import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken



@torch.no_grad()
def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    with open(SCREAMING_SNAKE_CASE	) as metadata_file:
        __UpperCamelCase     :int			  =		json.load(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Any			  =		LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE  ,			**metadata['''model_config''']	)

    # Load in the weights from the checkpoint_path
    __UpperCamelCase     :Optional[Any]			  =		torch.load(SCREAMING_SNAKE_CASE  ,			map_location='''cpu'''	)['''module''']

    # Load the entity vocab file
    __UpperCamelCase     :str			  =		load_original_entity_vocab(SCREAMING_SNAKE_CASE	)
    # add an entry for [MASK2]
    __UpperCamelCase     :Tuple			  =		max(entity_vocab.values()	) + 1
    config.entity_vocab_size += 1

    __UpperCamelCase     :Any			  =		XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']	)

    # Add special tokens to the token vocabulary for downstream tasks
    __UpperCamelCase     :Optional[int]			  =		AddedToken('''<ent>'''  ,			lstrip=SCREAMING_SNAKE_CASE  ,			rstrip=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :str			  =		AddedToken('''<ent2>'''  ,			lstrip=SCREAMING_SNAKE_CASE  ,			rstrip=SCREAMING_SNAKE_CASE	)
    tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]}	)
    config.vocab_size += 2

    print(f"""Saving tokenizer to {pytorch_dump_folder_path}"""	)
    tokenizer.save_pretrained(SCREAMING_SNAKE_CASE	)
    with open(os.path.join(SCREAMING_SNAKE_CASE  ,			'''tokenizer_config.json'''	)  ,			'''r'''	) as f:
        __UpperCamelCase     :Any			  =		json.load(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Any			  =		'''MLukeTokenizer'''
    with open(os.path.join(SCREAMING_SNAKE_CASE  ,			'''tokenizer_config.json'''	)  ,			'''w'''	) as f:
        json.dump(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    with open(os.path.join(SCREAMING_SNAKE_CASE  ,			MLukeTokenizer.vocab_files_names['''entity_vocab_file''']	)  ,			'''w'''	) as f:
        json.dump(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    __UpperCamelCase     :Optional[int]			  =		MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)

    # Initialize the embeddings of the special tokens
    __UpperCamelCase     :List[str]			  =		tokenizer.convert_tokens_to_ids(['''@''']	)[0]
    __UpperCamelCase     :Optional[int]			  =		tokenizer.convert_tokens_to_ids(['''#''']	)[0]

    __UpperCamelCase     :List[Any]			  =		state_dict['''embeddings.word_embeddings.weight''']
    __UpperCamelCase     :Tuple			  =		word_emb[ent_init_index].unsqueeze(0	)
    __UpperCamelCase     :List[str]			  =		word_emb[enta_init_index].unsqueeze(0	)
    __UpperCamelCase     :Tuple			  =		torch.cat([word_emb, ent_emb, enta_emb]	)
    # add special tokens for 'entity_predictions.bias'
    for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
        __UpperCamelCase     :Optional[int]			  =		state_dict[bias_name]
        __UpperCamelCase     :int			  =		decoder_bias[ent_init_index].unsqueeze(0	)
        __UpperCamelCase     :Optional[Any]			  =		decoder_bias[enta_init_index].unsqueeze(0	)
        __UpperCamelCase     :Optional[Any]			  =		torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]	)

