




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__					:  Optional[int]      							=				trt.Logger(trt.Logger.WARNING)
lowerCamelCase__					:  Tuple      							=				absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

lowerCamelCase__					:  str      							=				logging.getLogger(__name__)

lowerCamelCase__					:  Any      							=				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',
)

lowerCamelCase__					:  Tuple      							=				parser.parse_args()

if args.tokenizer_name:
   lowerCamelCase__					:  Union[str, 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)

lowerCamelCase__					:  Dict      							=				args.per_device_eval_batch_size

lowerCamelCase__					:  Dict      							=				(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
lowerCamelCase__					:  Dict      							=				True

lowerCamelCase__					:  Union[str, Any]      							=				'temp_engine/bert-fp32.engine'
if args.fpaa:
   lowerCamelCase__					:  Any      							=				'temp_engine/bert-fp16.engine'
if args.inta:
   lowerCamelCase__					:  Dict      							=				'temp_engine/bert-int8.engine'

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

lowerCamelCase__					:  Dict      							=				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__					:  Optional[Any]      							=				[network.get_input(i) for i in range(network.num_inputs)]
   lowerCamelCase__					:  Tuple      							=				[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

   with builder.create_builder_config() as config:
      lowerCamelCase__					:  List[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)
      lowerCamelCase__					:  Optional[Any]      							=				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__					:  Union[str, 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    UpperCAmelCase_				( __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      List[str]     ,     __UpperCAmelCase		:      Tuple     ,     __UpperCAmelCase		:      Optional[Any]     ,     __UpperCAmelCase		:      List[str]     ,     __UpperCAmelCase		:      List[Any]     ,     __UpperCAmelCase		:      List[Any]     ,     __UpperCAmelCase		:      List[Any]				)   ->					Union[str, Any]:
      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()     ,     __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
      SCREAMING_SNAKE_CASE_      		=     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
      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__					:  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.
   lowerCamelCase__					:  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.

lowerCamelCase__					:  Optional[int]      							=				raw_datasets['validation'].column_names

lowerCamelCase__					:  Tuple      							=				'question' if 'question' in column_names else column_names[0]
lowerCamelCase__					:  str      							=				'context' if 'context' in column_names else column_names[1]
lowerCamelCase__					:  Union[str, Any]      							=				'answers' if 'answers' in column_names else column_names[2]

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

lowerCamelCase__					:  Tuple      							=				min(args.max_seq_length, tokenizer.model_max_length)

def    UpperCAmelCase_				( __UpperCAmelCase		:      str				)   ->					Optional[Any]:
      # Some of the questions have lots of whitespace on the left, which is not useful and will make the
      # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
      # left whitespace
      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=__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.
      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(__UpperCAmelCase				)
            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__					:  Optional[Any]      							=				raw_datasets['validation']
# Validation Feature Creation
lowerCamelCase__					:  Union[str, 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',
)

lowerCamelCase__					:  Any      							=				default_data_collator

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

def    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[int]     ,     __UpperCAmelCase		:      Tuple     ,     __UpperCAmelCase		:      Tuple     ,     __UpperCAmelCase		:      Tuple="eval"				)   ->					Tuple:
      # Post-processing: we match the start logits and end logits to answers in the original context.
      SCREAMING_SNAKE_CASE_      		=     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:
            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=__UpperCAmelCase     ,     label_ids=__UpperCAmelCase				)


lowerCamelCase__					:  List[Any]      							=				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    UpperCAmelCase_				( __UpperCAmelCase		:      Tuple				)   ->					Union[str, Any]:
         return trt.volume(engine.get_binding_shape(__UpperCAmelCase				)				) * engine.get_binding_dtype(__UpperCAmelCase				).itemsize

   # Allocate device memory for inputs and outputs.
   lowerCamelCase__					:  Union[str, Any]      							=				[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

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

   # Create a stream in which to copy inputs/outputs and run inference.
   lowerCamelCase__					:  Union[str, 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}''')

   lowerCamelCase__					:  Optional[int]      							=				0.0
   lowerCamelCase__					:  Union[str, Any]      							=				0
   lowerCamelCase__					:  Union[str, Any]      							=				timeit.default_timer()

