
import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel


def 		UpperCAmelCase    (			a_	,     a_=1		)	->			str:







    """simple docstring"""



    if n_shave_prefix_segments >= 0:
        return ".".join(path.split("."		)[n_shave_prefix_segments:]		)
    else:
        return ".".join(path.split("."		)[:n_shave_prefix_segments]		)


def 		UpperCAmelCase    (			a_	,     a_=0		)	->			Union[str, Any]:







    """simple docstring"""



    __A         =				[]
    for old_item in old_list:
        __A         =				old_item
        __A         =				new_item.replace("block."	,     "resnets."		)
        __A         =				new_item.replace("conv_shorcut"	,     "conv1"		)
        __A         =				new_item.replace("in_shortcut"	,     "conv_shortcut"		)
        __A         =				new_item.replace("temb_proj"	,     "time_emb_proj"		)

        __A         =				shave_segments(a_	,     n_shave_prefix_segments=a_		)

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

    return mapping


def 		UpperCAmelCase    (			a_	,     a_=0	,     a_=False		)	->			Optional[Any]:







    """simple docstring"""



    __A         =				[]
    for old_item in old_list:
        __A         =				old_item

        # In `model.mid`, the layer is called `attn`.
        if not in_mid:
            __A         =				new_item.replace("attn"	,     "attentions"		)
        __A         =				new_item.replace(".k."	,     ".key."		)
        __A         =				new_item.replace(".v."	,     ".value."		)
        __A         =				new_item.replace(".q."	,     ".query."		)

        __A         =				new_item.replace("proj_out"	,     "proj_attn"		)
        __A         =				new_item.replace("norm"	,     "group_norm"		)

        __A         =				shave_segments(a_	,     n_shave_prefix_segments=a_		)
        mapping.append({"old": old_item, "new": new_item}		)

    return mapping


def 		UpperCAmelCase    (			a_	,     a_	,     a_	,     a_=None	,     a_=None	,     a_=None		)	->			Dict:







    """simple docstring"""



    assert isinstance(a_	,     a_		), "Paths should be a list of dicts containing 'old' and 'new' keys."

    if attention_paths_to_split is not None:
        if config is None:
            raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'."		)

        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.get("num_head_channels"	,     1		) // 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(a_		).squeeze()
            __A         =				key.reshape(a_		).squeeze()
            __A         =				value.reshape(a_		).squeeze()

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

        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
            continue

        __A         =				new_path.replace("down."	,     "down_blocks."		)
        __A         =				new_path.replace("up."	,     "up_blocks."		)

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

        if "attentions" in new_path:
            __A         =				old_checkpoint[path["old"]].squeeze()
        else:
            __A         =				old_checkpoint[path["old"]]


def 		UpperCAmelCase    (			a_	,     a_		)	->			Optional[int]:







    """simple docstring"""



    __A         =				{}

    __A         =				checkpoint["temb.dense.0.weight"]
    __A         =				checkpoint["temb.dense.0.bias"]
    __A         =				checkpoint["temb.dense.1.weight"]
    __A         =				checkpoint["temb.dense.1.bias"]

    __A         =				checkpoint["norm_out.weight"]
    __A         =				checkpoint["norm_out.bias"]

    __A         =				checkpoint["conv_in.weight"]
    __A         =				checkpoint["conv_in.bias"]
    __A         =				checkpoint["conv_out.weight"]
    __A         =				checkpoint["conv_out.bias"]

    __A         =				len({".".join(layer.split("."		)[:2]		) for layer in checkpoint if "down" in layer}		)
    __A         =				{
        layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(a_		)
    }

