





"""simple docstring"""
import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_snake_case     :		Tuple       						= False
_snake_case     :		Any       						= True
_snake_case     :		List[Any]       						= False


if __name__ == "__main__":
		_snake_case     :		Union[str, Any]       						= argparse.ArgumentParser()

		parser.add_argument(
		    '--repo_path',
		    default=None,
		    type=str,
		    required=True,
		    help='The config json file corresponding to the architecture.',
		)

		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

		_snake_case     :		Tuple       						= parser.parse_args()

		_snake_case     :		Optional[int]       						= {
		    'image_size': 'sample_size',
		    'num_res_blocks': 'layers_per_block',
		    'block_channels': 'block_out_channels',
		    'down_blocks': 'down_block_types',
		    'up_blocks': 'up_block_types',
		    'downscale_freq_shift': 'freq_shift',
		    'resnet_num_groups': 'norm_num_groups',
		    'resnet_act_fn': 'act_fn',
		    'resnet_eps': 'norm_eps',
		    'num_head_channels': 'attention_head_dim',
		}

		_snake_case     :		int       						= {
		    'time_steps': 'time_proj',
		    'mid': 'mid_block',
		    'downsample_blocks': 'down_blocks',
		    'upsample_blocks': 'up_blocks',
		}

		_snake_case     :		int       						= '' if has_file(args.repo_path, 'config.json') else 'unet'

		with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
				_snake_case     :		Optional[int]       						= reader.read()
				_snake_case     :		Tuple       						= json.loads(text)

		if do_only_config:
				for key in config_parameters_to_change.keys():
						config.pop(key, None)

		if has_file(args.repo_path, 'config.json'):
				_snake_case     :		Any       						= UNetaDModel(**config)
		else:
				_snake_case     :		Union[str, Any]       						= UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
				_snake_case     :		Tuple       						= class_name(**config)

		if do_only_config:
				model.save_config(os.path.join(args.repo_path, subfolder))

		_snake_case     :		str       						= dict(model.config)

		if do_only_renaming:
				for key, value in config_parameters_to_change.items():
						if key in config:
								_snake_case     :		Dict       						= config[key]
								del config[key]

				_snake_case     :		List[Any]       						= [k.replace('UNetRes', '') for k in config['down_block_types']]
				_snake_case     :		Optional[Any]       						= [k.replace('UNetRes', '') for k in config['up_block_types']]

		if do_only_weights:
				_snake_case     :		Dict       						= torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

				_snake_case     :		List[str]       						= {}
				for param_key, param_value in state_dict.items():
						if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
								continue
						_snake_case     :		int       						= False
						for key, new_key in key_parameters_to_change.items():
								if not has_changed and param_key.split('.')[0] == key:
										_snake_case     :		Dict       						= param_value
										_snake_case     :		Optional[int]       						= True
						if not has_changed:
								_snake_case     :		Dict       						= param_value

				model.load_state_dict(new_state_dict)
				model.save_pretrained(os.path.join(args.repo_path, subfolder))









"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case     :		Optional[int]       						= logging.get_logger(__name__)

_snake_case     :		Optional[int]       						= {
    '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 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							'''vivit'''


					def __init__(					self			:Optional[Any]     ,       __UpperCamelCase			:Dict=2_24     ,       __UpperCamelCase			:int=32     ,       __UpperCamelCase			:Union[str, Any]=[2, 16, 16]     ,       __UpperCamelCase			:Optional[Any]=3     ,       __UpperCamelCase			:Optional[Any]=7_68     ,       __UpperCamelCase			:Any=12     ,       __UpperCamelCase			:List[str]=12     ,       __UpperCamelCase			:List[str]=30_72     ,       __UpperCamelCase			:Any="gelu_fast"     ,       __UpperCamelCase			:List[Any]=0.0     ,       __UpperCamelCase			:str=0.0     ,       __UpperCamelCase			:Dict=0.02     ,       __UpperCamelCase			:Optional[Any]=1e-06     ,       __UpperCamelCase			:Dict=True     ,       **__UpperCamelCase			:Tuple     ,       ):
												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__(**__UpperCamelCase		)


