






import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowercase  :							Tuple			  =    {
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 10_00,
    '''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''',
}

lowercase  :							List[Any]			  =    {
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 10_00,
    '''block_out_channels''': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 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''',
}

lowercase  :							List[str]			  =    {
    '''sample_size''': 2_56,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 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''',
}

lowercase  :							Optional[Any]			  =    {
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

lowercase  :							List[str]			  =    {
    '''num_train_timesteps''': 2_01,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

lowercase  :							Tuple			  =    {
    '''num_train_timesteps''': 1_51,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

def      lowerCAmelCase__	(    _a       :			int    ):
		if isinstance(_a  ,      _a    ):
				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      lowerCAmelCase__	(    _a       :			Dict  ,      _a       :			int  ,      _a       :			Optional[int]  ,      _a       :			Dict  ,      _a       :			List[str]=False    ):
		snake_case_				:							Union[str, Any]						 =       checkpoint[F'''{old_prefix}.in_layers.0.weight''']
		snake_case_				:							Dict						 =       checkpoint[F'''{old_prefix}.in_layers.0.bias''']
		snake_case_				:							Tuple						 =       checkpoint[F'''{old_prefix}.in_layers.2.weight''']
		snake_case_				:							List[Any]						 =       checkpoint[F'''{old_prefix}.in_layers.2.bias''']
		snake_case_				:							Optional[Any]						 =       checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
		snake_case_				:							Dict						 =       checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
		snake_case_				:							Dict						 =       checkpoint[F'''{old_prefix}.out_layers.0.weight''']
		snake_case_				:							Optional[Any]						 =       checkpoint[F'''{old_prefix}.out_layers.0.bias''']
		snake_case_				:							List[Any]						 =       checkpoint[F'''{old_prefix}.out_layers.3.weight''']
		snake_case_				:							Optional[int]						 =       checkpoint[F'''{old_prefix}.out_layers.3.bias''']

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

		return new_checkpoint

def      lowerCAmelCase__	(    _a       :			List[str]  ,      _a       :			Union[str, Any]  ,      _a       :			Any  ,      _a       :			int  ,      _a       :			Tuple=None    ):
		snake_case_ ,       snake_case_ ,       snake_case_				:							Dict						 =       checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3  ,      dim=0    )
		snake_case_ ,       snake_case_ ,       snake_case_				:							List[Any]						 =       checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3  ,      dim=0    )

		snake_case_				:							List[Any]						 =       checkpoint[F'''{old_prefix}.norm.weight''']
		snake_case_				:							Union[str, Any]						 =       checkpoint[F'''{old_prefix}.norm.bias''']

		snake_case_				:							Tuple						 =       weight_q.squeeze(-1    ).squeeze(-1    )
		snake_case_				:							Any						 =       bias_q.squeeze(-1    ).squeeze(-1    )
		snake_case_				:							Union[str, Any]						 =       weight_k.squeeze(-1    ).squeeze(-1    )
		snake_case_				:							str						 =       bias_k.squeeze(-1    ).squeeze(-1    )
		snake_case_				:							Dict						 =       weight_v.squeeze(-1    ).squeeze(-1    )
		snake_case_				:							Union[str, Any]						 =       bias_v.squeeze(-1    ).squeeze(-1    )

		snake_case_				:							Tuple						 =       (
		    checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1    ).squeeze(-1    )
		)
		snake_case_				:							List[str]						 =       checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1    ).squeeze(-1    )

		return new_checkpoint

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			List[Any]    ):
		snake_case_				:							List[Any]						 =       torch.load(_a  ,      map_location="cpu"    )
		snake_case_				:							Optional[Any]						 =       {}

		snake_case_				:							Optional[Any]						 =       checkpoint["time_embed.0.weight"]
		snake_case_				:							Union[str, Any]						 =       checkpoint["time_embed.0.bias"]
		snake_case_				:							List[Any]						 =       checkpoint["time_embed.2.weight"]
		snake_case_				:							List[str]						 =       checkpoint["time_embed.2.bias"]

		if unet_config["num_class_embeds"] is not None:
				snake_case_				:							List[Any]						 =       checkpoint["label_emb.weight"]

		snake_case_				:							List[str]						 =       checkpoint["input_blocks.0.0.weight"]
		snake_case_				:							Tuple						 =       checkpoint["input_blocks.0.0.bias"]

		snake_case_				:							Any						 =       unet_config["down_block_types"]
		snake_case_				:							Optional[int]						 =       unet_config["layers_per_block"]
		snake_case_				:							str						 =       unet_config["attention_head_dim"]
		snake_case_				:							str						 =       unet_config["block_out_channels"]
		snake_case_				:							Union[str, Any]						 =       1
		snake_case_				:							Union[str, Any]						 =       channels_list[0]

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

				elif layer_type == "AttnDownBlock2D":
						for j in range(_a    ):
								snake_case_				:							Optional[int]						 =       F'''down_blocks.{i}.resnets.{j}'''
								snake_case_				:							Optional[Any]						 =       F'''input_blocks.{current_layer}.0'''
								snake_case_				:							Tuple						 =       True if j == 0 and downsample_block_has_skip else False
								snake_case_				:							List[str]						 =       convert_resnet(_a  ,      _a  ,      _a  ,      _a  ,      has_skip=_a    )
								snake_case_				:							List[Any]						 =       F'''down_blocks.{i}.attentions.{j}'''
								snake_case_				:							Union[str, Any]						 =       F'''input_blocks.{current_layer}.1'''
								snake_case_				:							Union[str, Any]						 =       convert_attention(
								    _a  ,      _a  ,      _a  ,      _a  ,      _a    )
								current_layer += 1

