
"""simple docstring"""







import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


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

lowerCamelCase__           =	logging.getLogger(__name__)

lowerCamelCase__           =	argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    """--onnx_model_path""",
    default=None,
    type=str,
    required=True,
    help="""Path to ONNX model: """,
)

parser.add_argument(
    """--output_dir""",
    default=None,
    type=str,
    required=True,
    help="""The output directory where the model checkpoints and predictions will be written.""",
)

# Other parameters

parser.add_argument(
    """--tokenizer_name""",
    default="""""",
    type=str,
    required=True,
    help="""Pretrained tokenizer name or path if not the same as model_name""",
)

parser.add_argument(
    """--version_2_with_negative""",
    action="""store_true""",
    help="""If true, the SQuAD examples contain some that do not have an answer.""",
)
parser.add_argument(
    """--null_score_diff_threshold""",
    type=float,
    default=0.0,
    help="""If null_score - best_non_null is greater than the threshold predict null.""",
)

parser.add_argument(
    """--max_seq_length""",
    default=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__           =	parser.parse_args()

if args.tokenizer_name:
							lowerCamelCase__           =	AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
							raise ValueError(
							    """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
							    """You can do it from another script, save it, and load it from here, using --tokenizer_name."""
							)

logger.info("""Training/evaluation parameters %s""", args)

lowerCamelCase__           =	args.per_device_eval_batch_size

lowerCamelCase__           =	(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
lowerCamelCase__           =	True

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

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

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

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

							with builder.create_builder_config() as config:
														lowerCamelCase__           =	1 << 50
														if STRICT_TYPES:
																					config.set_flag(trt.BuilderFlag.STRICT_TYPES)
														if args.fpaa:
																					config.set_flag(trt.BuilderFlag.FPaa)
														if args.inta:
																					config.set_flag(trt.BuilderFlag.INTa)
														lowerCamelCase__           =	builder.create_optimization_profile()
														config.add_optimization_profile(profile)
														for i in range(len(input_names)):
																					profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
														lowerCamelCase__           =	builder.build_engine(network, config)

														# serialize_engine and store in file (can be directly loaded and deserialized):
														with open(engine_name, """wb""") as f:
																					f.write(engine.serialize())
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Tuple		    =   np.asarray(inputs['input_ids'] ,						dtype=np.intaa	)
	__lowerCAmelCase    :		Union[str, Any]		    =   np.asarray(inputs['attention_mask'] ,						dtype=np.intaa	)
	__lowerCAmelCase    :		List[str]		    =   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
	__lowerCAmelCase    :		List[Any]		    =   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
	__lowerCAmelCase    :		str		    =   time.time()
	__lowerCAmelCase    :		Optional[Any]		    =   end_time - start_time
	__lowerCAmelCase    :		Optional[int]		    =   (h_outputa, h_outputa)
	# print(outputs)
	return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
lowerCamelCase__           =	Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
							datasets.utils.logging.set_verbosity_warning()
							transformers.utils.logging.set_verbosity_info()
else:
							datasets.utils.logging.set_verbosity_error()
							transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
							set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
							# Downloading and loading a dataset from the hub.
							lowerCamelCase__           =	load_dataset(args.dataset_name, args.dataset_config_name)
else:
							raise ValueError("""Evaluation requires a dataset name""")
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

lowerCamelCase__           =	raw_datasets["""validation"""].column_names

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

# Padding side determines if we do (question|context) or (context|question).
lowerCamelCase__           =	tokenizer.padding_side == """right"""

if args.max_seq_length > tokenizer.model_max_length:
							logger.warning(
							    f'The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'
							    f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'
							)

lowerCamelCase__           =	min(args.max_seq_length, tokenizer.model_max_length)
def 					__lowerCAmelCase       (_UpperCamelCase	):
	# 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
	__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    :		Union[str, 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=_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.
	__lowerCAmelCase    :		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.
	__lowerCAmelCase    :		List[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    :		Optional[int]		    =   tokenized_examples.sequence_ids(_UpperCamelCase	)
		__lowerCAmelCase    :		List[Any]		    =   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    :		List[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.
		__lowerCAmelCase    :		Optional[int]		    =   [
		    (o if sequence_ids[k] == context_index else None)
		    for k, o in enumerate(tokenized_examples['offset_mapping'][i]	)
		]

	return tokenized_examples


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

lowerCamelCase__           =	default_data_collator

lowerCamelCase__           =	eval_dataset.remove_columns(["""example_id""", """offset_mapping"""])
lowerCamelCase__           =	DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase="eval"	):
	# Post-processing: we match the start logits and end logits to answers in the original context.
	__lowerCAmelCase    :		List[str]		    =   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:
		__lowerCAmelCase    :		Tuple		    =   [
		    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
		]
	else:
		__lowerCAmelCase    :		int		    =   [{'id': k, 'prediction_text': v} for k, v in predictions.items()]

