



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 					__lowerCamelCase					(   lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__						):

				"""simple docstring"""


				lowercase__     :    List[Any]													=     state_dict.pop(lowerCamelCase__						)
				lowercase__     :    Any													=     val


def 					__lowerCamelCase					(   lowerCamelCase__						):

				"""simple docstring"""


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

				return new_state_dict


def 					__lowerCamelCase					(   lowerCamelCase__     , lowerCamelCase__=False						):

				"""simple docstring"""


				lowercase__     :    Optional[int]													=     """"""
				if is_panoptic:
								lowercase__     :    Dict													=     """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)
								lowercase__     :    List[Any]													=     state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""						)
								lowercase__     :    Union[str, Any]													=     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
								lowercase__     :    int													=     in_proj_weight[:256, :]
								lowercase__     :    Any													=     in_proj_bias[:256]
								lowercase__     :    str													=     in_proj_weight[256:512, :]
								lowercase__     :    Optional[int]													=     in_proj_bias[256:512]
								lowercase__     :    Dict													=     in_proj_weight[-256:, :]
								lowercase__     :    str													=     in_proj_bias[-256:]


def 					__lowerCamelCase					(   ):

				"""simple docstring"""


				lowercase__     :    Optional[int]													=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowercase__     :    Optional[Any]													=     Image.open(requests.get(lowerCamelCase__     , stream=lowerCamelCase__						).raw						)

				return im


@torch.no_grad()
def 					__lowerCamelCase					(   lowerCamelCase__     , lowerCamelCase__						):

				"""simple docstring"""


				lowercase__     :    Optional[Any]													=     ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								lowercase__     :    Any													=     """resnet101"""
				if "dc5" in model_name:
								lowercase__     :    Optional[int]													=     True
				lowercase__     :    int													=     """panoptic""" in model_name
				if is_panoptic:
								lowercase__     :    List[str]													=     250
				else:
								lowercase__     :    int													=     91
								lowercase__     :    int													=     """huggingface/label-files"""
								lowercase__     :    List[str]													=     """coco-detection-id2label.json"""
								lowercase__     :    Optional[Any]													=     json.load(open(hf_hub_download(lowerCamelCase__     , lowerCamelCase__     , repo_type="dataset"						)     , "r"						)						)
								lowercase__     :    Any													=     {int(lowerCamelCase__						): v for k, v in idalabel.items()}
								lowercase__     :    str													=     idalabel
								lowercase__     :    List[str]													=     {v: k for k, v in idalabel.items()}

				# load image processor
				lowercase__     :    Optional[int]													=     """coco_panoptic""" if is_panoptic else """coco_detection"""
				lowercase__     :    int													=     ConditionalDetrImageProcessor(format=lowerCamelCase__						)

				# prepare image
				lowercase__     :    List[str]													=     prepare_img()
				lowercase__     :    int													=     image_processor(images=lowerCamelCase__     , return_tensors="pt"						)
				lowercase__     :    Optional[Any]													=     encoding["""pixel_values"""]

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

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


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)












'''simple docstring'''







from __future__ import annotations

_A	:			Any         ={
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}

class _lowercase	:




							def __init__(							self:				Tuple			,							UpperCamelCase__:				dict[str, list[str]]			,							UpperCamelCase__:				str     ):
														lowerCamelCase__					:	str         =       graph
														# mapping node to its parent in resulting breadth first tree
														lowerCamelCase__					:	dict[str, str | None]         =       {}
														lowerCamelCase__					:	Any         =       source_vertex




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	List[str]         =       {self.source_vertex}
														lowerCamelCase__					:	List[str]         =       None
														lowerCamelCase__					:	Tuple         =       [self.source_vertex]  # first in first out queue

														while queue:
																					lowerCamelCase__					:	Tuple         =       queue.pop(0     )
																					for adjacent_vertex in self.graph[vertex]:
																												if adjacent_vertex not in visited:
																																			visited.add(UpperCamelCase__     )
																																			lowerCamelCase__					:	List[str]         =       vertex
																																			queue.append(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				str     ):
														if target_vertex == self.source_vertex:
																					return self.source_vertex

														lowerCamelCase__					:	Tuple         =       self.parent.get(UpperCamelCase__     )
														if target_vertex_parent is None:
																					lowerCamelCase__					:	int         =       (
																					    F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
																					)
																					raise ValueError(UpperCamelCase__     )

														return self.shortest_path(UpperCamelCase__     ) + F'''->{target_vertex}'''


if __name__ == "__main__":
			_A	:			int         =Graph(graph, '''G''')
			g.breath_first_search()
			print(g.shortest_path('''D'''))
			print(g.shortest_path('''G'''))
			print(g.shortest_path('''Foo'''))

