


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


SCREAMING_SNAKE_CASE			: Dict				   =					trt.Logger(trt.Logger.WARNING)
SCREAMING_SNAKE_CASE			: str				   =					absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

SCREAMING_SNAKE_CASE			: List[str]				   =					logging.getLogger(__name__)

SCREAMING_SNAKE_CASE			: Optional[int]				   =					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",
)

SCREAMING_SNAKE_CASE			: Tuple				   =					parser.parse_args()

if args.tokenizer_name:
 SCREAMING_SNAKE_CASE			: Optional[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)

SCREAMING_SNAKE_CASE			: Tuple				   =					args.per_device_eval_batch_size

SCREAMING_SNAKE_CASE			: str				   =					(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
SCREAMING_SNAKE_CASE			: Dict				   =					True

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

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

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

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

 with builder.create_builder_config() as config:
  SCREAMING_SNAKE_CASE			: str				   =					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)
  SCREAMING_SNAKE_CASE			: Union[str, 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)
  SCREAMING_SNAKE_CASE			: int				   =					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_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Tuple:
    _lowercase			:				List[Any] 						=						np.asarray(inputs['input_ids']				,					dtype=np.intaa					)
    _lowercase			:				Union[str, Any] 						=						np.asarray(inputs['attention_mask']				,					dtype=np.intaa					)
    _lowercase			:				Any 						=						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
    _lowercase			:				List[Any] 						=						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
    _lowercase			:				Any 						=						time.time()
    _lowercase			:				Dict 						=						end_time - start_time
    _lowercase			:				Union[str, Any] 						=						(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.
SCREAMING_SNAKE_CASE			: Dict				   =					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.
 SCREAMING_SNAKE_CASE			: str				   =					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.

SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					raw_datasets["validation"].column_names

SCREAMING_SNAKE_CASE			: Optional[Any]				   =					"question" if "question" in column_names else column_names[0]
SCREAMING_SNAKE_CASE			: str				   =					"context" if "context" in column_names else column_names[1]
SCREAMING_SNAKE_CASE			: List[Any]				   =					"answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
SCREAMING_SNAKE_CASE			: Any				   =					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}."
 )

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



def 							UpperCamelCase_( lowerCamelCase_					)       ->		str:
    # 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
    _lowercase			:				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.
    _lowercase			:				Optional[Any] 						=						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.
    _lowercase			:				str 						=						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.
    _lowercase			:				int 						=						[]

    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).
        _lowercase			:				Any 						=						tokenized_examples.sequence_ids(lowerCamelCase_					)
        _lowercase			:				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.
        _lowercase			:				Optional[Any] 						=						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.
        _lowercase			:				Dict 						=						[
            (o if sequence_ids[k] == context_index else None)
            for k, o in enumerate(tokenized_examples['offset_mapping'][i]					)
        ]

    return tokenized_examples


SCREAMING_SNAKE_CASE			: str				   =					raw_datasets["validation"]
# Validation Feature Creation
SCREAMING_SNAKE_CASE			: List[Any]				   =					eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc="Running tokenizer on validation dataset",
)

SCREAMING_SNAKE_CASE			: Tuple				   =					default_data_collator

SCREAMING_SNAKE_CASE			: List[str]				   =					eval_dataset.remove_columns(["example_id", "offset_mapping"])
SCREAMING_SNAKE_CASE			: List[str]				   =					DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_="eval"					)       ->		Union[str, Any]:
    # Post-processing: we match the start logits and end logits to answers in the original context.
    _lowercase			:				Dict 						=						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:
        _lowercase			:				Tuple 						=						[
            {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
        ]
    else:
        _lowercase			:				int 						=						[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

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


SCREAMING_SNAKE_CASE			: Tuple				   =					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_( lowerCamelCase_					)       ->		Optional[Any]:
     return trt.volume(engine.get_binding_shape(lowerCamelCase_					)					) * engine.get_binding_dtype(lowerCamelCase_					).itemsize

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

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

 # Create a stream in which to copy inputs/outputs and run inference.
 SCREAMING_SNAKE_CASE			: 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}")

 SCREAMING_SNAKE_CASE			: Dict				   =					0.0
 SCREAMING_SNAKE_CASE			: Optional[int]				   =					0
 SCREAMING_SNAKE_CASE			: str				   =					timeit.default_timer()

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

  SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE			: Any				   =					outputs
  SCREAMING_SNAKE_CASE			: str				   =					torch.tensor(start_logits)
  SCREAMING_SNAKE_CASE			: Dict				   =					torch.tensor(end_logits)

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

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

 if all_preds is not None:
  SCREAMING_SNAKE_CASE			: List[Any]				   =					nested_truncate(all_preds, len(eval_dataset))

 SCREAMING_SNAKE_CASE			: str				   =					timeit.default_timer() - start_time
 logger.info("  Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(eval_dataset))
 # Inference time from TRT
 logger.info("Average Inference Time = {:.3f} ms".format(total_time * 1000 / niter))
 logger.info("Total Inference Time =  {:.3f} ms".format(total_time * 1000))

