






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()
__snake_case    :    Optional[Any]         =     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__snake_case    :    Union[str, Any]         =     []
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 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[int]    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Optional[Any]		)  ->   Any:

						"""simple docstring"""



						lowerCAmelCase__    						=						state_dict.pop(UpperCamelCase_		)
						lowerCAmelCase__    						=						val







def 	_UpperCamelCase   (    UpperCamelCase_       :					str		)  ->   Any:

						"""simple docstring"""



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

						return new_state_dict







def 	_UpperCamelCase   (    UpperCamelCase_       :					Any    ,       UpperCamelCase_       :					List[Any]=False		)  ->   Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						''
						if is_panoptic:
												lowerCAmelCase__    						=						'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)
												lowerCAmelCase__    						=						state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"		)
												lowerCAmelCase__    						=						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__    						=						in_proj_weight[:256, :]
												lowerCAmelCase__    						=						in_proj_bias[:256]
												lowerCAmelCase__    						=						in_proj_weight[256:512, :]
												lowerCAmelCase__    						=						in_proj_bias[256:512]
												lowerCAmelCase__    						=						in_proj_weight[-256:, :]
												lowerCAmelCase__    						=						in_proj_bias[-256:]







def 	_UpperCamelCase   (    )  ->   List[str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						'http://images.cocodataset.org/val2017/000000039769.jpg'
						lowerCAmelCase__    						=						Image.open(requests.get(UpperCamelCase_    ,       stream=UpperCamelCase_		).raw		)

						return im







@torch.no_grad()
def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[int]    ,       UpperCamelCase_       :					Tuple		)  ->   Any:

						"""simple docstring"""



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

						# load image processor
						lowerCAmelCase__    						=						'coco_panoptic' if is_panoptic else 'coco_detection'
						lowerCAmelCase__    						=						ConditionalDetrImageProcessor(format=UpperCamelCase_		)

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

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

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


if __name__ == "__main__":
					__snake_case    :    Optional[int]         =     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."""
					)
					__snake_case    :    Any         =     parser.parse_args()
					convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)













from __future__ import annotations

import pandas as pd







def 	_UpperCamelCase   (    UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					int		)  ->   list[int]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[0] * no_of_processes
						lowerCAmelCase__    						=						[0] * no_of_processes
						# Copy the burst time into remaining_time[]
						for i in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						burst_time[i]

						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						9_9999_9999
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						False

						# Process until all processes are completed
						while complete != no_of_processes:
												for j in range(UpperCamelCase_		):
																		if arrival_time[j] <= increment_time and remaining_time[j] > 0:
																								if remaining_time[j] < minm:
																														lowerCAmelCase__    						=						remaining_time[j]
																														lowerCAmelCase__    						=						j
																														lowerCAmelCase__    						=						True

												if not check:
																		increment_time += 1
																		continue
												remaining_time[short] -= 1

												lowerCAmelCase__    						=						remaining_time[short]
												if minm == 0:
																		lowerCAmelCase__    						=						9_9999_9999

												if remaining_time[short] == 0:
																		complete += 1
																		lowerCAmelCase__    						=						False

																		# Find finish time of current process
																		lowerCAmelCase__    						=						increment_time + 1

																		# Calculate waiting time
																		lowerCAmelCase__    						=						finish_time - arrival_time[short]
																		lowerCAmelCase__    						=						finar - burst_time[short]

																		if waiting_time[short] < 0:
																								lowerCAmelCase__    						=						0

        # Increment time
												increment_time += 1
						return waiting_time







def 	_UpperCamelCase   (    UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					int    ,       UpperCamelCase_       :					list[int]		)  ->   list[int]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[0] * no_of_processes
						for i in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						burst_time[i] + waiting_time[i]
						return turn_around_time







def 	_UpperCamelCase   (    UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					int		)  ->   None:

						"""simple docstring"""



						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						for i in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						total_waiting_time + waiting_time[i]
												lowerCAmelCase__    						=						total_turn_around_time + turn_around_time[i]
						print(F"Average waiting time = {total_waiting_time / no_of_processes:.5f}"		)
						print('Average turn around time ='    ,       total_turn_around_time / no_of_processes		)


if __name__ == "__main__":
					print("""Enter how many process you want to analyze""")
					__snake_case    :    Dict         =     int(input())
					__snake_case    :    List[Any]         =     [0] * no_of_processes
					__snake_case    :    str         =     [0] * no_of_processes
					__snake_case    :    Optional[int]         =     list(range(1, no_of_processes + 1))

					for i in range(no_of_processes):
										print("""Enter the arrival time and burst time for process:--""" + str(i + 1))
										__snake_case     , __snake_case    :    Any         =     map(int, input().split())

					__snake_case    :    str         =     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

					__snake_case    :    Dict         =     burst_time
					__snake_case    :    Optional[int]         =     no_of_processes
					__snake_case    :    str         =     waiting_time
					__snake_case    :    int         =     calculate_turnaroundtime(bt, n, wt)

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					__snake_case    :    Dict         =     pd.DataFrame(
					    list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
					    columns=[
					        """Process""",
					        """BurstTime""",
					        """ArrivalTime""",
					        """WaitingTime""",
					        """TurnAroundTime""",
					    ],
					)

					# Printing the dataFrame
					pd.set_option("""display.max_rows""", fcfs.shape[0] + 1)
					print(fcfs)





