
"""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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
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}.multihead_attn.out_proj.weight""",
							        f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
							    )
							)
							rename_keys.append(
							    (
							        f"""transformer.decoder.layers.{i}.multihead_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"""))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
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"""),
    ]
)


def      lowerCAmelCase_     (	snake_case_	:    List[str]							,					snake_case_	:    Tuple							,					snake_case_	:    Optional[Any]    )     ->Any:
			lowerCamelCase__			:  int  =state_dict.pop(snake_case_    )
			lowerCamelCase__			:  Union[str, Any]  =val


def      lowerCAmelCase_     (	snake_case_	:    int    )     ->Optional[int]:
			lowerCamelCase__			:  Union[str, Any]  =OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									lowerCamelCase__			:  Optional[Any]  =key.replace('backbone.0.body'							,					'backbone.conv_encoder.model'    )
									lowerCamelCase__			:  Tuple  =value
						else:
									lowerCamelCase__			:  Tuple  =value

			return new_state_dict


def      lowerCAmelCase_     (	snake_case_	:    List[Any]							,					snake_case_	:    List[Any]=False    )     ->Tuple:
			lowerCamelCase__			:  Union[str, Any]  =''
			if is_panoptic:
						lowerCamelCase__			:  List[str]  ='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__			:  Tuple  =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__			:  Union[str, Any]  =in_proj_weight[:2_5_6, :]
						lowerCamelCase__			:  Optional[int]  =in_proj_bias[:2_5_6]
						lowerCamelCase__			:  int  =in_proj_weight[2_5_6:5_1_2, :]
						lowerCamelCase__			:  Any  =in_proj_bias[2_5_6:5_1_2]
						lowerCamelCase__			:  Tuple  =in_proj_weight[-2_5_6:, :]
						lowerCamelCase__			:  List[str]  =in_proj_bias[-2_5_6:]
			# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
			for i in range(6    ):
						# read in weights + bias of input projection layer of self-attention
						lowerCamelCase__			:  int  =state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""    )
						lowerCamelCase__			:  Tuple  =state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""    )
						# next, add query, keys and values (in that order) to the state dict
						lowerCamelCase__			:  Optional[Any]  =in_proj_weight[:2_5_6, :]
						lowerCamelCase__			:  List[str]  =in_proj_bias[:2_5_6]
						lowerCamelCase__			:  List[str]  =in_proj_weight[2_5_6:5_1_2, :]
						lowerCamelCase__			:  Dict  =in_proj_bias[2_5_6:5_1_2]
						lowerCamelCase__			:  List[str]  =in_proj_weight[-2_5_6:, :]
						lowerCamelCase__			:  Union[str, Any]  =in_proj_bias[-2_5_6:]
						# read in weights + bias of input projection layer of cross-attention
						lowerCamelCase__			:  List[Any]  =state_dict.pop(
						    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""    )
						lowerCamelCase__			:  Optional[Any]  =state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""    )
						# next, add query, keys and values (in that order) of cross-attention to the state dict
						lowerCamelCase__			:  Optional[Any]  =in_proj_weight_cross_attn[:2_5_6, :]
						lowerCamelCase__			:  Any  =in_proj_bias_cross_attn[:2_5_6]
						lowerCamelCase__			:  Dict  =in_proj_weight_cross_attn[2_5_6:5_1_2, :]
						lowerCamelCase__			:  str  =in_proj_bias_cross_attn[2_5_6:5_1_2]
						lowerCamelCase__			:  Dict  =in_proj_weight_cross_attn[-2_5_6:, :]
						lowerCamelCase__			:  Any  =in_proj_bias_cross_attn[-2_5_6:]


def      lowerCAmelCase_     (	)     ->int:
			lowerCamelCase__			:  Dict  ='http://images.cocodataset.org/val2017/000000039769.jpg'
			lowerCamelCase__			:  str  =Image.open(requests.get(snake_case_							,					stream=snake_case_    ).raw    )

