




'''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


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

lowerCamelCase_			=						logging.getLogger(__name__)

lowerCamelCase_			=						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=3_84,
    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=1_28,
    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',
)

lowerCamelCase_			=						parser.parse_args()

if args.tokenizer_name:
 lowerCamelCase_			=						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)

lowerCamelCase_			=						args.per_device_eval_batch_size

lowerCamelCase_			=						(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
lowerCamelCase_			=						True

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

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

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

 with builder.create_builder_config() as config:
  lowerCamelCase_			=						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)
  lowerCamelCase_			=						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)
  lowerCamelCase_			=						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_			(							__A    :			Dict	,					__A    :			List[Any]	,					__A    :			Tuple	,					__A    :			Optional[int]	,					__A    :			Union[str, Any]	,					__A    :			List[str]	,					__A    :			List[str]	,					__A    :			Optional[int]  )			->			str:
   _SCREAMING_SNAKE_CASE	=				np.asarray(inputs["input_ids"]	,					dtype=np.intaa  )
   _SCREAMING_SNAKE_CASE	=				np.asarray(inputs["attention_mask"]	,					dtype=np.intaa  )
   _SCREAMING_SNAKE_CASE	=				np.asarray(inputs["token_type_ids"]	,					dtype=np.intaa  )

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

lowerCamelCase_			=						raw_datasets['validation'].column_names

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

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

lowerCamelCase_			=						min(args.max_seq_length, tokenizer.model_max_length)
def 			SCREAMING_SNAKE_CASE_			(							__A    :			str  )			->			Tuple:
   # 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
   _SCREAMING_SNAKE_CASE	=				[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.
   _SCREAMING_SNAKE_CASE	=				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=__A	,					stride=args.doc_stride	,					return_overflowing_tokens=__A	,					return_offsets_mapping=__A	,					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.
   _SCREAMING_SNAKE_CASE	=				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.
   _SCREAMING_SNAKE_CASE	=				[]

   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).
      _SCREAMING_SNAKE_CASE	=				tokenized_examples.sequence_ids(__A  )
      _SCREAMING_SNAKE_CASE	=				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.
      _SCREAMING_SNAKE_CASE	=				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.
      _SCREAMING_SNAKE_CASE	=				[
          (o if sequence_ids[k] == context_index else None)
          for k, o in enumerate(tokenized_examples["offset_mapping"][i]  )
      ]

   return tokenized_examples


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

lowerCamelCase_			=						default_data_collator

lowerCamelCase_			=						eval_dataset.remove_columns(['example_id', 'offset_mapping'])
lowerCamelCase_			=						DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int	,					__A    :			Dict	,					__A    :			Dict	,					__A    :			List[Any]="eval"  )			->			int:
   # Post-processing: we match the start logits and end logits to answers in the original context.
   _SCREAMING_SNAKE_CASE	=				postprocess_qa_predictions(
       examples=__A	,					features=__A	,					predictions=__A	,					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=__A	,					)
   # Format the result to the format the metric expects.
   if args.version_2_with_negative:
      _SCREAMING_SNAKE_CASE	=				[
          {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
      ]
   else:
      _SCREAMING_SNAKE_CASE	=				[{"id": k, "prediction_text": v} for k, v in predictions.items()]

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


lowerCamelCase_			=						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_			(							__A    :			Dict  )			->			List[Any]:
    return trt.volume(engine.get_binding_shape(__A  )  ) * engine.get_binding_dtype(__A  ).itemsize

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

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

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

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

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

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

  # necessary to pad predictions and labels for being gathered
  lowerCamelCase_			=						accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
  lowerCamelCase_			=						accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

  lowerCamelCase_			=						(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
  lowerCamelCase_			=						logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)

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

 lowerCamelCase_			=						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 * 10_00 / niter))
 logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 10_00))






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

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






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







'''simple docstring'''



import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow







class 		lowercase_   :


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      return None







class 		lowercase_   :


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      return None







class 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   lowerCamelCase_      =						[
       # (model_name, model_kwargs)
       ('''bert-base-cased''', {}),
       ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
   ]



   @require_tf
   @slow
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCamelCase  ,				"tf"  ,				1_2  ,				**__lowerCamelCase   )



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				**__lowerCamelCase   )



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      from transformers import BertModel

      _SCREAMING_SNAKE_CASE	=				["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
      with NamedTemporaryFile(mode="w+t"   ) as vocab_file:
         vocab_file.write("\n".join(__lowerCamelCase   )   )
         vocab_file.flush()
         _SCREAMING_SNAKE_CASE	=				BertTokenizerFast(vocab_file.name   )

      with TemporaryDirectory() as bert_save_dir:
         _SCREAMING_SNAKE_CASE	=				BertModel(BertConfig(vocab_size=len(__lowerCamelCase   )   )   )
         model.save_pretrained(__lowerCamelCase   )
         self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				__lowerCamelCase   )