    # Initialize the query layers of the entity-aware self-attention mechanism
    for layer_index in range(config.num_hidden_layers	):
        for matrix_name in ["query.weight", "query.bias"]:
            __UpperCamelCase     :List[str]			  =		f"""encoder.layer.{layer_index}.attention.self."""
            __UpperCamelCase     :str			  =		state_dict[prefix + matrix_name]
            __UpperCamelCase     :Optional[Any]			  =		state_dict[prefix + matrix_name]
            __UpperCamelCase     :Union[str, Any]			  =		state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
    __UpperCamelCase     :int			  =		state_dict['''entity_embeddings.entity_embeddings.weight''']
    __UpperCamelCase     :Union[str, Any]			  =		entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0	)
    __UpperCamelCase     :Tuple			  =		torch.cat([entity_emb, entity_mask_emb]	)
    # add [MASK2] for 'entity_predictions.bias'
    __UpperCamelCase     :List[str]			  =		state_dict['''entity_predictions.bias''']
    __UpperCamelCase     :int			  =		entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0	)
    __UpperCamelCase     :int			  =		torch.cat([entity_prediction_bias, entity_mask_bias]	)

    __UpperCamelCase     :str			  =		LukeForMaskedLM(config=SCREAMING_SNAKE_CASE	).eval()

    state_dict.pop('''entity_predictions.decoder.weight'''	)
    state_dict.pop('''lm_head.decoder.weight'''	)
    state_dict.pop('''lm_head.decoder.bias'''	)
    __UpperCamelCase     :Any			  =		OrderedDict()
    for key, value in state_dict.items():
        if not (key.startswith('''lm_head'''	) or key.startswith('''entity_predictions'''	)):
            __UpperCamelCase     :Union[str, Any]			  =		state_dict[key]
        else:
            __UpperCamelCase     :Optional[int]			  =		state_dict[key]

    __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		model.load_state_dict(SCREAMING_SNAKE_CASE  ,			strict=SCREAMING_SNAKE_CASE	)

    if set(SCREAMING_SNAKE_CASE	) != {"luke.embeddings.position_ids"}:
        raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}"""	)
    if set(SCREAMING_SNAKE_CASE	) != {
        "lm_head.decoder.weight",
        "lm_head.decoder.bias",
        "entity_predictions.decoder.weight",
    }:
        raise ValueError(f"""Unexpected missing_keys: {missing_keys}"""	)

    model.tie_weights()
    assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
    assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

    # Check outputs
    __UpperCamelCase     :List[str]			  =		MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE  ,			task='''entity_classification'''	)

    __UpperCamelCase     :Dict			  =		'''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
    __UpperCamelCase     :Optional[Any]			  =		(0, 9)
    __UpperCamelCase     :Union[str, Any]			  =		tokenizer(SCREAMING_SNAKE_CASE  ,			entity_spans=[span]  ,			return_tensors='''pt'''	)

    __UpperCamelCase     :int			  =		model(**SCREAMING_SNAKE_CASE	)

    # Verify word hidden states
    if model_size == "large":
        raise NotImplementedError
    else:  # base
        __UpperCamelCase     :Optional[int]			  =		torch.Size((1, 33, 768)	)
        __UpperCamelCase     :Union[str, Any]			  =		torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]	)

    if not (outputs.last_hidden_state.shape == expected_shape):
        raise ValueError(
            f"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"""	)
    if not torch.allclose(outputs.last_hidden_state[0, :3, :3]  ,			SCREAMING_SNAKE_CASE  ,			atol=1e-4	):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        raise NotImplementedError
    else:  # base
        __UpperCamelCase     :Union[str, Any]			  =		torch.Size((1, 1, 768)	)
        __UpperCamelCase     :Union[str, Any]			  =		torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]	)

    if not (outputs.entity_last_hidden_state.shape == expected_shape):
        raise ValueError(
            f"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
            f""" {expected_shape}"""	)
    if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]  ,			SCREAMING_SNAKE_CASE  ,			atol=1e-4	):
        raise ValueError

    # Verify masked word/entity prediction
    __UpperCamelCase     :Optional[Any]			  =		MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Optional[int]			  =		'''Tokyo is the capital of <mask>.'''
    __UpperCamelCase     :Any			  =		(24, 30)
    __UpperCamelCase     :Optional[int]			  =		tokenizer(SCREAMING_SNAKE_CASE  ,			entity_spans=[span]  ,			return_tensors='''pt'''	)

