




'''simple docstring'''


from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)

@flax.struct.dataclass
class     A					(	_a					):
							lowercase_           =       42



@flax_register_to_config
class     A					(	nn.Module  ,_a  ,_a					):
							lowercase_           =       32
							lowercase_           =       4
							lowercase_           =       4
							lowercase_           =       (
							    "CrossAttnDownBlock2D",
							    "CrossAttnDownBlock2D",
							    "CrossAttnDownBlock2D",
							    "DownBlock2D",
							)
							lowercase_           =       ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
							lowercase_           =       False
							lowercase_           =       (320, 640, 1280, 1280)
							lowercase_           =       2
							lowercase_           =       8
							lowercase_           =       None
							lowercase_           =       1280
							lowercase_           =       0.0
							lowercase_           =       False
							lowercase_           =       jnp.floataa
							lowercase_           =       True
							lowercase_           =       0
							lowercase_           =       False







							def      __lowerCAmelCase    (						self :						Any							,    lowerCAmelCase_ :						jax.random.KeyArray      )    ->							FrozenDict:


														"""simple docstring"""


														_a			  =       (1, self.in_channels, self.sample_size, self.sample_size)
														_a			  =       jnp.zeros(lowerCAmelCase_							,    dtype=jnp.floataa      )
														_a			  =       jnp.ones((1,)							,    dtype=jnp.intaa      )
														_a			  =       jnp.zeros((1, 1, self.cross_attention_dim)							,    dtype=jnp.floataa      )

														_a  ,							_a			  =       jax.random.split(lowerCAmelCase_      )
														_a			  =       {'''params''': params_rng, '''dropout''': dropout_rng}

														return self.init(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_      )["params"]







							def      __lowerCAmelCase    (						self :						Union[str, Any]      )    ->							Optional[Any]:


														"""simple docstring"""


														_a			  =       self.block_out_channels
														_a			  =       block_out_channels[0] * 4

														if self.num_attention_heads is not None:
																					raise ValueError(
																					    '''At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.'''      )

														# If `num_attention_heads` is not defined (which is the case for most models)
														# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
														# The reason for this behavior is to correct for incorrectly named variables that were introduced
														# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
														# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
														# which is why we correct for the naming here.
														_a			  =       self.num_attention_heads or self.attention_head_dim

														# input
														_a			  =       nn.Conv(
														    block_out_channels[0]							,    kernel_size=(3, 3)							,    strides=(1, 1)							,    padding=((1, 1), (1, 1))							,    dtype=self.dtype							,    )

														# time
														_a			  =       FlaxTimesteps(
														    block_out_channels[0]							,    flip_sin_to_cos=self.flip_sin_to_cos							,    freq_shift=self.config.freq_shift      )
														_a			  =       FlaxTimestepEmbedding(lowerCAmelCase_							,    dtype=self.dtype      )

														_a			  =       self.only_cross_attention
														if isinstance(lowerCAmelCase_							,    lowerCAmelCase_      ):
																					_a			  =       (only_cross_attention,) * len(self.down_block_types      )

														if isinstance(lowerCAmelCase_							,    lowerCAmelCase_      ):
																					_a			  =       (num_attention_heads,) * len(self.down_block_types      )

														# down
														_a			  =       []
														_a			  =       block_out_channels[0]
														for i, down_block_type in enumerate(self.down_block_types      ):
																					_a			  =       output_channel
																					_a			  =       block_out_channels[i]
																					_a			  =       i == len(lowerCAmelCase_      ) - 1

																					if down_block_type == "CrossAttnDownBlock2D":
																												_a			  =       FlaxCrossAttnDownBlockaD(
																												    in_channels=lowerCAmelCase_							,    out_channels=lowerCAmelCase_							,    dropout=self.dropout							,    num_layers=self.layers_per_block							,    num_attention_heads=num_attention_heads[i]							,    add_downsample=not is_final_block							,    use_linear_projection=self.use_linear_projection							,    only_cross_attention=only_cross_attention[i]							,    use_memory_efficient_attention=self.use_memory_efficient_attention							,    dtype=self.dtype							,    )
																					else:
																												_a			  =       FlaxDownBlockaD(
																												    in_channels=lowerCAmelCase_							,    out_channels=lowerCAmelCase_							,    dropout=self.dropout							,    num_layers=self.layers_per_block							,    add_downsample=not is_final_block							,    dtype=self.dtype							,    )

																					down_blocks.append(lowerCAmelCase_      )
														_a			  =       down_blocks

