
import argparse

import torch

from diffusers import UNetaDConditionModel





def        lowerCAmelCase       (				snake_case__   :      List[Any]				,					snake_case__   :      int				,					snake_case__   :      Dict				,					snake_case__   :      str=None				,					snake_case__   :      List[Any]=None				,					snake_case__   :      Optional[int]=None )->			Tuple:
    assert isinstance(snake_case__				,					snake_case__ ), "Paths should be a list of dicts containing 'old' and 'new' keys."

    # Splits the attention layers into three variables.
    if attention_paths_to_split is not None:
        for path, path_map in attention_paths_to_split.items():
            A_								=					old_checkpoint[path]
            A_								=					old_tensor.shape[0] // 3

            A_								=					(-1, channels) if len(old_tensor.shape ) == 3 else (-1)

            A_								=					old_tensor.shape[0] // config["num_head_channels"] // 3

            A_								=					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
            A_	,	A_	,	A_								=					old_tensor.split(channels // num_heads				,					dim=1 )

            A_								=					query.reshape(snake_case__ )
            A_								=					key.reshape(snake_case__ )
            A_								=					value.reshape(snake_case__ )

    for path in paths:
        A_								=					path["new"]

        # These have already been assigned
        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
            continue

        if additional_replacements is not None:
            for replacement in additional_replacements:
                A_								=					new_path.replace(replacement["old"]				,					replacement["new"] )

        # proj_attn.weight has to be converted from conv 1D to linear
        A_								=					old_checkpoint[path["old"]]
        A_								=					["proj_attn.weight"]
        A_								=					["proj_out.weight", "proj_in.weight"]
        if any(k in new_path for k in names ):
            A_								=					weight[:, :, 0]
        elif any(k in new_path for k in names_a ) and len(weight.shape ) > 2 and ".attentions." not in new_path:
            A_								=					weight[:, :, 0]
        else:
            A_								=					weight





def        lowerCAmelCase       (				snake_case__   :      int				,					snake_case__   :      List[Any]=0 )->			List[Any]:
    A_								=					[]
    for old_item in old_list:
        A_								=					old_item

        #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
        #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

        #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
        #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

        #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

        mapping.append({"old": old_item, "new": new_item} )

    return mapping





def        lowerCAmelCase       (				snake_case__   :      Optional[int]				,					snake_case__   :      int=1 )->			Optional[Any]:
    if n_shave_prefix_segments >= 0:
        return ".".join(path.split("." )[n_shave_prefix_segments:] )
    else:
        return ".".join(path.split("." )[:n_shave_prefix_segments] )





def        lowerCAmelCase       (				snake_case__   :      Tuple				,					snake_case__   :      Union[str, Any]=0 )->			Dict:
    A_								=					[]
    for old_item in old_list:
        mapping.append({"old": old_item, "new": old_item} )

    return mapping





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any]				,					snake_case__   :      Optional[Any]=0 )->			Any:
    A_								=					[]
    for old_item in old_list:
        A_								=					old_item.replace("in_layers.0"				,					"norm1" )
        A_								=					new_item.replace("in_layers.2"				,					"conv1" )

        A_								=					new_item.replace("out_layers.0"				,					"norm2" )
        A_								=					new_item.replace("out_layers.3"				,					"conv2" )

        A_								=					new_item.replace("emb_layers.1"				,					"time_emb_proj" )
        A_								=					new_item.replace("skip_connection"				,					"conv_shortcut" )

        A_								=					shave_segments(snake_case__				,					n_shave_prefix_segments=snake_case__ )

        if "temopral_conv" not in old_item:
            mapping.append({"old": old_item, "new": new_item} )

    return mapping





def        lowerCAmelCase       (				snake_case__   :      str				,					snake_case__   :      int				,					snake_case__   :      Tuple=None				,					snake_case__   :      Any=False )->			List[str]:
    A_								=					{}
    A_								=					list(checkpoint.keys() )

    A_								=					"model.diffusion_model."

    # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
    if sum(k.startswith("model_ema" ) for k in keys ) > 100 and extract_ema:
        print(f'Checkpoint {path} has both EMA and non-EMA weights.' )
        print(
            "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
            " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." )
        for key in keys:
            if key.startswith("model.diffusion_model" ):
                A_								=					"model_ema." + "".join(key.split("." )[1:] )
                A_								=					checkpoint.pop(snake_case__ )
    else:
        if sum(k.startswith("model_ema" ) for k in keys ) > 100:
            print(
                "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
                " weights (usually better for inference), please make sure to add the `--extract_ema` flag." )

        for key in keys:
            A_								=					checkpoint.pop(snake_case__ )

    A_								=					{}

    A_								=					unet_state_dict["time_embed.0.weight"]
    A_								=					unet_state_dict["time_embed.0.bias"]
    A_								=					unet_state_dict["time_embed.2.weight"]
    A_								=					unet_state_dict["time_embed.2.bias"]

    if config["class_embed_type"] is None:
        # No parameters to port
        ...
    elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
        A_								=					unet_state_dict["label_emb.0.0.weight"]
        A_								=					unet_state_dict["label_emb.0.0.bias"]
        A_								=					unet_state_dict["label_emb.0.2.weight"]
        A_								=					unet_state_dict["label_emb.0.2.bias"]
    else:
        raise NotImplementedError(f'Not implemented `class_embed_type`: {config["class_embed_type"]}' )

    A_								=					unet_state_dict["input_blocks.0.0.weight"]
    A_								=					unet_state_dict["input_blocks.0.0.bias"]

    A_								=					[v for v in unet_state_dict if v.startswith("input_blocks.0.1" )]
    A_								=					renew_attention_paths(snake_case__ )
    A_								=					{"old": "input_blocks.0.1", "new": "transformer_in"}
    assign_to_checkpoint(snake_case__				,					snake_case__				,					snake_case__				,					additional_replacements=[meta_path]				,					config=snake_case__ )

    A_								=					unet_state_dict["out.0.weight"]
    A_								=					unet_state_dict["out.0.bias"]
    A_								=					unet_state_dict["out.2.weight"]
    A_								=					unet_state_dict["out.2.bias"]

    # Retrieves the keys for the input blocks only
    A_								=					len({".".join(layer.split("." )[:2] ) for layer in unet_state_dict if "input_blocks" in layer} )
    A_								=					{
        layer_id: [key for key in unet_state_dict if f'input_blocks.{layer_id}' in key]
        for layer_id in range(snake_case__ )
    }

    # Retrieves the keys for the middle blocks only
    A_								=					len({".".join(layer.split("." )[:2] ) for layer in unet_state_dict if "middle_block" in layer} )
    A_								=					{
        layer_id: [key for key in unet_state_dict if f'middle_block.{layer_id}' in key]
        for layer_id in range(snake_case__ )
    }

    # Retrieves the keys for the output blocks only
    A_								=					len({".".join(layer.split("." )[:2] ) for layer in unet_state_dict if "output_blocks" in layer} )
    A_								=					{
        layer_id: [key for key in unet_state_dict if f'output_blocks.{layer_id}' in key]
        for layer_id in range(snake_case__ )
    }

    for i in range(1				,					snake_case__ ):
        A_								=					(i - 1) // (config["layers_per_block"] + 1)
        A_								=					(i - 1) % (config["layers_per_block"] + 1)

        A_								=					[
            key for key in input_blocks[i] if f'input_blocks.{i}.0' in key and f'input_blocks.{i}.0.op' not in key
        ]
        A_								=					[key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]
        A_								=					[key for key in input_blocks[i] if f'input_blocks.{i}.2' in key]

        if f'input_blocks.{i}.op.weight' in unet_state_dict:
            A_								=					unet_state_dict.pop(
                f'input_blocks.{i}.op.weight' )
            A_								=					unet_state_dict.pop(
                f'input_blocks.{i}.op.bias' )

        A_								=					renew_resnet_paths(snake_case__ )
        A_								=					{"old": f'input_blocks.{i}.0', "new": f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
        assign_to_checkpoint(
            snake_case__				,					snake_case__				,					snake_case__				,					additional_replacements=[meta_path]				,					config=snake_case__ )

        A_								=					[key for key in resnets if "temopral_conv" in key]
        A_								=					renew_temp_conv_paths(snake_case__ )
        A_								=					{
            "old": f'input_blocks.{i}.0.temopral_conv',
            "new": f'down_blocks.{block_id}.temp_convs.{layer_in_block_id}',
        }
        assign_to_checkpoint(
            snake_case__				,					snake_case__				,					snake_case__				,					additional_replacements=[meta_path]				,					config=snake_case__ )

        if len(snake_case__ ):
            A_								=					renew_attention_paths(snake_case__ )
            A_								=					{"old": f'input_blocks.{i}.1', "new": f'down_blocks.{block_id}.attentions.{layer_in_block_id}'}
            assign_to_checkpoint(
                snake_case__				,					snake_case__				,					snake_case__				,					additional_replacements=[meta_path]				,					config=snake_case__ )

        if len(snake_case__ ):
            A_								=					renew_attention_paths(snake_case__ )
            A_								=					{
                "old": f'input_blocks.{i}.2',
                "new": f'down_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
            }
            assign_to_checkpoint(
                snake_case__				,					snake_case__				,					snake_case__				,					additional_replacements=[meta_path]				,					config=snake_case__ )

