




"""simple docstring"""
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def 						UpperCAmelCase				(			A__:    Optional[Any]							,				A__:    Tuple=1     )  ->     Optional[Any]:






			if n_shave_prefix_segments >= 0:
						return ".".join(path.split('.'     )[n_shave_prefix_segments:]     )
			else:
						return ".".join(path.split('.'     )[:n_shave_prefix_segments]     )





def 						UpperCAmelCase				(			A__:    int							,				A__:    Optional[Any]=0     )  ->     List[str]:
			__lowerCamelCase					:	str	       =				[]
			for old_item in old_list:
						__lowerCamelCase					:	Dict	       =				old_item.replace('in_layers.0'							,				'norm1'     )
						__lowerCamelCase					:	str	       =				new_item.replace('in_layers.2'							,				'conv1'     )

						__lowerCamelCase					:	Any	       =				new_item.replace('out_layers.0'							,				'norm2'     )
						__lowerCamelCase					:	List[Any]	       =				new_item.replace('out_layers.3'							,				'conv2'     )

						__lowerCamelCase					:	str	       =				new_item.replace('emb_layers.1'							,				'time_emb_proj'     )
						__lowerCamelCase					:	Tuple	       =				new_item.replace('skip_connection'							,				'conv_shortcut'     )

						__lowerCamelCase					:	List[str]	       =				shave_segments(A__							,				n_shave_prefix_segments=A__     )

						mapping.append({'old': old_item, 'new': new_item}     )

			return mapping





def 						UpperCAmelCase				(			A__:    Any							,				A__:    int=0     )  ->     List[Any]:
			__lowerCamelCase					:	Tuple	       =				[]
			for old_item in old_list:
						__lowerCamelCase					:	Dict	       =				old_item

						__lowerCamelCase					:	Tuple	       =				new_item.replace('norm.weight'							,				'group_norm.weight'     )
						__lowerCamelCase					:	Any	       =				new_item.replace('norm.bias'							,				'group_norm.bias'     )

						__lowerCamelCase					:	str	       =				new_item.replace('proj_out.weight'							,				'proj_attn.weight'     )
						__lowerCamelCase					:	Union[str, Any]	       =				new_item.replace('proj_out.bias'							,				'proj_attn.bias'     )

						__lowerCamelCase					:	Tuple	       =				shave_segments(A__							,				n_shave_prefix_segments=A__     )

						mapping.append({'old': old_item, 'new': new_item}     )

			return mapping





def 						UpperCAmelCase				(			A__:    Tuple							,				A__:    Union[str, Any]							,				A__:    int							,				A__:    Dict=None							,				A__:    Optional[int]=None							,				A__:    Optional[Any]=None     )  ->     Any:
			assert isinstance(A__							,				A__     ), "Paths should be a list of dicts containing 'old' and 'new' keys."

			# Splits the attention layers into three variables.
			if attention_paths_to_split is not None:
						for path, path_map in attention_paths_to_split.items():
									__lowerCamelCase					:	Optional[Any]	       =				old_checkpoint[path]
									__lowerCamelCase					:	int	       =				old_tensor.shape[0] // 3

									__lowerCamelCase					:	Tuple	       =				(-1, channels) if len(old_tensor.shape     ) == 3 else (-1)

									__lowerCamelCase					:	Dict	       =				old_tensor.shape[0] // config['num_head_channels'] // 3

									__lowerCamelCase					:	Any	       =				old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]     )
									__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Dict	       =				old_tensor.split(channels // num_heads							,				dim=1     )

									__lowerCamelCase					:	Dict	       =				query.reshape(A__     )
									__lowerCamelCase					:	List[Any]	       =				key.reshape(A__     )
									__lowerCamelCase					:	Dict	       =				value.reshape(A__     )

			for path in paths:
						__lowerCamelCase					:	Optional[Any]	       =				path['new']

						# These have already been assigned
						if attention_paths_to_split is not None and new_path in attention_paths_to_split:
									continue

						# Global renaming happens here
						__lowerCamelCase					:	str	       =				new_path.replace('middle_block.0'							,				'mid_block.resnets.0'     )
						__lowerCamelCase					:	Any	       =				new_path.replace('middle_block.1'							,				'mid_block.attentions.0'     )
						__lowerCamelCase					:	List[Any]	       =				new_path.replace('middle_block.2'							,				'mid_block.resnets.1'     )

						if additional_replacements is not None:
									for replacement in additional_replacements:
												__lowerCamelCase					:	List[Any]	       =				new_path.replace(replacement['old']							,				replacement['new']     )

