



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()
__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)




def 						UpperCAmelCase (    _lowerCamelCase     ):





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

			return orig_key




def 						UpperCAmelCase (    _lowerCamelCase     ):





			for i in range(6     ):
						# read in weights + bias of input projection layer of self-attention
						A				:       int														=    state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight"""     )
						A				:       Optional[Any]														=    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
						A				:       Optional[int]														=    in_proj_weight[:256, :]
						A				:       Optional[int]														=    in_proj_bias[:256]
						A				:       Optional[Any]														=    in_proj_weight[256:512, :]
						A				:       str														=    in_proj_bias[256:512]
						A				:       Dict														=    in_proj_weight[-256:, :]
						A				:       str														=    in_proj_bias[-256:]




def 						UpperCAmelCase (    ):
			A				:       Optional[Any]														=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			A				:       Any														=    Image.open(requests.get(_UpperCAmelCase			,     stream=_UpperCAmelCase     ).raw     )

			return im




@torch.no_grad()
def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     ):
			A				:       Optional[Any]														=    DeformableDetrConfig()
			# set config attributes
			if single_scale:
						A				:       List[str]														=    1
			A				:       Optional[int]														=    dilation
			A				:       Optional[int]														=    with_box_refine
			A				:       str														=    two_stage
			# set labels
			A				:       Union[str, Any]														=    91
			A				:       Tuple														=    '''huggingface/label-files'''
			A				:       List[Any]														=    '''coco-detection-id2label.json'''
			A				:       Dict														=    json.load(open(cached_download(hf_hub_url(_UpperCAmelCase			,     _UpperCAmelCase			,     repo_type="dataset"     )     )			,     "r"     )     )
			A				:       List[str]														=    {int(_UpperCAmelCase     ): v for k, v in idalabel.items()}
			A				:       str														=    idalabel
			A				:       Any														=    {v: k for k, v in idalabel.items()}

			# load image processor
			A				:       List[str]														=    DeformableDetrImageProcessor(format="coco_detection"     )

			# prepare image
			A				:       List[str]														=    prepare_img()
			A				:       Optional[Any]														=    image_processor(images=_UpperCAmelCase			,     return_tensors="pt"     )
			A				:       int														=    encoding['''pixel_values''']

			logger.info("Converting model..."     )

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

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

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

			# Push to hub
			if push_to_hub:
						A				:       int														=    '''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=_UpperCAmelCase			,     organization="nielsr"			,     commit_message="Add model"     )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE     =	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."""
							)
							__SCREAMING_SNAKE_CASE     =	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


__A       ={}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       =["BarthezTokenizer"]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       =["BarthezTokenizerFast"]


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

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_barthez_fast import BarthezTokenizerFast

else:
				import sys

				__A       =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





