






"""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


__a       :	Tuple             = trt.Logger(trt.Logger.WARNING)
__a       :	str             = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

__a       :	Any             = logging.getLogger(__name__)

__a       :	str             = argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    '--onnx_model_path',
    default=None,
    type=str,
    required=True,
    help='Path to ONNX model: ',
)

parser.add_argument(
    '--output_dir',
    default=None,
    type=str,
    required=True,
    help='The output directory where the model checkpoints and predictions will be written.',
)

# Other parameters

parser.add_argument(
    '--tokenizer_name',
    default='',
    type=str,
    required=True,
    help='Pretrained tokenizer name or path if not the same as model_name',
)

parser.add_argument(
    '--version_2_with_negative',
    action='store_true',
    help='If true, the SQuAD examples contain some that do not have an answer.',
)
parser.add_argument(
    '--null_score_diff_threshold',
    type=float,
    default=0.0,
    help='If null_score - best_non_null is greater than the threshold predict null.',
)

parser.add_argument(
    '--max_seq_length',
    default=384,
    type=int,
    help=(
        'The maximum total input sequence length after WordPiece tokenization. Sequences '
        'longer than this will be truncated, and sequences shorter than this will be padded.'
    ),
)
parser.add_argument(
    '--doc_stride',
    default=128,
    type=int,
    help='When splitting up a long document into chunks, how much stride to take between chunks.',
)

parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.')

parser.add_argument(
    '--n_best_size',
    default=20,
    type=int,
    help='The total number of n-best predictions to generate in the nbest_predictions.json output file.',
)
parser.add_argument(
    '--max_answer_length',
    default=30,
    type=int,
    help=(
        'The maximum length of an answer that can be generated. This is needed because the start '
        'and end predictions are not conditioned on one another.'
    ),
)

parser.add_argument('--seed', type=int, default=42, help='random seed for initialization')

parser.add_argument(
    '--dataset_name',
    type=str,
    default=None,
    required=True,
    help='The name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--dataset_config_name',
    type=str,
    default=None,
    help='The configuration name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.'
)
parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets')
parser.add_argument(
    '--fp16',
    action='store_true',
    help='Whether to use 16-bit (mixed) precision instead of 32-bit',
)
parser.add_argument(
    '--int8',
    action='store_true',
    help='Whether to use INT8',
)

__a       :	int             = parser.parse_args()

if args.tokenizer_name:
	__a       :	List[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)

__a       :	List[Any]             = args.per_device_eval_batch_size

__a       :	List[Any]             = (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__a       :	Any             = True

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

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

__a       :	List[str]             = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
	with open(args.onnx_model_path, 'rb') as model:
		if not parser.parse(model.read()):
			for error in range(parser.num_errors):
				print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
	__a       :	Optional[Any]             = [network.get_input(i) for i in range(network.num_inputs)]
	__a       :	Optional[int]             = [_input.name for _input in network_inputs]  # ex: ["actual_input1"]

	with builder.create_builder_config() as config:
		__a       :	Tuple             = 1 << 50
		if STRICT_TYPES:
			config.set_flag(trt.BuilderFlag.STRICT_TYPES)
		if args.fpaa:
			config.set_flag(trt.BuilderFlag.FPaa)
		if args.inta:
			config.set_flag(trt.BuilderFlag.INTa)
		__a       :	Dict             = builder.create_optimization_profile()
		config.add_optimization_profile(profile)
		for i in range(len(input_names)):
			profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
		__a       :	List[Any]             = 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_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					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() ,     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__          =  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__          =  time.time()
					a__          =  end_time - start_time
					a__          =  (h_outputa, h_outputa)
					# print(outputs)
					return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
__a       :	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.
	__a       :	Optional[Any]             = load_dataset(args.dataset_name, args.dataset_config_name)
else:
	raise ValueError('Evaluation requires a dataset name')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

__a       :	Tuple             = raw_datasets['validation'].column_names

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

# Padding side determines if we do (question|context) or (context|question).
__a       :	List[str]             = tokenizer.padding_side == 'right'

if args.max_seq_length > tokenizer.model_max_length:
	logger.warning(
	    F'''The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'''
	    F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'''
	)

__a       :	Dict             = min(args.max_seq_length, tokenizer.model_max_length)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					# 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__          =  [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=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__          =  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(lowerCamelCase_)
										a__          =  1 if pad_on_right else 0

										# One example can give several spans, this is the index of the example containing this span of text.
										a__          =  sample_mapping[i]
										tokenized_examples["example_id"].append(examples['''id'''][sample_index])

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

					return tokenized_examples


__a       :	Optional[int]             = raw_datasets['validation']
# Validation Feature Creation
__a       :	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',
)

__a       :	List[str]             = default_data_collator

__a       :	Union[str, Any]             = eval_dataset.remove_columns(['example_id', 'offset_mapping'])
__a       :	Union[str, Any]             = DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_="eval"):
					# Post-processing: we match the start logits and end logits to answers in the original context.
					a__          =  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__          =  [
										    {'''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=lowerCamelCase_ ,     label_ids=lowerCamelCase_)