        # proj_attn.weight has to be converted from conv 1D to linear
						if "proj_attn.weight" in new_path:
									__lowerCamelCase					:	Tuple	       =				old_checkpoint[path['old']][:, :, 0]
						else:
									__lowerCamelCase					:	Union[str, Any]	       =				old_checkpoint[path['old']]





def 						UpperCAmelCase				(			A__:    Union[str, Any]							,				A__:    Dict     )  ->     int:
			__lowerCamelCase					:	str	       =				{}

			__lowerCamelCase					:	Any	       =				checkpoint['time_embed.0.weight']
			__lowerCamelCase					:	Tuple	       =				checkpoint['time_embed.0.bias']
			__lowerCamelCase					:	str	       =				checkpoint['time_embed.2.weight']
			__lowerCamelCase					:	List[str]	       =				checkpoint['time_embed.2.bias']

			__lowerCamelCase					:	int	       =				checkpoint['input_blocks.0.0.weight']
			__lowerCamelCase					:	Optional[int]	       =				checkpoint['input_blocks.0.0.bias']

			__lowerCamelCase					:	int	       =				checkpoint['out.0.weight']
			__lowerCamelCase					:	List[Any]	       =				checkpoint['out.0.bias']
			__lowerCamelCase					:	List[str]	       =				checkpoint['out.2.weight']
			__lowerCamelCase					:	Union[str, Any]	       =				checkpoint['out.2.bias']

			# Retrieves the keys for the input blocks only
			__lowerCamelCase					:	Dict	       =				len({'.'.join(layer.split('.'     )[:2]     ) for layer in checkpoint if 'input_blocks' in layer}     )
			__lowerCamelCase					:	List[Any]	       =				{
			    layer_id: [key for key in checkpoint if f'''input_blocks.{layer_id}''' in key]
			    for layer_id in range(A__     )
			}

			# Retrieves the keys for the middle blocks only
			__lowerCamelCase					:	str	       =				len({'.'.join(layer.split('.'     )[:2]     ) for layer in checkpoint if 'middle_block' in layer}     )
			__lowerCamelCase					:	Union[str, Any]	       =				{
			    layer_id: [key for key in checkpoint if f'''middle_block.{layer_id}''' in key]
			    for layer_id in range(A__     )
			}

			# Retrieves the keys for the output blocks only
			__lowerCamelCase					:	str	       =				len({'.'.join(layer.split('.'     )[:2]     ) for layer in checkpoint if 'output_blocks' in layer}     )
			__lowerCamelCase					:	int	       =				{
			    layer_id: [key for key in checkpoint if f'''output_blocks.{layer_id}''' in key]
			    for layer_id in range(A__     )
			}

			for i in range(1							,				A__     ):
						__lowerCamelCase					:	Dict	       =				(i - 1) // (config['num_res_blocks'] + 1)
						__lowerCamelCase					:	str	       =				(i - 1) % (config['num_res_blocks'] + 1)

						__lowerCamelCase					:	str	       =				[key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
						__lowerCamelCase					:	int	       =				[key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]

						if f'''input_blocks.{i}.0.op.weight''' in checkpoint:
									__lowerCamelCase					:	Any	       =				checkpoint[
									    f'''input_blocks.{i}.0.op.weight'''
									]
									__lowerCamelCase					:	List[Any]	       =				checkpoint[
									    f'''input_blocks.{i}.0.op.bias'''
									]
									continue

						__lowerCamelCase					:	List[str]	       =				renew_resnet_paths(A__     )
						__lowerCamelCase					:	Union[str, Any]	       =				{'old': f'''input_blocks.{i}.0''', 'new': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
						__lowerCamelCase					:	int	       =				{'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
						assign_to_checkpoint(
						    A__							,				A__							,				A__							,				additional_replacements=[meta_path, resnet_op]							,				config=A__     )

						if len(A__     ):
									__lowerCamelCase					:	Dict	       =				renew_attention_paths(A__     )
									__lowerCamelCase					:	Any	       =				{
									    'old': f'''input_blocks.{i}.1''',
									    'new': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
									}
									__lowerCamelCase					:	Union[str, Any]	       =				{
									    f'''input_blocks.{i}.1.qkv.bias''': {
									        'key': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
									        'query': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
									        'value': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
									    },
									    f'''input_blocks.{i}.1.qkv.weight''': {
									        'key': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
									        'query': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
									        'value': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
									    },
									}
									assign_to_checkpoint(
									    A__							,				A__							,				A__							,				additional_replacements=[meta_path]							,				attention_paths_to_split=A__							,				config=A__							,				)