    __A         =				len({".".join(layer.split("."		)[:2]		) for layer in checkpoint if "up" in layer}		)
    __A         =				{layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(a_		)}

    for i in range(a_		):
        __A         =				(i - 1) // (config["layers_per_block"] + 1)

        if any("downsample" in layer for layer in down_blocks[i]		):
            __A         =				checkpoint[
                F'''down.{i}.downsample.op.weight'''
            ]
            __A         =				checkpoint[F'''down.{i}.downsample.op.bias''']
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

        if any("block" in layer for layer in down_blocks[i]		):
            __A         =				len(
                {".".join(shave_segments(a_	,     2		).split("."		)[:2]		) for layer in down_blocks[i] if "block" in layer}		)
            __A         =				{
                layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
                for layer_id in range(a_		)
            }

            if num_blocks > 0:
                for j in range(config["layers_per_block"]		):
                    __A         =				renew_resnet_paths(blocks[j]		)
                    assign_to_checkpoint(a_	,     a_	,     a_		)

        if any("attn" in layer for layer in down_blocks[i]		):
            __A         =				len(
                {".".join(shave_segments(a_	,     2		).split("."		)[:2]		) for layer in down_blocks[i] if "attn" in layer}		)
            __A         =				{
                layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
                for layer_id in range(a_		)
            }

            if num_attn > 0:
                for j in range(config["layers_per_block"]		):
                    __A         =				renew_attention_paths(attns[j]		)
                    assign_to_checkpoint(a_	,     a_	,     a_	,     config=a_		)

    __A         =				[key for key in checkpoint if "mid.block_1" in key]
    __A         =				[key for key in checkpoint if "mid.block_2" in key]
    __A         =				[key for key in checkpoint if "mid.attn_1" in key]

    # Mid new 2
    __A         =				renew_resnet_paths(a_		)
    assign_to_checkpoint(
        a_	,     a_	,     a_	,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]	,     )

    __A         =				renew_resnet_paths(a_		)
    assign_to_checkpoint(
        a_	,     a_	,     a_	,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]	,     )

    __A         =				renew_attention_paths(a_	,     in_mid=a_		)
    assign_to_checkpoint(
        a_	,     a_	,     a_	,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]	,     )

    for i in range(a_		):
        __A         =				num_up_blocks - 1 - i

        if any("upsample" in layer for layer in up_blocks[i]		):
            __A         =				checkpoint[
                F'''up.{i}.upsample.conv.weight'''
            ]
            __A         =				checkpoint[F'''up.{i}.upsample.conv.bias''']

        if any("block" in layer for layer in up_blocks[i]		):
            __A         =				len(
                {".".join(shave_segments(a_	,     2		).split("."		)[:2]		) for layer in up_blocks[i] if "block" in layer}		)
            __A         =				{
                layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(a_		)
            }

            if num_blocks > 0:
                for j in range(config["layers_per_block"] + 1		):
                    __A         =				{"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
                    __A         =				renew_resnet_paths(blocks[j]		)
                    assign_to_checkpoint(a_	,     a_	,     a_	,     additional_replacements=[replace_indices]		)

        if any("attn" in layer for layer in up_blocks[i]		):
            __A         =				len(
                {".".join(shave_segments(a_	,     2		).split("."		)[:2]		) for layer in up_blocks[i] if "attn" in layer}		)
            __A         =				{
                layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(a_		)
            }

            if num_attn > 0:
                for j in range(config["layers_per_block"] + 1		):
                    __A         =				{"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
                    __A         =				renew_attention_paths(attns[j]		)
                    assign_to_checkpoint(a_	,     a_	,     a_	,     additional_replacements=[replace_indices]		)

    __A         =				{k.replace("mid_new_2"	,     "mid_block"		): v for k, v in new_checkpoint.items()}
    return new_checkpoint


def 		UpperCAmelCase    (			a_	,     a_		)	->			Union[str, Any]:







    """simple docstring"""



    __A         =				{}

    __A         =				checkpoint["encoder.norm_out.weight"]
    __A         =				checkpoint["encoder.norm_out.bias"]

    __A         =				checkpoint["encoder.conv_in.weight"]
    __A         =				checkpoint["encoder.conv_in.bias"]
    __A         =				checkpoint["encoder.conv_out.weight"]
    __A         =				checkpoint["encoder.conv_out.bias"]

