






'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     torch.FloatTensor
       snake_case__    :     torch.FloatTensor
       snake_case__    :     Optional[torch.FloatTensor]   		=			None







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   lowerCAmelCase_  ):
       snake_case__    :     str   		=			2




       @register_to_config
       def __init__(					self      : List[Any]	,    A__      : float = 0.02	,    A__      : float = 100	,    A__      : float = 1.007	,    A__      : float = 80	,    A__      : float = 0.05	,    A__      : float = 50	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				sigma_max

            # setable values
            __lowerCamelCase						:       int				   =				None
            __lowerCamelCase						:       np.IntTensor				   =				None
            __lowerCamelCase						:       torch.FloatTensor				   =				None  # sigma(t_i)




       def 					a_					(					self      : str	,    A__      : torch.FloatTensor	,    A__      : Optional[int] = None					):


            """simple docstring"""







            return sample




       def 					a_					(					self      : Union[str, Any]	,    A__      : int	,    A__      : Union[str, torch.device] = None					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				num_inference_steps
            __lowerCamelCase						:       Optional[int]				   =				np.arange(0	,    self.num_inference_steps					)[::-1].copy()
            __lowerCamelCase						:       Tuple				   =				torch.from_numpy(A__					).to(A__					)
            __lowerCamelCase						:       Dict				   =				[
                (
                    self.config.sigma_max**2
                    * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
                )
                for i in self.timesteps
            ]
            __lowerCamelCase						:       Any				   =				torch.tensor(A__	,    dtype=torch.floataa	,    device=A__					)




       def 					a_					(					self      : str	,    A__      : torch.FloatTensor	,    A__      : float	,    A__      : Optional[torch.Generator] = None					):


            """simple docstring"""







            if self.config.s_min <= sigma <= self.config.s_max:
                 __lowerCamelCase						:       int				   =				min(self.config.s_churn / self.num_inference_steps	,    2**0.5 - 1					)
            else:
                 __lowerCamelCase						:       List[Any]				   =				0

            # sample eps ~ N(0, S_noise^2 * I)
            __lowerCamelCase						:       Union[str, Any]				   =				self.config.s_noise * randn_tensor(sample.shape	,    generator=A__					).to(sample.device					)
            __lowerCamelCase						:       Union[str, Any]				   =				sigma + gamma * sigma
            __lowerCamelCase						:       List[Any]				   =				sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

            return sample_hat, sigma_hat




       def 					a_					(					self      : Tuple	,    A__      : torch.FloatTensor	,    A__      : float	,    A__      : float	,    A__      : torch.FloatTensor	,    A__      : bool = True	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				sample_hat + sigma_hat * model_output
            __lowerCamelCase						:       Tuple				   =				(sample_hat - pred_original_sample) / sigma_hat
            __lowerCamelCase						:       Dict				   =				sample_hat + (sigma_prev - sigma_hat) * derivative

            if not return_dict:
                 return (sample_prev, derivative)

            return KarrasVeOutput(
                prev_sample=A__	,    derivative=A__	,    pred_original_sample=A__					)




       def 					a_					(					self      : Tuple	,    A__      : torch.FloatTensor	,    A__      : float	,    A__      : float	,    A__      : torch.FloatTensor	,    A__      : torch.FloatTensor	,    A__      : torch.FloatTensor	,    A__      : bool = True	,    ):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				sample_prev + sigma_prev * model_output
            __lowerCamelCase						:       List[Any]				   =				(sample_prev - pred_original_sample) / sigma_prev
            __lowerCamelCase						:       Union[str, Any]				   =				sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

            if not return_dict:
                 return (sample_prev, derivative)

            return KarrasVeOutput(
                prev_sample=A__	,    derivative=A__	,    pred_original_sample=A__					)




       def 					a_					(					self      : List[str]	,    A__      : str	,    A__      : str	,    A__      : Any					):


            """simple docstring"""







            raise NotImplementedError()











'''simple docstring'''

import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     # Initialise PyTorch model
     __lowerCamelCase						:       str				   =				RemBertConfig.from_json_file(_lowercase     )
     print("""Building PyTorch model from configuration: {}""".format(str(_lowercase     )     )     )
     __lowerCamelCase						:       List[Any]				   =				RemBertModel(_lowercase     )

     # Load weights from tf checkpoint
     load_tf_weights_in_rembert(_lowercase,						_lowercase,						_lowercase     )

     # Save pytorch-model
     print("""Save PyTorch model to {}""".format(_lowercase     )     )
     torch.save(model.state_dict(),						_lowercase     )


if __name__ == "__main__":
   UpperCAmelCase__						:Tuple          =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   UpperCAmelCase__						:List[Any]          =			parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



