



"""simple docstring"""






from sklearn.metrics import mean_squared_error

import datasets


lowerCAmelCase__      =    '''\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
'''

lowerCAmelCase__      =    '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


lowerCAmelCase__      =    '''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        "raw_values" : Returns a full set of errors in case of multioutput input.

        "uniform_average" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric("mse")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {\'mse\': 0.6123724356957945}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric("mse", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mse\': array([0.41666667, 1. ])}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,					_KWARGS_DESCRIPTION)
class 	__snake_case	(     datasets.Metric):




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






         """simple docstring"""






         return datasets.MetricInfo(
             description=_DESCRIPTION						, citation=_CITATION						, inputs_description=_KWARGS_DESCRIPTION						, features=datasets.Features(self._get_feature_types()		)						, reference_urls=[
                 '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
             ]						, )




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         if self.config_name == "multilist":
                return {
                    "predictions": datasets.Sequence(datasets.Value('''float'''		)		),
                    "references": datasets.Sequence(datasets.Value('''float'''		)		),
                }
         else:
                return {
                    "predictions": datasets.Value('''float'''		),
                    "references": datasets.Value('''float'''		),
                }




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]						, __lowerCAmelCase					:						List[str]						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						List[str]=None						, __lowerCAmelCase					:						Optional[int]="uniform_average"						, __lowerCAmelCase					:						Dict=True		):






         """simple docstring"""






         _lowerCamelCase						:    str         =			mean_squared_error(
             __lowerCAmelCase						, __lowerCAmelCase						, sample_weight=__lowerCAmelCase						, multioutput=__lowerCAmelCase						, squared=__lowerCAmelCase		)

         return {"mse": mse}







"""simple docstring"""






import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__      =    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase__      =    []
for i in range(6):
   # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
   rename_keys.append(
       (F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
   )
   rename_keys.append(
       (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
   )
   rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
   rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
   rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
   rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
   rename_keys.append(
       (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
   )
   rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
   rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
   rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
   # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
   )
   rename_keys.append(
       (
           F"""transformer.decoder.layers.{i}.cross_attn.out_proj.weight""",
           F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
       )
   )
   rename_keys.append(
       (
           F"""transformer.decoder.layers.{i}.cross_attn.out_proj.bias""",
           F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
       )
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
   rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
   rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
   rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
   rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))

   # q, k, v projections in self/cross-attention in decoder for conditional DETR
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", F"""decoder.layers.{i}.sa_qcontent_proj.weight""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", F"""decoder.layers.{i}.sa_kcontent_proj.weight""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", F"""decoder.layers.{i}.sa_qpos_proj.weight""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", F"""decoder.layers.{i}.sa_kpos_proj.weight""")
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.weight""", F"""decoder.layers.{i}.sa_v_proj.weight"""))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", F"""decoder.layers.{i}.ca_qcontent_proj.weight""")
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", F"""decoder.layers.{i}.ca_kcontent_proj.weight""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", F"""decoder.layers.{i}.ca_kpos_proj.weight""")
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.weight""", F"""decoder.layers.{i}.ca_v_proj.weight"""))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", F"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
   )

   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", F"""decoder.layers.{i}.sa_qcontent_proj.bias""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", F"""decoder.layers.{i}.sa_kcontent_proj.bias""")
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", F"""decoder.layers.{i}.sa_qpos_proj.bias"""))
   rename_keys.append((F"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", F"""decoder.layers.{i}.sa_kpos_proj.bias"""))
   rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.bias""", F"""decoder.layers.{i}.sa_v_proj.bias"""))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", F"""decoder.layers.{i}.ca_qcontent_proj.bias""")
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", F"""decoder.layers.{i}.ca_kcontent_proj.bias""")
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", F"""decoder.layers.{i}.ca_kpos_proj.bias"""))
   rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.bias""", F"""decoder.layers.{i}.ca_v_proj.bias"""))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", F"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
   )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)






def 	snake_case_  (				A_ :		str,    A_ :		Tuple,    A_ :		Union[str, Any]						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			state_dict.pop(A_						)
       _lowerCamelCase						:    Union[str, Any]         =			val






def 	snake_case_  (				A_ :		Any						):


       '''simple docstring'''





       _lowerCamelCase						:    Tuple         =			OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     _lowerCamelCase						:    List[Any]         =			key.replace('''backbone.0.body''',    '''backbone.conv_encoder.model'''						)
                     _lowerCamelCase						:    int         =			value
              else:
                     _lowerCamelCase						:    List[str]         =			value

       return new_state_dict






def 	snake_case_  (				A_ :		Optional[int],    A_ :		List[str]=False						):


       '''simple docstring'''





       _lowerCamelCase						:    Any         =			''''''
       if is_panoptic:
              _lowerCamelCase						:    Optional[Any]         =			'''conditional_detr.'''

