import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification



def A__  (			lowercase:     Optional[int]					)				->		List[str]:
       A						:     int         =[]
       embed.append(
           (
               F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
               F'stage{idx}.patch_embed.proj.weight',
           )					)
       embed.append(
           (
               F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
               F'stage{idx}.patch_embed.proj.bias',
           )					)
       embed.append(
           (
               F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
               F'stage{idx}.patch_embed.norm.weight',
           )					)
       embed.append(
           (
               F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
               F'stage{idx}.patch_embed.norm.bias',
           )					)
       return embed



def A__  (			lowercase:     List[Any],		lowercase:     List[Any]					)				->		Any:
       A						:     Optional[Any]         =[]
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
               F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
               F'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
               F'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
               F'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
               F'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
               F'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
               F'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
               F'stage{idx}.blocks.{cnt}.attn.proj.weight',
           )					)
       attention_weights.append(
           (
               F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
               F'stage{idx}.blocks.{cnt}.attn.proj.bias',
           )					)
       attention_weights.append(
           (F'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', F'stage{idx}.blocks.{cnt}.mlp.fc1.weight')					)
       attention_weights.append(
           (F'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', F'stage{idx}.blocks.{cnt}.mlp.fc1.bias')					)
       attention_weights.append(
           (F'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', F'stage{idx}.blocks.{cnt}.mlp.fc2.weight')					)
       attention_weights.append(
           (F'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', F'stage{idx}.blocks.{cnt}.mlp.fc2.bias')					)
       attention_weights.append(
           (F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', F'stage{idx}.blocks.{cnt}.norm1.weight')					)
       attention_weights.append(
           (F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', F'stage{idx}.blocks.{cnt}.norm1.bias')					)
       attention_weights.append(
           (F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', F'stage{idx}.blocks.{cnt}.norm2.weight')					)
       attention_weights.append(
           (F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', F'stage{idx}.blocks.{cnt}.norm2.bias')					)
       return attention_weights



def A__  (			lowercase:     int					)				->		Dict:
       A						:     List[str]         =[]
       token.append((F'cvt.encoder.stages.{idx}.cls_token', 'stage2.cls_token')					)
       return token



def A__  (			)				->		Optional[Any]:
       A						:     Tuple         =[]
       head.append(('layernorm.weight', 'norm.weight')					)
       head.append(('layernorm.bias', 'norm.bias')					)
       head.append(('classifier.weight', 'head.weight')					)
       head.append(('classifier.bias', 'head.bias')					)
       return head



def A__  (			lowercase:     List[str],		lowercase:     Union[str, Any],		lowercase:     Optional[Any],		lowercase:     List[Any]					)				->		Union[str, Any]:
       A						:     List[Any]         ='imagenet-1k-id2label.json'
       A						:     Optional[Any]         =1_000

       A						:     Optional[Any]         ='huggingface/label-files'
       A						:     Dict         =num_labels
       A						:     Optional[int]         =json.load(open(cached_download(hf_hub_url(lowercase,		lowercase,		repo_type='dataset'					)					),		'r'					)					)
       A						:     Union[str, Any]         ={int(lowercase					): v for k, v in idalabel.items()}

       A						:     Union[str, Any]         =idalabel
       A						:     int         ={v: k for k, v in idalabel.items()}

       A						:     str         =CvtConfig(num_labels=lowercase,		idalabel=lowercase,		labelaid=lowercase					)

       # For depth size 13 (13 = 1+2+10)
       if cvt_model.rsplit('/',		1					)[-1][4:6] == "13":
              A						:     Tuple         =[1, 2, 10]

       # For depth size 21 (21 = 1+4+16)
       elif cvt_model.rsplit('/',		1					)[-1][4:6] == "21":
              A						:     List[str]         =[1, 4, 16]

       # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
       else:
              A						:     Optional[int]         =[2, 2, 20]
              A						:     List[str]         =[3, 12, 16]
              A						:     Tuple         =[192, 768, 1_024]

       A						:     Optional[int]         =CvtForImageClassification(lowercase					)
       A						:     List[str]         =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'					)
       A						:     Union[str, Any]         =image_size
       A						:     int         =torch.load(lowercase,		map_location=torch.device('cpu'					)					)

       A						:     Any         =OrderedDict()
       A						:     Tuple         =[]

       for idx in range(len(config.depth					)					):
              if config.cls_token[idx]:
                     A						:     Optional[Any]         =list_of_state_dict + cls_token(lowercase					)
              A						:     Optional[int]         =list_of_state_dict + embeddings(lowercase					)
              for cnt in range(config.depth[idx]					):
                     A						:     Dict         =list_of_state_dict + attention(lowercase,		lowercase					)

       A						:     str         =list_of_state_dict + final()
       for gg in list_of_state_dict:
              print(lowercase					)
       for i in range(len(lowercase					)					):
              A						:     Dict         =original_weights[list_of_state_dict[i][1]]

       model.load_state_dict(lowercase					)
       model.save_pretrained(lowercase					)
       image_processor.save_pretrained(lowercase					)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
    _lowercase							:      Optional[int]      							=argparse.ArgumentParser()
    parser.add_argument(
        '''--cvt_model''',
        default='''cvt-w24''',
        type=str,
        help='''Name of the cvt model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--image_size''',
        default=3_8_4,
        type=int,
        help='''Input Image Size''',
    )
    parser.add_argument(
        '''--cvt_file_name''',
        default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
        type=str,
        help='''Input Image Size''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )

    _lowercase							:      Union[str, Any]      							=parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)







import heapq



def A__  (			lowercase:     dict					)				->		set[int]:
       A						:     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(lowercase,		[-1 * len(lowercase					), (key, value)]					)

       # chosen_vertices = set of chosen vertices
       A						:     Dict         =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
              A						:     List[str]         =heapq.heappop(lowercase					)[1][0]
              chosen_vertices.add(lowercase					)

              # 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]:
                            A						:     str         =elem[1][1].index(lowercase					)
                            del elem[1][1][index]
                            elem[0] += 1
        # re-order the queue
              heapq.heapify(lowercase					)
       return chosen_vertices


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    _lowercase							:      List[Any]      							={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)}''')






