






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel







def       A__				(   _a	:       List[str]		,	_a	:       str		,	_a	:       Any						):





 '''simple docstring'''







 snake_case__	:     int											=OmegaConf.load(_UpperCamelCase						)
 snake_case__	:     Tuple											=torch.load(_UpperCamelCase		,	map_location="""cpu"""						)["""model"""]
 snake_case__	:     Optional[int]											=list(state_dict.keys()						)

 # extract state_dict for VQVAE
 snake_case__	:     int											={}
 snake_case__	:     Any											="""first_stage_model."""
 for key in keys:
  if key.startswith(_UpperCamelCase						):
   snake_case__	:     str											=state_dict[key]

    # extract state_dict for UNetLDM
 snake_case__	:     Tuple											={}
 snake_case__	:     Any											="""model.diffusion_model."""
 for key in keys:
  if key.startswith(_UpperCamelCase						):
   snake_case__	:     int											=state_dict[key]

 snake_case__	:     Dict											=config.model.params.first_stage_config.params
 snake_case__	:     str											=config.model.params.unet_config.params

 snake_case__	:     Any											=VQModel(**_UpperCamelCase						).eval()
 vqvae.load_state_dict(_UpperCamelCase						)

 snake_case__	:     Optional[int]											=UNetLDMModel(**_UpperCamelCase						).eval()
 unet.load_state_dict(_UpperCamelCase						)

 snake_case__	:     Union[str, Any]											=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		,	)

 snake_case__	:     Optional[Any]											=LDMPipeline(_UpperCamelCase		,	_UpperCamelCase		,	_UpperCamelCase						)
 pipeline.save_pretrained(_UpperCamelCase						)


if __name__ == "__main__":
       __lowerCamelCase						:		int				      = 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						:		Tuple				      = parser.parse_args()

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










from typing import Mapping

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


a_								=       logging.get_logger(__name__)

a_								=       {
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	"""t5"""
 lowerCAmelCase__							       =	["""past_key_values"""]
 lowerCAmelCase__							       =	{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}




 def __init__(    self						,					__UpperCAmelCase=32128						,					__UpperCAmelCase=512						,					__UpperCAmelCase=64						,					__UpperCAmelCase=2048						,					__UpperCAmelCase=6						,					__UpperCAmelCase=None						,					__UpperCAmelCase=8						,					__UpperCAmelCase=32						,					__UpperCAmelCase=128						,					__UpperCAmelCase=0.1						,					__UpperCAmelCase=1E-6						,					__UpperCAmelCase=1.0						,					__UpperCAmelCase="relu"						,					__UpperCAmelCase=True						,					__UpperCAmelCase=True						,					__UpperCAmelCase=0						,					__UpperCAmelCase=1						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     vocab_size
    __lowerCamelCase    				=     d_model
    __lowerCamelCase    				=     d_kv
    __lowerCamelCase    				=     d_ff
    __lowerCamelCase    				=     num_layers
    __lowerCamelCase    				=     (
        num_decoder_layers if num_decoder_layers is not None else self.num_layers
    )  # default = symmetry
    __lowerCamelCase    				=     num_heads
    __lowerCamelCase    				=     relative_attention_num_buckets
    __lowerCamelCase    				=     relative_attention_max_distance
    __lowerCamelCase    				=     dropout_rate
    __lowerCamelCase    				=     layer_norm_epsilon
    __lowerCamelCase    				=     initializer_factor
    __lowerCamelCase    				=     feed_forward_proj
    __lowerCamelCase    				=     use_cache

    __lowerCamelCase    				=     self.feed_forward_proj.split('''-'''					)
    __lowerCamelCase    				=     act_info[-1]
    __lowerCamelCase    				=     act_info[0] == '''gated'''

    if len(__UpperCAmelCase					) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase					) > 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":
       __lowerCamelCase    				=     '''gelu_new'''

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



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     {
        '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
        '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
    }
    if self.use_past:
       __lowerCamelCase    				=     '''past_encoder_sequence + sequence'''
       __lowerCamelCase    				=     {0: '''batch'''}
       __lowerCamelCase    				=     {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
    else:
       __lowerCamelCase    				=     {0: '''batch''', 1: '''decoder_sequence'''}
       __lowerCamelCase    				=     {0: '''batch''', 1: '''decoder_sequence'''}

    if self.use_past:
       self.fill_with_past_key_values_(__UpperCAmelCase						,					direction='''inputs'''					)

    return common_inputs




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return 13






