
import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__a							  =						"""src/transformers"""

# Matches is_xxx_available()
__a							  =						re.compile(r"""is\_([a-z_]*)_available()""")
# Matches from xxx import bla
__a							  =						re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
__a							  =						re.compile(r"""^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)""")


__a							  =						"""
{0} = None
"""

__a							  =						"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


__a							  =						"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""

def    UpperCamelCase_ (	a_		)     ->List[Any]:
		if _re_test_backend.search(a_		) is None:
				return None
		A				=[b[0] for b in _re_backend.findall(a_		)]
		backends.sort()
		return "_and_".join(a_		)

def    UpperCamelCase_ (	)     ->Dict:
		with open(os.path.join(a_  ,				"__init__.py"		)  ,				"r"  ,				encoding="utf-8"  ,				newline="\n"		) as f:
				A				=f.readlines()

		# Get to the point we do the actual imports for type checking
		A				=0
		while not lines[line_index].startswith("if TYPE_CHECKING"		):
				line_index += 1

		A				={}
		# Go through the end of the file
		while line_index < len(a_		):
				# If the line is an if is_backend_available, we grab all objects associated.
				A				=find_backend(lines[line_index]		)
				if backend is not None:
						while not lines[line_index].startswith("    else:"		):
								line_index += 1
						line_index += 1

						A				=[]
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]		) <= 1 or lines[line_index].startswith(" " * 8		):
								A				=lines[line_index]
								A				=_re_single_line_import.search(a_		)
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(", "		)		)
								elif line.startswith(" " * 12		):
										objects.append(line[12:-2]		)
								line_index += 1

						A				=objects
				else:
						line_index += 1

		return backend_specific_objects

def    UpperCamelCase_ (	a_  ,				a_		)     ->str:
		if name.isupper():
				return DUMMY_CONSTANT.format(a_		)
		elif name.islower():
				return DUMMY_FUNCTION.format(a_  ,				a_		)
		else:
				return DUMMY_CLASS.format(a_  ,				a_		)

def    UpperCamelCase_ (	a_=None		)     ->List[str]:
		if backend_specific_objects is None:
				A				=read_init()
		# For special correspondence backend to module name as used in the function requires_modulename
		A				={}

		for backend, objects in backend_specific_objects.items():
				A				="[" + ", ".join(f'''"{b}"''' for b in backend.split("_and_"		)		) + "]"
				A				="# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
				dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
				dummy_file += "\n".join([create_dummy_object(a_  ,				a_		) for o in objects]		)
				A				=dummy_file

		return dummy_files

def    UpperCamelCase_ (	a_=False		)     ->List[Any]:
		A				=create_dummy_files()
		# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
		A				={"torch": "pt"}

		# Locate actual dummy modules and read their content.
		A				=os.path.join(a_  ,				"utils"		)
		A				={
		    backend: os.path.join(a_  ,				f'''dummy_{short_names.get(a_  ,				a_		)}_objects.py'''		)
		    for backend in dummy_files.keys()
		}

		A				={}
		for backend, file_path in dummy_file_paths.items():
				if os.path.isfile(a_		):
						with open(a_  ,				"r"  ,				encoding="utf-8"  ,				newline="\n"		) as f:
								A				=f.read()
				else:
						A				=""

		for backend in dummy_files.keys():
				if dummy_files[backend] != actual_dummies[backend]:
						if overwrite:
								print(
								    f'''Updating transformers.utils.dummy_{short_names.get(a_  ,				a_		)}_objects.py as the main '''
								    "__init__ has new objects."		)
								with open(dummy_file_paths[backend]  ,				"w"  ,				encoding="utf-8"  ,				newline="\n"		) as f:
										f.write(dummy_files[backend]		)
						else:
								raise ValueError(
								    "The main __init__ has objects that are not present in "
								    f'''transformers.utils.dummy_{short_names.get(a_  ,				a_		)}_objects.py. Run `make fix-copies` '''
								    "to fix this."		)


if __name__ == "__main__":
	__a							  =						argparse.ArgumentParser()
	parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
	__a							  =						parser.parse_args()

	check_dummies(args.fix_and_overwrite)






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

		return orig_key

def    UpperCamelCase_ (	a_		)     ->Dict:
		# transformer decoder self-attention layers
		for i in range(6		):
				# read in weights + bias of input projection layer of self-attention
				A				=state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''		)
				A				=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				=in_proj_weight[:256, :]
				A				=in_proj_bias[:256]
				A				=in_proj_weight[256:512, :]
				A				=in_proj_bias[256:512]
				A				=in_proj_weight[-256:, :]
				A				=in_proj_bias[-256:]

def    UpperCamelCase_ (	)     ->Any:
		A				="http://images.cocodataset.org/val2017/000000039769.jpg"
		A				=Image.open(requests.get(a_  ,				stream=a_		).raw		)

		return im

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

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

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

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

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

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

		A				=torch.tensor(
		    [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]		)
		A				=torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]		)

		if single_scale:
				A				=torch.tensor(
				    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]		)
				A				=torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]		)

		if single_scale and dilation:
				A				=torch.tensor(
				    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]		)
				A				=torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]		)

		if with_box_refine:
				A				=torch.tensor(
				    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]		)
				A				=torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]		)

		if with_box_refine and two_stage:
				A				=torch.tensor(
				    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]		)
				A				=torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]		)

		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:
				A				="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__":
	__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,
	)