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

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

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






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE			: List[str]				   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			: Any				   =					{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}






class _lowerCamelCase(		_a  ):
       lowercase_					:      Any       					=     """deta"""
       lowercase_					:      Union[str, Any]       					=     {
           """hidden_size""": """d_model""",
           """num_attention_heads""": """encoder_attention_heads""",
       }
       def __init__(   self, lowerCamelCase=None, lowerCamelCase=9_00, lowerCamelCase=20_48, lowerCamelCase=6, lowerCamelCase=20_48, lowerCamelCase=8, lowerCamelCase=6, lowerCamelCase=10_24, lowerCamelCase=8, lowerCamelCase=0.0, lowerCamelCase=True, lowerCamelCase="relu", lowerCamelCase=2_56, lowerCamelCase=0.1, lowerCamelCase=0.0, lowerCamelCase=0.0, lowerCamelCase=0.0_2, lowerCamelCase=1.0, lowerCamelCase=True, lowerCamelCase=False, lowerCamelCase="sine", lowerCamelCase=5, lowerCamelCase=4, lowerCamelCase=4, lowerCamelCase=True, lowerCamelCase=3_00, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=1, lowerCamelCase=5, lowerCamelCase=2, lowerCamelCase=1, lowerCamelCase=1, lowerCamelCase=5, lowerCamelCase=2, lowerCamelCase=0.1, lowerCamelCase=0.2_5, **lowerCamelCase, )			->  Any:




           """simple docstring"""

           if backbone_config is None:
               logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.')
               _lowercase			:				List[Any] 						=						CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'])
           else:
               if isinstance(lowerCamelCase, lowerCamelCase):
                   _lowercase			:				Dict 						=						backbone_config.pop('model_type')
                   _lowercase			:				int 						=						CONFIG_MAPPING[backbone_model_type]
                   _lowercase			:				Union[str, Any] 						=						config_class.from_dict(lowerCamelCase)

           _lowercase			:				Union[str, Any] 						=						backbone_config
           _lowercase			:				Any 						=						num_queries
           _lowercase			:				Union[str, Any] 						=						max_position_embeddings
           _lowercase			:				Union[str, Any] 						=						d_model
           _lowercase			:				Optional[int] 						=						encoder_ffn_dim
           _lowercase			:				Optional[int] 						=						encoder_layers
           _lowercase			:				Optional[Any] 						=						encoder_attention_heads
           _lowercase			:				Optional[Any] 						=						decoder_ffn_dim
           _lowercase			:				Dict 						=						decoder_layers
           _lowercase			:				Tuple 						=						decoder_attention_heads
           _lowercase			:				Union[str, Any] 						=						dropout
           _lowercase			:				Optional[Any] 						=						attention_dropout
           _lowercase			:				int 						=						activation_dropout
           _lowercase			:				Tuple 						=						activation_function
           _lowercase			:				List[Any] 						=						init_std
           _lowercase			:				Union[str, Any] 						=						init_xavier_std
           _lowercase			:				int 						=						encoder_layerdrop
           _lowercase			:				Optional[int] 						=						auxiliary_loss
           _lowercase			:				Dict 						=						position_embedding_type
           # deformable attributes
           _lowercase			:				Any 						=						num_feature_levels
           _lowercase			:				str 						=						encoder_n_points
           _lowercase			:				Any 						=						decoder_n_points
           _lowercase			:				List[str] 						=						two_stage
           _lowercase			:				Dict 						=						two_stage_num_proposals
           _lowercase			:				Any 						=						with_box_refine
           _lowercase			:				List[Any] 						=						assign_first_stage
           if two_stage is True and with_box_refine is False:
               raise ValueError('If two_stage is True, with_box_refine must be True.')
           # Hungarian matcher
           _lowercase			:				List[Any] 						=						class_cost
           _lowercase			:				Optional[int] 						=						bbox_cost
           _lowercase			:				str 						=						giou_cost
           # Loss coefficients
           _lowercase			:				Optional[int] 						=						mask_loss_coefficient
           _lowercase			:				int 						=						dice_loss_coefficient
           _lowercase			:				List[Any] 						=						bbox_loss_coefficient
           _lowercase			:				Optional[Any] 						=						giou_loss_coefficient
           _lowercase			:				str 						=						eos_coefficient
           _lowercase			:				int 						=						focal_alpha
           super().__init__(is_encoder_decoder=lowerCamelCase, **lowerCamelCase)
       @property
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           return self.encoder_attention_heads
       @property
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           return self.d_model




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




           """simple docstring"""

           _lowercase			:				int 						=						copy.deepcopy(self.__dict__)
           _lowercase			:				Optional[int] 						=						self.backbone_config.to_dict()
           _lowercase			:				Optional[Any] 						=						self.__class__.model_type
           return output


