

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()
A											=     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A											=     []
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 a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= state_dict.pop(lowercase__	)
	snake_case_      		= val





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			snake_case_      		= key.replace('backbone.0.body'			, 'backbone.conv_encoder.model'	)
			snake_case_      		= value
		else:
			snake_case_      		= value

	return new_state_dict





def a(lowercase__			, lowercase__=False	):





	'''simple docstring'''
	snake_case_      		= ''
	if is_panoptic:
		snake_case_      		= '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)
		snake_case_      		= state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""	)
		snake_case_      		= 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
		snake_case_      		= in_proj_weight[:256, :]
		snake_case_      		= in_proj_bias[:256]
		snake_case_      		= in_proj_weight[256:512, :]
		snake_case_      		= in_proj_bias[256:512]
		snake_case_      		= in_proj_weight[-256:, :]
		snake_case_      		= in_proj_bias[-256:]





def a():





	'''simple docstring'''
	snake_case_      		= 'http://images.cocodataset.org/val2017/000000039769.jpg'
	snake_case_      		= Image.open(requests.get(lowercase__			, stream=lowercase__	).raw	)

	return im





@torch.no_grad()
def a(lowercase__			, lowercase__	):





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

	# load image processor
	snake_case_      		= 'coco_panoptic' if is_panoptic else 'coco_detection'
	snake_case_      		= ConditionalDetrImageProcessor(format=lowercase__	)

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

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

	# load original model from torch hub
	snake_case_      		= torch.hub.load('DeppMeng/ConditionalDETR'			, lowercase__			, pretrained=lowercase__	).eval()
	snake_case_      		= conditional_detr.state_dict()
	# rename keys
	for src, dest in rename_keys:
		if is_panoptic:
			snake_case_      		= 'conditional_detr.' + src
		rename_key(lowercase__			, lowercase__			, lowercase__	)
	snake_case_      		= 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
	snake_case_      		= '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'	)
			):
				snake_case_      		= state_dict.pop(lowercase__	)
				snake_case_      		= val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				snake_case_      		= state_dict.pop(lowercase__	)
				snake_case_      		= val
			elif key.startswith('bbox_attention'	) or key.startswith('mask_head'	):
				continue
			else:
				snake_case_      		= state_dict.pop(lowercase__	)
				snake_case_      		= val
		else:
			if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
				snake_case_      		= state_dict.pop(lowercase__	)
				snake_case_      		= val
    # finally, create HuggingFace model and load state dict
	snake_case_      		= ConditionalDetrForSegmentation(lowercase__	) if is_panoptic else ConditionalDetrForObjectDetection(lowercase__	)
	model.load_state_dict(lowercase__	)
	model.eval()
	model.push_to_hub(repo_id=lowercase__			, organization='DepuMeng'			, commit_message='Add model'	)
	# verify our conversion
	snake_case_      		= conditional_detr(lowercase__	)
	snake_case_      		= 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__":
		A											=     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.'
		)
		A											=     parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)



class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= name
				snake_case_      		= val


			def __str__(    self							):

				"""simple docstring"""


				return f"""{self.__class__.__name__}({self.name}, {self.val})"""







			def __lt__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return self.val < other.val







class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= {}
				snake_case_      		= {}
				snake_case_      		= self.build_heap(__UpperCamelCase							)


			def __getitem__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return self.get_value(__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return (idx - 1) // 2


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return idx * 2 + 1


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return idx * 2 + 2


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return self.heap_dict[key]


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= len(__UpperCamelCase							) - 1
				snake_case_      		= self.get_parent_idx(__UpperCamelCase							)

				for idx, i in enumerate(__UpperCamelCase							):
					snake_case_      		= idx
					snake_case_      		= i.val

				for i in range(__UpperCamelCase ,				-1 ,				-1							):
					self.sift_down(__UpperCamelCase ,				__UpperCamelCase							)
				return array


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				while True:
					snake_case_      		= self.get_left_child_idx(__UpperCamelCase							)  # noqa: E741
					snake_case_      		= self.get_right_child_idx(__UpperCamelCase							)

					snake_case_      		= idx
					if l < len(__UpperCamelCase							) and array[l] < array[idx]:
						snake_case_      		= l
					if r < len(__UpperCamelCase							) and array[r] < array[smallest]:
						snake_case_      		= r

					if smallest != idx:
						snake_case_		,       snake_case_      		= array[smallest], array[idx]
						(
						    (
						    snake_case_
						)		,       (
						    snake_case_
						)		,       
						)      		= (
						    self.idx_of_element[array[smallest]],
						    self.idx_of_element[array[idx]],
						)
						snake_case_      		= smallest
					else:
						break


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.get_parent_idx(__UpperCamelCase							)
				while p >= 0 and self.heap[p] > self.heap[idx]:
					snake_case_		,       snake_case_      		= self.heap[idx], self.heap[p]
					snake_case_		,       snake_case_      		= (
					    self.idx_of_element[self.heap[idx]],
					    self.idx_of_element[self.heap[p]],
					)
					snake_case_      		= p
					snake_case_      		= self.get_parent_idx(__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return self.heap[0]


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.heap[-1], self.heap[0]
				snake_case_		,       snake_case_      		= (
				    self.idx_of_element[self.heap[-1]],
				    self.idx_of_element[self.heap[0]],
				)

				snake_case_      		= self.heap.pop()
				del self.idx_of_element[x]
				self.sift_down(0 ,				self.heap							)
				return x


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				self.heap.append(__UpperCamelCase							)
				snake_case_      		= len(self.heap							) - 1
				snake_case_      		= node.val
				self.sift_up(len(self.heap							) - 1							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return len(self.heap							) == 0







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				assert (
				    self.heap[self.idx_of_element[node]].val > new_value
				), "newValue must be less that current value"
				snake_case_      		= new_value
				snake_case_      		= new_value
				self.sift_up(self.idx_of_element[node]							)







A											=     Node('R', -1)
A											=     Node('B', 6)
A											=     Node('A', 3)
A											=     Node('X', 1)
A											=     Node('E', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
A											=     MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
		print(i)

print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
		print(i)

if __name__ == "__main__":
		import doctest

		doctest.testmod()
