
import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__		:Union[str, Any]			  =   logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase__		: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}.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 encoder + 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.encoder.norm.weight", "encoder.layernorm.weight"),
        ("transformer.encoder.norm.bias", "encoder.layernorm.bias"),
        ("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   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						state_dict.pop(lowerCAmelCase__		)
					lowercase						     =						val



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					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   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						''''''

					# 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   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase     ,						lowercase						     =						image.size
					lowercase						     =						max(lowerCAmelCase__			,      lowerCAmelCase__		)
					lowercase						     =						800 if '''detection''' in checkpoint_url else 1000
					lowercase						     =						target_max_size / current_max_size
					lowercase						     =						image.resize((int(round(scale * width		)		), int(round(scale * height		)		))		)

					return resized_image



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						F.to_tensor(lowerCAmelCase__		)
					lowercase						     =						F.normalize(lowerCAmelCase__			,      mean=[0.4_85, 0.4_56, 0.4_06]			,      std=[0.2_29, 0.2_24, 0.2_25]		)
					return image



@torch.no_grad()
def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					logger.info('''Converting model...'''		)

					# load original state dict
					lowercase						     =						torch.hub.load_state_dict_from_url(lowerCAmelCase__			,      map_location='''cpu'''		)
					# rename keys
					for src, dest in rename_keys:
										rename_key(lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		)
					lowercase						     =						rename_backbone_keys(lowerCAmelCase__		)
					# query, key and value matrices need special treatment
					read_in_q_k_v(lowerCAmelCase__		)
					# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
					lowercase						     =						'''model.'''
					for key in state_dict.copy().keys():
										if not key.startswith('''class_labels_classifier'''		) and not key.startswith('''bbox_predictor'''		):
															lowercase						     =						state_dict.pop(lowerCAmelCase__		)
															lowercase						     =						val
    # create HuggingFace model and load state dict
					lowercase						     =						TableTransformerConfig(
					    backbone='''resnet18'''			,      mask_loss_coefficient=1			,      dice_loss_coefficient=1			,      ce_loss_coefficient=1			,      bbox_loss_coefficient=5			,      giou_loss_coefficient=2			,      eos_coefficient=0.4			,      class_cost=1			,      bbox_cost=5			,      giou_cost=2			,      )

					if "detection" in checkpoint_url:
										lowercase						     =						15
										lowercase						     =						2
										lowercase						     =						{0: '''table''', 1: '''table rotated'''}
										lowercase						     =						idalabel
										lowercase						     =						{v: k for k, v in idalabel.items()}
					else:
										lowercase						     =						125
										lowercase						     =						6
										lowercase						     =						{
										    0: '''table''',
										    1: '''table column''',
										    2: '''table row''',
										    3: '''table column header''',
										    4: '''table projected row header''',
										    5: '''table spanning cell''',
										}
										lowercase						     =						idalabel
										lowercase						     =						{v: k for k, v in idalabel.items()}

					lowercase						     =						DetrImageProcessor(
					    format='''coco_detection'''			,      max_size=800 if '''detection''' in checkpoint_url else 1000		)
					lowercase						     =						TableTransformerForObjectDetection(lowerCAmelCase__		)
					model.load_state_dict(lowerCAmelCase__		)
					model.eval()

					# verify our conversion
					lowercase						     =						'''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
					lowercase						     =						hf_hub_download(repo_id='''nielsr/example-pdf'''			,      repo_type='''dataset'''			,      filename=lowerCAmelCase__		)
					lowercase						     =						Image.open(lowerCAmelCase__		).convert('''RGB'''		)
					lowercase						     =						normalize(resize(lowerCAmelCase__			,      lowerCAmelCase__		)		).unsqueeze(0		)

					lowercase						     =						model(lowerCAmelCase__		)

					if "detection" in checkpoint_url:
										lowercase						     =						(1, 15, 3)
										lowercase						     =						torch.tensor(
										    [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]		)
										lowercase						     =						torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]		)

					else:
										lowercase						     =						(1, 125, 7)
										lowercase						     =						torch.tensor(
										    [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]		)
										lowercase						     =						torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]		)