	__lowerCAmelCase    :		Optional[int]		    =   [{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
	return EvalPrediction(predictions=_UpperCamelCase ,						label_ids=_UpperCamelCase	)


lowerCamelCase__           =	load_metric("""squad_v2""" if args.version_2_with_negative else """squad""")

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

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

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

							# Create a stream in which to copy inputs/outputs and run inference.
							lowerCamelCase__           =	cuda.Stream()

							# Evaluation
							logger.info("""***** Running Evaluation *****""")
							logger.info(f'  Num examples = {len(eval_dataset)}')
							logger.info(f'  Batch size = {args.per_device_eval_batch_size}')

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

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

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

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

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

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

							lowerCamelCase__           =	timeit.default_timer() - start_time
							logger.info("""  Evaluation done in total %f secs (%f sec per example)""", evalTime, evalTime / len(eval_dataset))
							# Inference time from TRT
							logger.info("""Average Inference Time = {:.3f} ms""".format(total_time * 1_000 / niter))
							logger.info("""Total Inference Time =  {:.3f} ms""".format(total_time * 1_000))
							logger.info("""Total Number of Inference =  %d""", niter)

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

"""simple docstring"""







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__           =	logging.get_logger(__name__)
def 					__lowerCAmelCase       (_UpperCamelCase	):
	# initialize config
	if "resnet-50" in model_name:
		__lowerCAmelCase    :		int		    =   ResNetConfig.from_pretrained('microsoft/resnet-50'	)
	elif "resnet-101" in model_name:
		__lowerCAmelCase    :		Union[str, Any]		    =   ResNetConfig.from_pretrained('microsoft/resnet-101'	)
	else:
		raise ValueError('Model name should include either resnet50 or resnet101'	)

	__lowerCAmelCase    :		Union[str, Any]		    =   DetrConfig(use_timm_backbone=_UpperCamelCase ,						backbone_config=_UpperCamelCase	)

	# set label attributes
	__lowerCAmelCase    :		Optional[int]		    =   'panoptic' in model_name
	if is_panoptic:
		__lowerCAmelCase    :		Dict		    =   250
	else:
		__lowerCAmelCase    :		List[str]		    =   91
		__lowerCAmelCase    :		Tuple		    =   'huggingface/label-files'
		__lowerCAmelCase    :		str		    =   'coco-detection-id2label.json'
		__lowerCAmelCase    :		Optional[int]		    =   json.load(open(hf_hub_download(_UpperCamelCase ,						_UpperCamelCase ,						repo_type='dataset'	) ,						'r'	)	)
		__lowerCAmelCase    :		List[Any]		    =   {int(_UpperCamelCase	): v for k, v in idalabel.items()}
		__lowerCAmelCase    :		List[Any]		    =   idalabel
		__lowerCAmelCase    :		Dict		    =   {v: k for k, v in idalabel.items()}

	return config, is_panoptic
def 					__lowerCAmelCase       (_UpperCamelCase	):
	# here we list all keys to be renamed (original name on the left, our name on the right)
	__lowerCAmelCase    :		Tuple		    =   []

	# stem
	# fmt: off
	rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')	)
	rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')	)
	rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')	)
	rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')	)
	rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')	)
	# stages
	for stage_idx in range(len(config.backbone_config.depths	)	):
		for layer_idx in range(config.backbone_config.depths[stage_idx]	):
			# shortcut
			if layer_idx == 0:
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
				    )	)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
				    )	)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
				    )	)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
				    )	)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
				    )	)
			# 3 convs
			for i in range(3	):
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
				    )	)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
				    )	)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
				    )	)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
				    )	)
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
				    )	)
    # fmt: on

