
"""simple docstring"""
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from pathlib import Path

from packaging import version

from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
from ..utils import logging
from ..utils.import_utils import is_optimum_available
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import get_preprocessor


_a   :      Optional[Any]       =				"""1.5.0"""

_a   :      Optional[Any]       =				["""vision-encoder-decoder"""]
def 					a__ ( a :					Any		):

  """simple docstring"""







  if is_optimum_available():
    from optimum.version import __version__ as optimum_version

    _snake_case  :    Dict            = version.parse(a		)
    if parsed_optimum_version < version.parse(a		):
      raise RuntimeError(
          f'transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You '
          "can upgrade optimum by running: pip install -U optimum[exporters]"		)
  else:
    raise RuntimeError(
        "transformers.onnx requires optimum to run, you can install the library by running: pip install "
        "optimum[exporters]"		)
  _snake_case  :    str            = [
      sys.executable,
      "-m",
      "optimum.exporters.onnx",
      f'--model {args.model}',
      f'--task {args.feature}',
      f'--framework {args.framework}' if args.framework is not None else "",
      f'{args.output}',
  ]
  _snake_case  :    str            = subprocess.Popen(" ".join(a		)							,  stdout=subprocess.PIPE							,  shell=a		)
  proc.wait()

  logger.info(
      "The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as "
      "transformers.onnx is deprecated, and will be removed in v5. You can find more information here: "
      "https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."		)
def 					a__ ( a :					Dict		):

  """simple docstring"""







  _snake_case  :    Tuple            = args.output if args.output.is_file() else args.output.joinpath("model.onnx"		)
  if not args.output.parent.exists():
    args.output.parent.mkdir(parents=a		)

  # Allocate the model
  _snake_case  :    Optional[Any]            = FeaturesManager.get_model_from_feature(
      args.feature							,  args.model							,  framework=args.framework							,  cache_dir=args.cache_dir		)

  _snake_case      ,       _snake_case  :    Optional[Any]            = FeaturesManager.check_supported_model_or_raise(a							,  feature=args.feature		)
  _snake_case  :    Union[str, Any]            = model_onnx_config(model.config		)

  if model_kind in ENCODER_DECODER_MODELS:
    _snake_case  :    Optional[Any]            = model.get_encoder()
    _snake_case  :    Dict            = model.get_decoder()

    _snake_case  :    Any            = onnx_config.get_encoder_config(encoder_model.config		)
    _snake_case  :    str            = onnx_config.get_decoder_config(
        encoder_model.config							,  decoder_model.config							,  feature=args.feature		)

    if args.opset is None:
      _snake_case  :    str            = max(encoder_onnx_config.default_onnx_opset							,  decoder_onnx_config.default_onnx_opset		)

    if args.opset < min(encoder_onnx_config.default_onnx_opset							,  decoder_onnx_config.default_onnx_opset		):
      raise ValueError(
          f'Opset {args.opset} is not sufficient to export {model_kind}. At least '
          f' {min(encoder_onnx_config.default_onnx_opset							,  decoder_onnx_config.default_onnx_opset		)} is required.'		)

    _snake_case  :    Any            = AutoFeatureExtractor.from_pretrained(args.model		)

    _snake_case      ,       _snake_case  :    str            = export(
        a							,  a							,  a							,  args.opset							,  args.output.parent.joinpath("encoder_model.onnx"		)							,  )

    validate_model_outputs(
        a							,  a							,  a							,  args.output.parent.joinpath("encoder_model.onnx"		)							,  a							,  args.atol if args.atol else encoder_onnx_config.atol_for_validation							,  )

    _snake_case  :    str            = AutoTokenizer.from_pretrained(args.model		)

