

SCREAMING_SNAKE_CASE						:Union[str, Any]      =			[
    ["""ab-e1""", """ac-e3""", """ad-e5""", """bc-e4""", """bd-e2""", """be-e6""", """bh-e12""", """cd-e2""", """ce-e4""",
     """de-e1""", """df-e8""", """dg-e5""", """dh-e10""", """ef-e3""", """eg-e2""", """fg-e6""", """gh-e6""", """hi-e3"""],
    ["""ab-e1""", """ac-e3""", """ad-e5""", """bc-e4""", """bd-e2""", """be-e6""", """cd-e2""", """de-e1""", """df-e8""",
     """ef-e3""", """eg-e2""", """fg-e6"""],
    ["""ab-e1""", """ac-e3""", """bc-e4""", """bd-e2""", """de-e1""", """df-e8""", """dg-e5""", """ef-e3""", """eg-e2""",
     """eh-e12""", """fg-e6""", """fh-e10""", """gh-e6"""],
    ["""ab-e1""", """ac-e3""", """bc-e4""", """bd-e2""", """bh-e12""", """cd-e2""", """df-e8""", """dh-e10"""],
    ["""ab-e1""", """ac-e3""", """ad-e5""", """bc-e4""", """bd-e2""", """cd-e2""", """ce-e4""", """de-e1""", """df-e8""",
     """dg-e5""", """ef-e3""", """eg-e2""", """fg-e6"""]
]




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		List[str]:



    """simple docstring"""






    UpperCamelCase_          =		set()
    for row in edge_array:
        for item in row:
            distinct_edge.add(item[0]       )
    return list(SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Union[str, Any]:



    """simple docstring"""






    UpperCamelCase_          =		["0"] * len(SCREAMING_SNAKE_CASE_       )
    for i, row in enumerate(SCREAMING_SNAKE_CASE_       ):
        for item in row:
            if distinct_edge in item[0]:
                UpperCamelCase_          =		"1"
                break
    return "".join(SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		Union[str, Any]:



    """simple docstring"""






    UpperCamelCase_          =		get_distinct_edge(SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		{}

    for item in distinct_edge:
        UpperCamelCase_          =		get_bitcode(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
        # print('bit',bit)
        # bt=''.join(bit)
        UpperCamelCase_          =		bit.count("1"       )
        UpperCamelCase_          =		[s, bit]
    # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
    UpperCamelCase_          =		[
        [k, v[0], v[1]]
        for k, v in sorted(frequency_table.items()       ,		key=lambda SCREAMING_SNAKE_CASE_       : v[1][0]       ,		reverse=SCREAMING_SNAKE_CASE_       )
    ]
    return sorted_frequency_table




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		Optional[Any]:



    """simple docstring"""






    UpperCamelCase_          =		{}
    for _, item in enumerate(SCREAMING_SNAKE_CASE_       ):
        nodes.setdefault(item[2]       ,		[]       ).append(item[0]       )
    return nodes




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		List[str]:



    """simple docstring"""






    UpperCamelCase_          =		{}
    for key, value in nodes.items():
        UpperCamelCase_          =		value
    return cluster




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		str:



    """simple docstring"""






    return [i * 1_0_0 / len(SCREAMING_SNAKE_CASE_       ) for i in cluster]




def        lowerCAmelCase(	)->		None:



    """simple docstring"""






    print("\nNodes\n"       )
    for key, value in nodes.items():
        print(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    print("\nSupport\n"       )
    print(SCREAMING_SNAKE_CASE_       )
    print("\n Cluster \n"       )
    for key, value in sorted(cluster.items()       ,		reverse=SCREAMING_SNAKE_CASE_       ):
        print(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    print("\n Graph\n"       )
    for key, value in graph.items():
        print(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    print("\n Edge List of Frequent subgraphs \n"       )
    for edge_list in freq_subgraph_edge_list:
        print(SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Dict:



