






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_a   			=						logging.get_logger(__name__)


def __A					(   __lowerCAmelCase			)->						str:





						"""simple docstring"""



						if "backbone.0.body" in orig_key:
												_UpperCAmelCase             =    orig_key.replace('backbone.0.body'				,	'backbone.conv_encoder.model'			)
						if "transformer" in orig_key:
												_UpperCAmelCase             =    orig_key.replace('transformer.'				,	''			)
						if "norm1" in orig_key:
												if "encoder" in orig_key:
																		_UpperCAmelCase             =    orig_key.replace('norm1'				,	'self_attn_layer_norm'			)
												else:
																		_UpperCAmelCase             =    orig_key.replace('norm1'				,	'encoder_attn_layer_norm'			)
						if "norm2" in orig_key:
												if "encoder" in orig_key:
																		_UpperCAmelCase             =    orig_key.replace('norm2'				,	'final_layer_norm'			)
												else:
																		_UpperCAmelCase             =    orig_key.replace('norm2'				,	'self_attn_layer_norm'			)
						if "norm3" in orig_key:
												_UpperCAmelCase             =    orig_key.replace('norm3'				,	'final_layer_norm'			)
						if "linear1" in orig_key:
												_UpperCAmelCase             =    orig_key.replace('linear1'				,	'fc1'			)
						if "linear2" in orig_key:
												_UpperCAmelCase             =    orig_key.replace('linear2'				,	'fc2'			)
						if "query_embed" in orig_key:
												_UpperCAmelCase             =    orig_key.replace('query_embed'				,	'query_position_embeddings'			)
						if "cross_attn" in orig_key:
												_UpperCAmelCase             =    orig_key.replace('cross_attn'				,	'encoder_attn'			)

						return orig_key


def __A					(   __lowerCAmelCase			)->						Dict:





						"""simple docstring"""



						for i in range(6			):
												# read in weights + bias of input projection layer of self-attention
												_UpperCAmelCase             =    state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""			)
												_UpperCAmelCase             =    state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_bias"""			)
												# next, add query, keys and values (in that order) to the state dict
												_UpperCAmelCase             =    in_proj_weight[:256, :]
												_UpperCAmelCase             =    in_proj_bias[:256]
												_UpperCAmelCase             =    in_proj_weight[256:512, :]
												_UpperCAmelCase             =    in_proj_bias[256:512]
												_UpperCAmelCase             =    in_proj_weight[-256:, :]
												_UpperCAmelCase             =    in_proj_bias[-256:]


def __A					(   )->						List[str]:





						"""simple docstring"""



						_UpperCAmelCase             =    'http://images.cocodataset.org/val2017/000000039769.jpg'
						_UpperCAmelCase             =    Image.open(requests.get(__lowerCAmelCase				,	stream=__lowerCAmelCase			).raw			)

						return im


@torch.no_grad()
def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	)->						List[str]:





						"""simple docstring"""



						_UpperCAmelCase             =    DeformableDetrConfig()
						# set config attributes
						if single_scale:
												_UpperCAmelCase             =    1
						_UpperCAmelCase             =    dilation
						_UpperCAmelCase             =    with_box_refine
						_UpperCAmelCase             =    two_stage
						# set labels
						_UpperCAmelCase             =    91
						_UpperCAmelCase             =    'huggingface/label-files'
						_UpperCAmelCase             =    'coco-detection-id2label.json'
						_UpperCAmelCase             =    json.load(open(cached_download(hf_hub_url(__lowerCAmelCase				,	__lowerCAmelCase				,	repo_type='dataset'			)			)				,	'r'			)			)
						_UpperCAmelCase             =    {int(__lowerCAmelCase			): v for k, v in idalabel.items()}
						_UpperCAmelCase             =    idalabel
						_UpperCAmelCase             =    {v: k for k, v in idalabel.items()}

						# load image processor
						_UpperCAmelCase             =    DeformableDetrImageProcessor(format='coco_detection'			)

						# prepare image
						_UpperCAmelCase             =    prepare_img()
						_UpperCAmelCase             =    image_processor(images=__lowerCAmelCase				,	return_tensors='pt'			)
						_UpperCAmelCase             =    encoding['pixel_values']

						logger.info('Converting model...'			)

						# load original state dict
						_UpperCAmelCase             =    torch.load(__lowerCAmelCase				,	map_location='cpu'			)['model']
						# rename keys
						for key in state_dict.copy().keys():
												_UpperCAmelCase             =    state_dict.pop(__lowerCAmelCase			)
												_UpperCAmelCase             =    val
						# query, key and value matrices need special treatment
						read_in_q_k_v(__lowerCAmelCase			)
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						_UpperCAmelCase             =    'model.'
						for key in state_dict.copy().keys():
												if not key.startswith('class_embed'			) and not key.startswith('bbox_embed'			):
																		_UpperCAmelCase             =    state_dict.pop(__lowerCAmelCase			)
																		_UpperCAmelCase             =    val
    # finally, create HuggingFace model and load state dict
						_UpperCAmelCase             =    DeformableDetrForObjectDetection(__lowerCAmelCase			)
						model.load_state_dict(__lowerCAmelCase			)
						model.eval()