   @require_tf
   @slow
   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE	=				self._test_export(__lowerCamelCase  ,				"tf"  ,				1_2  ,				**__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				quantize(Path(__lowerCamelCase   )   )

         # Ensure the actual quantized model is not bigger than the original one
         if quantized_path.stat().st_size >= Path(__lowerCamelCase   ).stat().st_size:
            self.fail("Quantized model is bigger than initial ONNX model"   )



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE	=				self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				**__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				quantize(__lowerCamelCase   )

         # Ensure the actual quantized model is not bigger than the original one
         if quantized_path.stat().st_size >= Path(__lowerCamelCase   ).stat().st_size:
            self.fail("Quantized model is bigger than initial ONNX model"   )



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Optional[int]=None  ,				**__lowerCamelCase :     int   ):

      """simple docstring"""
      try:
         # Compute path
         with TemporaryDirectory() as tempdir:
            _SCREAMING_SNAKE_CASE	=				Path(__lowerCamelCase   ).joinpath("model.onnx"   )

         # Remove folder if exists
         if path.parent.exists():
            path.parent.rmdir()

         # Export
         convert(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   )

         return path
      except Exception as e:
         self.fail(__lowerCamelCase   )



   @require_torch
   @require_tokenizers
   @slow
   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      from transformers import BertModel

      _SCREAMING_SNAKE_CASE	=				BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"   )   )
      _SCREAMING_SNAKE_CASE	=				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"   )
      self._test_infer_dynamic_axis(__lowerCamelCase  ,				__lowerCamelCase  ,				"pt"   )



   @require_tf
   @require_tokenizers
   @slow
   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      from transformers import TFBertModel

      _SCREAMING_SNAKE_CASE	=				TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"   )   )
      _SCREAMING_SNAKE_CASE	=				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"   )
      self._test_infer_dynamic_axis(__lowerCamelCase  ,				__lowerCamelCase  ,				"tf"   )



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				FeatureExtractionPipeline(__lowerCamelCase  ,				__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				infer_shapes(__lowerCamelCase  ,				__lowerCamelCase   )

      # Assert all variables are present
      self.assertEqual(len(__lowerCamelCase   )  ,				len(__lowerCamelCase   )   )
      self.assertTrue(all(var_name in shapes for var_name in variable_names   )   )
      self.assertSequenceEqual(variable_names[:3]  ,				__lowerCamelCase   )
      self.assertSequenceEqual(variable_names[3:]  ,				__lowerCamelCase   )

      # Assert inputs are {0: batch, 1: sequence}
      for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
         self.assertDictEqual(shapes[var_name]  ,				{0: "batch", 1: "sequence"}   )

      # Assert outputs are {0: batch, 1: sequence} and {0: batch}
      self.assertDictEqual(shapes["output_0"]  ,				{0: "batch", 1: "sequence"}   )
      self.assertDictEqual(shapes["output_1"]  ,				{0: "batch"}   )



   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				["input_ids", "attention_mask", "token_type_ids"]
      _SCREAMING_SNAKE_CASE	=				{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				ensure_valid_input(FuncContiguousArgs()  ,				__lowerCamelCase  ,				__lowerCamelCase   )

      # Should have exactly the same number of args (all are valid)
      self.assertEqual(len(__lowerCamelCase   )  ,				3   )

      # Should have exactly the same input names
      self.assertEqual(set(__lowerCamelCase   )  ,				set(__lowerCamelCase   )   )

      # Parameter should be reordered according to their respective place in the function:
      # (input_ids, token_type_ids, attention_mask)
      self.assertEqual(__lowerCamelCase  ,				(tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"])   )

      # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				ensure_valid_input(FuncNonContiguousArgs()  ,				__lowerCamelCase  ,				__lowerCamelCase   )

      # Should have exactly the one arg (all before the one not provided "some_other_args")
      self.assertEqual(len(__lowerCamelCase   )  ,				1   )
      self.assertEqual(len(__lowerCamelCase   )  ,				1   )

      # Should have only "input_ids"
      self.assertEqual(inputs_args[0]  ,				tokens["input_ids"]   )
      self.assertEqual(ordered_input_names[0]  ,				"input_ids"   )



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				generate_identified_filename(Path("/home/something/my_fake_model.onnx"   )  ,				"-test"   )
      self.assertEqual("/home/something/my_fake_model-test.onnx"  ,				generated.as_posix()   )

