


from collections import OrderedDict
from typing import Mapping

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


snake_case				:    int       				=     logging.get_logger(__name__)

snake_case				:    List[Any]       				=     {
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'roformer'




 def __init__(		self					,  _lowerCamelCase=5_0000					,  _lowerCamelCase=None					,  _lowerCamelCase=768					,  _lowerCamelCase=12					,  _lowerCamelCase=12					,  _lowerCamelCase=3072					,  _lowerCamelCase="gelu"					,  _lowerCamelCase=0.1					,  _lowerCamelCase=0.1					,  _lowerCamelCase=1536					,  _lowerCamelCase=2					,  _lowerCamelCase=0.02					,  _lowerCamelCase=1e-12					,  _lowerCamelCase=0					,  _lowerCamelCase=False					,  _lowerCamelCase=True					,  **_lowerCamelCase					,  ):
       super().__init__(pad_token_id=_lowerCamelCase					,  **_lowerCamelCase    )

       a						:Union[str, Any] 							=       vocab_size
       a						:Optional[int] 							=       hidden_size if embedding_size is None else embedding_size
       a						:List[str] 							=       hidden_size
       a						:Tuple 							=       num_hidden_layers
       a						:Union[str, Any] 							=       num_attention_heads
       a						:Any 							=       hidden_act
       a						:int 							=       intermediate_size
       a						:List[Any] 							=       hidden_dropout_prob
       a						:Dict 							=       attention_probs_dropout_prob
       a						:Dict 							=       max_position_embeddings
       a						:Union[str, Any] 							=       type_vocab_size
       a						:List[Any] 							=       initializer_range
       a						:Optional[Any] 							=       layer_norm_eps
       a						:Optional[Any] 							=       rotary_value
       a						:List[Any] 							=       use_cache







class 		_snake_case  (      _snake_case							):




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.task == "multiple-choice":
             a						:Any 							=       {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
             a						:List[str] 							=       {0: '''batch''', 1: '''sequence'''}
       a						:Optional[int] 							=       {0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
               ('''token_type_ids''', dynamic_axis),
           ]    )










"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Optional[Any]							):
    _UpperCAmelCase							:  int											=    OmegaConf.load(UpperCamelCase__							)
    _UpperCAmelCase							:  str											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)['''model''']
    _UpperCAmelCase							:  Optional[Any]											=    list(state_dict.keys()							)

    # extract state_dict for VQVAE
    _UpperCAmelCase							:  Any											=    {}
    _UpperCAmelCase							:  Any											=    '''first_stage_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    # extract state_dict for UNetLDM
    _UpperCAmelCase							:  Tuple											=    {}
    _UpperCAmelCase							:  int											=    '''model.diffusion_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    _UpperCAmelCase							:  List[str]											=    config.model.params.first_stage_config.params
    _UpperCAmelCase							:  Union[str, Any]											=    config.model.params.unet_config.params

    _UpperCAmelCase							:  Any											=    VQModel(**UpperCamelCase__							).eval()
    vqvae.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  Union[str, Any]											=    UNetLDMModel(**UpperCamelCase__							).eval()
    unet.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  int											=    DDIMScheduler(
        timesteps=config.model.params.timesteps     ,       beta_schedule='''scaled_linear'''     ,       beta_start=config.model.params.linear_start     ,       beta_end=config.model.params.linear_end     ,       clip_sample=UpperCamelCase__     ,       )

    _UpperCAmelCase							:  Optional[Any]											=    LDMPipeline(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
    pipeline.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  _lowerCAmelCase    :Union[str, Any]     =					argparse.ArgumentParser()
  parser.add_argument('--checkpoint_path', type=str, required=True)
  parser.add_argument('--config_path', type=str, required=True)
  parser.add_argument('--output_path', type=str, required=True)
  _lowerCAmelCase    :List[Any]     =					parser.parse_args()

  convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


