





"""simple docstring"""




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


a						: List[Any]     	=			{
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1000,
    '''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''',
}

a						: int     	=			{
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1000,
    '''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''',
}

a						: Tuple     	=			{
    '''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''',
}

a						: int     	=			{
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

a						: Tuple     	=			{
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

a						: List[Any]     	=			{
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Any					) ->Union[str, Any]:






	'''simple docstring'''






	if isinstance(_lowercase    ,       _lowercase					):
		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       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Dict    ,       _lowercase						:    Union[str, Any]    ,       _lowercase						:    List[Any]    ,       _lowercase						:    Tuple    ,       _lowercase						:    Any=False					) ->str:






	'''simple docstring'''






	a      :		Dict	=				checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
	a      :		List[str]	=				checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
	a      :		List[Any]	=				checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
	a      :		str	=				checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
	a      :		Optional[int]	=				checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
	a      :		str	=				checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
	a      :		List[str]	=				checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
	a      :		Dict	=				checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
	a      :		List[Any]	=				checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
	a      :		Dict	=				checkpoint[F"""{old_prefix}.out_layers.3.bias"""]

	if has_skip:
		a      :		str	=				checkpoint[F"""{old_prefix}.skip_connection.weight"""]
		a      :		List[Any]	=				checkpoint[F"""{old_prefix}.skip_connection.bias"""]

	return new_checkpoint






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    str    ,       _lowercase						:    int    ,       _lowercase						:    Tuple    ,       _lowercase						:    List[str]=None					) ->Tuple:






	'''simple docstring'''






	a,       a,       a      :		Any	=				checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3    ,       dim=0					)
	a,       a,       a      :		int	=				checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3    ,       dim=0					)

	a      :		Tuple	=				checkpoint[F"""{old_prefix}.norm.weight"""]
	a      :		Dict	=				checkpoint[F"""{old_prefix}.norm.bias"""]

	a      :		Dict	=				weight_q.squeeze(-1					).squeeze(-1					)
	a      :		Optional[int]	=				bias_q.squeeze(-1					).squeeze(-1					)
	a      :		Tuple	=				weight_k.squeeze(-1					).squeeze(-1					)
	a      :		Optional[int]	=				bias_k.squeeze(-1					).squeeze(-1					)
	a      :		List[Any]	=				weight_v.squeeze(-1					).squeeze(-1					)
	a      :		Optional[Any]	=				bias_v.squeeze(-1					).squeeze(-1					)

	a      :		List[str]	=				(
	    checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1					).squeeze(-1					)
	)
	a      :		Optional[int]	=				checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1					).squeeze(-1					)

	return new_checkpoint






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    Any					) ->Tuple:






	'''simple docstring'''






	a      :		str	=				torch.load(_lowercase    ,       map_location="cpu"					)
	a      :		List[Any]	=				{}

	a      :		List[str]	=				checkpoint["time_embed.0.weight"]
	a      :		Tuple	=				checkpoint["time_embed.0.bias"]
	a      :		List[Any]	=				checkpoint["time_embed.2.weight"]
	a      :		Dict	=				checkpoint["time_embed.2.bias"]

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

	a      :		Any	=				checkpoint["input_blocks.0.0.weight"]
	a      :		Optional[int]	=				checkpoint["input_blocks.0.0.bias"]

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

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

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

		if i != len(_lowercase					) - 1:
			a      :		List[Any]	=				F"""down_blocks.{i}.downsamplers.0"""
			a      :		Any	=				F"""input_blocks.{current_layer}.0"""
			a      :		Union[str, Any]	=				convert_resnet(_lowercase    ,       _lowercase    ,       _lowercase    ,       _lowercase					)
			current_layer += 1

		a      :		Dict	=				current_channels

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

	a      :		Optional[int]	=				0
	a      :		int	=				unet_config["up_block_types"]

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

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

			if i != len(_lowercase					) - 1:
				a      :		Any	=				F"""up_blocks.{i}.upsamplers.0"""
				a      :		Union[str, Any]	=				F"""output_blocks.{current_layer-1}.2"""
				a      :		Optional[int]	=				convert_resnet(_lowercase    ,       _lowercase    ,       _lowercase    ,       _lowercase					)

	a      :		Any	=				checkpoint["out.0.weight"]
	a      :		int	=				checkpoint["out.0.bias"]
	a      :		int	=				checkpoint["out.2.weight"]
	a      :		Dict	=				checkpoint["out.2.bias"]

	return new_checkpoint


if __name__ == "__main__":
		a						: Optional[int]     	=			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.''')

		a						: List[str]     	=			parser.parse_args()
		a						: Tuple     	=			strabool(args.class_cond)

		a						: int     	=			os.path.basename(args.unet_path)
		print(F'''Checkpoint: {ckpt_name}''')

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

		if not args.class_cond:
				a						: Optional[int]     	=			None

		a						: Optional[int]     	=			con_pt_to_diffuser(args.unet_path, unet_config)

		a						: 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:
				a						: List[str]     	=			CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				a						: 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)):
				a						: Any     	=			CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

		a						: int     	=			CMStochasticIterativeScheduler(**scheduler_config)

		a						: int     	=			ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)










# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a		:       Union[str, Any]			     = {"configuration_timm_backbone": ["TimmBackboneConfig"]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:       Tuple			     = ["TimmBackbone"]


if TYPE_CHECKING:
       from .configuration_timm_backbone import TimmBackboneConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_timm_backbone import TimmBackbone

else:
       import sys

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