						_UpperCAmelCase             =    'cuda' if torch.cuda.is_available() else 'cpu'
						model.to(__lowerCAmelCase			)
						# verify our conversion
						_UpperCAmelCase             =    model(pixel_values.to(__lowerCAmelCase			)			)

						_UpperCAmelCase             =    torch.tensor(
						    [[-9.66_45, -4.34_49, -5.87_05], [-9.70_35, -3.85_04, -5.07_24], [-10.56_34, -5.33_79, -7.51_16]]			)
						_UpperCAmelCase             =    torch.tensor([[0.86_93, 0.22_89, 0.24_92], [0.31_50, 0.54_89, 0.58_45], [0.55_63, 0.75_80, 0.85_18]]			)

						if single_scale:
												_UpperCAmelCase             =    torch.tensor(
												    [[-9.90_51, -4.25_41, -6.48_52], [-9.69_47, -4.08_54, -6.80_33], [-10.06_65, -5.84_70, -7.70_03]]			)
												_UpperCAmelCase             =    torch.tensor([[0.72_92, 0.49_91, 0.55_32], [0.79_59, 0.24_26, 0.42_36], [0.75_82, 0.35_18, 0.44_51]]			)

						if single_scale and dilation:
												_UpperCAmelCase             =    torch.tensor(
												    [[-8.96_52, -4.10_74, -5.66_35], [-9.05_96, -4.94_47, -6.60_75], [-10.11_78, -4.52_75, -6.26_71]]			)
												_UpperCAmelCase             =    torch.tensor([[0.76_65, 0.41_30, 0.47_69], [0.83_64, 0.18_41, 0.33_91], [0.62_61, 0.38_95, 0.79_78]]			)

						if with_box_refine:
												_UpperCAmelCase             =    torch.tensor(
												    [[-8.88_95, -5.41_87, -6.81_53], [-8.47_06, -6.16_68, -7.61_84], [-9.00_42, -5.53_59, -6.91_41]]			)
												_UpperCAmelCase             =    torch.tensor([[0.78_28, 0.22_08, 0.43_23], [0.08_92, 0.59_96, 0.13_19], [0.55_24, 0.63_89, 0.89_14]]			)

						if with_box_refine and two_stage:
												_UpperCAmelCase             =    torch.tensor(
												    [[-6.71_08, -4.32_13, -6.37_77], [-8.90_14, -6.17_99, -6.72_40], [-6.93_15, -4.47_35, -6.22_98]]			)
												_UpperCAmelCase             =    torch.tensor([[0.25_83, 0.54_99, 0.46_83], [0.76_52, 0.90_68, 0.48_82], [0.54_90, 0.27_63, 0.05_64]]			)

						print('Logits:'				,	outputs.logits[0, :3, :3]			)

						assert torch.allclose(outputs.logits[0, :3, :3]				,	expected_logits.to(__lowerCAmelCase			)				,	atol=1E-4			)
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]				,	expected_boxes.to(__lowerCAmelCase			)				,	atol=1E-4			)

						print('Everything ok!'			)

						# Save model and image processor
						logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""			)
						Path(__lowerCAmelCase			).mkdir(exist_ok=__lowerCAmelCase			)
						model.save_pretrained(__lowerCAmelCase			)
						image_processor.save_pretrained(__lowerCAmelCase			)

						# Push to hub
						if push_to_hub:
												_UpperCAmelCase             =    'deformable-detr'
												model_name += "-single-scale" if single_scale else ""
												model_name += "-dc5" if dilation else ""
												model_name += "-with-box-refine" if with_box_refine else ""
												model_name += "-two-stage" if two_stage else ""
												print('Pushing model to hub...'			)
												model.push_to_hub(repo_path_or_name=__lowerCAmelCase				,	organization='nielsr'				,	commit_message='Add model'			)


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

		parser.add_argument(
		    '''--checkpoint_path''',
		    type=str,
		    default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
		    help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
		)
		parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
		parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
		parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
		parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path to the folder to output PyTorch model.''',
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
		)
		_a   			=						parser.parse_args()
		convert_deformable_detr_checkpoint(
		    args.checkpoint_path,
		    args.single_scale,
		    args.dilation,
		    args.with_box_refine,
		    args.two_stage,
		    args.pytorch_dump_folder_path,
		    args.push_to_hub,
		)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a   			=						{'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a   			=						[
		    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''QDQBertForMaskedLM''',
		    '''QDQBertForMultipleChoice''',
		    '''QDQBertForNextSentencePrediction''',
		    '''QDQBertForQuestionAnswering''',
		    '''QDQBertForSequenceClassification''',
		    '''QDQBertForTokenClassification''',
		    '''QDQBertLayer''',
		    '''QDQBertLMHeadModel''',
		    '''QDQBertModel''',
		    '''QDQBertPreTrainedModel''',
		    '''load_tf_weights_in_qdqbert''',
		]


if TYPE_CHECKING:
		from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_qdqbert import (
				    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    QDQBertForMaskedLM,
				    QDQBertForMultipleChoice,
				    QDQBertForNextSentencePrediction,
				    QDQBertForQuestionAnswering,
				    QDQBertForSequenceClassification,
				    QDQBertForTokenClassification,
				    QDQBertLayer,
				    QDQBertLMHeadModel,
				    QDQBertModel,
				    QDQBertPreTrainedModel,
				    load_tf_weights_in_qdqbert,
				)


else:
		import sys

		_a   			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
