



import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


a_    :  Tuple						  =		False
a_    :  Optional[int]						  =		True
a_    :  Any						  =		False


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

     a_    :  Tuple						  =		parser.parse_args()

     a_    :  Optional[Any]						  =		{
         "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",
     }

     a_    :  Tuple						  =		{
         "time_steps": "time_proj",
         "mid": "mid_block",
         "downsample_blocks": "down_blocks",
         "upsample_blocks": "up_blocks",
     }

     a_    :  Any						  =		"" 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:
          a_    :  List[Any]						  =		reader.read()
          a_    :  List[Any]						  =		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'):
          a_    :  List[Any]						  =		UNetaDModel(**config)
     else:
          a_    :  int						  =		UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
          a_    :  Optional[Any]						  =		class_name(**config)

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

     a_    :  Any						  =		dict(model.config)

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

          a_    :  List[str]						  =		[k.replace('UNetRes', '') for k in config["down_block_types"]]
          a_    :  Any						  =		[k.replace('UNetRes', '') for k in config["up_block_types"]]

     if do_only_weights:
          a_    :  Optional[int]						  =		torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

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

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

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


_snake_case      :							List[str]     	=    logging.get_logger(__name__)

_snake_case      :							int     	=    {
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  List[str]				   =					'''switch_transformers'''
						UpperCamelCase      :  Dict				   =					['''past_key_values''']
						UpperCamelCase      :  Union[str, Any]				   =					{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





						def __init__(			self							:int		,    lowerCAmelCase__							:str=32128		,    lowerCAmelCase__							:Union[str, Any]=768		,    lowerCAmelCase__							:Tuple=64		,    lowerCAmelCase__							:Any=2048		,    lowerCAmelCase__							:Optional[int]=64		,    lowerCAmelCase__							:Any=12		,    lowerCAmelCase__							:Any=3		,    lowerCAmelCase__							:Union[str, Any]=12		,    lowerCAmelCase__							:Tuple=3		,    lowerCAmelCase__							:str=12		,    lowerCAmelCase__							:Dict=8		,    lowerCAmelCase__							:Union[str, Any]=False		,    lowerCAmelCase__							:List[str]=0.01		,    lowerCAmelCase__							:Union[str, Any]="float32"		,    lowerCAmelCase__							:Optional[int]=False		,    lowerCAmelCase__							:Optional[Any]=32		,    lowerCAmelCase__							:int=128		,    lowerCAmelCase__							:Optional[Any]=0.1		,    lowerCAmelCase__							:Optional[Any]=1E-6		,    lowerCAmelCase__							:Any=0.0_01		,    lowerCAmelCase__							:Union[str, Any]=0.0_01		,    lowerCAmelCase__							:List[Any]=1.0		,    lowerCAmelCase__							:Optional[Any]="relu"		,    lowerCAmelCase__							:Optional[int]=True		,    lowerCAmelCase__							:List[Any]=False		,    lowerCAmelCase__							:int=True		,    lowerCAmelCase__							:int=0		,    lowerCAmelCase__							:Optional[Any]=1		,    **lowerCAmelCase__							:List[Any]		,    )  ->Dict:
										lowercase         =      vocab_size
										lowercase         =      d_model
										lowercase         =      d_kv
										lowercase         =      d_ff

										lowercase         =      num_sparse_encoder_layers

										lowercase         =      num_layers
										lowercase         =      (
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										lowercase         =      num_sparse_decoder_layers

										# This tells us, each how many encoder layer we'll have to set a sparse layer.
										if self.num_sparse_encoder_layers > 0:
														lowercase         =      self.num_layers // self.num_sparse_encoder_layers
										else:
														lowercase         =      self.num_layers  # HACK: this will create 0 sparse layers

										# This tells us, each how many encoder layer we'll have to set a sparse layer.
										if self.num_sparse_decoder_layers > 0:
														lowercase         =      self.num_decoder_layers // self.num_sparse_decoder_layers
										else:
														lowercase         =      self.num_decoder_layers  # HACK: this will create 0 sparse layers

										lowercase         =      num_heads
										lowercase         =      num_experts
										lowercase         =      expert_capacity
										lowercase         =      router_bias
										lowercase         =      router_jitter_noise
										if router_dtype not in ["float32", "float16", "bfloat16"]:
														raise ValueError(F'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''     )
										lowercase         =      router_dtype

										lowercase         =      router_ignore_padding_tokens
										lowercase         =      relative_attention_num_buckets
										lowercase         =      relative_attention_max_distance

										lowercase         =      dropout_rate
										lowercase         =      layer_norm_epsilon
										lowercase         =      initializer_factor
										lowercase         =      feed_forward_proj
										lowercase         =      use_cache
										lowercase         =      add_router_probs

										lowercase         =      router_z_loss_coef
										lowercase         =      router_aux_loss_coef

										lowercase         =      self.feed_forward_proj.split("-"     )
										lowercase         =      act_info[-1]
										lowercase         =      act_info[0] == "gated"

										if len(lowerCAmelCase__     ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase__     ) > 2:
														raise ValueError(
														    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
														    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
														    "'gated-gelu' or 'relu'"     )

										# for backwards compatibility
										if feed_forward_proj == "gated-gelu":
														lowercase         =      "gelu_new"

										super().__init__(
										    pad_token_id=lowerCAmelCase__		,    eos_token_id=lowerCAmelCase__		,    is_encoder_decoder=lowerCAmelCase__		,    **lowerCAmelCase__		,    )
