





"""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


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

_UpperCamelCase							=      logging.getLogger(__name__)

_UpperCamelCase							=      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',
)

_UpperCamelCase							=      parser.parse_args()

if args.tokenizer_name:
							_UpperCamelCase							=      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)

_UpperCamelCase							=      args.per_device_eval_batch_size

_UpperCamelCase							=      (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_UpperCamelCase							=      True

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

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

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

							with builder.create_builder_config() as config:
														_UpperCamelCase							=      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)
														_UpperCamelCase							=      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)
														_UpperCamelCase							=      builder.build_engine(network, config)

														# serialize_engine and store in file (can be directly loaded and deserialized):
														with open(engine_name, 'wb') as f:
																					f.write(engine.serialize())



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Dict    ,						SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							List[Any]    ,						SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]    ,						SCREAMING_SNAKE_CASE       :							Any   ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         =np.asarray(inputs['''input_ids''']    ,						dtype=np.intaa   )
							__lowerCamelCase      :      List[Any]         =np.asarray(inputs['''attention_mask''']    ,						dtype=np.intaa   )
							__lowerCamelCase      :      Union[str, Any]         =np.asarray(inputs['''token_type_ids''']    ,						dtype=np.intaa   )

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

_UpperCamelCase							=      raw_datasets['validation'].column_names

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

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

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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =[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.
							__lowerCamelCase      :      Optional[int]         =tokenizer(
							    examples[question_column_name if pad_on_right else context_column_name]    ,						examples[context_column_name if pad_on_right else question_column_name]    ,						truncation='''only_second''' if pad_on_right else '''only_first'''    ,						max_length=SCREAMING_SNAKE_CASE    ,						stride=args.doc_stride    ,						return_overflowing_tokens=SCREAMING_SNAKE_CASE    ,						return_offsets_mapping=SCREAMING_SNAKE_CASE    ,						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.
							__lowerCamelCase      :      Tuple         =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.
							__lowerCamelCase      :      Union[str, Any]         =[]

							for i in range(len(tokenized_examples['''input_ids''']   )   ):
														# Grab the sequence corresponding to that example (to know what is the context and what is the question).
														__lowerCamelCase      :      Dict         =tokenized_examples.sequence_ids(SCREAMING_SNAKE_CASE   )
														__lowerCamelCase      :      str         =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.
														__lowerCamelCase      :      int         =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.
														__lowerCamelCase      :      List[str]         =[
														    (o if sequence_ids[k] == context_index else None)
														    for k, o in enumerate(tokenized_examples['''offset_mapping'''][i]   )
														]

							return tokenized_examples


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

_UpperCamelCase							=      default_data_collator

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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Union[str, Any]    ,						SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							Optional[Any]    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]="eval"   ):


							'''simple docstring'''







							__lowerCamelCase      :      Any         =postprocess_qa_predictions(
							    examples=SCREAMING_SNAKE_CASE    ,						features=SCREAMING_SNAKE_CASE    ,						predictions=SCREAMING_SNAKE_CASE    ,						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=SCREAMING_SNAKE_CASE    ,						)
							# Format the result to the format the metric expects.
							if args.version_2_with_negative:
														__lowerCamelCase      :      Dict         =[
														    {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
														]
							else:
														__lowerCamelCase      :      Optional[Any]         =[{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

							__lowerCamelCase      :      str         =[{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
							return EvalPrediction(predictions=SCREAMING_SNAKE_CASE    ,						label_ids=SCREAMING_SNAKE_CASE   )


_UpperCamelCase							=      load_metric('squad_v2' if args.version_2_with_negative else 'squad')

# Evaluation!
logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path)
with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
							# setup for TRT inferrence
							for i in range(len(input_names)):
														context.set_binding_shape(i, INPUT_SHAPE)
							assert context.all_binding_shapes_specified



							def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[Any]   ):


														'''simple docstring'''







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

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

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

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

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

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

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

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

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

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

							_UpperCamelCase							=      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)

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











"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_UpperCamelCase							=      logging.get_logger(__name__)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Union[tf.Tensor, np.ndarray]   ):


							'''simple docstring'''







							if isinstance(SCREAMING_SNAKE_CASE    ,						np.ndarray   ):
														return list(tensor.shape   )

							__lowerCamelCase      :      Tuple         =tf.shape(SCREAMING_SNAKE_CASE   )

							if tensor.shape == tf.TensorShape(SCREAMING_SNAKE_CASE   ):
														return dynamic

							__lowerCamelCase      :      Union[str, Any]         =tensor.shape.as_list()

							return [dynamic[i] if s is None else s for i, s in enumerate(SCREAMING_SNAKE_CASE   )]



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							tf.Tensor    ,						SCREAMING_SNAKE_CASE       :							Optional[int] = None    ,						SCREAMING_SNAKE_CASE       :							Optional[str] = None   ):


							'''simple docstring'''







							return tf.nn.softmax(logits=logits + 1E-9    ,						axis=SCREAMING_SNAKE_CASE    ,						name=SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Dict    ,						SCREAMING_SNAKE_CASE       :							Tuple=1E-5    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]=-1   ):