    """simple docstring"""






    for i in cluster[ca]:
        UpperCamelCase_          =		0
        UpperCamelCase_          =		ca + 1
        while ca < max(cluster.keys()       ):
            for j in cluster[ca]:
                if int(SCREAMING_SNAKE_CASE_       ,		2       ) & int(SCREAMING_SNAKE_CASE_       ,		2       ) == int(SCREAMING_SNAKE_CASE_       ,		2       ):
                    if tuple(nodes[i]       ) in graph:
                        graph[tuple(nodes[i]       )].append(nodes[j]       )
                    else:
                        UpperCamelCase_          =		[nodes[j]]
                    count += 1
            if count == 0:
                UpperCamelCase_          =		ca + 1
            else:
                break




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    UpperCamelCase_          =		cluster[max(cluster.keys()       )]
    UpperCamelCase_          =		"Header"
    UpperCamelCase_          =		{}
    for i in x:
        if (["Header"],) in graph:
            graph[(["Header"],)].append(x[i]       )
        else:
            UpperCamelCase_          =		[x[i]]
    for i in x:
        UpperCamelCase_          =		[["Header"]]
    UpperCamelCase_          =		1
    while i < max(SCREAMING_SNAKE_CASE_       ) - 1:
        create_edge(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
        UpperCamelCase_          =		i + 1
    return graph




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=None       )->		Tuple:



    """simple docstring"""






    UpperCamelCase_          =		(path or []) + [start]
    if start == end:
        paths.append(SCREAMING_SNAKE_CASE_       )
    for node in graph[start]:
        if tuple(SCREAMING_SNAKE_CASE_       ) not in path:
            my_dfs(SCREAMING_SNAKE_CASE_       ,		tuple(SCREAMING_SNAKE_CASE_       )       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Optional[int]:



    """simple docstring"""






    UpperCamelCase_          =		int(s / 1_0_0 * (len(SCREAMING_SNAKE_CASE_       ) - 1)       )
    for i in cluster[k]:
        my_dfs(SCREAMING_SNAKE_CASE_       ,		tuple(cluster[k][i]       )       ,		(["Header"],)       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		int:



    """simple docstring"""






    UpperCamelCase_          =		[]
    for edges in paths:
        UpperCamelCase_          =		[]
        for j in range(len(SCREAMING_SNAKE_CASE_       ) - 1       ):
            UpperCamelCase_          =		list(edges[j]       )
            for e in temp:
                UpperCamelCase_          =		(e[0], e[1])
                el.append(SCREAMING_SNAKE_CASE_       )
        freq_sub_el.append(SCREAMING_SNAKE_CASE_       )
    return freq_sub_el




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		List[str]:



    """simple docstring"""






    for i in range(len(SCREAMING_SNAKE_CASE_       )       ):
        for j in range(len(edge_array[i]       )       ):
            UpperCamelCase_          =		edge_array[i][j].split("-"       )
            UpperCamelCase_          =		t


if __name__ == "__main__":
 preprocess(edge_array)
 SCREAMING_SNAKE_CASE						:Any      =			get_frequency_table(edge_array)
 SCREAMING_SNAKE_CASE						:Optional[Any]      =			get_nodes(frequency_table)
 SCREAMING_SNAKE_CASE						:Union[str, Any]      =			get_cluster(nodes)
 SCREAMING_SNAKE_CASE						:Optional[int]      =			get_support(cluster)
 SCREAMING_SNAKE_CASE						:str      =			construct_graph(cluster, nodes)
 find_freq_subgraph_given_support(60, cluster, graph)
 SCREAMING_SNAKE_CASE						:list      =			[]
 SCREAMING_SNAKE_CASE						:Dict      =			freq_subgraphs_edge_list(paths)
 print_all()



import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_          =		pt_attn_layer.in_proj_weight.chunk(3       ,		dim=0       )
    UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_          =		pt_attn_layer.in_proj_bias.chunk(3       ,		dim=0       )

    UpperCamelCase_          =		pt_attn_layer.out_proj.weight
    UpperCamelCase_          =		pt_attn_layer.out_proj.bias

