_allocated()
        # make sure that less than 2.9 GB is allocated
        assert mem_bytes < 2.9 * 10**9




'''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


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

__magic_name__			=      logging.getLogger(__name__)

__magic_name__			=      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',
)

__magic_name__			=      parser.parse_args()

if args.tokenizer_name:
     __magic_name__			=      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)

__magic_name__			=      args.per_device_eval_batch_size

__magic_name__			=      (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__magic_name__			=      True

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

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

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

     with builder.create_builder_config() as config:
          __magic_name__			=      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)
          __magic_name__			=      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)
          __magic_name__			=      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      lowerCamelCase					(      lowerCamelCase  :      Any							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      str							,						lowerCamelCase  :      str							,						lowerCamelCase  :      Any							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      str							,						lowerCamelCase  :      List[str]):
      A_						:  str          =				np.asarray(inputs["""input_ids"""]							,						dtype=np.intaa)
      A_						:  int          =				np.asarray(inputs["""attention_mask"""]							,						dtype=np.intaa)
      A_						:  Optional[int]          =				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
      A_						:  List[Any]          =				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
      A_						:  str          =				time.time()
      A_						:  Tuple          =				end_time - start_time
      A_						:  Any          =				(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.
__magic_name__			=      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.
     __magic_name__			=      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.

__magic_name__			=      raw_datasets['validation'].column_names

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

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

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




def      lowerCamelCase					(      lowerCamelCase  :      Dict):
      # 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
      A_						:  List[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.
      A_						:  Optional[int]          =				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.
      A_						:  List[str]          =				tokenized_examples.pop("""overflow_to_sample_mapping""")

      # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
      # corresponding example_id and we will store the offset mappings.
      A_						:  Union[str, Any]          =				[]

      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_						:  Any          =				tokenized_examples.sequence_ids(lowerCamelCase)
            A_						:  Tuple          =				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_						:  Union[str, Any]          =				sample_mapping[i]
            tokenized_examples["example_id"].append(examples["""id"""][sample_index])

            # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
            # position is part of the context or not.
            A_						:  Dict          =				[
                (o if sequence_ids[k] == context_index else None)
                for k, o in enumerate(tokenized_examples["""offset_mapping"""][i])
            ]

      return tokenized_examples


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

__magic_name__			=      default_data_collator

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




def      lowerCamelCase					(      lowerCamelCase  :      Tuple							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      Optional[Any]="eval"):
      # Post-processing: we match the start logits and end logits to answers in the original context.
      A_						:  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:
            A_						:  Dict          =				[
                {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
            ]
      else:
            A_						:  Union[str, Any]          =				[{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

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


__magic_name__			=      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      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]):
           return trt.volume(engine.get_binding_shape(lowerCamelCase)) * engine.get_binding_dtype(lowerCamelCase).itemsize

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

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

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

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

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

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

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

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

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

     __magic_name__			=      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_000 / niter))
     logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1_000))






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

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






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