	for i in range(config.encoder_layers	):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (
		        F"transformer.encoder.layers.{i}.self_attn.out_proj.weight",
		        F"encoder.layers.{i}.self_attn.out_proj.weight",
		    )	)
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")	)
		rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight")	)
		rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias")	)
		rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight")	)
		rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias")	)
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")	)
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias")	)
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight")	)
		rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias")	)
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (
		        F"transformer.decoder.layers.{i}.self_attn.out_proj.weight",
		        F"decoder.layers.{i}.self_attn.out_proj.weight",
		    )	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")	)
		rename_keys.append(
		    (
		        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
		        F"decoder.layers.{i}.encoder_attn.out_proj.weight",
		    )	)
		rename_keys.append(
		    (
		        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
		        F"decoder.layers.{i}.encoder_attn.out_proj.bias",
		    )	)
		rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight")	)
		rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias")	)
		rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight")	)
		rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias")	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias")	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight")	)
		rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias")	)

	# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
	rename_keys.extend(
	    [
	        ('input_proj.weight', 'input_projection.weight'),
	        ('input_proj.bias', 'input_projection.bias'),
	        ('query_embed.weight', 'query_position_embeddings.weight'),
	        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
	        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
	        ('class_embed.weight', 'class_labels_classifier.weight'),
	        ('class_embed.bias', 'class_labels_classifier.bias'),
	        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
	        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
	        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
	        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
	        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
	        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
	    ]	)

	return rename_keys
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		str		    =   state_dict.pop(_UpperCamelCase	)
	__lowerCAmelCase    :		Dict		    =   val
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=False	):
	__lowerCAmelCase    :		Tuple		    =   ''
	if is_panoptic:
		__lowerCAmelCase    :		Union[str, Any]		    =   'detr.'

	# first: transformer encoder
	for i in range(6	):
		# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
		__lowerCAmelCase    :		Optional[int]		    =   state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"	)
		__lowerCAmelCase    :		List[Any]		    =   state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"	)
		# next, add query, keys and values (in that order) to the state dict
		__lowerCAmelCase    :		int		    =   in_proj_weight[:256, :]
		__lowerCAmelCase    :		Tuple		    =   in_proj_bias[:256]
		__lowerCAmelCase    :		Dict		    =   in_proj_weight[256:512, :]
		__lowerCAmelCase    :		Optional[int]		    =   in_proj_bias[256:512]
		__lowerCAmelCase    :		int		    =   in_proj_weight[-256:, :]
		__lowerCAmelCase    :		int		    =   in_proj_bias[-256:]
	# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
	for i in range(6	):
		# read in weights + bias of input projection layer of self-attention
		__lowerCAmelCase    :		int		    =   state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"	)
		__lowerCAmelCase    :		List[str]		    =   state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"	)
		# next, add query, keys and values (in that order) to the state dict
		__lowerCAmelCase    :		List[str]		    =   in_proj_weight[:256, :]
		__lowerCAmelCase    :		int		    =   in_proj_bias[:256]
		__lowerCAmelCase    :		Any		    =   in_proj_weight[256:512, :]
		__lowerCAmelCase    :		Any		    =   in_proj_bias[256:512]
		__lowerCAmelCase    :		Any		    =   in_proj_weight[-256:, :]
		__lowerCAmelCase    :		Optional[int]		    =   in_proj_bias[-256:]
		# read in weights + bias of input projection layer of cross-attention
		__lowerCAmelCase    :		Union[str, Any]		    =   state_dict.pop(
		    F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"	)
		__lowerCAmelCase    :		Optional[Any]		    =   state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"	)
		# next, add query, keys and values (in that order) of cross-attention to the state dict
		__lowerCAmelCase    :		List[Any]		    =   in_proj_weight_cross_attn[:256, :]
		__lowerCAmelCase    :		Union[str, Any]		    =   in_proj_bias_cross_attn[:256]
		__lowerCAmelCase    :		Optional[Any]		    =   in_proj_weight_cross_attn[256:512, :]
		__lowerCAmelCase    :		Any		    =   in_proj_bias_cross_attn[256:512]
		__lowerCAmelCase    :		str		    =   in_proj_weight_cross_attn[-256:, :]
		__lowerCAmelCase    :		Optional[int]		    =   in_proj_bias_cross_attn[-256:]
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		List[str]		    =   'http://images.cocodataset.org/val2017/000000039769.jpg'
	__lowerCAmelCase    :		Any		    =   Image.open(requests.get(_UpperCamelCase ,						stream=_UpperCamelCase	).raw	)

	return im
@torch.no_grad()
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=None ,						_UpperCamelCase=False	):
	__lowerCAmelCase    ,     __lowerCAmelCase    :		int		    =   get_detr_config(_UpperCamelCase	)

