






'''simple docstring'''







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_A	:			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''',
}

_A	:			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''',
}

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





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       Any:
							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  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase=False      )      ->       int:
							lowerCamelCase__					:	Union[str, Any]         =       checkpoint[f'''{old_prefix}.in_layers.0.weight''']
							lowerCamelCase__					:	List[Any]         =       checkpoint[f'''{old_prefix}.in_layers.0.bias''']
							lowerCamelCase__					:	Optional[int]         =       checkpoint[f'''{old_prefix}.in_layers.2.weight''']
							lowerCamelCase__					:	List[str]         =       checkpoint[f'''{old_prefix}.in_layers.2.bias''']
							lowerCamelCase__					:	int         =       checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
							lowerCamelCase__					:	Optional[Any]         =       checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
							lowerCamelCase__					:	Optional[int]         =       checkpoint[f'''{old_prefix}.out_layers.0.weight''']
							lowerCamelCase__					:	Optional[Any]         =       checkpoint[f'''{old_prefix}.out_layers.0.bias''']
							lowerCamelCase__					:	int         =       checkpoint[f'''{old_prefix}.out_layers.3.weight''']
							lowerCamelCase__					:	Union[str, Any]         =       checkpoint[f'''{old_prefix}.out_layers.3.bias''']

							if has_skip:
														lowerCamelCase__					:	Any         =       checkpoint[f'''{old_prefix}.skip_connection.weight''']
														lowerCamelCase__					:	Any         =       checkpoint[f'''{old_prefix}.skip_connection.bias''']

							return new_checkpoint





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase=None      )      ->       Any:
							lowerCamelCase__					:	str         =       checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3				,			dim=0      )
							lowerCamelCase__					:	List[str]         =       checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3				,			dim=0      )

							lowerCamelCase__					:	int         =       checkpoint[f'''{old_prefix}.norm.weight''']
							lowerCamelCase__					:	Union[str, Any]         =       checkpoint[f'''{old_prefix}.norm.bias''']

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

							lowerCamelCase__					:	Optional[int]         =       (
							    checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1      ).squeeze(-1      )
							)
							lowerCamelCase__					:	Optional[Any]         =       checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1      ).squeeze(-1      )

							return new_checkpoint





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       Dict:
							lowerCamelCase__					:	Union[str, Any]         =       torch.load(a_				,			map_location="""cpu"""      )
							lowerCamelCase__					:	Union[str, Any]         =       {}

							lowerCamelCase__					:	List[Any]         =       checkpoint['''time_embed.0.weight''']
							lowerCamelCase__					:	Dict         =       checkpoint['''time_embed.0.bias''']
							lowerCamelCase__					:	int         =       checkpoint['''time_embed.2.weight''']
							lowerCamelCase__					:	Tuple         =       checkpoint['''time_embed.2.bias''']

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

							lowerCamelCase__					:	Dict         =       checkpoint['''input_blocks.0.0.weight''']
							lowerCamelCase__					:	List[str]         =       checkpoint['''input_blocks.0.0.bias''']

							lowerCamelCase__					:	Optional[Any]         =       unet_config['''down_block_types''']
							lowerCamelCase__					:	List[Any]         =       unet_config['''layers_per_block''']
							lowerCamelCase__					:	List[Any]         =       unet_config['''attention_head_dim''']
							lowerCamelCase__					:	str         =       unet_config['''block_out_channels''']
							lowerCamelCase__					:	str         =       1
							lowerCamelCase__					:	Any         =       channels_list[0]

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

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

														if i != len(a_      ) - 1:
																					lowerCamelCase__					:	Dict         =       f'''down_blocks.{i}.downsamplers.0'''
																					lowerCamelCase__					:	List[str]         =       f'''input_blocks.{current_layer}.0'''
																					lowerCamelCase__					:	Any         =       convert_resnet(a_				,			a_				,			a_				,			a_      )
																					current_layer += 1

														lowerCamelCase__					:	str         =       current_channels

							# hardcoded the mid-block for now
							lowerCamelCase__					:	Union[str, Any]         =       '''mid_block.resnets.0'''
							lowerCamelCase__					:	Optional[int]         =       '''middle_block.0'''
							lowerCamelCase__					:	Optional[Any]         =       convert_resnet(a_				,			a_				,			a_				,			a_      )
							lowerCamelCase__					:	Optional[Any]         =       '''mid_block.attentions.0'''
							lowerCamelCase__					:	List[Any]         =       '''middle_block.1'''
							lowerCamelCase__					:	List[str]         =       convert_attention(a_				,			a_				,			a_				,			a_				,			a_      )
							lowerCamelCase__					:	Optional[Any]         =       '''mid_block.resnets.1'''
							lowerCamelCase__					:	List[Any]         =       '''middle_block.2'''
							lowerCamelCase__					:	Dict         =       convert_resnet(a_				,			a_				,			a_				,			a_      )