    __UpperCamelCase     :Tuple			  =		model(**SCREAMING_SNAKE_CASE	)

    __UpperCamelCase     :int			  =		encoding['''input_ids'''][0].tolist()
    __UpperCamelCase     :int			  =		input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''	)	)
    __UpperCamelCase     :Optional[int]			  =		outputs.logits[0][mask_position_id].argmax(dim=-1	)
    assert "Japan" == tokenizer.decode(SCREAMING_SNAKE_CASE	)

    __UpperCamelCase     :int			  =		outputs.entity_logits[0][0].argmax().item()
    __UpperCamelCase     :Union[str, Any]			  =		[
        entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
    ]
    assert [e for e in multilingual_predicted_entities if e.startswith('''en:'''	)][0] == "en:Japan"

    # Finally, save our PyTorch model and tokenizer
    print('''Saving PyTorch model to {}'''.format(SCREAMING_SNAKE_CASE	)	)
    model.save_pretrained(SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		['''[MASK]''', '''[PAD]''', '''[UNK]''']

    __UpperCamelCase     :List[Any]			  =		[json.loads(SCREAMING_SNAKE_CASE	) for line in open(SCREAMING_SNAKE_CASE	)]

    __UpperCamelCase     :int			  =		{}
    for entry in data:
        __UpperCamelCase     :int			  =		entry['''id''']
        for entity_name, language in entry["entities"]:
            if entity_name in SPECIAL_TOKENS:
                __UpperCamelCase     :Optional[int]			  =		entity_id
                break
            __UpperCamelCase     :Tuple			  =		f"""{language}:{entity_name}"""
            __UpperCamelCase     :Union[str, Any]			  =		entity_id
    return new_mapping


if __name__ == "__main__":
    __lowercase							 =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
    parser.add_argument(
        '''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
    )
    parser.add_argument(
        '''--entity_vocab_path''',
        default=None,
        type=str,
        help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
    )
    parser.add_argument(
        '''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
    )
    __lowercase							 =					parser.parse_args()
    convert_luke_checkpoint(
        args.checkpoint_path,
        args.metadata_path,
        args.entity_vocab_path,
        args.pytorch_dump_folder_path,
        args.model_size,
    )








import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowerCAmelCase					:			str  				=			{
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1_000,
    "block_out_channels": [32, 64],
    "attention_head_dim": 8,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

_lowerCAmelCase					:			List[Any]  				=			{
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1_000,
    "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

_lowerCAmelCase					:			str  				=			{
    "sample_size": 256,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "default",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

_lowerCAmelCase					:			List[str]  				=			{
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_lowerCAmelCase					:			Optional[Any]  				=			{
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_lowerCAmelCase					:			List[str]  				=			{
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






			if isinstance(_snake_case       ,     _snake_case	):
						return v
			if v.lower() in ("yes", "true", "t", "y", "1"):
						return True
			elif v.lower() in ("no", "false", "f", "n", "0"):
						return False
			else:
						raise argparse.ArgumentTypeError('boolean value expected'	)






def        UpperCamelCase_(							_snake_case   :  Tuple       ,     _snake_case   :  List[str]       ,     _snake_case   :  Any       ,     _snake_case   :  str       ,     _snake_case   :  Union[str, Any]=False	):




			"""simple docstring"""






