



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()
UpperCamelCase__			=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase__			=    []
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 			UpperCAmelCase__     (     lowercase__    ,							lowercase__    ,							lowercase__ )			->   Union[str, Any]:
						__lowercase											=					state_dict.pop(lowercase__ )
						__lowercase											=					val






def 			UpperCAmelCase__     (     lowercase__ )			->   Any:
						__lowercase											=					OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		__lowercase											=					key.replace("""backbone.0.body"""    ,							"""backbone.conv_encoder.model""" )
																		__lowercase											=					value
												else:
																		__lowercase											=					value

						return new_state_dict






def 			UpperCAmelCase__     (     lowercase__    ,							lowercase__=False )			->   Union[str, Any]:
						__lowercase											=					""""""
						if is_panoptic:
												__lowercase											=					"""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											=					state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight" )
												__lowercase											=					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											=					in_proj_weight[:256, :]
												__lowercase											=					in_proj_bias[:256]
												__lowercase											=					in_proj_weight[256:512, :]
												__lowercase											=					in_proj_bias[256:512]
												__lowercase											=					in_proj_weight[-256:, :]
												__lowercase											=					in_proj_bias[-256:]
						# 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
												__lowercase											=					state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight" )
												__lowercase											=					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
												__lowercase											=					in_proj_weight[:256, :]
												__lowercase											=					in_proj_bias[:256]
												__lowercase											=					in_proj_weight[256:512, :]
												__lowercase											=					in_proj_bias[256:512]
												__lowercase											=					in_proj_weight[-256:, :]
												__lowercase											=					in_proj_bias[-256:]
												# read in weights + bias of input projection layer of cross-attention
												__lowercase											=					state_dict.pop(
												    F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight" )
												__lowercase											=					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
												__lowercase											=					in_proj_weight_cross_attn[:256, :]
												__lowercase											=					in_proj_bias_cross_attn[:256]
												__lowercase											=					in_proj_weight_cross_attn[256:512, :]
												__lowercase											=					in_proj_bias_cross_attn[256:512]
												__lowercase											=					in_proj_weight_cross_attn[-256:, :]
												__lowercase											=					in_proj_bias_cross_attn[-256:]






def 			UpperCAmelCase__     (     )			->   str:
						__lowercase											=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						__lowercase											=					Image.open(requests.get(lowercase__    ,							stream=lowercase__ ).raw )

						return im






@torch.no_grad()
def 			UpperCAmelCase__     (     lowercase__    ,							lowercase__ )			->   Tuple:
						__lowercase											=					DetrConfig()
						# set backbone and dilation attributes
						if "resnet101" in model_name:
												__lowercase											=					"""resnet101"""
						if "dc5" in model_name:
												__lowercase											=					True
						__lowercase											=					"""panoptic""" in model_name
						if is_panoptic:
												__lowercase											=					250
						else:
												__lowercase											=					91
												__lowercase											=					"""huggingface/label-files"""
												__lowercase											=					"""coco-detection-id2label.json"""
												__lowercase											=					json.load(open(hf_hub_download(lowercase__    ,							lowercase__    ,							repo_type="""dataset""" )    ,							"""r""" ) )
												__lowercase											=					{int(lowercase__ ): v for k, v in idalabel.items()}
												__lowercase											=					idalabel
												__lowercase											=					{v: k for k, v in idalabel.items()}

						# load image processor
						__lowercase											=					"""coco_panoptic""" if is_panoptic else """coco_detection"""
						__lowercase											=					DetrImageProcessor(format=lowercase__ )

						# prepare image
						__lowercase											=					prepare_img()
						__lowercase											=					image_processor(images=lowercase__    ,							return_tensors="""pt""" )
						__lowercase											=					encoding["""pixel_values"""]

