





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

SCREAMING_SNAKE_CASE_		     = logging.getLogger(__name__)

SCREAMING_SNAKE_CASE_		     = 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_8_4,
    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_2_8,
    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=2_0,
    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=3_0,
    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=4_2, 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_		     = parser.parse_args()

if args.tokenizer_name:
      SCREAMING_SNAKE_CASE_		     = 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_		     = args.per_device_eval_batch_size

SCREAMING_SNAKE_CASE_		     = (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
SCREAMING_SNAKE_CASE_		     = True

SCREAMING_SNAKE_CASE_		     = """temp_engine/bert-fp32.engine"""
if args.fpaa:
      SCREAMING_SNAKE_CASE_		     = """temp_engine/bert-fp16.engine"""
if args.inta:
      SCREAMING_SNAKE_CASE_		     = """temp_engine/bert-int8.engine"""

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

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

      with builder.create_builder_config() as config:
            SCREAMING_SNAKE_CASE_		     = 1 << 5_0
            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_		     = 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_		     = 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      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     int:


      '''simple docstring'''


      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()				,		_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
      SCREAMING_SNAKE_CASE	=		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
      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.
SCREAMING_SNAKE_CASE_		     = 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_		     = 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_		     = raw_datasets["""validation"""].column_names

SCREAMING_SNAKE_CASE_		     = """question""" if """question""" in column_names else column_names[0]
SCREAMING_SNAKE_CASE_		     = """context""" if """context""" in column_names else column_names[1]
SCREAMING_SNAKE_CASE_		     = """answers""" if """answers""" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
SCREAMING_SNAKE_CASE_		     = 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_		     = min(args.max_seq_length, tokenizer.model_max_length)



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Optional[Any]:


      '''simple docstring'''


      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=_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.
      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(_SCREAMING_SNAKE_CASE			)
            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


SCREAMING_SNAKE_CASE_		     = raw_datasets["""validation"""]
# Validation Feature Creation
SCREAMING_SNAKE_CASE_		     = 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_		     = default_data_collator

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



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE="eval"			)				->     Optional[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		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:
            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=_SCREAMING_SNAKE_CASE				,		label_ids=_SCREAMING_SNAKE_CASE			)


SCREAMING_SNAKE_CASE_		     = 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      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Optional[int]:


            '''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.
      SCREAMING_SNAKE_CASE_		     = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

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

      # Create a stream in which to copy inputs/outputs and run inference.
      SCREAMING_SNAKE_CASE_		     = 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_		     = 0.0
      SCREAMING_SNAKE_CASE_		     = 0
      SCREAMING_SNAKE_CASE_		     = timeit.default_timer()

      SCREAMING_SNAKE_CASE_		     = None
      for step, batch in enumerate(eval_dataloader):
            SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_		     = 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_		     = outputs
            SCREAMING_SNAKE_CASE_		     = torch.tensor(start_logits)
            SCREAMING_SNAKE_CASE_		     = torch.tensor(end_logits)

            # necessary to pad predictions and labels for being gathered
            SCREAMING_SNAKE_CASE_		     = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_0_0)
            SCREAMING_SNAKE_CASE_		     = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

            SCREAMING_SNAKE_CASE_		     = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
            SCREAMING_SNAKE_CASE_		     = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_0_0)

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

      SCREAMING_SNAKE_CASE_		     = 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_0_0_0 / niter))
      logger.info("""Total Inference Time =  {:.3f} ms""".format(total_time * 1_0_0_0))



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

SCREAMING_SNAKE_CASE_		     = post_processing_function(eval_examples, eval_dataset, all_preds)
SCREAMING_SNAKE_CASE_		     = 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_		     = logging.get_logger(__name__)

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

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			str				=    "deta"
     __snake_case					:			int				=    {
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }






     def __init__(							self		:					List[Any]		,lowerCamelCase__		:					Any=None		,lowerCamelCase__		:					Optional[int]=900		,lowerCamelCase__		:					Optional[Any]=2048		,lowerCamelCase__		:					Optional[Any]=6		,lowerCamelCase__		:					Dict=2048		,lowerCamelCase__		:					Optional[int]=8		,lowerCamelCase__		:					List[str]=6		,lowerCamelCase__		:					Union[str, Any]=1024		,lowerCamelCase__		:					str=8		,lowerCamelCase__		:					Union[str, Any]=0.0		,lowerCamelCase__		:					Dict=True		,lowerCamelCase__		:					Dict="relu"		,lowerCamelCase__		:					int=256		,lowerCamelCase__		:					Optional[Any]=0.1		,lowerCamelCase__		:					Dict=0.0		,lowerCamelCase__		:					Dict=0.0		,lowerCamelCase__		:					Dict=0.02		,lowerCamelCase__		:					List[Any]=1.0		,lowerCamelCase__		:					List[str]=True		,lowerCamelCase__		:					int=False		,lowerCamelCase__		:					int="sine"		,lowerCamelCase__		:					Any=5		,lowerCamelCase__		:					Optional[int]=4		,lowerCamelCase__		:					Optional[int]=4		,lowerCamelCase__		:					List[Any]=True		,lowerCamelCase__		:					Optional[int]=300		,lowerCamelCase__		:					Optional[Any]=True		,lowerCamelCase__		:					Optional[Any]=True		,lowerCamelCase__		:					Optional[int]=1		,lowerCamelCase__		:					Tuple=5		,lowerCamelCase__		:					Tuple=2		,lowerCamelCase__		:					Dict=1		,lowerCamelCase__		:					str=1		,lowerCamelCase__		:					List[Any]=5		,lowerCamelCase__		:					Tuple=2		,lowerCamelCase__		:					Union[str, Any]=0.1		,lowerCamelCase__		:					List[str]=0.25		,**lowerCamelCase__		:					int		,)  ->	List[Any]:




           '''simple docstring'''


           if backbone_config is None:
                 logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
                 SCREAMING_SNAKE_CASE	=		CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""] )
           else:
                 if isinstance(lowerCamelCase__		,lowerCamelCase__ ):
                       SCREAMING_SNAKE_CASE	=		backbone_config.pop("""model_type""" )
                       SCREAMING_SNAKE_CASE	=		CONFIG_MAPPING[backbone_model_type]
                       SCREAMING_SNAKE_CASE	=		config_class.from_dict(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		backbone_config
           SCREAMING_SNAKE_CASE	=		num_queries
           SCREAMING_SNAKE_CASE	=		max_position_embeddings
           SCREAMING_SNAKE_CASE	=		d_model
           SCREAMING_SNAKE_CASE	=		encoder_ffn_dim
           SCREAMING_SNAKE_CASE	=		encoder_layers
           SCREAMING_SNAKE_CASE	=		encoder_attention_heads
           SCREAMING_SNAKE_CASE	=		decoder_ffn_dim
           SCREAMING_SNAKE_CASE	=		decoder_layers
           SCREAMING_SNAKE_CASE	=		decoder_attention_heads
           SCREAMING_SNAKE_CASE	=		dropout
           SCREAMING_SNAKE_CASE	=		attention_dropout
           SCREAMING_SNAKE_CASE	=		activation_dropout
           SCREAMING_SNAKE_CASE	=		activation_function
           SCREAMING_SNAKE_CASE	=		init_std
           SCREAMING_SNAKE_CASE	=		init_xavier_std
           SCREAMING_SNAKE_CASE	=		encoder_layerdrop
           SCREAMING_SNAKE_CASE	=		auxiliary_loss
           SCREAMING_SNAKE_CASE	=		position_embedding_type
           # deformable attributes
           SCREAMING_SNAKE_CASE	=		num_feature_levels
           SCREAMING_SNAKE_CASE	=		encoder_n_points
           SCREAMING_SNAKE_CASE	=		decoder_n_points
           SCREAMING_SNAKE_CASE	=		two_stage
           SCREAMING_SNAKE_CASE	=		two_stage_num_proposals
           SCREAMING_SNAKE_CASE	=		with_box_refine
           SCREAMING_SNAKE_CASE	=		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
           SCREAMING_SNAKE_CASE	=		class_cost
           SCREAMING_SNAKE_CASE	=		bbox_cost
           SCREAMING_SNAKE_CASE	=		giou_cost
           # Loss coefficients
           SCREAMING_SNAKE_CASE	=		mask_loss_coefficient
           SCREAMING_SNAKE_CASE	=		dice_loss_coefficient
           SCREAMING_SNAKE_CASE	=		bbox_loss_coefficient
           SCREAMING_SNAKE_CASE	=		giou_loss_coefficient
           SCREAMING_SNAKE_CASE	=		eos_coefficient
           SCREAMING_SNAKE_CASE	=		focal_alpha
           super().__init__(is_encoder_decoder=lowerCamelCase__		,**lowerCamelCase__ )






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	int:




           '''simple docstring'''


           return self.encoder_attention_heads






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	int:




           '''simple docstring'''


           return self.d_model






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		copy.deepcopy(self.__dict__ )
           SCREAMING_SNAKE_CASE	=		self.backbone_config.to_dict()
           SCREAMING_SNAKE_CASE	=		self.__class__.model_type
           return output