			__lowerCamelCase					:	Optional[int]	       =				middle_blocks[0]
			__lowerCamelCase					:	List[Any]	       =				middle_blocks[1]
			__lowerCamelCase					:	Tuple	       =				middle_blocks[2]

			__lowerCamelCase					:	Dict	       =				renew_resnet_paths(A__     )
			assign_to_checkpoint(A__							,				A__							,				A__							,				config=A__     )

			__lowerCamelCase					:	Any	       =				renew_resnet_paths(A__     )
			assign_to_checkpoint(A__							,				A__							,				A__							,				config=A__     )

			__lowerCamelCase					:	Tuple	       =				renew_attention_paths(A__     )
			__lowerCamelCase					:	int	       =				{
			    'middle_block.1.qkv.bias': {
			        'key': 'mid_block.attentions.0.key.bias',
			        'query': 'mid_block.attentions.0.query.bias',
			        'value': 'mid_block.attentions.0.value.bias',
			    },
			    'middle_block.1.qkv.weight': {
			        'key': 'mid_block.attentions.0.key.weight',
			        'query': 'mid_block.attentions.0.query.weight',
			        'value': 'mid_block.attentions.0.value.weight',
			    },
			}
			assign_to_checkpoint(
			    A__							,				A__							,				A__							,				attention_paths_to_split=A__							,				config=A__     )

			for i in range(A__     ):
						__lowerCamelCase					:	Optional[Any]	       =				i // (config['num_res_blocks'] + 1)
						__lowerCamelCase					:	int	       =				i % (config['num_res_blocks'] + 1)
						__lowerCamelCase					:	int	       =				[shave_segments(A__							,				2     ) for name in output_blocks[i]]
						__lowerCamelCase					:	List[str]	       =				{}

						for layer in output_block_layers:
									__lowerCamelCase       ,      __lowerCamelCase					:	int	       =				layer.split('.'     )[0], shave_segments(A__							,				1     )
									if layer_id in output_block_list:
												output_block_list[layer_id].append(A__     )
									else:
												__lowerCamelCase					:	Union[str, Any]	       =				[layer_name]

						if len(A__     ) > 1:
									__lowerCamelCase					:	Optional[int]	       =				[key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
									__lowerCamelCase					:	Union[str, Any]	       =				[key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]

									__lowerCamelCase					:	str	       =				renew_resnet_paths(A__     )
									__lowerCamelCase					:	Optional[int]	       =				renew_resnet_paths(A__     )

									__lowerCamelCase					:	Union[str, Any]	       =				{'old': f'''output_blocks.{i}.0''', 'new': f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
									assign_to_checkpoint(A__							,				A__							,				A__							,				additional_replacements=[meta_path]							,				config=A__     )

									if ["conv.weight", "conv.bias"] in output_block_list.values():
												__lowerCamelCase					:	List[Any]	       =				list(output_block_list.values()     ).index(['conv.weight', 'conv.bias']     )
												__lowerCamelCase					:	Any	       =				checkpoint[
												    f'''output_blocks.{i}.{index}.conv.weight'''
												]
												__lowerCamelCase					:	Optional[Any]	       =				checkpoint[
												    f'''output_blocks.{i}.{index}.conv.bias'''
												]

												# Clear attentions as they have been attributed above.
												if len(A__     ) == 2:
															__lowerCamelCase					:	str	       =				[]

									if len(A__     ):
												__lowerCamelCase					:	List[Any]	       =				renew_attention_paths(A__     )
												__lowerCamelCase					:	Optional[int]	       =				{
												    'old': f'''output_blocks.{i}.1''',
												    'new': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
												}
												__lowerCamelCase					:	int	       =				{
												    f'''output_blocks.{i}.1.qkv.bias''': {
												        'key': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
												        'query': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
												        'value': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
												    },
												    f'''output_blocks.{i}.1.qkv.weight''': {
												        'key': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
												        'query': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
												        'value': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
												    },
												}
												assign_to_checkpoint(
												    A__							,				A__							,				A__							,				additional_replacements=[meta_path]							,				attention_paths_to_split=to_split if any('qkv' in key for key in attentions     ) else None							,				config=A__							,				)
						else:
									__lowerCamelCase					:	Any	       =				renew_resnet_paths(A__							,				n_shave_prefix_segments=1     )
									for path in resnet_0_paths:
												__lowerCamelCase					:	Optional[Any]	       =				'.'.join(['output_blocks', str(A__     ), path['old']]     )
												__lowerCamelCase					:	Tuple	       =				'.'.join(['up_blocks', str(A__     ), 'resnets', str(A__     ), path['new']]     )