       # first: transformer encoder
       for i in range(6						):
              # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
              _lowerCamelCase						:    Optional[int]         =			state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''						)
              _lowerCamelCase						:    Dict         =			state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''						)
              # next, add query, keys and values (in that order) to the state dict
              _lowerCamelCase						:    str         =			in_proj_weight[:2_56, :]
              _lowerCamelCase						:    int         =			in_proj_bias[:2_56]
              _lowerCamelCase						:    str         =			in_proj_weight[2_56:5_12, :]
              _lowerCamelCase						:    Optional[Any]         =			in_proj_bias[2_56:5_12]
              _lowerCamelCase						:    List[Any]         =			in_proj_weight[-2_56:, :]
              _lowerCamelCase						:    List[str]         =			in_proj_bias[-2_56:]






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    List[str]         =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       _lowerCamelCase						:    Any         =			Image.open(requests.get(A_,    stream=A_						).raw						)

       return im






@torch.no_grad()
def 	snake_case_  (				A_ :		Optional[Any],    A_ :		List[Any]						):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[Any]         =			ConditionalDetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              _lowerCamelCase						:    Union[str, Any]         =			'''resnet101'''
       if "dc5" in model_name:
              _lowerCamelCase						:    Optional[int]         =			True
       _lowerCamelCase						:    Tuple         =			'''panoptic''' in model_name
       if is_panoptic:
              _lowerCamelCase						:    Optional[int]         =			2_50
       else:
              _lowerCamelCase						:    int         =			91
              _lowerCamelCase						:    List[str]         =			'''huggingface/label-files'''
              _lowerCamelCase						:    Any         =			'''coco-detection-id2label.json'''
              _lowerCamelCase						:    Optional[int]         =			json.load(open(hf_hub_download(A_,    A_,    repo_type='''dataset'''						),    '''r'''						)						)
              _lowerCamelCase						:    List[str]         =			{int(A_						): v for k, v in idalabel.items()}
              _lowerCamelCase						:    List[str]         =			idalabel
              _lowerCamelCase						:    str         =			{v: k for k, v in idalabel.items()}

       # load image processor
       _lowerCamelCase						:    int         =			'''coco_panoptic''' if is_panoptic else '''coco_detection'''
       _lowerCamelCase						:    Any         =			ConditionalDetrImageProcessor(format=A_						)

       # prepare image
       _lowerCamelCase						:    Optional[int]         =			prepare_img()
       _lowerCamelCase						:    str         =			image_processor(images=A_,    return_tensors='''pt'''						)
       _lowerCamelCase						:    Union[str, Any]         =			encoding['''pixel_values''']

       logger.info(F'''Converting model {model_name}...'''						)

       # load original model from torch hub
       _lowerCamelCase						:    int         =			torch.hub.load('''DeppMeng/ConditionalDETR''',    A_,    pretrained=A_						).eval()
       _lowerCamelCase						:    Tuple         =			conditional_detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     _lowerCamelCase						:    Optional[Any]         =			'''conditional_detr.''' + src
              rename_key(A_,    A_,    A_						)
       _lowerCamelCase						:    Dict         =			rename_backbone_keys(A_						)
       # query, key and value matrices need special treatment
       read_in_q_k_v(A_,    is_panoptic=A_						)
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       _lowerCamelCase						:    Optional[int]         =			'''conditional_detr.model.''' if is_panoptic else '''model.'''
       for key in state_dict.copy().keys():
              if is_panoptic:
                     if (
                         key.startswith('''conditional_detr'''						)
                         and not key.startswith('''class_labels_classifier'''						)
                         and not key.startswith('''bbox_predictor'''						)
                     ):
                            _lowerCamelCase						:    List[Any]         =			state_dict.pop(A_						)
                            _lowerCamelCase						:    int         =			val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            _lowerCamelCase						:    List[str]         =			state_dict.pop(A_						)
                            _lowerCamelCase						:    Optional[Any]         =			val
                     elif key.startswith('''bbox_attention'''						) or key.startswith('''mask_head'''						):
                            continue
                     else:
                            _lowerCamelCase						:    Optional[Any]         =			state_dict.pop(A_						)
                            _lowerCamelCase						:    Any         =			val
              else:
                     if not key.startswith('''class_labels_classifier'''						) and not key.startswith('''bbox_predictor'''						):
                            _lowerCamelCase						:    int         =			state_dict.pop(A_						)
                            _lowerCamelCase						:    str         =			val
    # finally, create HuggingFace model and load state dict
       _lowerCamelCase						:    Dict         =			ConditionalDetrForSegmentation(A_						) if is_panoptic else ConditionalDetrForObjectDetection(A_						)
       model.load_state_dict(A_						)
       model.eval()
       model.push_to_hub(repo_id=A_,    organization='''DepuMeng''',    commit_message='''Add model'''						)
       # verify our conversion
       _lowerCamelCase						:    Dict         =			conditional_detr(A_						)
       _lowerCamelCase						:    Optional[int]         =			model(A_						)
       assert torch.allclose(outputs.logits,    original_outputs['''pred_logits'''],    atol=1E-4						)
       assert torch.allclose(outputs.pred_boxes,    original_outputs['''pred_boxes'''],    atol=1E-4						)
       if is_panoptic:
              assert torch.allclose(outputs.pred_masks,    original_outputs['''pred_masks'''],    atol=1E-4						)

       # 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_						)


if __name__ == "__main__":
   lowerCAmelCase__      =    argparse.ArgumentParser()

   parser.add_argument(
       '''--model_name''',
       default='''conditional_detr_resnet50''',
       type=str,
       help='''Name of the CONDITIONAL_DETR model you\'d like to convert.''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
   )
   lowerCAmelCase__      =    parser.parse_args()
   convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


