



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()
UpperCamelCase_									=   logging.get_logger(__name__)







def       lowerCamelCase_  (    _a       :						List[str]    ):



    '''simple docstring'''




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

    return orig_key







def       lowerCamelCase_  (    _a       :						Optional[Any]    ):



    '''simple docstring'''




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







def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Dict  		=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    UpperCAmelCase_     :       Union[str, Any]  		=					Image.open(requests.get(_a   ,		stream=_a    ).raw    )

    return im







@torch.no_grad()
def       lowerCamelCase_  (    _a       :						Tuple   ,		_a       :						List[str]   ,		_a       :						Tuple   ,		_a       :						Optional[Any]   ,		_a       :						Tuple   ,		_a       :						List[str]   ,		_a       :						Union[str, Any]   ,		):



    '''simple docstring'''




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

    # load image processor
    UpperCAmelCase_     :       str  		=					DeformableDetrImageProcessor(format="""coco_detection"""    )

    # prepare image
    UpperCAmelCase_     :       Tuple  		=					prepare_img()
    UpperCAmelCase_     :       Tuple  		=					image_processor(images=_a   ,		return_tensors="""pt"""    )
    UpperCAmelCase_     :       Union[str, Any]  		=					encoding["""pixel_values"""]

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

    # load original state dict
    UpperCAmelCase_     :       List[str]  		=					torch.load(_a   ,		map_location="""cpu"""    )["""model"""]
    # rename keys
    for key in state_dict.copy().keys():
        UpperCAmelCase_     :       int  		=					state_dict.pop(_a    )
        UpperCAmelCase_     :       List[str]  		=					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
    UpperCAmelCase_     :       Dict  		=					"""model."""
    for key in state_dict.copy().keys():
        if not key.startswith("""class_embed"""    ) and not key.startswith("""bbox_embed"""    ):
            UpperCAmelCase_     :       str  		=					state_dict.pop(_a    )
            UpperCAmelCase_     :       List[str]  		=					val
    # finally, create HuggingFace model and load state dict
    UpperCAmelCase_     :       Union[str, Any]  		=					DeformableDetrForObjectDetection(_a    )
    model.load_state_dict(_a    )
    model.eval()

    UpperCAmelCase_     :       int  		=					"""cuda""" if torch.cuda.is_available() else """cpu"""
    model.to(_a    )
    # verify our conversion
    UpperCAmelCase_     :       Dict  		=					model(pixel_values.to(_a    )    )

    UpperCAmelCase_     :       List[Any]  		=					torch.tensor(
        [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]    )
    UpperCAmelCase_     :       Tuple  		=					torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]    )

    if single_scale:
        UpperCAmelCase_     :       Tuple  		=					torch.tensor(
            [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]    )
        UpperCAmelCase_     :       Optional[Any]  		=					torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]    )

    if single_scale and dilation:
        UpperCAmelCase_     :       List[Any]  		=					torch.tensor(
            [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]    )
        UpperCAmelCase_     :       Optional[Any]  		=					torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]    )

    if with_box_refine:
        UpperCAmelCase_     :       List[Any]  		=					torch.tensor(
            [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]    )
        UpperCAmelCase_     :       Optional[int]  		=					torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]    )

    if with_box_refine and two_stage:
        UpperCAmelCase_     :       Any  		=					torch.tensor(
            [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]    )
        UpperCAmelCase_     :       List[str]  		=					torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]    )

    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:
        UpperCAmelCase_     :       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=_a   ,		organization="""nielsr"""   ,		commit_message="""Add model"""    )


if __name__ == "__main__":
      UpperCamelCase_									=   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.'''
      )
      UpperCamelCase_									=   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,
      )








import numpy as np


class      __SCREAMING_SNAKE_CASE       :






						def __init__(						self  ,    _UpperCamelCase=5  ,    _UpperCamelCase=5 ):






												"""simple docstring"""
												lowerCAmelCase__         =    depth
												lowerCAmelCase__         =    0
												lowerCAmelCase__         =    None
												lowerCAmelCase__         =    None
												lowerCAmelCase__         =    min_leaf_size
												lowerCAmelCase__         =    None