			__a        =checkpoint[F'{old_prefix}.in_layers.0.weight']
			__a        =checkpoint[F'{old_prefix}.in_layers.0.bias']
			__a        =checkpoint[F'{old_prefix}.in_layers.2.weight']
			__a        =checkpoint[F'{old_prefix}.in_layers.2.bias']
			__a        =checkpoint[F'{old_prefix}.emb_layers.1.weight']
			__a        =checkpoint[F'{old_prefix}.emb_layers.1.bias']
			__a        =checkpoint[F'{old_prefix}.out_layers.0.weight']
			__a        =checkpoint[F'{old_prefix}.out_layers.0.bias']
			__a        =checkpoint[F'{old_prefix}.out_layers.3.weight']
			__a        =checkpoint[F'{old_prefix}.out_layers.3.bias']

			if has_skip:
						__a        =checkpoint[F'{old_prefix}.skip_connection.weight']
						__a        =checkpoint[F'{old_prefix}.skip_connection.bias']

			return new_checkpoint






def        UpperCamelCase_(							_snake_case   :  Dict       ,     _snake_case   :  Union[str, Any]       ,     _snake_case   :  Union[str, Any]       ,     _snake_case   :  Optional[Any]       ,     _snake_case   :  Optional[int]=None	):




			"""simple docstring"""






			__a      ,    __a      ,    __a        =checkpoint[F'{old_prefix}.qkv.weight'].chunk(3       ,     dim=0	)
			__a      ,    __a      ,    __a        =checkpoint[F'{old_prefix}.qkv.bias'].chunk(3       ,     dim=0	)

			__a        =checkpoint[F'{old_prefix}.norm.weight']
			__a        =checkpoint[F'{old_prefix}.norm.bias']

			__a        =weight_q.squeeze(-1	).squeeze(-1	)
			__a        =bias_q.squeeze(-1	).squeeze(-1	)
			__a        =weight_k.squeeze(-1	).squeeze(-1	)
			__a        =bias_k.squeeze(-1	).squeeze(-1	)
			__a        =weight_v.squeeze(-1	).squeeze(-1	)
			__a        =bias_v.squeeze(-1	).squeeze(-1	)

			__a        =(
			    checkpoint[F'{old_prefix}.proj_out.weight'].squeeze(-1	).squeeze(-1	)
			)
			__a        =checkpoint[F'{old_prefix}.proj_out.bias'].squeeze(-1	).squeeze(-1	)

			return new_checkpoint






def        UpperCamelCase_(							_snake_case   :  str       ,     _snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     map_location='cpu'	)
			__a        ={}

			__a        =checkpoint['time_embed.0.weight']
			__a        =checkpoint['time_embed.0.bias']
			__a        =checkpoint['time_embed.2.weight']
			__a        =checkpoint['time_embed.2.bias']

			if unet_config["num_class_embeds"] is not None:
						__a        =checkpoint['label_emb.weight']

			__a        =checkpoint['input_blocks.0.0.weight']
			__a        =checkpoint['input_blocks.0.0.bias']

			__a        =unet_config['down_block_types']
			__a        =unet_config['layers_per_block']
			__a        =unet_config['attention_head_dim']
			__a        =unet_config['block_out_channels']
			__a        =1
			__a        =channels_list[0]

			for i, layer_type in enumerate(_snake_case	):
						__a        =channels_list[i]
						__a        =current_channels != prev_channels
						if layer_type == "ResnetDownsampleBlock2D":
									for j in range(_snake_case	):
												__a        =F'down_blocks.{i}.resnets.{j}'
												__a        =F'input_blocks.{current_layer}.0'
												__a        =True if j == 0 and downsample_block_has_skip else False
												__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     has_skip=_snake_case	)
												current_layer += 1

						elif layer_type == "AttnDownBlock2D":
									for j in range(_snake_case	):
												__a        =F'down_blocks.{i}.resnets.{j}'
												__a        =F'input_blocks.{current_layer}.0'
												__a        =True if j == 0 and downsample_block_has_skip else False
												__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     has_skip=_snake_case	)
												__a        =F'down_blocks.{i}.attentions.{j}'
												__a        =F'input_blocks.{current_layer}.1'
												__a        =convert_attention(
												    _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
												current_layer += 1

						if i != len(_snake_case	) - 1:
									__a        =F'down_blocks.{i}.downsamplers.0'
									__a        =F'input_blocks.{current_layer}.0'
									__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
									current_layer += 1