														# mid
														_a			  =       FlaxUNetMidBlockaDCrossAttn(
														    in_channels=block_out_channels[-1]							,    dropout=self.dropout							,    num_attention_heads=num_attention_heads[-1]							,    use_linear_projection=self.use_linear_projection							,    use_memory_efficient_attention=self.use_memory_efficient_attention							,    dtype=self.dtype							,    )

														# up
														_a			  =       []
														_a			  =       list(reversed(lowerCAmelCase_      )      )
														_a			  =       list(reversed(lowerCAmelCase_      )      )
														_a			  =       list(reversed(lowerCAmelCase_      )      )
														_a			  =       reversed_block_out_channels[0]
														for i, up_block_type in enumerate(self.up_block_types      ):
																					_a			  =       output_channel
																					_a			  =       reversed_block_out_channels[i]
																					_a			  =       reversed_block_out_channels[min(i + 1							,    len(lowerCAmelCase_      ) - 1      )]

																					_a			  =       i == len(lowerCAmelCase_      ) - 1

																					if up_block_type == "CrossAttnUpBlock2D":
																												_a			  =       FlaxCrossAttnUpBlockaD(
																												    in_channels=lowerCAmelCase_							,    out_channels=lowerCAmelCase_							,    prev_output_channel=lowerCAmelCase_							,    num_layers=self.layers_per_block + 1							,    num_attention_heads=reversed_num_attention_heads[i]							,    add_upsample=not is_final_block							,    dropout=self.dropout							,    use_linear_projection=self.use_linear_projection							,    only_cross_attention=only_cross_attention[i]							,    use_memory_efficient_attention=self.use_memory_efficient_attention							,    dtype=self.dtype							,    )
																					else:
																												_a			  =       FlaxUpBlockaD(
																												    in_channels=lowerCAmelCase_							,    out_channels=lowerCAmelCase_							,    prev_output_channel=lowerCAmelCase_							,    num_layers=self.layers_per_block + 1							,    add_upsample=not is_final_block							,    dropout=self.dropout							,    dtype=self.dtype							,    )

																					up_blocks.append(lowerCAmelCase_      )
																					_a			  =       output_channel
														_a			  =       up_blocks

														# out
														_a			  =       nn.GroupNorm(num_groups=32							,    epsilon=1e-5      )
														_a			  =       nn.Conv(
														    self.out_channels							,    kernel_size=(3, 3)							,    strides=(1, 1)							,    padding=((1, 1), (1, 1))							,    dtype=self.dtype							,    )







							def __call__(						self :						Tuple							,    lowerCAmelCase_ :						str							,    lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						Union[str, Any]							,    lowerCAmelCase_ :						Union[str, Any]=None							,    lowerCAmelCase_ :						Tuple=None							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						bool = False							,    )    ->							Union[FlaxUNetaDConditionOutput, Tuple]:


														"""simple docstring"""


														if not isinstance(lowerCAmelCase_							,    jnp.ndarray      ):
																					_a			  =       jnp.array([timesteps]							,    dtype=jnp.intaa      )
														elif isinstance(lowerCAmelCase_							,    jnp.ndarray      ) and len(timesteps.shape      ) == 0:
																					_a			  =       timesteps.astype(dtype=jnp.floataa      )
																					_a			  =       jnp.expand_dims(lowerCAmelCase_							,    0      )

														_a			  =       self.time_proj(lowerCAmelCase_      )
														_a			  =       self.time_embedding(lowerCAmelCase_      )