			return im


@torch.no_grad()
def      lowerCAmelCase_     (	snake_case_	:    str							,					snake_case_	:    Dict    )     ->List[Any]:
			lowerCamelCase__			:  Tuple  =DetrConfig()
			# set backbone and dilation attributes
			if "resnet101" in model_name:
						lowerCamelCase__			:  Optional[Any]  ='resnet101'
			if "dc5" in model_name:
						lowerCamelCase__			:  Any  =True
			lowerCamelCase__			:  List[str]  ='panoptic' in model_name
			if is_panoptic:
						lowerCamelCase__			:  Optional[Any]  =2_5_0
			else:
						lowerCamelCase__			:  int  =9_1
						lowerCamelCase__			:  List[str]  ='huggingface/label-files'
						lowerCamelCase__			:  List[Any]  ='coco-detection-id2label.json'
						lowerCamelCase__			:  List[Any]  =json.load(open(hf_hub_download(snake_case_							,					snake_case_							,					repo_type='dataset'    )							,					'r'    )    )
						lowerCamelCase__			:  List[str]  ={int(snake_case_    ): v for k, v in idalabel.items()}
						lowerCamelCase__			:  str  =idalabel
						lowerCamelCase__			:  Tuple  ={v: k for k, v in idalabel.items()}

			# load image processor
			lowerCamelCase__			:  Tuple  ='coco_panoptic' if is_panoptic else 'coco_detection'
			lowerCamelCase__			:  Dict  =DetrImageProcessor(format=snake_case_    )

			# prepare image
			lowerCamelCase__			:  Dict  =prepare_img()
			lowerCamelCase__			:  Union[str, Any]  =image_processor(images=snake_case_							,					return_tensors='pt'    )
			lowerCamelCase__			:  str  =encoding['pixel_values']

			logger.info(f"""Converting model {model_name}..."""    )

			# load original model from torch hub
			lowerCamelCase__			:  Tuple  =torch.hub.load('facebookresearch/detr'							,					snake_case_							,					pretrained=snake_case_    ).eval()
			lowerCamelCase__			:  int  =detr.state_dict()
			# rename keys
			for src, dest in rename_keys:
						if is_panoptic:
									lowerCamelCase__			:  List[Any]  ='detr.' + src
						rename_key(snake_case_							,					snake_case_							,					snake_case_    )
			lowerCamelCase__			:  Any  =rename_backbone_keys(snake_case_    )
			# query, key and value matrices need special treatment
			read_in_q_k_v(snake_case_							,					is_panoptic=snake_case_    )
			# 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[Any]  ='detr.model.' if is_panoptic else 'model.'
			for key in state_dict.copy().keys():
						if is_panoptic:
									if (
									    key.startswith('detr'    )
									    and not key.startswith('class_labels_classifier'    )
									    and not key.startswith('bbox_predictor'    )
									):
												lowerCamelCase__			:  Optional[Any]  =state_dict.pop(snake_case_    )
												lowerCamelCase__			:  List[str]  =val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												lowerCamelCase__			:  Tuple  =state_dict.pop(snake_case_    )
												lowerCamelCase__			:  int  =val
									elif key.startswith('bbox_attention'    ) or key.startswith('mask_head'    ):
												continue
									else:
												lowerCamelCase__			:  Dict  =state_dict.pop(snake_case_    )
												lowerCamelCase__			:  int  =val
						else:
									if not key.startswith('class_labels_classifier'    ) and not key.startswith('bbox_predictor'    ):
												lowerCamelCase__			:  Union[str, Any]  =state_dict.pop(snake_case_    )
												lowerCamelCase__			:  str  =val
    # finally, create HuggingFace model and load state dict
			lowerCamelCase__			:  Tuple  =DetrForSegmentation(snake_case_    ) if is_panoptic else DetrForObjectDetection(snake_case_    )
			model.load_state_dict(snake_case_    )
			model.eval()
			# verify our conversion
			lowerCamelCase__			:  int  =detr(snake_case_    )
			lowerCamelCase__			:  int  =model(snake_case_    )
			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(snake_case_    ).mkdir(exist_ok=snake_case_    )
			model.save_pretrained(snake_case_    )
			image_processor.save_pretrained(snake_case_    )


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

							parser.add_argument(
							    """--model_name""", default="""detr_resnet50""", type=str, help="""Name of the 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_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

"""simple docstring"""


from sklearn.neural_network import MLPClassifier

lowerCAmelCase			=		[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCAmelCase			=		[0, 1, 0, 0]


lowerCAmelCase			=		MLPClassifier(
    solver="""lbfgs""", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


lowerCAmelCase			=		[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
lowerCAmelCase			=		clf.predict(test)






def      lowerCAmelCase_     (	snake_case_	:    Tuple    )     ->List[Any]:
			return list(snake_case_    )


if __name__ == "__main__":
							import doctest

							doctest.testmod()