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

				snake_case_				:							Optional[Any]						 =       current_channels

		# hardcoded the mid-block for now
		snake_case_				:							Optional[int]						 =       "mid_block.resnets.0"
		snake_case_				:							int						 =       "middle_block.0"
		snake_case_				:							List[Any]						 =       convert_resnet(_a  ,      _a  ,      _a  ,      _a    )
		snake_case_				:							Optional[int]						 =       "mid_block.attentions.0"
		snake_case_				:							Any						 =       "middle_block.1"
		snake_case_				:							Optional[Any]						 =       convert_attention(_a  ,      _a  ,      _a  ,      _a  ,      _a    )
		snake_case_				:							Tuple						 =       "mid_block.resnets.1"
		snake_case_				:							int						 =       "middle_block.2"
		snake_case_				:							Dict						 =       convert_resnet(_a  ,      _a  ,      _a  ,      _a    )

		snake_case_				:							Tuple						 =       0
		snake_case_				:							int						 =       unet_config["up_block_types"]

		for i, layer_type in enumerate(_a    ):
				if layer_type == "ResnetUpsampleBlock2D":
						for j in range(layers_per_block + 1    ):
								snake_case_				:							Optional[Any]						 =       F'''up_blocks.{i}.resnets.{j}'''
								snake_case_				:							int						 =       F'''output_blocks.{current_layer}.0'''
								snake_case_				:							Dict						 =       convert_resnet(_a  ,      _a  ,      _a  ,      _a  ,      has_skip=_a    )
								current_layer += 1

						if i != len(_a    ) - 1:
								snake_case_				:							Tuple						 =       F'''up_blocks.{i}.upsamplers.0'''
								snake_case_				:							Optional[int]						 =       F'''output_blocks.{current_layer-1}.1'''
								snake_case_				:							int						 =       convert_resnet(_a  ,      _a  ,      _a  ,      _a    )
				elif layer_type == "AttnUpBlock2D":
						for j in range(layers_per_block + 1    ):
								snake_case_				:							Optional[int]						 =       F'''up_blocks.{i}.resnets.{j}'''
								snake_case_				:							Dict						 =       F'''output_blocks.{current_layer}.0'''
								snake_case_				:							Tuple						 =       convert_resnet(_a  ,      _a  ,      _a  ,      _a  ,      has_skip=_a    )
								snake_case_				:							Any						 =       F'''up_blocks.{i}.attentions.{j}'''
								snake_case_				:							Optional[Any]						 =       F'''output_blocks.{current_layer}.1'''
								snake_case_				:							List[str]						 =       convert_attention(
								    _a  ,      _a  ,      _a  ,      _a  ,      _a    )
								current_layer += 1

						if i != len(_a    ) - 1:
								snake_case_				:							List[str]						 =       F'''up_blocks.{i}.upsamplers.0'''
								snake_case_				:							Dict						 =       F'''output_blocks.{current_layer-1}.2'''
								snake_case_				:							Optional[Any]						 =       convert_resnet(_a  ,      _a  ,      _a  ,      _a    )

		snake_case_				:							Optional[Any]						 =       checkpoint["out.0.weight"]
		snake_case_				:							Optional[Any]						 =       checkpoint["out.0.bias"]
		snake_case_				:							str						 =       checkpoint["out.2.weight"]
		snake_case_				:							Tuple						 =       checkpoint["out.2.bias"]

		return new_checkpoint


if __name__ == "__main__":
							lowercase  :							Union[str, 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.''')

							lowercase  :							List[str]			  =    parser.parse_args()
							lowercase  :							Optional[Any]			  =    strabool(args.class_cond)

							lowercase  :							Any			  =    os.path.basename(args.unet_path)
							print(F"""Checkpoint: {ckpt_name}""")

							# Get U-Net config
							if "imagenet64" in ckpt_name:
														lowercase  :							str			  =    IMAGENET_64_UNET_CONFIG
							elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
														lowercase  :							Union[str, Any]			  =    LSUN_256_UNET_CONFIG
							elif "test" in ckpt_name:
														lowercase  :							Tuple			  =    TEST_UNET_CONFIG
							else:
														raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

							if not args.class_cond:
														lowercase  :							Dict			  =    None

							lowercase  :							Union[str, Any]			  =    con_pt_to_diffuser(args.unet_path, unet_config)

							lowercase  :							Union[str, Any]			  =    UNetaDModel(**unet_config)
							image_unet.load_state_dict(converted_unet_ckpt)

							# Get scheduler config
							if "cd" in ckpt_name or "test" in ckpt_name:
														lowercase  :							Any			  =    CD_SCHEDULER_CONFIG
							elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
														lowercase  :							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)):
														lowercase  :							Tuple			  =    CT_LSUN_256_SCHEDULER_CONFIG
							else:
														raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

							lowercase  :							Tuple			  =    CMStochasticIterativeScheduler(**scheduler_config)

							lowercase  :							List[Any]			  =    ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
							consistency_model.save_pretrained(args.dump_path)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase  :							str			  =    {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							List[str]			  =    [
							    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BioGptForCausalLM''',
							    '''BioGptForTokenClassification''',
							    '''BioGptForSequenceClassification''',
							    '''BioGptModel''',
							    '''BioGptPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase  :							Tuple			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