														# 2. pre-process
														_a			  =       jnp.transpose(lowerCAmelCase_							,    (0, 2, 3, 1)      )
														_a			  =       self.conv_in(lowerCAmelCase_      )

														# 3. down
														_a			  =       (sample,)
														for down_block in self.down_blocks:
																					if isinstance(lowerCAmelCase_							,    lowerCAmelCase_      ):
																												_a  ,							_a			  =       down_block(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    deterministic=not train      )
																					else:
																												_a  ,							_a			  =       down_block(lowerCAmelCase_							,    lowerCAmelCase_							,    deterministic=not train      )
																					down_block_res_samples += res_samples

														if down_block_additional_residuals is not None:
																					_a			  =       ()

																					for down_block_res_sample, down_block_additional_residual in zip(
																					    lowerCAmelCase_							,    lowerCAmelCase_      ):
																												down_block_res_sample += down_block_additional_residual
																												new_down_block_res_samples += (down_block_res_sample,)

																					_a			  =       new_down_block_res_samples

														# 4. mid
														_a			  =       self.mid_block(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    deterministic=not train      )

														if mid_block_additional_residual is not None:
																					sample += mid_block_additional_residual

														# 5. up
														for up_block in self.up_blocks:
																					_a			  =       down_block_res_samples[-(self.layers_per_block + 1) :]
																					_a			  =       down_block_res_samples[: -(self.layers_per_block + 1)]
																					if isinstance(lowerCAmelCase_							,    lowerCAmelCase_      ):
																												_a			  =       up_block(
																												    lowerCAmelCase_							,    temb=lowerCAmelCase_							,    encoder_hidden_states=lowerCAmelCase_							,    res_hidden_states_tuple=lowerCAmelCase_							,    deterministic=not train							,    )
																					else:
																												_a			  =       up_block(lowerCAmelCase_							,    temb=lowerCAmelCase_							,    res_hidden_states_tuple=lowerCAmelCase_							,    deterministic=not train      )

        # 6. post-process
														_a			  =       self.conv_norm_out(lowerCAmelCase_      )
														_a			  =       nn.silu(lowerCAmelCase_      )
														_a			  =       self.conv_out(lowerCAmelCase_      )
														_a			  =       jnp.transpose(lowerCAmelCase_							,    (0, 3, 1, 2)      )

														if not return_dict:
																					return (sample,)

														return FlaxUNetaDConditionOutput(sample=lowerCAmelCase_      )







'''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


_snake_case   :   int     							=	trt.Logger(trt.Logger.WARNING)
_snake_case   :   List[Any]     							=	absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

_snake_case   :   Optional[int]     							=	logging.getLogger(__name__)

_snake_case   :   Dict     							=	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',
)

_snake_case   :   Any     							=	parser.parse_args()

if args.tokenizer_name:
				_snake_case   :   Any     							=	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)

_snake_case   :   Any     							=	args.per_device_eval_batch_size

_snake_case   :   List[str]     							=	(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_snake_case   :   Any     							=	True

_snake_case   :   Optional[Any]     							=	'temp_engine/bert-fp32.engine'
if args.fpaa:
				_snake_case   :   Optional[int]     							=	'temp_engine/bert-fp16.engine'
if args.inta:
				_snake_case   :   Optional[Any]     							=	'temp_engine/bert-int8.engine'

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

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

				with builder.create_builder_config() as config:
								_snake_case   :   Union[str, Any]     							=	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)
								_snake_case   :   Optional[int]     							=	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)
								_snake_case   :   str     							=	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 	snake_case_					(UpperCamelCase :						Tuple				,		UpperCamelCase :						Dict				,		UpperCamelCase :						List[Any]				,		UpperCamelCase :						List[Any]				,		UpperCamelCase :						List[Any]				,		UpperCamelCase :						Tuple				,		UpperCamelCase :						Any				,		UpperCamelCase :						str   ):