												__lowerCamelCase					:	Any	       =				checkpoint[old_path]

			return new_checkpoint


if __name__ == "__main__":
					a_				:    Union[str, Any]										=     argparse.ArgumentParser()

					parser.add_argument(
					    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
					)

					parser.add_argument(
					    '''--config_file''',
					    default=None,
					    type=str,
					    required=True,
					    help='''The config json file corresponding to the architecture.''',
					)

					parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

					a_				:    Dict										=     parser.parse_args()

					a_				:    List[Any]										=     torch.load(args.checkpoint_path)

					with open(args.config_file) as f:
										a_				:    List[str]										=     json.loads(f.read())

					a_				:    Union[str, Any]										=     convert_ldm_checkpoint(checkpoint, config)

					if "ldm" in config:
										del config["ldm"]

					a_				:    Any										=     UNetaDModel(**config)
					model.load_state_dict(converted_checkpoint)

					try:
										a_				:    Tuple										=     DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
										a_				:    Optional[Any]										=     VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

										a_				:    Optional[Any]										=     LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
										pipe.save_pretrained(args.dump_path)
					except:  # noqa: E722
										model.save_pretrained(args.dump_path)









"""simple docstring"""
from __future__ import annotations

import pandas as pd





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    list[int]							,				A__:    int     )  ->     list[int]:
			__lowerCamelCase					:	List[Any]	       =				[0] * no_of_processes
			__lowerCamelCase					:	Any	       =				[0] * no_of_processes
			# Copy the burst time into remaining_time[]
			for i in range(A__     ):
						__lowerCamelCase					:	Dict	       =				burst_time[i]

			__lowerCamelCase					:	Any	       =				0
			__lowerCamelCase					:	Tuple	       =				0
			__lowerCamelCase					:	Union[str, Any]	       =				999999999
			__lowerCamelCase					:	str	       =				0
			__lowerCamelCase					:	Optional[int]	       =				False

			# Process until all processes are completed
			while complete != no_of_processes:
						for j in range(A__     ):
									if arrival_time[j] <= increment_time and remaining_time[j] > 0:
												if remaining_time[j] < minm:
															__lowerCamelCase					:	List[Any]	       =				remaining_time[j]
															__lowerCamelCase					:	List[str]	       =				j
															__lowerCamelCase					:	Optional[int]	       =				True

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

						__lowerCamelCase					:	Optional[Any]	       =				remaining_time[short]
						if minm == 0:
									__lowerCamelCase					:	Optional[int]	       =				999999999

						if remaining_time[short] == 0:
									complete += 1
									__lowerCamelCase					:	List[str]	       =				False

									# Find finish time of current process
									__lowerCamelCase					:	Dict	       =				increment_time + 1

									# Calculate waiting time
									__lowerCamelCase					:	Any	       =				finish_time - arrival_time[short]
									__lowerCamelCase					:	Dict	       =				finar - burst_time[short]

									if waiting_time[short] < 0:
												__lowerCamelCase					:	Optional[Any]	       =				0

        # Increment time
						increment_time += 1
			return waiting_time





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    int							,				A__:    list[int]     )  ->     list[int]:
			__lowerCamelCase					:	List[Any]	       =				[0] * no_of_processes
			for i in range(A__     ):
						__lowerCamelCase					:	Tuple	       =				burst_time[i] + waiting_time[i]
			return turn_around_time





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    list[int]							,				A__:    int     )  ->     None:
			__lowerCamelCase					:	int	       =				0
			__lowerCamelCase					:	Dict	       =				0
			for i in range(A__     ):
						__lowerCamelCase					:	str	       =				total_waiting_time + waiting_time[i]
						__lowerCamelCase					:	Union[str, Any]	       =				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''')
					a_				:    int										=     int(input())
					a_				:    List[str]										=     [0] * no_of_processes
					a_				:    int										=     [0] * no_of_processes
					a_				:    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))
										a_						,							a_				:    Union[str, Any]										=     map(int, input().split())

					a_				:    Any										=     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

					a_				:    List[str]										=     burst_time
					a_				:    List[Any]										=     no_of_processes
					a_				:    Tuple										=     waiting_time
					a_				:    Optional[Any]										=     calculate_turnaroundtime(bt, n, wt)

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					a_				:    List[Any]										=     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)