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												if labels.ndim != 1:
																		print('Error: Input labels must be one dimensional' )

												return np.mean((labels - prediction) ** 2 )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												if x.ndim != 1:
																		print('Error: Input data set must be one dimensional' )
																		return
												if len(_UpperCamelCase ) != len(_UpperCamelCase ):
																		print('Error: X and y have different lengths' )
																		return
												if y.ndim != 1:
																		print('Error: Data set labels must be one dimensional' )
																		return

												if len(_UpperCamelCase ) < 2 * self.min_leaf_size:
																		lowerCAmelCase__         =    np.mean(_UpperCamelCase )
																		return

												if self.depth == 1:
																		lowerCAmelCase__         =    np.mean(_UpperCamelCase )
																		return

												lowerCAmelCase__         =    0
												lowerCAmelCase__         =    self.mean_squared_error(_UpperCamelCase  ,    np.mean(_UpperCamelCase ) ) * 2
												for i in range(len(_UpperCamelCase ) ):
																		if len(x[:i] ) < self.min_leaf_size:
																								continue
																		elif len(x[i:] ) < self.min_leaf_size:
																								continue
																		else:
																								lowerCAmelCase__         =    self.mean_squared_error(x[:i]  ,    np.mean(y[:i] ) )
																								lowerCAmelCase__         =    self.mean_squared_error(x[i:]  ,    np.mean(y[i:] ) )
																								lowerCAmelCase__         =    error_left + error_right
																								if error < min_error:
																														lowerCAmelCase__         =    i
																														lowerCAmelCase__         =    error

												if best_split != 0:
																		lowerCAmelCase__         =    x[:best_split]
																		lowerCAmelCase__         =    y[:best_split]
																		lowerCAmelCase__         =    x[best_split:]
																		lowerCAmelCase__         =    y[best_split:]

																		lowerCAmelCase__         =    x[best_split]
																		lowerCAmelCase__         =    DecisionTree(
																		    depth=self.depth - 1  ,    min_leaf_size=self.min_leaf_size )
																		lowerCAmelCase__         =    DecisionTree(
																		    depth=self.depth - 1  ,    min_leaf_size=self.min_leaf_size )
																		self.left.train(_UpperCamelCase  ,    _UpperCamelCase )
																		self.right.train(_UpperCamelCase  ,    _UpperCamelCase )
												else:
																		lowerCAmelCase__         =    np.mean(_UpperCamelCase )

												return







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												if self.prediction is not None:
																		return self.prediction
												elif self.left or self.right is not None:
																		if x >= self.decision_boundary:
																								return self.right.predict(_UpperCamelCase )
																		else:
																								return self.left.predict(_UpperCamelCase )
												else:
																		print('Error: Decision tree not yet trained' )
																		return None




class      __SCREAMING_SNAKE_CASE       :






						@staticmethod
						def    UpperCamelCase__   (						_UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    float(0 )
												for label in labels:
																		squared_error_sum += (label - prediction) ** 2

												return float(squared_error_sum / labels.size )







def 						_UpperCamelCase (     )	-> List[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    np.arange(-1.0     ,							1.0     ,							0.005		)
						lowerCAmelCase__         =    np.sin(UpperCamelCase_		)

						lowerCAmelCase__         =    DecisionTree(depth=10     ,							min_leaf_size=10		)
						tree.train(UpperCamelCase_     ,							UpperCamelCase_		)

						lowerCAmelCase__         =    (np.random.rand(10		) * 2) - 1
						lowerCAmelCase__         =    np.array([tree.predict(UpperCamelCase_		) for x in test_cases]		)
						lowerCAmelCase__         =    np.mean((predictions - test_cases) ** 2		)

						print('Test values: ' + str(UpperCamelCase_		)		)
						print('Predictions: ' + str(UpperCamelCase_		)		)
						print('Average error: ' + str(UpperCamelCase_		)		)


if __name__ == "__main__":
					main()
					import doctest

					doctest.testmod(name="""mean_squarred_error""", verbose=True)