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

						# load original model from torch hub
						__lowercase											=					torch.hub.load("""facebookresearch/detr"""    ,							lowercase__    ,							pretrained=lowercase__ ).eval()
						__lowercase											=					detr.state_dict()
						# rename keys
						for src, dest in rename_keys:
												if is_panoptic:
																		__lowercase											=					"""detr.""" + src
												rename_key(lowercase__    ,							lowercase__    ,							lowercase__ )
						__lowercase											=					rename_backbone_keys(lowercase__ )
						# query, key and value matrices need special treatment
						read_in_q_k_v(lowercase__    ,							is_panoptic=lowercase__ )
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						__lowercase											=					"""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""" )
																		):
																								__lowercase											=					state_dict.pop(lowercase__ )
																								__lowercase											=					val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								__lowercase											=					state_dict.pop(lowercase__ )
																								__lowercase											=					val
																		elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
																								continue
																		else:
																								__lowercase											=					state_dict.pop(lowercase__ )
																								__lowercase											=					val
												else:
																		if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
																								__lowercase											=					state_dict.pop(lowercase__ )
																								__lowercase											=					val
    # finally, create HuggingFace model and load state dict
						__lowercase											=					DetrForSegmentation(lowercase__ ) if is_panoptic else DetrForObjectDetection(lowercase__ )
						model.load_state_dict(lowercase__ )
						model.eval()
						# verify our conversion
						__lowercase											=					detr(lowercase__ )
						__lowercase											=					model(lowercase__ )
						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(lowercase__ ).mkdir(exist_ok=lowercase__ )
						model.save_pretrained(lowercase__ )
						image_processor.save_pretrained(lowercase__ )


if __name__ == "__main__":
			UpperCamelCase__			=    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."
			)
			UpperCamelCase__			=    parser.parse_args()
			convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










from __future__ import annotations

from typing import Generic, TypeVar

UpperCamelCase__			=    TypeVar("T")
class       _lowerCAmelCase (			Generic[T]      ):






			"""simple docstring"""





			def __init__(					self						:							int      ,  lowercase						:							T )    ->       Union[str, Any]:
									"""simple docstring"""







									__lowercase											=					data  # Assign data
									__lowercase											=					None  # Initialize next as null
									__lowercase											=					None  # Initialize prev as null





class       _lowerCAmelCase (			Generic[T]      ):






			"""simple docstring"""





			def __init__(					self						:							Any )    ->       None:
									"""simple docstring"""







									__lowercase											=					None





			def 		snake_case__      (					self						:							str      ,  lowercase						:							T )    ->       None:
									"""simple docstring"""







									if self.head is None:
															__lowercase											=					Node(lowercase )
									else:
															__lowercase											=					Node(lowercase )
															__lowercase											=					new_node
															__lowercase											=					self.head
															__lowercase											=					None
															__lowercase											=					new_node





			def 		snake_case__      (					self						:							Optional[Any] )    ->       T | None:
									"""simple docstring"""







									if self.head is None:
															return None
									else:
															assert self.head is not None
															__lowercase											=					self.head.data
															__lowercase											=					self.head.next
															if self.head is not None:
																					__lowercase											=					None
															return temp





			def 		snake_case__      (					self						:							Optional[Any] )    ->       T | None:
									"""simple docstring"""







									return self.head.data if self.head is not None else None





			def __len__(					self						:							List[str] )    ->       int:
									"""simple docstring"""







									__lowercase											=					self.head
									__lowercase											=					0
									while temp is not None:
															count += 1
															__lowercase											=					temp.next
									return count





			def 		snake_case__      (					self						:							Optional[Any] )    ->       bool:
									"""simple docstring"""







									return self.head is None







			def 		snake_case__      (					self						:							Optional[Any] )    ->       None:
									"""simple docstring"""







									print("""stack elements are:""" )
									__lowercase											=					self.head
									while temp is not None:
															print(temp.data      ,  end="""->""" )
															__lowercase											=					temp.next


# Code execution starts here
if __name__ == "__main__":
			# Start with the empty stack
			UpperCamelCase__			=    Stack()

			# Insert 4 at the beginning. So stack becomes 4->None
			print("Stack operations using Doubly LinkedList")
			stack.push(4)

			# Insert 5 at the beginning. So stack becomes 4->5->None
			stack.push(5)

			# Insert 6 at the beginning. So stack becomes 4->5->6->None
			stack.push(6)

			# Insert 7 at the beginning. So stack becomes 4->5->6->7->None
			stack.push(7)

			# Print the stack
			stack.print_stack()

			# Print the top element
			print("\nTop element is ", stack.top())

			# Print the stack size
			print("Size of the stack is ", len(stack))

			# pop the top element
			stack.pop()

			# pop the top element
			stack.pop()

			# two elements have now been popped off
			stack.print_stack()

			# Print True if the stack is empty else False
			print("\nstack is empty:", stack.is_empty())