							lowerCamelCase__					:	Optional[Any]         =       0
							lowerCamelCase__					:	Dict         =       unet_config['''up_block_types''']

							for i, layer_type in enumerate(a_      ):
														if layer_type == "ResnetUpsampleBlock2D":
																					for j in range(layers_per_block + 1      ):
																												lowerCamelCase__					:	List[Any]         =       f'''up_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__					:	Optional[int]         =       f'''output_blocks.{current_layer}.0'''
																												lowerCamelCase__					:	Tuple         =       convert_resnet(a_				,			a_				,			a_				,			a_				,			has_skip=a_      )
																												current_layer += 1

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

																					if i != len(a_      ) - 1:
																												lowerCamelCase__					:	str         =       f'''up_blocks.{i}.upsamplers.0'''
																												lowerCamelCase__					:	Union[str, Any]         =       f'''output_blocks.{current_layer-1}.2'''
																												lowerCamelCase__					:	List[str]         =       convert_resnet(a_				,			a_				,			a_				,			a_      )

							lowerCamelCase__					:	Optional[Any]         =       checkpoint['''out.0.weight''']
							lowerCamelCase__					:	Tuple         =       checkpoint['''out.0.bias''']
							lowerCamelCase__					:	Dict         =       checkpoint['''out.2.weight''']
							lowerCamelCase__					:	str         =       checkpoint['''out.2.bias''']

							return new_checkpoint


if __name__ == "__main__":
			_A	:			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.''')

			_A	:			List[str]         =parser.parse_args()
			_A	:			Optional[int]         =strabool(args.class_cond)

			_A	:			List[str]         =os.path.basename(args.unet_path)
			print(F'Checkpoint: {ckpt_name}')

			# Get U-Net config
			if "imagenet64" in ckpt_name:
						_A	:			List[Any]         =IMAGENET_64_UNET_CONFIG
			elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
						_A	:			Optional[Any]         =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	:			Tuple         =con_pt_to_diffuser(args.unet_path, unet_config)

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

			_A	:			Optional[Any]         =CMStochasticIterativeScheduler(**scheduler_config)

			_A	:			Any         =ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
			consistency_model.save_pretrained(args.dump_path)








"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      Any							=  '''xmod'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=3_05_22		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=5_12		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-12		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__="absolute"		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=("en_XX",)		,SCREAMING_SNAKE_CASE__=None		,**SCREAMING_SNAKE_CASE__		,)			->      Optional[Any]:





             """simple docstring"""






             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  vocab_size
             __SCREAMING_SNAKE_CASE   :List[Any]						=  hidden_size
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_hidden_layers
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_attention_heads
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  hidden_act
             __SCREAMING_SNAKE_CASE   :Tuple						=  intermediate_size
             __SCREAMING_SNAKE_CASE   :Dict						=  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE   :str						=  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  max_position_embeddings
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  type_vocab_size
             __SCREAMING_SNAKE_CASE   :str						=  initializer_range
             __SCREAMING_SNAKE_CASE   :List[Any]						=  layer_norm_eps
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  position_embedding_type
             __SCREAMING_SNAKE_CASE   :Any						=  use_cache
             __SCREAMING_SNAKE_CASE   :List[str]						=  classifier_dropout
             __SCREAMING_SNAKE_CASE   :Any						=  pre_norm
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_reduction_factor
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_layer_norm
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_reuse_layer_norm
             __SCREAMING_SNAKE_CASE   :Tuple						=  ln_before_adapter
             __SCREAMING_SNAKE_CASE   :Any						=  list(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  default_language






class 					_SCREAMING_SNAKE_CASE(   A  ):




       @property
       def     _UpperCamelCase     (						self							)			->      Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             if self.task == "multiple-choice":
                   __SCREAMING_SNAKE_CASE   :Tuple						=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
             else:
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  {0: '''batch''', 1: '''sequence'''}
             return OrderedDict(
                 [
                     ('''input_ids''', dynamic_axis),
                     ('''attention_mask''', dynamic_axis),
                 ]							)