    __A         =				checkpoint["decoder.norm_out.weight"]
    __A         =				checkpoint["decoder.norm_out.bias"]

    __A         =				checkpoint["decoder.conv_in.weight"]
    __A         =				checkpoint["decoder.conv_in.bias"]
    __A         =				checkpoint["decoder.conv_out.weight"]
    __A         =				checkpoint["decoder.conv_out.bias"]

    __A         =				len({".".join(layer.split("."		)[:3]		) for layer in checkpoint if "down" in layer}		)
    __A         =				{
        layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(a_		)
    }

    __A         =				len({".".join(layer.split("."		)[:3]		) for layer in checkpoint if "up" in layer}		)
    __A         =				{layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(a_		)}

    for i in range(a_		):
        __A         =				(i - 1) // (config["layers_per_block"] + 1)

        if any("downsample" in layer for layer in down_blocks[i]		):
            __A         =				checkpoint[
                F'''encoder.down.{i}.downsample.conv.weight'''
            ]
            __A         =				checkpoint[
                F'''encoder.down.{i}.downsample.conv.bias'''
            ]

        if any("block" in layer for layer in down_blocks[i]		):
            __A         =				len(
                {".".join(shave_segments(a_	,     3		).split("."		)[:3]		) for layer in down_blocks[i] if "block" in layer}		)
            __A         =				{
                layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
                for layer_id in range(a_		)
            }

            if num_blocks > 0:
                for j in range(config["layers_per_block"]		):
                    __A         =				renew_resnet_paths(blocks[j]		)
                    assign_to_checkpoint(a_	,     a_	,     a_		)

        if any("attn" in layer for layer in down_blocks[i]		):
            __A         =				len(
                {".".join(shave_segments(a_	,     3		).split("."		)[:3]		) for layer in down_blocks[i] if "attn" in layer}		)
            __A         =				{
                layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
                for layer_id in range(a_		)
            }

            if num_attn > 0:
                for j in range(config["layers_per_block"]		):
                    __A         =				renew_attention_paths(attns[j]		)
                    assign_to_checkpoint(a_	,     a_	,     a_	,     config=a_		)

    __A         =				[key for key in checkpoint if "mid.block_1" in key]
    __A         =				[key for key in checkpoint if "mid.block_2" in key]
    __A         =				[key for key in checkpoint if "mid.attn_1" in key]

    # Mid new 2
    __A         =				renew_resnet_paths(a_		)
    assign_to_checkpoint(
        a_	,     a_	,     a_	,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]	,     )

    __A         =				renew_resnet_paths(a_		)
    assign_to_checkpoint(
        a_	,     a_	,     a_	,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]	,     )

    __A         =				renew_attention_paths(a_	,     in_mid=a_		)
    assign_to_checkpoint(
        a_	,     a_	,     a_	,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]	,     )

    for i in range(a_		):
        __A         =				num_up_blocks - 1 - i

        if any("upsample" in layer for layer in up_blocks[i]		):
            __A         =				checkpoint[
                F'''decoder.up.{i}.upsample.conv.weight'''
            ]
            __A         =				checkpoint[
                F'''decoder.up.{i}.upsample.conv.bias'''
            ]

        if any("block" in layer for layer in up_blocks[i]		):
            __A         =				len(
                {".".join(shave_segments(a_	,     3		).split("."		)[:3]		) for layer in up_blocks[i] if "block" in layer}		)
            __A         =				{
                layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(a_		)
            }

            if num_blocks > 0:
                for j in range(config["layers_per_block"] + 1		):
                    __A         =				{"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
                    __A         =				renew_resnet_paths(blocks[j]		)
                    assign_to_checkpoint(a_	,     a_	,     a_	,     additional_replacements=[replace_indices]		)

        if any("attn" in layer for layer in up_blocks[i]		):
            __A         =				len(
                {".".join(shave_segments(a_	,     3		).split("."		)[:3]		) for layer in up_blocks[i] if "attn" in layer}		)
            __A         =				{
                layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(a_		)
            }

            if num_attn > 0:
                for j in range(config["layers_per_block"] + 1		):
                    __A         =				{"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
                    __A         =				renew_attention_paths(attns[j]		)
                    assign_to_checkpoint(a_	,     a_	,     a_	,     additional_replacements=[replace_indices]		)

