import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
    _lowercase							:      str      							=argparse.ArgumentParser()

    parser.add_argument(
        '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
    )
    parser.add_argument(
        '''--original_config_file''',
        type=str,
        required=True,
        help='''The YAML config file corresponding to the original architecture.''',
    )
    parser.add_argument(
        '''--num_in_channels''',
        default=None,
        type=int,
        help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
    )
    parser.add_argument(
        '''--image_size''',
        default=5_1_2,
        type=int,
        help=(
            '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
            ''' Base. Use 768 for Stable Diffusion v2.'''
        ),
    )
    parser.add_argument(
        '''--extract_ema''',
        action='''store_true''',
        help=(
            '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
            ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
            ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
        ),
    )
    parser.add_argument(
        '''--upcast_attention''',
        action='''store_true''',
        help=(
            '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
            ''' diffusion 2.1.'''
        ),
    )
    parser.add_argument(
        '''--from_safetensors''',
        action='''store_true''',
        help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
    )
    parser.add_argument(
        '''--to_safetensors''',
        action='''store_true''',
        help='''Whether to store pipeline in safetensors format or not.''',
    )
    parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
    parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')



    def A__  (			lowercase:     Union[str, Any]					)				->		Any:
           if string == "True":
                  return True
           elif string == "False":
                  return False
           else:
                  raise ValueError(F'could not parse string as bool {string}'					)

    parser.add_argument(
        '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
    )

    parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

    _lowercase							:      List[str]      							=parser.parse_args()

    _lowercase							:      Optional[int]      							=download_controlnet_from_original_ckpt(
        checkpoint_path=args.checkpoint_path,
        original_config_file=args.original_config_file,
        image_size=args.image_size,
        extract_ema=args.extract_ema,
        num_in_channels=args.num_in_channels,
        upcast_attention=args.upcast_attention,
        from_safetensors=args.from_safetensors,
        device=args.device,
        use_linear_projection=args.use_linear_projection,
        cross_attention_dim=args.cross_attention_dim,
    )
    controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)







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)}''')