							'''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()				,		UpperCamelCase   )
							cuda.memcpy_htod_async(d_inputs[1]				,		attention_mask.ravel()				,		UpperCamelCase   )
							cuda.memcpy_htod_async(d_inputs[2]				,		token_type_ids.ravel()				,		UpperCamelCase   )
							# start time
							_a			  =       time.time()
							# Run inference
							context.execute_async(
							    bindings=[int(UpperCamelCase   ) for d_inp in d_inputs] + [int(UpperCamelCase   ), int(UpperCamelCase   )]				,		stream_handle=stream.handle   )
							# Transfer predictions back from GPU
							cuda.memcpy_dtoh_async(UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
							cuda.memcpy_dtoh_async(UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
							# 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.
_snake_case   :   Optional[int]     							=	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.
				_snake_case   :   int     							=	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.

_snake_case   :   Dict     							=	raw_datasets['validation'].column_names

_snake_case   :   Optional[Any]     							=	'question' if 'question' in column_names else column_names[0]
_snake_case   :   Optional[Any]     							=	'context' if 'context' in column_names else column_names[1]
_snake_case   :   Tuple     							=	'answers' if 'answers' in column_names else column_names[2]

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

_snake_case   :   int     							=	min(args.max_seq_length, tokenizer.model_max_length)
def 	snake_case_					(UpperCamelCase :						List[Any]   ):





							'''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=UpperCamelCase				,		stride=args.doc_stride				,		return_overflowing_tokens=UpperCamelCase				,		return_offsets_mapping=UpperCamelCase				,		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(UpperCamelCase   )
														_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


_snake_case   :   List[Any]     							=	raw_datasets['validation']
# Validation Feature Creation
_snake_case   :   List[Any]     							=	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',
)

_snake_case   :   Optional[Any]     							=	default_data_collator

_snake_case   :   Any     							=	eval_dataset.remove_columns(['example_id', 'offset_mapping'])
_snake_case   :   Any     							=	DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def 	snake_case_					(UpperCamelCase :						Union[str, Any]				,		UpperCamelCase :						List[str]				,		UpperCamelCase :						str				,		UpperCamelCase :						Tuple="eval"   ):





							'''simple docstring'''


							_a			  =       postprocess_qa_predictions(
							    examples=UpperCamelCase				,		features=UpperCamelCase				,		predictions=UpperCamelCase				,		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=UpperCamelCase				,		)
							# 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=UpperCamelCase				,		label_ids=UpperCamelCase   )


_snake_case   :   str     							=	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 	snake_case_					(UpperCamelCase :						Optional[int]   ):





											'''simple docstring'''


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

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

				# Allocate output buffer
				_snake_case   :   Any     							=	cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
				_snake_case   :   Dict     							=	cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
				_snake_case   :   Optional[int]     							=	cuda.mem_alloc(h_outputa.nbytes)
				_snake_case   :   List[Any]     							=	cuda.mem_alloc(h_outputa.nbytes)

				# Create a stream in which to copy inputs/outputs and run inference.
				_snake_case   :   Optional[Any]     							=	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}''')

				_snake_case   :   List[str]     							=	0.0
				_snake_case   :   Union[str, Any]     							=	0
				_snake_case   :   List[Any]     							=	timeit.default_timer()

				_snake_case   :   Optional[Any]     							=	None
				for step, batch in enumerate(eval_dataloader):
								_snake_case							,       _snake_case   :   Optional[int]     							=	model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
								total_time += infer_time
								niter += 1

								_snake_case							,       _snake_case   :   Tuple     							=	outputs
								_snake_case   :   List[Any]     							=	torch.tensor(start_logits)
								_snake_case   :   List[str]     							=	torch.tensor(end_logits)

								# necessary to pad predictions and labels for being gathered
								_snake_case   :   Union[str, Any]     							=	accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
								_snake_case   :   Optional[int]     							=	accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

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

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

				_snake_case   :   str     							=	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)

_snake_case   :   Union[str, Any]     							=	post_processing_function(eval_examples, eval_dataset, all_preds)
_snake_case   :   Dict     							=	metric.compute(predictions=prediction.predictions, references=prediction.label_ids)

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

