


"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase     =  logging.get_logger(__name__)


def __lowercase     (      lowerCamelCase_  :      Any						,					lowerCamelCase_  :      Dict=False						,					lowerCamelCase_  :      int=False  ):
				SCREAMING_SNAKE_CASE__     							=				"""backbone.""" if is_semantic else """"""

				SCREAMING_SNAKE_CASE__     							=				[]
				for i in range(config.num_hidden_layers  ):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((F'''{prefix}blocks.{i}.norm1.weight''', F'''beit.encoder.layer.{i}.layernorm_before.weight''')  )
								rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''beit.encoder.layer.{i}.layernorm_before.bias''')  )
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''beit.encoder.layer.{i}.attention.output.dense.weight''')  )
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''beit.encoder.layer.{i}.attention.output.dense.bias''')  )
								rename_keys.append((F'''{prefix}blocks.{i}.norm2.weight''', F'''beit.encoder.layer.{i}.layernorm_after.weight''')  )
								rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''beit.encoder.layer.{i}.layernorm_after.bias''')  )
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''beit.encoder.layer.{i}.intermediate.dense.weight''')  )
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''beit.encoder.layer.{i}.intermediate.dense.bias''')  )
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''beit.encoder.layer.{i}.output.dense.weight''')  )
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''beit.encoder.layer.{i}.output.dense.bias''')  )

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        (F'''{prefix}cls_token''', "beit.embeddings.cls_token"),
				        (F'''{prefix}patch_embed.proj.weight''', "beit.embeddings.patch_embeddings.projection.weight"),
				        (F'''{prefix}patch_embed.proj.bias''', "beit.embeddings.patch_embeddings.projection.bias"),
				        (F'''{prefix}pos_embed''', "beit.embeddings.position_embeddings"),
				    ]  )

				if has_lm_head:
								# mask token + layernorm
								rename_keys.extend(
								    [
								        ("mask_token", "beit.embeddings.mask_token"),
								        ("norm.weight", "layernorm.weight"),
								        ("norm.bias", "layernorm.bias"),
								    ]  )
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("fc_norm.weight", "beit.pooler.layernorm.weight"),
								        ("fc_norm.bias", "beit.pooler.layernorm.bias"),
								        ("head.weight", "classifier.weight"),
								        ("head.bias", "classifier.bias"),
								    ]  )

				return rename_keys


def __lowercase     (      lowerCamelCase_  :      Any						,					lowerCamelCase_  :      str						,					lowerCamelCase_  :      Optional[Any]=False						,					lowerCamelCase_  :      int=False  ):





				for i in range(config.num_hidden_layers  ):
								SCREAMING_SNAKE_CASE__     							=				"""backbone.""" if is_semantic else """"""
								# queries, keys and values
								SCREAMING_SNAKE_CASE__     							=				state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''  )
								SCREAMING_SNAKE_CASE__     							=				state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''  )
								SCREAMING_SNAKE_CASE__     							=				state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''  )

								SCREAMING_SNAKE_CASE__     							=				in_proj_weight[
								    : config.hidden_size, :
								]
								SCREAMING_SNAKE_CASE__     							=				q_bias
								SCREAMING_SNAKE_CASE__     							=				in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								SCREAMING_SNAKE_CASE__     							=				in_proj_weight[
								    -config.hidden_size :, :
								]
								SCREAMING_SNAKE_CASE__     							=				v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								SCREAMING_SNAKE_CASE__     							=				state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''  )
								SCREAMING_SNAKE_CASE__     							=				state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''  )

								SCREAMING_SNAKE_CASE__     							=				gamma_a
								SCREAMING_SNAKE_CASE__     							=				gamma_a


def __lowercase     (      lowerCamelCase_  :      Tuple						,					lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      int  ):
				SCREAMING_SNAKE_CASE__     							=				dct.pop(SCREAMING_SNAKE_CASE__  )
				SCREAMING_SNAKE_CASE__     							=				val


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				SCREAMING_SNAKE_CASE__     							=				Image.open(requests.get(SCREAMING_SNAKE_CASE__						,					stream=SCREAMING_SNAKE_CASE__  ).raw  )
				return im


@torch.no_grad()
def __lowercase     (      lowerCamelCase_  :      Any						,					lowerCamelCase_  :      Optional[int]						,					lowerCamelCase_  :      Union[str, Any]=False  ):
				SCREAMING_SNAKE_CASE__     							=				False if """rvlcdip""" in checkpoint_url else True
				SCREAMING_SNAKE_CASE__     							=				BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE__						,					use_mask_token=SCREAMING_SNAKE_CASE__  )

				# size of the architecture
				if "large" in checkpoint_url or "dit-l" in checkpoint_url:
								SCREAMING_SNAKE_CASE__     							=				1024
								SCREAMING_SNAKE_CASE__     							=				4096
								SCREAMING_SNAKE_CASE__     							=				24
								SCREAMING_SNAKE_CASE__     							=				16

				# labels
				if "rvlcdip" in checkpoint_url:
								SCREAMING_SNAKE_CASE__     							=				16
								SCREAMING_SNAKE_CASE__     							=				"""huggingface/label-files"""
								SCREAMING_SNAKE_CASE__     							=				"""rvlcdip-id2label.json"""
								SCREAMING_SNAKE_CASE__     							=				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__						,					SCREAMING_SNAKE_CASE__						,					repo_type="dataset"  )						,					"r"  )  )
								SCREAMING_SNAKE_CASE__     							=				{int(SCREAMING_SNAKE_CASE__  ): v for k, v in idalabel.items()}
								SCREAMING_SNAKE_CASE__     							=				idalabel
								SCREAMING_SNAKE_CASE__     							=				{v: k for k, v in idalabel.items()}