    A_								=					middle_blocks[0]
    A_								=					[key for key in resnet_a if "temopral_conv" in key]
    A_								=					middle_blocks[1]
    A_								=					middle_blocks[2]
    A_								=					middle_blocks[3]
    A_								=					[key for key in resnet_a if "temopral_conv" in key]

    A_								=					renew_resnet_paths(snake_case__ )
    A_								=					{"old": "middle_block.0", "new": "mid_block.resnets.0"}
    assign_to_checkpoint(
        snake_case__				,					snake_case__				,					snake_case__				,					config=snake_case__				,					additional_replacements=[meta_path] )

    A_								=					renew_temp_conv_paths(snake_case__ )
    A_								=					{"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
    assign_to_checkpoint(
        snake_case__				,					snake_case__				,					snake_case__				,					config=snake_case__				,					additional_replacements=[meta_path] )

    A_								=					renew_resnet_paths(snake_case__ )
    A_								=					{"old": "middle_block.3", "new": "mid_block.resnets.1"}
    assign_to_checkpoint(
        snake_case__				,					snake_case__				,					snake_case__				,					config=snake_case__				,					additional_replacements=[meta_path] )

    A_								=					renew_temp_conv_paths(snake_case__ )
    A_								=					{"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
    assign_to_checkpoint(
        snake_case__				,					snake_case__				,					snake_case__				,					config=snake_case__				,					additional_replacements=[meta_path] )

    A_								=					renew_attention_paths(snake_case__ )
    A_								=					{"old": "middle_block.1", "new": "mid_block.attentions.0"}
    assign_to_checkpoint(
        snake_case__				,					snake_case__				,					snake_case__				,					additional_replacements=[meta_path]				,					config=snake_case__ )

    A_								=					renew_attention_paths(snake_case__ )
    A_								=					{"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
    assign_to_checkpoint(
        snake_case__				,					snake_case__				,					snake_case__				,					additional_replacements=[meta_path]				,					config=snake_case__ )

    for i in range(snake_case__ ):
        A_								=					i // (config["layers_per_block"] + 1)
        A_								=					i % (config["layers_per_block"] + 1)
        A_								=					[shave_segments(snake_case__				,					2 ) for name in output_blocks[i]]
        A_								=					{}

        for layer in output_block_layers:
            A_	,	A_								=					layer.split("." )[0], shave_segments(snake_case__				,					1 )
            if layer_id in output_block_list:
                output_block_list[layer_id].append(snake_case__ )
            else:
                A_								=					[layer_name]

        if len(snake_case__ ) > 1:
            A_								=					[key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
            A_								=					[key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]
            A_								=					[key for key in output_blocks[i] if f'output_blocks.{i}.2' in key]

            A_								=					renew_resnet_paths(snake_case__ )
            A_								=					renew_resnet_paths(snake_case__ )

            A_								=					{"old": f'output_blocks.{i}.0', "new": f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
            assign_to_checkpoint(
                snake_case__				,					snake_case__				,					snake_case__				,					additional_replacements=[meta_path]				,					config=snake_case__ )

            A_								=					[key for key in resnets if "temopral_conv" in key]
            A_								=					renew_temp_conv_paths(snake_case__ )
            A_								=					{
                "old": f'output_blocks.{i}.0.temopral_conv',
                "new": f'up_blocks.{block_id}.temp_convs.{layer_in_block_id}',
            }
            assign_to_checkpoint(
                snake_case__				,					snake_case__				,					snake_case__				,					additional_replacements=[meta_path]				,					config=snake_case__ )

            A_								=					{k: sorted(snake_case__ ) for k, v in output_block_list.items()}
            if ["conv.bias", "conv.weight"] in output_block_list.values():
                A_								=					list(output_block_list.values() ).index(["conv.bias", "conv.weight"] )
                A_								=					unet_state_dict[
                    f'output_blocks.{i}.{index}.conv.weight'
                ]
                A_								=					unet_state_dict[
                    f'output_blocks.{i}.{index}.conv.bias'
                ]

