

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




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



    """simple docstring"""






    if "model" in orig_key:
        UpperCamelCase_          =		orig_key.replace("model."       ,		""       )
    if "norm1" in orig_key:
        UpperCamelCase_          =		orig_key.replace("norm1"       ,		"attention.output.LayerNorm"       )
    if "norm2" in orig_key:
        UpperCamelCase_          =		orig_key.replace("norm2"       ,		"output.LayerNorm"       )
    if "norm" in orig_key:
        UpperCamelCase_          =		orig_key.replace("norm"       ,		"LayerNorm"       )
    if "transformer" in orig_key:
        UpperCamelCase_          =		orig_key.split("."       )[0].split("_"       )[-1]
        UpperCamelCase_          =		orig_key.replace(f"transformer_{layer_num}"       ,		f"encoder.layer.{layer_num}"       )
    if "mha.attn" in orig_key:
        UpperCamelCase_          =		orig_key.replace("mha.attn"       ,		"attention.self"       )
    if "mha" in orig_key:
        UpperCamelCase_          =		orig_key.replace("mha"       ,		"attention"       )
    if "W_q" in orig_key:
        UpperCamelCase_          =		orig_key.replace("W_q"       ,		"self.query"       )
    if "W_k" in orig_key:
        UpperCamelCase_          =		orig_key.replace("W_k"       ,		"self.key"       )
    if "W_v" in orig_key:
        UpperCamelCase_          =		orig_key.replace("W_v"       ,		"self.value"       )
    if "ff.0" in orig_key:
        UpperCamelCase_          =		orig_key.replace("ff.0"       ,		"intermediate.dense"       )
    if "ff.2" in orig_key:
        UpperCamelCase_          =		orig_key.replace("ff.2"       ,		"output.dense"       )
    if "ff" in orig_key:
        UpperCamelCase_          =		orig_key.replace("ff"       ,		"output.dense"       )
    if "mlm_class" in orig_key:
        UpperCamelCase_          =		orig_key.replace("mlm.mlm_class"       ,		"cls.predictions.decoder"       )
    if "mlm" in orig_key:
        UpperCamelCase_          =		orig_key.replace("mlm"       ,		"cls.predictions.transform"       )
    if "backbone.backbone.encoders" in orig_key:
        UpperCamelCase_          =		orig_key.replace("backbone.backbone.encoders"       ,		"encoder.layer"       )
    if "cls" not in orig_key:
        UpperCamelCase_          =		"mra." + orig_key

    return orig_key




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		str:



    """simple docstring"""






    for key in orig_state_dict.copy().keys():
        UpperCamelCase_          =		orig_state_dict.pop(SCREAMING_SNAKE_CASE_       )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            UpperCamelCase_          =		val

    UpperCamelCase_          =		orig_state_dict["cls.predictions.decoder.bias"]
    UpperCamelCase_          =		torch.arange(SCREAMING_SNAKE_CASE_       ).expand((1, -1)       ) + 2

    return orig_state_dict




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



    """simple docstring"""






    UpperCamelCase_          =		torch.load(SCREAMING_SNAKE_CASE_       ,		map_location="cpu"       )["model_state_dict"]
    UpperCamelCase_          =		MraConfig.from_json_file(SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		MraForMaskedLM(SCREAMING_SNAKE_CASE_       )

    UpperCamelCase_          =		convert_checkpoint_helper(config.max_position_embeddings       ,		SCREAMING_SNAKE_CASE_       )

    print(model.load_state_dict(SCREAMING_SNAKE_CASE_       )       )
    model.eval()
    model.save_pretrained(SCREAMING_SNAKE_CASE_       )

    print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"       )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE						:int      =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Mra pytorch checkpoint."""
 )
 parser.add_argument(
     """--config_file""",
     default=None,
     type=str,
     required=True,
     help="""The json file for Mra model config.""",
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 SCREAMING_SNAKE_CASE						:List[str]      =			parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



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)