				# load state_dict of original model, remove and rename some keys
				SCREAMING_SNAKE_CASE__     							=				torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__						,					map_location="cpu"  )["""model"""]

				SCREAMING_SNAKE_CASE__     							=				create_rename_keys(SCREAMING_SNAKE_CASE__						,					has_lm_head=SCREAMING_SNAKE_CASE__  )
				for src, dest in rename_keys:
								rename_key(SCREAMING_SNAKE_CASE__						,					SCREAMING_SNAKE_CASE__						,					SCREAMING_SNAKE_CASE__  )
				read_in_q_k_v(SCREAMING_SNAKE_CASE__						,					SCREAMING_SNAKE_CASE__						,					has_lm_head=SCREAMING_SNAKE_CASE__  )

				# load HuggingFace model
				SCREAMING_SNAKE_CASE__     							=				BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE__  ) if has_lm_head else BeitForImageClassification(SCREAMING_SNAKE_CASE__  )
				model.eval()
				model.load_state_dict(SCREAMING_SNAKE_CASE__  )

				# Check outputs on an image
				SCREAMING_SNAKE_CASE__     							=				BeitImageProcessor(
				    size=config.image_size						,					resample=PILImageResampling.BILINEAR						,					do_center_crop=SCREAMING_SNAKE_CASE__  )
				SCREAMING_SNAKE_CASE__     							=				prepare_img()

				SCREAMING_SNAKE_CASE__     							=				image_processor(images=SCREAMING_SNAKE_CASE__						,					return_tensors="pt"  )
				SCREAMING_SNAKE_CASE__     							=				encoding["""pixel_values"""]

				SCREAMING_SNAKE_CASE__     							=				model(SCREAMING_SNAKE_CASE__  )
				SCREAMING_SNAKE_CASE__     							=				outputs.logits

				# verify logits
				SCREAMING_SNAKE_CASE__     							=				[1, 16] if """rvlcdip""" in checkpoint_url else [1, 196, 8192]
				assert logits.shape == torch.Size(SCREAMING_SNAKE_CASE__  ), "Shape of logits not as expected"

				Path(SCREAMING_SNAKE_CASE__  ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__  )
				print(F'''Saving model to {pytorch_dump_folder_path}'''  )
				model.save_pretrained(SCREAMING_SNAKE_CASE__  )
				print(F'''Saving image processor to {pytorch_dump_folder_path}'''  )
				image_processor.save_pretrained(SCREAMING_SNAKE_CASE__  )

				if push_to_hub:
								if has_lm_head:
												SCREAMING_SNAKE_CASE__     							=				"""dit-base""" if """base""" in checkpoint_url else """dit-large"""
								else:
												SCREAMING_SNAKE_CASE__     							=				"""dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
								image_processor.push_to_hub(
								    repo_path_or_name=Path(SCREAMING_SNAKE_CASE__						,					SCREAMING_SNAKE_CASE__  )						,					organization="nielsr"						,					commit_message="Add image processor"						,					use_temp_dir=SCREAMING_SNAKE_CASE__						,					)
								model.push_to_hub(
								    repo_path_or_name=Path(SCREAMING_SNAKE_CASE__						,					SCREAMING_SNAKE_CASE__  )						,					organization="nielsr"						,					commit_message="Add model"						,					use_temp_dir=SCREAMING_SNAKE_CASE__						,					)


if __name__ == "__main__":
	_lowerCamelCase     =  argparse.ArgumentParser()

	parser.add_argument(
	    '--checkpoint_url',
	    default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
	    type=str,
	    help='URL to the original PyTorch checkpoint (.pth file).',
	)
	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',
	)
	_lowerCamelCase     =  parser.parse_args()
	convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""






import heapq


def __lowercase     (      lowerCamelCase_  :      dict  ):
				SCREAMING_SNAKE_CASE__     							=				[]

				# for each node and his adjacency list add them and the rank of the node to queue
				# using heapq module the queue will be filled like a Priority Queue
				# heapq works with a min priority queue, so I used -1*len(v) to build it
				for key, value in graph.items():
								# O(log(n))
								heapq.heappush(lowerCamelCase_						,					[-1 * len(lowerCamelCase_  ), (key, value)]  )

				# chosen_vertices = set of chosen vertices
				SCREAMING_SNAKE_CASE__     							=				set()

				# while queue isn't empty and there are still edges
				#   (queue[0][0] is the rank of the node with max rank)
				while queue and queue[0][0] != 0:
								# extract vertex with max rank from queue and add it to chosen_vertices
								SCREAMING_SNAKE_CASE__     							=				heapq.heappop(lowerCamelCase_  )[1][0]
								chosen_vertices.add(lowerCamelCase_  )

								# Remove all arcs adjacent to argmax
								for elem in queue:
												# if v haven't adjacent node, skip
												if elem[0] == 0:
																continue
												# if argmax is reachable from elem
												# remove argmax from elem's adjacent list and update his rank
												if argmax in elem[1][1]:
																SCREAMING_SNAKE_CASE__     							=				elem[1][1].index(lowerCamelCase_  )
																del elem[1][1][index]
																elem[0] += 1
        # re-order the queue
								heapq.heapify(lowerCamelCase_  )
				return chosen_vertices


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowerCamelCase     =  {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
	print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
