
"""simple docstring"""


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 					_lowerCamelCase(__UpperCamelCase )       ->				Optional[int]:
	if "backbone.0.body" in orig_key:
		_lowerCAmelCase			=orig_key.replace("""backbone.0.body"""					,				"""backbone.conv_encoder.model""" )
	if "transformer" in orig_key:
		_lowerCAmelCase			=orig_key.replace("""transformer."""					,				"""""" )
	if "norm1" in orig_key:
		if "encoder" in orig_key:
			_lowerCAmelCase			=orig_key.replace("""norm1"""					,				"""self_attn_layer_norm""" )
		else:
			_lowerCAmelCase			=orig_key.replace("""norm1"""					,				"""encoder_attn_layer_norm""" )
	if "norm2" in orig_key:
		if "encoder" in orig_key:
			_lowerCAmelCase			=orig_key.replace("""norm2"""					,				"""final_layer_norm""" )
		else:
			_lowerCAmelCase			=orig_key.replace("""norm2"""					,				"""self_attn_layer_norm""" )
	if "norm3" in orig_key:
		_lowerCAmelCase			=orig_key.replace("""norm3"""					,				"""final_layer_norm""" )
	if "linear1" in orig_key:
		_lowerCAmelCase			=orig_key.replace("""linear1"""					,				"""fc1""" )
	if "linear2" in orig_key:
		_lowerCAmelCase			=orig_key.replace("""linear2"""					,				"""fc2""" )
	if "query_embed" in orig_key:
		_lowerCAmelCase			=orig_key.replace("""query_embed"""					,				"""query_position_embeddings""" )
	if "cross_attn" in orig_key:
		_lowerCAmelCase			=orig_key.replace("""cross_attn"""					,				"""encoder_attn""" )

	return orig_key



def 					_lowerCamelCase(__UpperCamelCase )       ->				Union[str, Any]:
	# transformer decoder self-attention layers
	for i in range(6 ):
		# read in weights + bias of input projection layer of self-attention
		_lowerCAmelCase			=state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight''' )
		_lowerCAmelCase			=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
		_lowerCAmelCase			=in_proj_weight[:256, :]
		_lowerCAmelCase			=in_proj_bias[:256]
		_lowerCAmelCase			=in_proj_weight[256:512, :]
		_lowerCAmelCase			=in_proj_bias[256:512]
		_lowerCAmelCase			=in_proj_weight[-256:, :]
		_lowerCAmelCase			=in_proj_bias[-256:]



def 					_lowerCamelCase()       ->				Tuple:
	_lowerCAmelCase			="http://images.cocodataset.org/val2017/000000039769.jpg"
	_lowerCAmelCase			=Image.open(requests.get(__UpperCamelCase					,				stream=__UpperCamelCase ).raw )

	return im



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

	# load image processor
	_lowerCAmelCase			=DeformableDetrImageProcessor(format="""coco_detection""" )

	# prepare image
	_lowerCAmelCase			=prepare_img()
	_lowerCAmelCase			=image_processor(images=__UpperCamelCase					,				return_tensors="""pt""" )
	_lowerCAmelCase			=encoding["pixel_values"]

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

	# load original state dict
	_lowerCAmelCase			=torch.load(__UpperCamelCase					,				map_location="""cpu""" )["model"]
	# rename keys
	for key in state_dict.copy().keys():
		_lowerCAmelCase			=state_dict.pop(__UpperCamelCase )
		_lowerCAmelCase			=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
	_lowerCAmelCase			="model."
	for key in state_dict.copy().keys():
		if not key.startswith("""class_embed""" ) and not key.startswith("""bbox_embed""" ):
			_lowerCAmelCase			=state_dict.pop(__UpperCamelCase )
			_lowerCAmelCase			=val
    # finally, create HuggingFace model and load state dict
	_lowerCAmelCase			=DeformableDetrForObjectDetection(__UpperCamelCase )
	model.load_state_dict(__UpperCamelCase )
	model.eval()

	_lowerCAmelCase			="cuda" if torch.cuda.is_available() else "cpu"
	model.to(__UpperCamelCase )
	# verify our conversion
	_lowerCAmelCase			=model(pixel_values.to(__UpperCamelCase ) )

	_lowerCAmelCase			=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]] )
	_lowerCAmelCase			=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:
		_lowerCAmelCase			=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]] )
		_lowerCAmelCase			=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:
		_lowerCAmelCase			=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]] )
		_lowerCAmelCase			=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:
		_lowerCAmelCase			=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]] )
		_lowerCAmelCase			=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:
		_lowerCAmelCase			=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]] )
		_lowerCAmelCase			=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:
		_lowerCAmelCase			="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__":
			__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,
			)





"""simple docstring"""


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A 		= {
    'configuration_convnextv2': [
        'CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConvNextV2Config',
    ]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= [
			    'CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'ConvNextV2ForImageClassification',
			    'ConvNextV2Model',
			    'ConvNextV2PreTrainedModel',
			    'ConvNextV2Backbone',
			]


if TYPE_CHECKING:
			from .configuration_convnextva import (
			    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    ConvNextVaConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_convnextva import (
						    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ConvNextVaBackbone,
						    ConvNextVaForImageClassification,
						    ConvNextVaModel,
						    ConvNextVaPreTrainedModel,
						)

else:
			import sys

			__A 		= _LazyModule(__name__, globals()['__file__'], _import_structure)



