






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

def      _lowerCamelCase	(						__A    :							int )      ->					List[Any]:
    # vision encoder
    if "img_encoder.pos_embed" in name:
        _UpperCAmelCase				:      Any			  =				name.replace('''img_encoder.pos_embed'''		,		'''vision_model.embeddings.position_embeddings''' )
    if "img_encoder.patch_embed.proj" in name:
        _UpperCAmelCase				:      List[Any]			  =				name.replace('''img_encoder.patch_embed.proj'''		,		'''vision_model.embeddings.patch_embeddings.projection''' )
    if "img_encoder.patch_embed.norm" in name:
        _UpperCAmelCase				:      Optional[int]			  =				name.replace('''img_encoder.patch_embed.norm'''		,		'''vision_model.embeddings.layernorm''' )
    if "img_encoder.layers" in name:
        _UpperCAmelCase				:      List[Any]			  =				name.replace('''img_encoder.layers'''		,		'''vision_model.encoder.stages''' )
    if "blocks" in name and "res" not in name:
        _UpperCAmelCase				:      int			  =				name.replace('''blocks'''		,		'''layers''' )
    if "attn" in name and "pre_assign" not in name:
        _UpperCAmelCase				:      int			  =				name.replace('''attn'''		,		'''self_attn''' )
    if "proj" in name and "self_attn" in name and "text" not in name:
        _UpperCAmelCase				:      Optional[Any]			  =				name.replace('''proj'''		,		'''out_proj''' )
    if "pre_assign_attn.attn.proj" in name:
        _UpperCAmelCase				:      Any			  =				name.replace('''pre_assign_attn.attn.proj'''		,		'''pre_assign_attn.attn.out_proj''' )
    if "norm1" in name:
        _UpperCAmelCase				:      List[str]			  =				name.replace('''norm1'''		,		'''layer_norm1''' )
    if "norm2" in name and "pre_assign" not in name:
        _UpperCAmelCase				:      Optional[Any]			  =				name.replace('''norm2'''		,		'''layer_norm2''' )
    if "img_encoder.norm" in name:
        _UpperCAmelCase				:      Optional[int]			  =				name.replace('''img_encoder.norm'''		,		'''vision_model.layernorm''' )
    # text encoder
    if "text_encoder.token_embedding" in name:
        _UpperCAmelCase				:      List[str]			  =				name.replace('''text_encoder.token_embedding'''		,		'''text_model.embeddings.token_embedding''' )
    if "text_encoder.positional_embedding" in name:
        _UpperCAmelCase				:      Tuple			  =				name.replace('''text_encoder.positional_embedding'''		,		'''text_model.embeddings.position_embedding.weight''' )
    if "text_encoder.transformer.resblocks." in name:
        _UpperCAmelCase				:      Any			  =				name.replace('''text_encoder.transformer.resblocks.'''		,		'''text_model.encoder.layers.''' )
    if "ln_1" in name:
        _UpperCAmelCase				:      Any			  =				name.replace('''ln_1'''		,		'''layer_norm1''' )
    if "ln_2" in name:
        _UpperCAmelCase				:      Dict			  =				name.replace('''ln_2'''		,		'''layer_norm2''' )
    if "c_fc" in name:
        _UpperCAmelCase				:      List[Any]			  =				name.replace('''c_fc'''		,		'''fc1''' )
    if "c_proj" in name:
        _UpperCAmelCase				:      Union[str, Any]			  =				name.replace('''c_proj'''		,		'''fc2''' )
    if "text_encoder" in name:
        _UpperCAmelCase				:      Dict			  =				name.replace('''text_encoder'''		,		'''text_model''' )
    if "ln_final" in name:
        _UpperCAmelCase				:      Optional[int]			  =				name.replace('''ln_final'''		,		'''final_layer_norm''' )
    # projection layers
    if "img_projector.linear_hidden." in name:
        _UpperCAmelCase				:      Optional[int]			  =				name.replace('''img_projector.linear_hidden.'''		,		'''visual_projection.''' )
    if "img_projector.linear_out." in name:
        _UpperCAmelCase				:      str			  =				name.replace('''img_projector.linear_out.'''		,		'''visual_projection.3.''' )
    if "text_projector.linear_hidden" in name:
        _UpperCAmelCase				:      Optional[Any]			  =				name.replace('''text_projector.linear_hidden'''		,		'''text_projection''' )
    if "text_projector.linear_out" in name:
        _UpperCAmelCase				:      Dict			  =				name.replace('''text_projector.linear_out'''		,		'''text_projection.3''' )

    return name

def      _lowerCamelCase	(						__A    :							Any		,		__A    :							Union[str, Any] )      ->					List[Any]:
    for key in orig_state_dict.copy().keys():
        _UpperCAmelCase				:      Optional[int]			  =				orig_state_dict.pop(__A )