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

      lowerCamelCase__							,				lowerCamelCase__					:  Dict      							=				outputs
      lowerCamelCase__					:  Union[str, Any]      							=				torch.tensor(start_logits)
      lowerCamelCase__					:  int      							=				torch.tensor(end_logits)

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

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

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

   lowerCamelCase__					:  Dict      							=				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)

lowerCamelCase__					:  Any      							=				post_processing_function(eval_examples, eval_dataset, all_preds)
lowerCamelCase__					:  int      							=				metric.compute(predictions=prediction.predictions, references=prediction.label_ids)





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





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Any							,							_lowerCAmelCase    :						Optional[int]=2							,							_lowerCAmelCase    :						Any=3							,							_lowerCAmelCase    :						Tuple=64							,							_lowerCAmelCase    :						List[str]=None		):
       SCREAMING_SNAKE_CASE_      		=     np.random.default_rng(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     length
       SCREAMING_SNAKE_CASE_      		=     rng.normal(size=(length,)		).astype(np.floataa		)
       SCREAMING_SNAKE_CASE_      		=     a * self.x + b + rng.normal(scale=0.1							,							size=(length,)		).astype(np.floataa		)



 def __len__(				self    :						Optional[int]		):
       return self.length




 def __getitem__(				self    :						str							,							_lowerCAmelCase    :						Union[str, Any]		):
       return {"x": self.x[i], "y": self.y[i]}





class 			lowerCamelCase_				( torch.nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						Dict=0							,							_lowerCAmelCase    :						List[str]=0							,							_lowerCAmelCase    :						str=False		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
       SCREAMING_SNAKE_CASE_      		=     True




 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]=None		):
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
             SCREAMING_SNAKE_CASE_      		=     False
       return x * self.a[0] + self.b[0]





class 			lowerCamelCase_				( torch.nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Optional[int]							,							_lowerCAmelCase    :						Any=0							,							_lowerCAmelCase    :						Any=0							,							_lowerCAmelCase    :						Optional[Any]=False		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor(_lowerCAmelCase		).float()		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor(_lowerCAmelCase		).float()		)
       SCREAMING_SNAKE_CASE_      		=     True




 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						Optional[int]=None		):
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
             SCREAMING_SNAKE_CASE_      		=     False
       return x * self.a + self.b


def    UpperCAmelCase_				( __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      int = 16				)   ->					Union[str, Any]:
      from datasets import load_dataset
      from transformers import AutoTokenizer

      SCREAMING_SNAKE_CASE_      		=     AutoTokenizer.from_pretrained('bert-base-cased'				)
      SCREAMING_SNAKE_CASE_      		=     {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
      SCREAMING_SNAKE_CASE_      		=     load_dataset('csv'     ,     data_files=__UpperCAmelCase				)
      SCREAMING_SNAKE_CASE_      		=     datasets['train'].unique('label'				)

      SCREAMING_SNAKE_CASE_      		=     {v: i for i, v in enumerate(__UpperCAmelCase				)}

      def tokenize_function(__UpperCAmelCase		:      Optional[int]				):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE_      		=     tokenizer(
                examples['sentence1']     ,     examples['sentence2']     ,     truncation=__UpperCAmelCase     ,     max_length=__UpperCAmelCase     ,     padding='max_length'				)
            if "label" in examples:
                  SCREAMING_SNAKE_CASE_      		=     [label_to_id[l] for l in examples['label']]
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      SCREAMING_SNAKE_CASE_      		=     datasets.map(
          __UpperCAmelCase     ,     batched=__UpperCAmelCase     ,     remove_columns=['sentence1', 'sentence2', 'label']     ,     )

      def collate_fn(__UpperCAmelCase		:      Dict				):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(__UpperCAmelCase     ,     padding='max_length'     ,     max_length=1_28     ,     return_tensors='pt'				)
            return tokenizer.pad(__UpperCAmelCase     ,     padding='longest'     ,     return_tensors='pt'				)

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE_      		=     DataLoader(tokenized_datasets['train']     ,     shuffle=__UpperCAmelCase     ,     collate_fn=__UpperCAmelCase     ,     batch_size=2				)
      SCREAMING_SNAKE_CASE_      		=     DataLoader(tokenized_datasets['validation']     ,     shuffle=__UpperCAmelCase     ,     collate_fn=__UpperCAmelCase     ,     batch_size=1				)

      return train_dataloader, eval_dataloader