





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()
__UpperCamelCase:	Any = logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase:	Optional[int] = []
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       SCREAMING_SNAKE_CASE__  (       _lowercase     : Tuple   ,    _lowercase     : Optional[Any]   ,    _lowercase     : List[str]			)   ->							Optional[int]:




		'''simple docstring'''
		lowercase__					:      Optional[Any]      =		state_dict.pop(_lowercase			)
		lowercase__					:      List[Any]      =		val


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Any			)   ->							Union[str, Any]:




		'''simple docstring'''
		lowercase__					:      Dict      =		OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						lowercase__					:      Union[str, Any]      =		key.replace('backbone.0.body'   ,    'backbone.conv_encoder.model'			)
						lowercase__					:      Optional[Any]      =		value
				else:
						lowercase__					:      Any      =		value

		return new_state_dict


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : str   ,    _lowercase     : Dict=False			)   ->							str:




		'''simple docstring'''
		lowercase__					:      Tuple      =		''
		if is_panoptic:
				lowercase__					:      List[str]      =		'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__					:      List[str]      =		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__					:      Optional[Any]      =		in_proj_weight[:256, :]
				lowercase__					:      List[Any]      =		in_proj_bias[:256]
				lowercase__					:      Tuple      =		in_proj_weight[256:512, :]
				lowercase__					:      Dict      =		in_proj_bias[256:512]
				lowercase__					:      Tuple      =		in_proj_weight[-256:, :]
				lowercase__					:      List[str]      =		in_proj_bias[-256:]


def       SCREAMING_SNAKE_CASE__  (       )   ->							List[str]:




		'''simple docstring'''
		lowercase__					:      List[Any]      =		'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowercase__					:      Tuple      =		Image.open(requests.get(_lowercase   ,    stream=_lowercase			).raw			)

		return im


@torch.no_grad()
def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]   ,    _lowercase     : Union[str, Any]			)   ->							int:




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

		# load image processor
		lowercase__					:      Dict      =		'coco_panoptic' if is_panoptic else 'coco_detection'
		lowercase__					:      Tuple      =		ConditionalDetrImageProcessor(format=_lowercase			)

		# prepare image
		lowercase__					:      Union[str, Any]      =		prepare_img()
		lowercase__					:      int      =		image_processor(images=_lowercase   ,    return_tensors='pt'			)
		lowercase__					:      List[str]      =		encoding['pixel_values']

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

		# load original model from torch hub
		lowercase__					:      Optional[int]      =		torch.hub.load('DeppMeng/ConditionalDETR'   ,    _lowercase   ,    pretrained=_lowercase			).eval()
		lowercase__					:      int      =		conditional_detr.state_dict()
		# rename keys
		for src, dest in rename_keys:
				if is_panoptic:
						lowercase__					:      int      =		'conditional_detr.' + src
				rename_key(_lowercase   ,    _lowercase   ,    _lowercase			)
		lowercase__					:      str      =		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__					:      str      =		'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__					:      List[Any]      =		state_dict.pop(_lowercase			)
								lowercase__					:      List[Any]      =		val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								lowercase__					:      str      =		state_dict.pop(_lowercase			)
								lowercase__					:      List[str]      =		val
						elif key.startswith('bbox_attention'			) or key.startswith('mask_head'			):
								continue
						else:
								lowercase__					:      List[Any]      =		state_dict.pop(_lowercase			)
								lowercase__					:      int      =		val
				else:
						if not key.startswith('class_labels_classifier'			) and not key.startswith('bbox_predictor'			):
								lowercase__					:      Any      =		state_dict.pop(_lowercase			)
								lowercase__					:      Tuple      =		val
    # finally, create HuggingFace model and load state dict
		lowercase__					:      int      =		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
		lowercase__					:      List[str]      =		conditional_detr(_lowercase			)
		lowercase__					:      int      =		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:	Union[str, Any] = 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."""
			)
			__UpperCamelCase:	Union[str, Any] = parser.parse_args()
			convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









from __future__ import annotations


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[float]   ,    _lowercase     : Tuple			)   ->							int:




		'''simple docstring'''
		print(f"""Vertex\tShortest Distance from vertex {src}"""			)
		for i, d in enumerate(_lowercase			):
				print(f"""{i}\t\t{d}"""			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : list[float]   ,    _lowercase     : int			)   ->							Any:




		'''simple docstring'''
		for j in range(_lowercase			):
				lowercase__   ,				lowercase__   ,				lowercase__					:      Dict      =		(graph[j][k] for k in ['src', 'dst', 'weight'])
				if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
						return True
		return False


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : int   ,    _lowercase     : int   ,    _lowercase     : int			)   ->							list[float]:




		'''simple docstring'''
		lowercase__					:      Dict      =		[float('inf'			)] * vertex_count
		lowercase__					:      Dict      =		0.0

		for _ in range(vertex_count - 1			):
				for j in range(_lowercase			):
						lowercase__   ,				lowercase__   ,				lowercase__					:      int      =		(graph[j][k] for k in ['src', 'dst', 'weight'])

						if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
								lowercase__					:      str      =		distance[u] + w

		lowercase__					:      str      =		check_negative_cycle(_lowercase   ,    _lowercase   ,    _lowercase			)
		if negative_cycle_exists:
				raise Exception('Negative cycle found'			)

		return distance


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__UpperCamelCase:	Optional[int] = int(input("""Enter number of vertices: """).strip())
			__UpperCamelCase:	Union[str, Any] = int(input("""Enter number of edges: """).strip())

			__UpperCamelCase:	list[dict[str, int]] = [{} for _ in range(E)]

			for i in range(E):
						print("""Edge """, i + 1)
						__UpperCamelCase,	__UpperCamelCase,	__UpperCamelCase:	List[str] = (
						    int(x)
						    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
						)
						__UpperCamelCase:	List[Any] = {"""src""": src, """dst""": dest, """weight""": weight}

			__UpperCamelCase:	Optional[int] = int(input("""\nEnter shortest path source:""").strip())
			__UpperCamelCase:	Dict = bellman_ford(graph, V, E, source)
			print_distance(shortest_distance, 0)