    _snake_case      ,       _snake_case  :    List[Any]            = export(
        a							,  a							,  a							,  args.opset							,  args.output.parent.joinpath("decoder_model.onnx"		)							,  )

    validate_model_outputs(
        a							,  a							,  a							,  args.output.parent.joinpath("decoder_model.onnx"		)							,  a							,  args.atol if args.atol else decoder_onnx_config.atol_for_validation							,  )
    logger.info(
        f'All good, model saved at: {args.output.parent.joinpath("encoder_model.onnx"		).as_posix()},'
        f' {args.output.parent.joinpath("decoder_model.onnx"		).as_posix()}'		)

  else:
    # Instantiate the appropriate preprocessor
    if args.preprocessor == "auto":
      _snake_case  :    int            = get_preprocessor(args.model		)
    elif args.preprocessor == "tokenizer":
      _snake_case  :    str            = AutoTokenizer.from_pretrained(args.model		)
    elif args.preprocessor == "image_processor":
      _snake_case  :    Dict            = AutoImageProcessor.from_pretrained(args.model		)
    elif args.preprocessor == "feature_extractor":
      _snake_case  :    Optional[Any]            = AutoFeatureExtractor.from_pretrained(args.model		)
    elif args.preprocessor == "processor":
      _snake_case  :    int            = AutoProcessor.from_pretrained(args.model		)
    else:
      raise ValueError(f'Unknown preprocessor type \'{args.preprocessor}\''		)

    # Ensure the requested opset is sufficient
    if args.opset is None:
      _snake_case  :    List[str]            = onnx_config.default_onnx_opset

    if args.opset < onnx_config.default_onnx_opset:
      raise ValueError(
          f'Opset {args.opset} is not sufficient to export {model_kind}. '
          f'At least  {onnx_config.default_onnx_opset} is required.'		)

    _snake_case      ,       _snake_case  :    Tuple            = export(
        a							,  a							,  a							,  args.opset							,  args.output							,  )

    if args.atol is None:
      _snake_case  :    Optional[Any]            = onnx_config.atol_for_validation

    validate_model_outputs(a							,  a							,  a							,  args.output							,  a							,  args.atol		)
    logger.info(f'All good, model saved at: {args.output.as_posix()}'		)
    warnings.warn(
        "The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend"
        " using optimum.exporters.onnx in future. You can find more information here:"
        " https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."							,  a							,  )
def 					a__ ( ):

  """simple docstring"""







  _snake_case  :    List[Any]            = ArgumentParser("Hugging Face Transformers ONNX exporter"		)
  parser.add_argument(
      "-m"							,  "--model"							,  type=a							,  required=a							,  help="Model ID on huggingface.co or path on disk to load model from."		)
  parser.add_argument(
      "--feature"							,  default="default"							,  help="The type of features to export the model with."							,  )
  parser.add_argument("--opset"							,  type=a							,  default=a							,  help="ONNX opset version to export the model with."		)
  parser.add_argument(
      "--atol"							,  type=a							,  default=a							,  help="Absolute difference tolerance when validating the model."		)
  parser.add_argument(
      "--framework"							,  type=a							,  choices=["pt", "tf"]							,  default=a							,  help=(
          "The framework to use for the ONNX export."
          " If not provided, will attempt to use the local checkpoint's original framework"
          " or what is available in the environment."
      )							,  )
  parser.add_argument("output"							,  type=a							,  help="Path indicating where to store generated ONNX model."		)
  parser.add_argument("--cache_dir"							,  type=a							,  default=a							,  help="Path indicating where to store cache."		)
  parser.add_argument(
      "--preprocessor"							,  type=a							,  choices=["auto", "tokenizer", "feature_extractor", "image_processor", "processor"]							,  default="auto"							,  help="Which type of preprocessor to use. 'auto' tries to automatically detect it."							,  )
  parser.add_argument(
      "--export_with_transformers"							,  action="store_true"							,  help=(
          "Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be "
          "useful when exporting a model supported in transformers but not in optimum, otherwise it is not "
          "recommended."
      )							,  )

  _snake_case  :    str            = parser.parse_args()
  if args.export_with_transformers or not is_optimum_available():
    export_with_transformers(a		)
  else:
    export_with_optimum(a		)


if __name__ == "__main__":
    _a   :      int       =				logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
    logger.setLevel(logging.INFO)
    main()



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,
	)