					assert outputs.logits.shape == expected_shape
					assert torch.allclose(outputs.logits[0, :3, :3]			,      lowerCAmelCase__			,      atol=1E-4		)
					assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,      lowerCAmelCase__			,      atol=1E-4		)
					print('''Looks ok!'''		)

					if pytorch_dump_folder_path is not None:
										# Save model and image processor
										logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'		)
										Path(lowerCAmelCase__		).mkdir(exist_ok=lowerCAmelCase__		)
										model.save_pretrained(lowerCAmelCase__		)
										image_processor.save_pretrained(lowerCAmelCase__		)

					if push_to_hub:
										# Push model to HF hub
										logger.info('''Pushing model to the hub...'''		)
										lowercase						     =						(
										    '''microsoft/table-transformer-detection'''
										    if '''detection''' in checkpoint_url
										    else '''microsoft/table-transformer-structure-recognition'''
										)
										model.push_to_hub(lowerCAmelCase__		)
										image_processor.push_to_hub(lowerCAmelCase__		)


if __name__ == "__main__":
							lowercase__		:Dict			  =   argparse.ArgumentParser()

							parser.add_argument(
							    "--checkpoint_url",
							    default="https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth",
							    type=str,
							    choices=[
							        "https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth",
							        "https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth",
							    ],
							    help="URL of the Table Transformer checkpoint 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."
							)
							parser.add_argument(
							    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
							)
							lowercase__		:List[str]			  =   parser.parse_args()
							convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






from statistics import mean

import numpy as np



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						0
					# Number of processes finished
					lowercase						     =						0
					# Displays the finished process.
					# If it is 0, the performance is completed if it is 1, before the performance.
					lowercase						     =						[0] * no_of_process
					# List to include calculation results
					lowercase						     =						[0] * no_of_process

					# Sort by arrival time.
					lowercase						     =						[burst_time[i] for i in np.argsort(lowerCAmelCase__		)]
					lowercase						     =						[process_name[i] for i in np.argsort(lowerCAmelCase__		)]
					arrival_time.sort()

					while no_of_process > finished_process_count:
										lowercase						     =						0
										while finished_process[i] == 1:
															i += 1
										if current_time < arrival_time[i]:
															lowercase						     =						arrival_time[i]

										lowercase						     =						0
										# Index showing the location of the process being performed
										lowercase						     =						0
										# Saves the current response ratio.
										lowercase						     =						0
										for i in range(0			,      lowerCAmelCase__		):
															if finished_process[i] == 0 and arrival_time[i] <= current_time:
																				lowercase						     =						(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
																				    i
																				]
															if response_ratio < temp:
																				lowercase						     =						temp
																				lowercase						     =						i

        # Calculate the turn around time
										lowercase						     =						current_time + burst_time[loc] - arrival_time[loc]
										current_time += burst_time[loc]
										# Indicates that the process has been performed.
										lowercase						     =						1
										# Increase finished_process_count by 1
										finished_process_count += 1

					return turn_around_time



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						[0] * no_of_process
					for i in range(0			,      lowerCAmelCase__		):
										lowercase						     =						turn_around_time[i] - burst_time[i]
					return waiting_time


if __name__ == "__main__":
							lowercase__		:Dict			  =   5
							lowercase__		:str			  =   ["A", "B", "C", "D", "E"]
							lowercase__		:Optional[int]			  =   [1, 2, 3, 4, 5]
							lowercase__		:List[Any]			  =   [1, 2, 3, 4, 5]

							lowercase__		:List[str]			  =   calculate_turn_around_time(
							    process_name, arrival_time, burst_time, no_of_process
							)
							lowercase__		:List[Any]			  =   calculate_waiting_time(
							    process_name, turn_around_time, burst_time, no_of_process
							)

							print("Process name \tArrival time \tBurst time \tTurn around time \tWaiting time")
							for i in range(0, no_of_process):
														print(
														    F'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
														    F'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
														)

							print(F'average waiting time : {mean(waiting_time):.5f}')
							print(F'average turn around time : {mean(turn_around_time):.5f}')