    __A         =				{k.replace("mid_new_2"	,     "mid_block"		): v for k, v in new_checkpoint.items()}
    __A         =				checkpoint["quant_conv.weight"]
    __A         =				checkpoint["quant_conv.bias"]
    if "quantize.embedding.weight" in checkpoint:
        __A         =				checkpoint["quantize.embedding.weight"]
    __A         =				checkpoint["post_quant_conv.weight"]
    __A         =				checkpoint["post_quant_conv.bias"]

    return new_checkpoint


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE							:List[Any]											=       argparse.ArgumentParser()

     parser.add_argument(
         '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
     )

     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The config json file corresponding to the architecture.',
     )

     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

     SCREAMING_SNAKE_CASE							:List[Any]											=       parser.parse_args()
     SCREAMING_SNAKE_CASE							:Optional[int]											=       torch.load(args.checkpoint_path)

     with open(args.config_file) as f:
          SCREAMING_SNAKE_CASE							:str											=       json.loads(f.read())

     # unet case
     SCREAMING_SNAKE_CASE							:int											=       {key.split('.')[0] for key in checkpoint.keys()}
     if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
          SCREAMING_SNAKE_CASE							:int											=       convert_vq_autoenc_checkpoint(checkpoint, config)
     else:
          SCREAMING_SNAKE_CASE							:Dict											=       convert_ddpm_checkpoint(checkpoint, config)

     if "ddpm" in config:
          del config["ddpm"]

     if config["_class_name"] == "VQModel":
          SCREAMING_SNAKE_CASE							:Union[str, Any]											=       VQModel(**config)
          model.load_state_dict(converted_checkpoint)
          model.save_pretrained(args.dump_path)
     elif config["_class_name"] == "AutoencoderKL":
          SCREAMING_SNAKE_CASE							:Union[str, Any]											=       AutoencoderKL(**config)
          model.load_state_dict(converted_checkpoint)
          model.save_pretrained(args.dump_path)
     else:
          SCREAMING_SNAKE_CASE							:Optional[int]											=       UNetaDModel(**config)
          model.load_state_dict(converted_checkpoint)

          SCREAMING_SNAKE_CASE							:Tuple											=       DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

          SCREAMING_SNAKE_CASE							:Optional[int]											=       DDPMPipeline(unet=model, scheduler=scheduler)
          pipe.save_pretrained(args.dump_path)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE							:str											=       {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:int											=       ['DebertaV2TokenizerFast']

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFDebertaV2ForMaskedLM',
					    'TFDebertaV2ForQuestionAnswering',
					    'TFDebertaV2ForSequenceClassification',
					    'TFDebertaV2ForTokenClassification',
					    'TFDebertaV2Model',
					    'TFDebertaV2PreTrainedModel',
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'DebertaV2ForMaskedLM',
					    'DebertaV2ForMultipleChoice',
					    'DebertaV2ForQuestionAnswering',
					    'DebertaV2ForSequenceClassification',
					    'DebertaV2ForTokenClassification',
					    'DebertaV2Model',
					    'DebertaV2PreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_deberta_va import (
					    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DebertaVaConfig,
					    DebertaVaOnnxConfig,
					)
					from .tokenization_deberta_va import DebertaVaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_deberta_va import (
										    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDebertaVaForMaskedLM,
										    TFDebertaVaForQuestionAnswering,
										    TFDebertaVaForSequenceClassification,
										    TFDebertaVaForTokenClassification,
										    TFDebertaVaModel,
										    TFDebertaVaPreTrainedModel,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_deberta_va import (
										    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DebertaVaForMaskedLM,
										    DebertaVaForMultipleChoice,
										    DebertaVaForQuestionAnswering,
										    DebertaVaForSequenceClassification,
										    DebertaVaForTokenClassification,
										    DebertaVaModel,
										    DebertaVaPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE							:List[Any]											=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
