
# using dfs for finding eulerian path traversal





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase=None				):
    snake_case__    =					(path or []) + [u]
    for v in graph[u]:
        if visited_edge[u][v] is False:
            snake_case__    ,       snake_case__    =					True, True
            snake_case__    =					dfs(__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				)
    return path





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					0
    snake_case__    =					-1
    for i in range(__lowerCAmelCase				):
        if i not in graph.keys():
            continue
        if len(graph[i]				) % 2 == 1:
            odd_degree_nodes += 1
            snake_case__    =					i
    if odd_degree_nodes == 0:
        return 1, odd_node
    if odd_degree_nodes == 2:
        return 2, odd_node
    return 3, odd_node





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					[[False for _ in range(max_node + 1				)] for _ in range(max_node + 1				)]
    snake_case__    ,       snake_case__    =					check_circuit_or_path(__lowerCAmelCase     ,		__lowerCAmelCase				)
    if check == 3:
        print("graph is not Eulerian"				)
        print("no path"				)
        return
    snake_case__    =					1
    if check == 2:
        snake_case__    =					odd_node
        print("graph has a Euler path"				)
    if check == 1:
        print("graph has a Euler cycle"				)
    snake_case__    =					dfs(__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				)
    print(__lowerCAmelCase				)





def 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					{1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
    snake_case__    =					{1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
    snake_case__    =					{1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
    snake_case__    =					{1: [2, 3], 2: [1, 3], 3: [1, 2]}
    snake_case__    =					{
        1: [],
        2: []
        # all degree is zero
    }
    snake_case__    =					10
    check_euler(__lowerCAmelCase     ,		__lowerCAmelCase				)
    check_euler(__lowerCAmelCase     ,		__lowerCAmelCase				)
    check_euler(__lowerCAmelCase     ,		__lowerCAmelCase				)
    check_euler(__lowerCAmelCase     ,		__lowerCAmelCase				)
    check_euler(__lowerCAmelCase     ,		__lowerCAmelCase				)


if __name__ == "__main__":
     main()





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    # vision encoder
    if "img_encoder.pos_embed" in name:
        snake_case__    =					name.replace("img_encoder.pos_embed"     ,		"vision_model.embeddings.position_embeddings"				)
    if "img_encoder.patch_embed.proj" in name:
        snake_case__    =					name.replace("img_encoder.patch_embed.proj"     ,		"vision_model.embeddings.patch_embeddings.projection"				)
    if "img_encoder.patch_embed.norm" in name:
        snake_case__    =					name.replace("img_encoder.patch_embed.norm"     ,		"vision_model.embeddings.layernorm"				)
    if "img_encoder.layers" in name:
        snake_case__    =					name.replace("img_encoder.layers"     ,		"vision_model.encoder.stages"				)
    if "blocks" in name and "res" not in name:
        snake_case__    =					name.replace("blocks"     ,		"layers"				)
    if "attn" in name and "pre_assign" not in name:
        snake_case__    =					name.replace("attn"     ,		"self_attn"				)
    if "proj" in name and "self_attn" in name and "text" not in name:
        snake_case__    =					name.replace("proj"     ,		"out_proj"				)
    if "pre_assign_attn.attn.proj" in name:
        snake_case__    =					name.replace("pre_assign_attn.attn.proj"     ,		"pre_assign_attn.attn.out_proj"				)
    if "norm1" in name:
        snake_case__    =					name.replace("norm1"     ,		"layer_norm1"				)
    if "norm2" in name and "pre_assign" not in name:
        snake_case__    =					name.replace("norm2"     ,		"layer_norm2"				)
    if "img_encoder.norm" in name:
        snake_case__    =					name.replace("img_encoder.norm"     ,		"vision_model.layernorm"				)
    # text encoder
    if "text_encoder.token_embedding" in name:
        snake_case__    =					name.replace("text_encoder.token_embedding"     ,		"text_model.embeddings.token_embedding"				)
    if "text_encoder.positional_embedding" in name:
        snake_case__    =					name.replace("text_encoder.positional_embedding"     ,		"text_model.embeddings.position_embedding.weight"				)
    if "text_encoder.transformer.resblocks." in name:
        snake_case__    =					name.replace("text_encoder.transformer.resblocks."     ,		"text_model.encoder.layers."				)
    if "ln_1" in name:
        snake_case__    =					name.replace("ln_1"     ,		"layer_norm1"				)
    if "ln_2" in name:
        snake_case__    =					name.replace("ln_2"     ,		"layer_norm2"				)
    if "c_fc" in name:
        snake_case__    =					name.replace("c_fc"     ,		"fc1"				)
    if "c_proj" in name:
        snake_case__    =					name.replace("c_proj"     ,		"fc2"				)
    if "text_encoder" in name:
        snake_case__    =					name.replace("text_encoder"     ,		"text_model"				)
    if "ln_final" in name:
        snake_case__    =					name.replace("ln_final"     ,		"final_layer_norm"				)
    # projection layers
    if "img_projector.linear_hidden." in name:
        snake_case__    =					name.replace("img_projector.linear_hidden."     ,		"visual_projection."				)
    if "img_projector.linear_out." in name:
        snake_case__    =					name.replace("img_projector.linear_out."     ,		"visual_projection.3."				)
    if "text_projector.linear_hidden" in name:
        snake_case__    =					name.replace("text_projector.linear_hidden"     ,		"text_projection"				)
    if "text_projector.linear_out" in name:
        snake_case__    =					name.replace("text_projector.linear_out"     ,		"text_projection.3"				)