							'''simple docstring'''







							if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
														raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.'''   )

							# Get mean and variance on the axis to be normalized
							__lowerCamelCase    ,						__lowerCamelCase      :      Dict         =tf.nn.moments(SCREAMING_SNAKE_CASE    ,						axes=[axis]    ,						keepdims=SCREAMING_SNAKE_CASE   )

							if axis != -1:
														# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
														# on every dimension except axis
														__lowerCamelCase      :      Dict         =[1] * inputs.shape.rank
														__lowerCamelCase      :      Dict         =shape_list(SCREAMING_SNAKE_CASE   )[axis]
														__lowerCamelCase      :      List[str]         =tf.reshape(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
														__lowerCamelCase      :      List[str]         =tf.reshape(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							# Compute layer normalization using the batch_normalization
							# function.
							__lowerCamelCase      :      int         =tf.nn.batch_normalization(
							    SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						offset=SCREAMING_SNAKE_CASE    ,						scale=SCREAMING_SNAKE_CASE    ,						variance_epsilon=SCREAMING_SNAKE_CASE    ,						)
							return outputs



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[Any]    ,						SCREAMING_SNAKE_CASE       :							Dict=0    ,						SCREAMING_SNAKE_CASE       :							int=-1   ):


							'''simple docstring'''







							if end_dim < 0:
														end_dim += input.shape.rank
							if start_dim < 0:
														start_dim += input.shape.rank

							if start_dim == end_dim:
														return input

							__lowerCamelCase      :      List[str]         =tf.shape(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[Any]         =tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]   )
							__lowerCamelCase      :      int         =tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]    ,						axis=0   )
							return tf.reshape(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							tf.Tensor   ):


							'''simple docstring'''







							if not isinstance(SCREAMING_SNAKE_CASE    ,						tf.Tensor   ):
														__lowerCamelCase      :      Union[str, Any]         =tf.convert_to_tensor(SCREAMING_SNAKE_CASE   )  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														__lowerCamelCase      :      str         =encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														__lowerCamelCase      :      Optional[int]         =encoder_attention_mask[:, None, None, :]
							# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
							# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
							# /transformer/transformer_layers.py#L270
							# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
							# encoder_extended_attention_mask.transpose(-1, -2))
							__lowerCamelCase      :      int         =(
							    tf.cast(1    ,						encoder_attention_mask.dtype   ) - encoder_extended_attention_mask
							) * encoder_extended_attention_mask.dtype.min

							return encoder_extended_attention_mask



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							tf.Tensor    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							str = "input_ids"   ):


							'''simple docstring'''







							tf.debugging.assert_less(
							    SCREAMING_SNAKE_CASE    ,						tf.cast(SCREAMING_SNAKE_CASE    ,						dtype=tensor.dtype   )    ,						message=(
							        F'The maximum value of {tensor_name} ({tf.math.reduce_max(SCREAMING_SNAKE_CASE   )}) must be smaller than the embedding '
							        F'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
							    )    ,						)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[int]         =64512
							# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
							# because in that case even chunking the array would not make the saving
							# possible.
							__lowerCamelCase      :      Any         =[x for x in data if len(SCREAMING_SNAKE_CASE   ) > HDF5_OBJECT_HEADER_LIMIT]

							# Expecting this to never be true.
							if bad_attributes:
														raise RuntimeError(
														    '''The following attributes cannot be saved to HDF5 file because '''
														    F'they are larger than {HDF5_OBJECT_HEADER_LIMIT} '
														    F'bytes: {bad_attributes}'   )

							__lowerCamelCase      :      Tuple         =np.asarray(SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      Any         =1
							__lowerCamelCase      :      Optional[Any]         =np.array_split(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							# This will never loop forever thanks to the test above.
							while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data   ):
														num_chunks += 1
														__lowerCamelCase      :      Dict         =np.array_split(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      List[Any]         =chunk_data
							else:
														__lowerCamelCase      :      Optional[int]         =data



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							Optional[int]   ):


							'''simple docstring'''







							if name in group.attrs:
														__lowerCamelCase      :      Optional[int]         =[n.decode('''utf8'''   ) if hasattr(SCREAMING_SNAKE_CASE    ,						'''decode'''   ) else n for n in group.attrs[name]]
							else:
														__lowerCamelCase      :      Tuple         =[]
														__lowerCamelCase      :      List[str]         =0
														while "%s%d" % (name, chunk_id) in group.attrs:
																					data.extend(
																					    [n.decode('''utf8'''   ) if hasattr(SCREAMING_SNAKE_CASE    ,						'''decode'''   ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]   )
																					chunk_id += 1
							return data



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Dict   ):


							'''simple docstring'''







							def _expand_single_ad_tensor(SCREAMING_SNAKE_CASE       :							List[str]   ):
														if isinstance(SCREAMING_SNAKE_CASE    ,						tf.Tensor   ) and t.shape.rank == 1:
																					return tf.expand_dims(SCREAMING_SNAKE_CASE    ,						axis=-1   )
														return t

							return tf.nest.map_structure(_expand_single_ad_tensor    ,						SCREAMING_SNAKE_CASE   )