    UpperCamelCase_          =		q_proj
    UpperCamelCase_          =		q_proj_bias

    UpperCamelCase_          =		k_proj
    UpperCamelCase_          =		k_proj_bias

    UpperCamelCase_          =		v_proj
    UpperCamelCase_          =		v_proj_bias

    UpperCamelCase_          =		out_proj_weights
    UpperCamelCase_          =		out_proj_bias




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Union[str, Any]:



    """simple docstring"""






    copy_linear(hf_mlp.fca       ,		pt_mlp.c_fc       )
    copy_linear(hf_mlp.fca       ,		pt_mlp.c_proj       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    UpperCamelCase_          =		pt_linear.weight
    UpperCamelCase_          =		pt_linear.bias




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    copy_linear(hf_layer.layer_norma       ,		pt_layer.ln_a       )
    copy_linear(hf_layer.layer_norma       ,		pt_layer.ln_a       )

    # copy MLP
    copy_mlp(hf_layer.mlp       ,		pt_layer.mlp       )

    # copy attn
    copy_attn_layer(hf_layer.self_attn       ,		pt_layer.attn       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    for hf_layer, pt_layer in zip(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        copy_layer(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Optional[Any]:



    """simple docstring"""






    UpperCamelCase_          =		pt_model.token_embedding.weight
    UpperCamelCase_          =		pt_model.positional_embedding

    # copy layer norm
    copy_linear(hf_encoder.final_layer_norm       ,		pt_model.ln_final       )

    # copy hidden layers
    copy_layers(hf_encoder.encoder.layers       ,		pt_model.transformer.resblocks       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    UpperCamelCase_          =		pt_model.text_projection.data.T

    # copy text encoder
    copy_encoder(hf_model.text_model       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    UpperCamelCase_          =		pt_model.visual.proj.data.T

    # copy layer norms
    copy_linear(hf_model.vision_model.pre_layrnorm       ,		pt_model.visual.ln_pre       )
    copy_linear(hf_model.vision_model.post_layernorm       ,		pt_model.visual.ln_post       )

    # copy embeds
    UpperCamelCase_          =		pt_model.visual.conva.weight.data
    UpperCamelCase_          =		pt_model.visual.class_embedding
    UpperCamelCase_          =		pt_model.visual.positional_embedding.data

    # copy encoder
    copy_layers(hf_model.vision_model.encoder.layers       ,		pt_model.visual.transformer.resblocks       )




@torch.no_grad()
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=None       )->		List[Any]:



    """simple docstring"""






    if config_path is not None:
        UpperCamelCase_          =		CLIPConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
    else:
        UpperCamelCase_          =		CLIPConfig(projection_dim=5_1_2       ,		text_config={}       ,		vision_config={}       )

    UpperCamelCase_          =		CLIPModel(SCREAMING_SNAKE_CASE_       ).eval()

    UpperCamelCase_   ,    UpperCamelCase_          =		load(SCREAMING_SNAKE_CASE_       ,		device="cpu"       ,		jit=SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		pt_model.eval()

    copy_text_model_and_projection(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    copy_vison_model_and_projection(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		pt_model.logit_scale

    UpperCamelCase_          =		torch.arange(0       ,		7_7       ).unsqueeze(0       )
    UpperCamelCase_          =		torch.randn(1       ,		3       ,		2_2_4       ,		2_2_4       )

    UpperCamelCase_          =		hf_model(input_ids=SCREAMING_SNAKE_CASE_       ,		pixel_values=SCREAMING_SNAKE_CASE_       ,		return_dict=SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		hf_outputs.logits_per_image
    UpperCamelCase_          =		hf_outputs.logits_per_text
    UpperCamelCase_   ,    UpperCamelCase_          =		pt_model(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )

    assert torch.allclose(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		atol=1E-3       )
    assert torch.allclose(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		atol=1E-3       )

    hf_model.save_pretrained(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE						:Any      =			argparse.ArgumentParser()
 parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
 parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
 parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
 SCREAMING_SNAKE_CASE						:Dict      =			parser.parse_args()

 convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
