

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase					:			str  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Any  				=			{
    "google/vivit-b-16x2-kinetics400": (
        "https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'vivit'





			def __init__(		self					,  __snake_case=224					,  __snake_case=32					,  __snake_case=[2, 16, 16]					,  __snake_case=3					,  __snake_case=768					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu_fast"					,  __snake_case=0.0					,  __snake_case=0.0					,  __snake_case=0.02					,  __snake_case=1e-06					,  __snake_case=True					,  **__snake_case					,  )   -> List[Any]:




						'''simple docstring'''
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =intermediate_size
						__a        =hidden_act
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =initializer_range
						__a        =layer_norm_eps

						__a        =image_size
						__a        =num_frames
						__a        =tubelet_size
						__a        =num_channels
						__a        =qkv_bias

						super().__init__(**__snake_case					)









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)






