


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()
lowerCAmelCase__       :	Optional[Any]     				=logging.get_logger(__name__)






def 		a__       (							A__							):
			if "backbone.0.body" in orig_key:
						SCREAMING_SNAKE_CASE_						:			List[Any]       						= orig_key.replace('backbone.0.body',	'backbone.conv_encoder.model'							)
			if "transformer" in orig_key:
						SCREAMING_SNAKE_CASE_						:			Optional[int]       						= orig_key.replace('transformer.',	''							)
			if "norm1" in orig_key:
						if "encoder" in orig_key:
									SCREAMING_SNAKE_CASE_						:			Optional[int]       						= orig_key.replace('norm1',	'self_attn_layer_norm'							)
						else:
									SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= orig_key.replace('norm1',	'encoder_attn_layer_norm'							)
			if "norm2" in orig_key:
						if "encoder" in orig_key:
									SCREAMING_SNAKE_CASE_						:			List[Any]       						= orig_key.replace('norm2',	'final_layer_norm'							)
						else:
									SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= orig_key.replace('norm2',	'self_attn_layer_norm'							)
			if "norm3" in orig_key:
						SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= orig_key.replace('norm3',	'final_layer_norm'							)
			if "linear1" in orig_key:
						SCREAMING_SNAKE_CASE_						:			List[Any]       						= orig_key.replace('linear1',	'fc1'							)
			if "linear2" in orig_key:
						SCREAMING_SNAKE_CASE_						:			int       						= orig_key.replace('linear2',	'fc2'							)
			if "query_embed" in orig_key:
						SCREAMING_SNAKE_CASE_						:			Any       						= orig_key.replace('query_embed',	'query_position_embeddings'							)
			if "cross_attn" in orig_key:
						SCREAMING_SNAKE_CASE_						:			int       						= orig_key.replace('cross_attn',	'encoder_attn'							)

			return orig_key






def 		a__       (							A__							):
			# transformer decoder self-attention layers
			for i in range(6							):
						# read in weights + bias of input projection layer of self-attention
						SCREAMING_SNAKE_CASE_						:			Any       						= state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''							)
						SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 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
						SCREAMING_SNAKE_CASE_						:			Optional[int]       						= in_proj_weight[:2_5_6, :]
						SCREAMING_SNAKE_CASE_						:			List[str]       						= in_proj_bias[:2_5_6]
						SCREAMING_SNAKE_CASE_						:			Tuple       						= in_proj_weight[2_5_6:5_1_2, :]
						SCREAMING_SNAKE_CASE_						:			List[str]       						= in_proj_bias[2_5_6:5_1_2]
						SCREAMING_SNAKE_CASE_						:			Optional[int]       						= in_proj_weight[-2_5_6:, :]
						SCREAMING_SNAKE_CASE_						:			Dict       						= in_proj_bias[-2_5_6:]






def 		a__       (							):
			SCREAMING_SNAKE_CASE_						:			Any       						= 'http://images.cocodataset.org/val2017/000000039769.jpg'
			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= Image.open(requests.get(A__,	stream=A__							).raw							)

			return im






@torch.no_grad()
def 		a__       (							A__,	A__,	A__,	A__,	A__,	A__,	A__,	):
			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= DeformableDetrConfig()
			# set config attributes
			if single_scale:
						SCREAMING_SNAKE_CASE_						:			str       						= 1
			SCREAMING_SNAKE_CASE_						:			Tuple       						= dilation
			SCREAMING_SNAKE_CASE_						:			List[Any]       						= with_box_refine
			SCREAMING_SNAKE_CASE_						:			int       						= two_stage
			# set labels
			SCREAMING_SNAKE_CASE_						:			Dict       						= 9_1
			SCREAMING_SNAKE_CASE_						:			Any       						= 'huggingface/label-files'
			SCREAMING_SNAKE_CASE_						:			str       						= 'coco-detection-id2label.json'
			SCREAMING_SNAKE_CASE_						:			Tuple       						= json.load(open(cached_download(hf_hub_url(A__,	A__,	repo_type='dataset'							)							),	'r'							)							)
			SCREAMING_SNAKE_CASE_						:			List[Any]       						= {int(A__							): v for k, v in idalabel.items()}
			SCREAMING_SNAKE_CASE_						:			str       						= idalabel
			SCREAMING_SNAKE_CASE_						:			int       						= {v: k for k, v in idalabel.items()}

			# load image processor
			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= DeformableDetrImageProcessor(format='coco_detection'							)

			# prepare image
			SCREAMING_SNAKE_CASE_						:			str       						= prepare_img()
			SCREAMING_SNAKE_CASE_						:			int       						= image_processor(images=A__,	return_tensors='pt'							)
			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= encoding['pixel_values']

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

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

			SCREAMING_SNAKE_CASE_						:			Any       						= 'cuda' if torch.cuda.is_available() else 'cpu'
			model.to(A__							)
			# verify our conversion
			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= model(pixel_values.to(A__							)							)

			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 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]]							)
			SCREAMING_SNAKE_CASE_						:			List[str]       						= 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:
						SCREAMING_SNAKE_CASE_						:			List[Any]       						= 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]]							)
						SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 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:
						SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= 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]]							)
						SCREAMING_SNAKE_CASE_						:			Tuple       						= 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:
						SCREAMING_SNAKE_CASE_						:			List[str]       						= 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]]							)
						SCREAMING_SNAKE_CASE_						:			Tuple       						= 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:
						SCREAMING_SNAKE_CASE_						:			int       						= 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]]							)
						SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= 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(A__							),	atol=1E-4							)
			assert torch.allclose(outputs.pred_boxes[0, :3, :3],	expected_boxes.to(A__							),	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(A__							).mkdir(exist_ok=A__							)
			model.save_pretrained(A__							)
			image_processor.save_pretrained(A__							)

			# Push to hub
			if push_to_hub:
						SCREAMING_SNAKE_CASE_						:			Dict       						= '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=A__,	organization='nielsr',	commit_message='Add model'							)


if __name__ == "__main__":
				lowerCAmelCase__       :	Tuple     				=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.'
				)
				lowerCAmelCase__       :	Dict     				=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_sentencepiece_available, is_tokenizers_available


lowerCAmelCase__       :	List[Any]     				={}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__       :	Optional[Any]     				=['MBart50Tokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__       :	Any     				=['MBart50TokenizerFast']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa import MBartaaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
				import sys

				lowerCAmelCase__       :	Optional[int]     				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