						__a        =current_channels

			# hardcoded the mid-block for now
			__a        ='mid_block.resnets.0'
			__a        ='middle_block.0'
			__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
			__a        ='mid_block.attentions.0'
			__a        ='middle_block.1'
			__a        =convert_attention(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
			__a        ='mid_block.resnets.1'
			__a        ='middle_block.2'
			__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)

			__a        =0
			__a        =unet_config['up_block_types']

			for i, layer_type in enumerate(_snake_case	):
						if layer_type == "ResnetUpsampleBlock2D":
									for j in range(layers_per_block + 1	):
												__a        =F'up_blocks.{i}.resnets.{j}'
												__a        =F'output_blocks.{current_layer}.0'
												__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     has_skip=_snake_case	)
												current_layer += 1

									if i != len(_snake_case	) - 1:
												__a        =F'up_blocks.{i}.upsamplers.0'
												__a        =F'output_blocks.{current_layer-1}.1'
												__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
						elif layer_type == "AttnUpBlock2D":
									for j in range(layers_per_block + 1	):
												__a        =F'up_blocks.{i}.resnets.{j}'
												__a        =F'output_blocks.{current_layer}.0'
												__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     has_skip=_snake_case	)
												__a        =F'up_blocks.{i}.attentions.{j}'
												__a        =F'output_blocks.{current_layer}.1'
												__a        =convert_attention(
												    _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
												current_layer += 1

									if i != len(_snake_case	) - 1:
												__a        =F'up_blocks.{i}.upsamplers.0'
												__a        =F'output_blocks.{current_layer-1}.2'
												__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)

			__a        =checkpoint['out.0.weight']
			__a        =checkpoint['out.0.bias']
			__a        =checkpoint['out.2.weight']
			__a        =checkpoint['out.2.bias']

			return new_checkpoint


if __name__ == "__main__":
						_lowerCAmelCase					:			Optional[Any]  				=			argparse.ArgumentParser()

						parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
						parser.add_argument(
						    "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
						)
						parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

						_lowerCAmelCase					:			Optional[Any]  				=			parser.parse_args()
						_lowerCAmelCase					:			Optional[Any]  				=			strabool(args.class_cond)

						_lowerCAmelCase					:			Dict  				=			os.path.basename(args.unet_path)
						print(f'''Checkpoint: {ckpt_name}''')

						# Get U-Net config
						if "imagenet64" in ckpt_name:
												_lowerCAmelCase					:			Tuple  				=			IMAGENET_64_UNET_CONFIG
						elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
												_lowerCAmelCase					:			Optional[int]  				=			LSUN_256_UNET_CONFIG
						elif "test" in ckpt_name:
												_lowerCAmelCase					:			int  				=			TEST_UNET_CONFIG
						else:
												raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

						if not args.class_cond:
												_lowerCAmelCase					:			List[Any]  				=			None

						_lowerCAmelCase					:			Tuple  				=			con_pt_to_diffuser(args.unet_path, unet_config)

						_lowerCAmelCase					:			Optional[int]  				=			UNetaDModel(**unet_config)
						image_unet.load_state_dict(converted_unet_ckpt)

						# Get scheduler config
						if "cd" in ckpt_name or "test" in ckpt_name:
												_lowerCAmelCase					:			int  				=			CD_SCHEDULER_CONFIG
						elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
												_lowerCAmelCase					:			Optional[int]  				=			CT_IMAGENET_64_SCHEDULER_CONFIG
						elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
												_lowerCAmelCase					:			List[str]  				=			CT_LSUN_256_SCHEDULER_CONFIG
						else:
												raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

						_lowerCAmelCase					:			Any  				=			CMStochasticIterativeScheduler(**scheduler_config)

						_lowerCAmelCase					:			str  				=			ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
						consistency_model.save_pretrained(args.dump_path)