    return name





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    for key in orig_state_dict.copy().keys():
        snake_case__    =					orig_state_dict.pop(__lowerCAmelCase				)

        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
            snake_case__    =					key.split("."				)
            snake_case__    ,       snake_case__    =					int(key_split[2]				), int(key_split[4]				)
            snake_case__    =					config.vision_config.hidden_size
            if "weight" in key:
                snake_case__    =					val[:dim, :]
                snake_case__    =					val[dim : dim * 2, :]
                snake_case__    =					val[-dim:, :]
            else:
                snake_case__    =					val[:dim]
                snake_case__    =					val[dim : dim * 2]
                snake_case__    =					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
            snake_case__    =					key.split("."				)
            snake_case__    =					int(key_split[3]				)
            snake_case__    =					config.text_config.hidden_size
            if "weight" in key:
                snake_case__    =					val[:dim, :]
                snake_case__    =					val[
                    dim : dim * 2, :
                ]
                snake_case__    =					val[-dim:, :]
            else:
                snake_case__    =					val[:dim]
                snake_case__    =					val[dim : dim * 2]
                snake_case__    =					val[-dim:]
        else:
            snake_case__    =					rename_key(__lowerCAmelCase				)
            # 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
            ):
                snake_case__    =					val.squeeze_()
            else:
                snake_case__    =					val

    return orig_state_dict





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





@torch.no_grad()
def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase="groupvit-gcc-yfcc"     ,		__lowerCAmelCase=False				):
    snake_case__    =					GroupViTConfig()
    snake_case__    =					GroupViTModel(__lowerCAmelCase				).eval()

    snake_case__    =					torch.load(__lowerCAmelCase     ,		map_location="cpu"				)["model"]
    snake_case__    =					convert_state_dict(__lowerCAmelCase     ,		__lowerCAmelCase				)
    snake_case__    ,       snake_case__    =					model.load_state_dict(__lowerCAmelCase     ,		strict=__lowerCAmelCase				)
    assert missing_keys == ["text_model.embeddings.position_ids"]
    assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__lowerCAmelCase				) == 0)

    # verify result
    snake_case__    =					CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"				)
    snake_case__    =					prepare_img()
    snake_case__    =					processor(text=["a photo of a cat", "a photo of a dog"]     ,		images=__lowerCAmelCase     ,		padding=__lowerCAmelCase     ,		return_tensors="pt"				)

    with torch.no_grad():
        snake_case__    =					model(**__lowerCAmelCase				)

    if model_name == "groupvit-gcc-yfcc":
        snake_case__    =					torch.tensor([[13.3_523, 6.3_629]]				)
    elif model_name == "groupvit-gcc-redcaps":
        snake_case__    =					torch.tensor([[16.1_873, 8.6_230]]				)
    else:
        raise ValueError(F"""Model name {model_name} not supported."""				)
    assert torch.allclose(outputs.logits_per_image     ,		__lowerCAmelCase     ,		atol=1E-3				)

    processor.save_pretrained(__lowerCAmelCase				)
    model.save_pretrained(__lowerCAmelCase				)
    print("Successfully saved processor and model to"     ,		__lowerCAmelCase				)

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


if __name__ == "__main__":
     __magic_name__  							=	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`.''',
     )
     __magic_name__  							=	parser.parse_args()

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