	# load original model from torch hub
	__lowerCAmelCase    :		List[str]		    =   {
	    'detr-resnet-50': 'detr_resnet50',
	    'detr-resnet-101': 'detr_resnet101',
	}
	logger.info(F"Converting model {model_name}..."	)
	__lowerCAmelCase    :		List[Any]		    =   torch.hub.load('facebookresearch/detr' ,						model_name_to_original_name[model_name] ,						pretrained=_UpperCamelCase	).eval()
	__lowerCAmelCase    :		str		    =   detr.state_dict()
	# rename keys
	for src, dest in create_rename_keys(_UpperCamelCase	):
		if is_panoptic:
			__lowerCAmelCase    :		List[str]		    =   'detr.' + src
		rename_key(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)
	# query, key and value matrices need special treatment
	read_in_q_k_v(_UpperCamelCase ,						is_panoptic=_UpperCamelCase	)
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	__lowerCAmelCase    :		Optional[Any]		    =   'detr.model.' if is_panoptic else 'model.'
	for key in state_dict.copy().keys():
		if is_panoptic:
			if (
			    key.startswith('detr'	)
			    and not key.startswith('class_labels_classifier'	)
			    and not key.startswith('bbox_predictor'	)
			):
				__lowerCAmelCase    :		Optional[Any]		    =   state_dict.pop(_UpperCamelCase	)
				__lowerCAmelCase    :		Tuple		    =   val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				__lowerCAmelCase    :		List[Any]		    =   state_dict.pop(_UpperCamelCase	)
				__lowerCAmelCase    :		Union[str, Any]		    =   val
			elif key.startswith('bbox_attention'	) or key.startswith('mask_head'	):
				continue
			else:
				__lowerCAmelCase    :		Optional[Any]		    =   state_dict.pop(_UpperCamelCase	)
				__lowerCAmelCase    :		Union[str, Any]		    =   val
		else:
			if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
				__lowerCAmelCase    :		Dict		    =   state_dict.pop(_UpperCamelCase	)
				__lowerCAmelCase    :		Optional[int]		    =   val

    # finally, create HuggingFace model and load state dict
	__lowerCAmelCase    :		Tuple		    =   DetrForSegmentation(_UpperCamelCase	) if is_panoptic else DetrForObjectDetection(_UpperCamelCase	)
	model.load_state_dict(_UpperCamelCase	)
	model.eval()

	# verify our conversion on an image
	__lowerCAmelCase    :		Optional[Any]		    =   'coco_panoptic' if is_panoptic else 'coco_detection'
	__lowerCAmelCase    :		List[str]		    =   DetrImageProcessor(format=_UpperCamelCase	)

	__lowerCAmelCase    :		Union[str, Any]		    =   processor(images=prepare_img() ,						return_tensors='pt'	)
	__lowerCAmelCase    :		Optional[int]		    =   encoding['pixel_values']

	__lowerCAmelCase    :		Optional[Any]		    =   detr(_UpperCamelCase	)
	__lowerCAmelCase    :		Union[str, Any]		    =   model(_UpperCamelCase	)

	assert torch.allclose(outputs.logits ,						original_outputs['pred_logits'] ,						atol=1e-3	)
	assert torch.allclose(outputs.pred_boxes ,						original_outputs['pred_boxes'] ,						atol=1e-3	)
	if is_panoptic:
		assert torch.allclose(outputs.pred_masks ,						original_outputs['pred_masks'] ,						atol=1e-4	)
	print('Looks ok!'	)

	if pytorch_dump_folder_path is not None:
		# Save model and image processor
		logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."	)
		Path(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
		model.save_pretrained(_UpperCamelCase	)
		processor.save_pretrained(_UpperCamelCase	)

	if push_to_hub:
		# Upload model and image processor to the hub
		logger.info('Uploading PyTorch model and image processor to the hub...'	)
		model.push_to_hub(F"nielsr/{model_name}"	)
		processor.push_to_hub(F"nielsr/{model_name}"	)


if __name__ == "__main__":
							lowerCamelCase__           =	argparse.ArgumentParser()

							parser.add_argument(
							    """--model_name""",
							    default="""detr-resnet-50""",
							    type=str,
							    choices=["""detr-resnet-50""", """detr-resnet-101"""],
							    help="""Name of the DETR model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
							)
							parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
							lowerCamelCase__           =	parser.parse_args()
							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)