                # Clear attentions as they have been attributed above.
                if len(snake_case__ ) == 2:
                    A_								=					[]

            if len(snake_case__ ):
                A_								=					renew_attention_paths(snake_case__ )
                A_								=					{
                    "old": f'output_blocks.{i}.1',
                    "new": f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
                }
                assign_to_checkpoint(
                    snake_case__				,					snake_case__				,					snake_case__				,					additional_replacements=[meta_path]				,					config=snake_case__ )

            if len(snake_case__ ):
                A_								=					renew_attention_paths(snake_case__ )
                A_								=					{
                    "old": f'output_blocks.{i}.2',
                    "new": f'up_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
                }
                assign_to_checkpoint(
                    snake_case__				,					snake_case__				,					snake_case__				,					additional_replacements=[meta_path]				,					config=snake_case__ )
        else:
            A_								=					renew_resnet_paths(snake_case__				,					n_shave_prefix_segments=1 )
            for path in resnet_0_paths:
                A_								=					".".join(["output_blocks", str(snake_case__ ), path["old"]] )
                A_								=					".".join(["up_blocks", str(snake_case__ ), "resnets", str(snake_case__ ), path["new"]] )
                A_								=					unet_state_dict[old_path]

            A_								=					[l for l in output_block_layers if "temopral_conv" in l]
            for path in temopral_conv_paths:
                A_								=					path.split("temopral_conv." )[-1]
                A_								=					".".join(["output_blocks", str(snake_case__ ), str(snake_case__ ), "temopral_conv", pruned_path] )
                A_								=					".".join(["up_blocks", str(snake_case__ ), "temp_convs", str(snake_case__ ), pruned_path] )
                A_								=					unet_state_dict[old_path]

    return new_checkpoint


if __name__ == "__main__":
     __magic_name__  :  Any     			=   argparse.ArgumentParser()

     parser.add_argument(
         '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
     )
     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
     __magic_name__  :  int     			=   parser.parse_args()

     __magic_name__  :  Union[str, Any]     			=   torch.load(args.checkpoint_path, map_location='cpu')
     __magic_name__  :  Tuple     			=   UNetaDConditionModel()

     __magic_name__  :  str     			=   convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

     __magic_name__  :  Any     			=   set(unet.state_dict().keys()) - set(converted_ckpt.keys())
     __magic_name__  :  Tuple     			=   set(converted_ckpt.keys()) - set(unet.state_dict().keys())

     assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

     # load state_dict
     unet.load_state_dict(converted_ckpt)

     unet.save_pretrained(args.dump_path)

     # -- finish converting the unet --








import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			List[str]:
    A_								=					[
        "encoder.version",
        "decoder.version",
        "model.encoder.version",
        "model.decoder.version",
        "_float_tensor",
        "decoder.output_projection.weight",
    ]
    for k in ignore_keys:
        state_dict.pop(snake_case__				,					snake_case__ )





def        lowerCAmelCase       (				snake_case__   :      Dict )->			str:
    A_	,	A_								=					emb.weight.shape
    A_								=					nn.Linear(snake_case__				,					snake_case__				,					bias=snake_case__ )
    A_								=					emb.weight.data
    return lin_layer





def        lowerCAmelCase       (				snake_case__   :      Any				,					snake_case__   :      Optional[int]="uclanlp/plbart-base"				,					snake_case__   :      Any=False				,					snake_case__   :      str=False )->			str:
    A_								=					torch.load(snake_case__				,					map_location="cpu" )["model"]
    remove_ignore_keys_(snake_case__ )
    A_								=					state_dict["encoder.embed_tokens.weight"].shape[0]

    A_								=					PLBartConfig.from_pretrained(snake_case__				,					vocab_size=snake_case__ )

    A_								=					state_dict["decoder.embed_tokens.weight"]
    if not classification:
        A_								=					PLBartForConditionalGeneration(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        if finetuned:
            A_								=					make_linear_from_emb(model.model.shared )

    else:
        A_								=					{}
        for key, value in state_dict.copy().items():
            if key.startswith("classification_heads.sentence_classification_head" ):
                A_								=					value
                state_dict.pop(snake_case__ )
        A_								=					PLBartForSequenceClassification(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        model.classification_head.load_state_dict(snake_case__ )

    return model


if __name__ == "__main__":
     __magic_name__  :  int     			=   argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('fairseq_path', type=str, help='model.pt on local filesystem.')
     parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument(
         '--hf_config',
         default='uclanlp/plbart-base',
         type=str,
         help='Which huggingface architecture to use: plbart-base',
     )
     parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
     parser.add_argument(
         '--classification', action='store_true', help='whether the model is a classification checkpoint'
     )
     __magic_name__  :  Union[str, Any]     			=   parser.parse_args()
     __magic_name__  :  List[str]     			=   convert_fairseq_plbart_checkpoint_from_disk(
         args.fairseq_path,
         hf_config_path=args.hf_config,
         finetuned=args.finetuned,
         classification=args.classification,
     )
     model.save_pretrained(args.pytorch_dump_folder_path)