        if "qkv" in key:
            # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
            # we need to split them up into separate matrices/vectors
            _UpperCAmelCase				:      int			  =				key.split('''.''' )
            _UpperCAmelCase  , _UpperCAmelCase				:      Optional[int]			  =				int(key_split[2] ), int(key_split[4] )
            _UpperCAmelCase				:      Dict			  =				config.vision_config.hidden_size
            if "weight" in key:
                _UpperCAmelCase				:      Union[str, Any]			  =				val[:dim, :]
                _UpperCAmelCase				:      str			  =				val[dim : dim * 2, :]
                _UpperCAmelCase				:      List[str]			  =				val[-dim:, :]
            else:
                _UpperCAmelCase				:      Optional[Any]			  =				val[:dim]
                _UpperCAmelCase				:      Dict			  =				val[dim : dim * 2]
                _UpperCAmelCase				:      Union[str, Any]			  =				val[-dim:]
        elif "in_proj" in key:
            # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
            # we need to split them up into separate matrices/vectors
            _UpperCAmelCase				:      Union[str, Any]			  =				key.split('''.''' )
            _UpperCAmelCase				:      int			  =				int(key_split[3] )
            _UpperCAmelCase				:      int			  =				config.text_config.hidden_size
            if "weight" in key:
                _UpperCAmelCase				:      Any			  =				val[:dim, :]
                _UpperCAmelCase				:      int			  =				val[
                    dim : dim * 2, :
                ]
                _UpperCAmelCase				:      List[str]			  =				val[-dim:, :]
            else:
                _UpperCAmelCase				:      Dict			  =				val[:dim]
                _UpperCAmelCase				:      List[str]			  =				val[dim : dim * 2]
                _UpperCAmelCase				:      Dict			  =				val[-dim:]
        else:
            _UpperCAmelCase				:      Any			  =				rename_key(__A )
            # squeeze if necessary
            if (
                "text_projection.0" in new_name
                or "text_projection.3" in new_name
                or "visual_projection.0" in new_name
                or "visual_projection.3" in new_name
            ):
                _UpperCAmelCase				:      Union[str, Any]			  =				val.squeeze_()
            else:
                _UpperCAmelCase				:      List[str]			  =				val

    return orig_state_dict

def      _lowerCamelCase	(						)      ->					List[str]:
    _UpperCAmelCase				:      Any			  =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    _UpperCAmelCase				:      List[str]			  =				Image.open(requests.get(__A		,		stream=__A ).raw )
    return im

@torch.no_grad()
def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							Optional[Any]		,		__A    :							List[str]="groupvit-gcc-yfcc"		,		__A    :							Tuple=False )      ->					Union[str, Any]:
    _UpperCAmelCase				:      Optional[Any]			  =				GroupViTConfig()
    _UpperCAmelCase				:      List[Any]			  =				GroupViTModel(__A ).eval()

    _UpperCAmelCase				:      List[Any]			  =				torch.load(__A		,		map_location='''cpu''' )['''model''']
    _UpperCAmelCase				:      int			  =				convert_state_dict(__A		,		__A )
    _UpperCAmelCase  , _UpperCAmelCase				:      int			  =				model.load_state_dict(__A		,		strict=__A )
    assert missing_keys == ["text_model.embeddings.position_ids"]
    assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__A ) == 0)

    # verify result
    _UpperCAmelCase				:      Union[str, Any]			  =				CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
    _UpperCAmelCase				:      int			  =				prepare_img()
    _UpperCAmelCase				:      Union[str, Any]			  =				processor(text=['''a photo of a cat''', '''a photo of a dog''']		,		images=__A		,		padding=__A		,		return_tensors='''pt''' )

    with torch.no_grad():
        _UpperCAmelCase				:      List[Any]			  =				model(**__A )

    if model_name == "groupvit-gcc-yfcc":
        _UpperCAmelCase				:      Dict			  =				torch.tensor([[13.3523, 6.3629]] )
    elif model_name == "groupvit-gcc-redcaps":
        _UpperCAmelCase				:      Union[str, Any]			  =				torch.tensor([[16.1873, 8.6230]] )
    else:
        raise ValueError(f'''Model name {model_name} not supported.''' )
    assert torch.allclose(outputs.logits_per_image		,		__A		,		atol=1e-3 )

    processor.save_pretrained(__A )
    model.save_pretrained(__A )
    print('''Successfully saved processor and model to'''		,		__A )

    if push_to_hub:
        print('''Pushing to the hub...''' )
        processor.push_to_hub(__A		,		organization='''nielsr''' )
        model.push_to_hub(__A		,		organization='''nielsr''' )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
  )
  parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
  parser.add_argument(
      '--model_name',
      default='groupvit-gccy-fcc',
      type=str,
      help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
  )
  parser.add_argument(
      '--push_to_hub',
      action='store_true',
      help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()

  convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)












import heapq

def      _lowerCamelCase	(						__A    :							dict )      ->					set[int]:
    _UpperCAmelCase				:      list[list]			  =				[]

    # 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(__A		,		[-1 * len(__A ), (key, value)] )

    # chosen_vertices = set of chosen vertices
    _UpperCAmelCase				:      Union[str, Any]			  =				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
        _UpperCAmelCase				:      Tuple			  =				heapq.heappop(__A )[1][0]
        chosen_vertices.add(__A )

        # 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]:
                _UpperCAmelCase				:      Dict			  =				elem[1][1].index(__A )
                del elem[1][1][index]
                elem[0] += 1
        # re-order the queue
        heapq.heapify(__A )
    return chosen_vertices


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  SCREAMING_SNAKE_CASE 							=						{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)}')