__a       :	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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
						return trt.volume(engine.get_binding_shape(lowerCamelCase_)) * engine.get_binding_dtype(lowerCamelCase_).itemsize

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

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

	# Create a stream in which to copy inputs/outputs and run inference.
	__a       :	Tuple             = cuda.Stream()

	# Evaluation
	logger.info('***** Running Evaluation *****')
	logger.info(F'''  Num examples = {len(eval_dataset)}''')
	logger.info(F'''  Batch size = {args.per_device_eval_batch_size}''')

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

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

		__a       , __a       :	Optional[Any]             = outputs
		__a       :	Dict             = torch.tensor(start_logits)
		__a       :	Optional[int]             = torch.tensor(end_logits)

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

		__a       :	List[Any]             = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
		__a       :	Optional[int]             = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

	if all_preds is not None:
		__a       :	Optional[int]             = nested_truncate(all_preds, len(eval_dataset))

	__a       :	Optional[Any]             = timeit.default_timer() - start_time
	logger.info('  Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset))
	# Inference time from TRT
	logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 1000 / niter))
	logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1000))
	logger.info('Total Number of Inference =  %d', niter)

__a       :	str             = post_processing_function(eval_examples, eval_dataset, all_preds)
__a       :	Any             = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(F'''Evaluation metrics: {eval_metric}''')










"""simple docstring"""







import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
	    FlaxRobertaPreLayerNormForCausalLM,
	    FlaxRobertaPreLayerNormForMaskedLM,
	    FlaxRobertaPreLayerNormForMultipleChoice,
	    FlaxRobertaPreLayerNormForQuestionAnswering,
	    FlaxRobertaPreLayerNormForSequenceClassification,
	    FlaxRobertaPreLayerNormForTokenClassification,
	    FlaxRobertaPreLayerNormModel,
	)







class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def __init__(   self:     List[str]				,		__A:     Optional[Any]				,		__A:     List[Any]=13				,		__A:     Optional[int]=7				,		__A:     Any=True				,		__A:     str=True				,		__A:     Any=True				,		__A:     str=True				,		__A:     Optional[Any]=99				,		__A:     Union[str, Any]=32				,		__A:     str=5				,		__A:     Any=4				,		__A:     List[str]=37				,		__A:     Union[str, Any]="gelu"				,		__A:     str=0.1				,		__A:     Tuple=0.1				,		__A:     Optional[Any]=512				,		__A:     Union[str, Any]=16				,		__A:     str=2				,		__A:     Any=0.0_2				,		__A:     Tuple=4				,		):

												'''simple docstring'''

												a__          =  parent
												a__          =  batch_size
												a__          =  seq_length
												a__          =  is_training
												a__          =  use_attention_mask
												a__          =  use_token_type_ids
												a__          =  use_labels
												a__          =  vocab_size
												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  intermediate_size
												a__          =  hidden_act
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  max_position_embeddings
												a__          =  type_vocab_size
												a__          =  type_sequence_label_size
												a__          =  initializer_range
												a__          =  num_choices




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size    )

												a__          =  None
												if self.use_attention_mask:
																	a__          =  random_attention_mask([self.batch_size, self.seq_length]    )

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

												a__          =  RobertaPreLayerNormConfig(
												    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				,		is_decoder=__A				,		initializer_range=self.initializer_range				,		)

												return config, input_ids, token_type_ids, attention_mask




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												a__       ,a__       ,a__       ,a__          =  config_and_inputs
												a__          =  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
												return config, inputs_dict





							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												a__       ,a__       ,a__       ,a__          =  config_and_inputs

												a__          =  True
												a__          =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
												a__          =  ids_tensor([self.batch_size, self.seq_length]				,		vocab_size=2    )

												return (
												    config,
												    input_ids,
												    token_type_ids,
												    encoder_hidden_states,
												    encoder_attention_mask,
												)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =True

							_SCREAMING_SNAKE_CASE    =(
							    (
							        FlaxRobertaPreLayerNormModel,
							        FlaxRobertaPreLayerNormForCausalLM,
							        FlaxRobertaPreLayerNormForMaskedLM,
							        FlaxRobertaPreLayerNormForSequenceClassification,
							        FlaxRobertaPreLayerNormForTokenClassification,
							        FlaxRobertaPreLayerNormForMultipleChoice,
							        FlaxRobertaPreLayerNormForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormModelTester(self    )





							@slow
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												for model_class_name in self.all_model_classes:
																	a__          =  model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )
																	a__          =  model(np.ones((1, 1)    )    )
																	self.assertIsNotNone(__A    )







@require_flax
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )

												a__          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,		dtype=jnp.intaa    )
												a__          =  model(__A    )[0]
												a__          =  [1, 11, 50265]
												self.assertEqual(list(output.shape    )				,		__A    )
												# compare the actual values for a slice.
												a__          =  np.array(
												    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]				,		dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )





							@slow
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )

												a__          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,		dtype=jnp.intaa    )
												a__          =  model(__A    )[0]
												# compare the actual values for a slice.
												a__          =  np.array(
												    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]				,		dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )


