






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

   UpperCamelCase_						=  (
       ('layer.', 'layer_'),
       ('word_embeddings.weight', 'word_embeddings'),
       ('position_embeddings.weight', 'position_embeddings'),
       ('token_type_embeddings.weight', 'token_type_embeddings'),
       ('.', '/'),
       ('LayerNorm/weight', 'LayerNorm/gamma'),
       ('LayerNorm/bias', 'LayerNorm/beta'),
       ('weight', 'kernel'),
   )

   if not os.path.isdir(__lowercase):
      os.makedirs(__lowercase)

   UpperCamelCase_						=  model.state_dict()

   def to_tf_var_name(__lowercase):
      for patt, repl in iter(__lowercase):
         UpperCamelCase_						=  name.replace(__lowercase     ,   __lowercase)
      return f"""bert/{name}"""

   def create_tf_var(__lowercase     ,   __lowercase     ,   __lowercase):
      UpperCamelCase_						=  tf.dtypes.as_dtype(tensor.dtype)
      UpperCamelCase_						=  tf.get_variable(dtype=__lowercase     ,   shape=tensor.shape     ,   name=__lowercase     ,   initializer=tf.zeros_initializer())
      session.run(tf.variables_initializer([tf_var]))
      session.run(__lowercase)
      return tf_var

   tf.reset_default_graph()
   with tf.Session() as session:
      for var_name in state_dict:
         UpperCamelCase_						=  to_tf_var_name(__lowercase)
         UpperCamelCase_						=  state_dict[var_name].numpy()
         if any(x in var_name for x in tensors_to_transpose):
            UpperCamelCase_						=  torch_tensor.T
         UpperCamelCase_						=  create_tf_var(tensor=__lowercase     ,   name=__lowercase     ,   session=__lowercase)
         tf.keras.backend.set_value(__lowercase     ,   __lowercase)
         UpperCamelCase_						=  session.run(__lowercase)
         print(f"""Successfully created {tf_name}: {np.allclose(__lowercase     ,   __lowercase)}""")

      UpperCamelCase_						=  tf.train.Saver(tf.trainable_variables())
      saver.save(__lowercase     ,   os.path.join(__lowercase     ,   model_name.replace('-'     ,   '_') + '.ckpt'))






def      _snake_case     (__lowercase=None):
   UpperCamelCase_						=  argparse.ArgumentParser()
   parser.add_argument('--model_name'     ,   type=__lowercase     ,   required=__lowercase     ,   help='model name e.g. bert-base-uncased')
   parser.add_argument(
       '--cache_dir'     ,   type=__lowercase     ,   default=__lowercase     ,   required=__lowercase     ,   help='Directory containing pytorch model')
   parser.add_argument('--pytorch_model_path'     ,   type=__lowercase     ,   required=__lowercase     ,   help='/path/to/<pytorch-model-name>.bin')
   parser.add_argument('--tf_cache_dir'     ,   type=__lowercase     ,   required=__lowercase     ,   help='Directory in which to save tensorflow model')
   UpperCamelCase_						=  parser.parse_args(__lowercase)

   UpperCamelCase_						=  BertModel.from_pretrained(
       pretrained_model_name_or_path=args.model_name     ,   state_dict=torch.load(args.pytorch_model_path)     ,   cache_dir=args.cache_dir     ,   )

   convert_pytorch_checkpoint_to_tf(model=__lowercase     ,   ckpt_dir=args.tf_cache_dir     ,   model_name=args.model_name)


if __name__ == "__main__":
 main()













# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

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


@dataclass
class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = 42
  A_						       = 42




class     _a							(	UpperCAmelCase__    ,							UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = 1




  @register_to_config
  def __init__(  self		,		_UpperCAmelCase = 2000		,		_UpperCAmelCase = 0.1_5		,		_UpperCAmelCase = 0.0_1		,		_UpperCAmelCase = 1_3_4_8.0		,		_UpperCAmelCase = 1e-5		,		_UpperCAmelCase = 1		,		)      ->			Tuple:
     # standard deviation of the initial noise distribution
     UpperCamelCase_						=  sigma_max

     # setable values
     UpperCamelCase_						=  None

     self.set_sigmas(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None			)      ->			torch.FloatTensor:
     return sample




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None			)      ->			str:
     UpperCamelCase_						=  sampling_eps if sampling_eps is not None else self.config.sampling_eps

     UpperCamelCase_						=  torch.linspace(1		,		_UpperCAmelCase		,		_UpperCAmelCase		,		device=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None			)      ->			Any:
     UpperCamelCase_						=  sigma_min if sigma_min is not None else self.config.sigma_min
     UpperCamelCase_						=  sigma_max if sigma_max is not None else self.config.sigma_max
     UpperCamelCase_						=  sampling_eps if sampling_eps is not None else self.config.sampling_eps
     if self.timesteps is None:
        self.set_timesteps(_UpperCAmelCase		,		_UpperCAmelCase			)

     UpperCamelCase_						=  sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
     UpperCamelCase_						=  torch.exp(torch.linspace(math.log(_UpperCAmelCase			)		,		math.log(_UpperCAmelCase			)		,		_UpperCAmelCase			)			)
     UpperCamelCase_						=  torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[Any]:
     return torch.where(
         timesteps == 0		,		torch.zeros_like(t.to(timesteps.device			)			)		,		self.discrete_sigmas[timesteps - 1].to(timesteps.device			)		,		)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = True		,		)      ->			Union[SdeVeOutput, Tuple]:
     if self.timesteps is None:
        raise ValueError(
            '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'			)

     UpperCamelCase_						=  timestep * torch.ones(
         sample.shape[0]		,		device=sample.device			)  # torch.repeat_interleave(timestep, sample.shape[0])
     UpperCamelCase_						=  (timestep * (len(self.timesteps			) - 1)).long()

     # mps requires indices to be in the same device, so we use cpu as is the default with cuda
     UpperCamelCase_						=  timesteps.to(self.discrete_sigmas.device			)

     UpperCamelCase_						=  self.discrete_sigmas[timesteps].to(sample.device			)
     UpperCamelCase_						=  self.get_adjacent_sigma(_UpperCAmelCase		,		_UpperCAmelCase			).to(sample.device			)
     UpperCamelCase_						=  torch.zeros_like(_UpperCAmelCase			)
     UpperCamelCase_						=  (sigma**2 - adjacent_sigma**2) ** 0.5

     # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
     # also equation 47 shows the analog from SDE models to ancestral sampling methods
     UpperCamelCase_						=  diffusion.flatten()
     while len(diffusion.shape			) < len(sample.shape			):
        UpperCamelCase_						=  diffusion.unsqueeze(-1			)
     UpperCamelCase_						=  drift - diffusion**2 * model_output

     #  equation 6: sample noise for the diffusion term of
     UpperCamelCase_						=  randn_tensor(
         sample.shape		,		layout=sample.layout		,		generator=_UpperCAmelCase		,		device=sample.device		,		dtype=sample.dtype			)
     UpperCamelCase_						=  sample - drift  # subtract because `dt` is a small negative timestep
     # TODO is the variable diffusion the correct scaling term for the noise?
     UpperCamelCase_						=  prev_sample_mean + diffusion * noise  # add impact of diffusion field g

     if not return_dict:
        return (prev_sample, prev_sample_mean)

     return SdeVeOutput(prev_sample=_UpperCAmelCase		,		prev_sample_mean=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = True		,		)      ->			Union[SchedulerOutput, Tuple]:
     if self.timesteps is None:
        raise ValueError(
            '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'			)

     # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
     # sample noise for correction
     UpperCamelCase_						=  randn_tensor(sample.shape		,		layout=sample.layout		,		generator=_UpperCAmelCase			).to(sample.device			)

     # compute step size from the model_output, the noise, and the snr
     UpperCamelCase_						=  torch.norm(model_output.reshape(model_output.shape[0]		,		-1			)		,		dim=-1			).mean()
     UpperCamelCase_						=  torch.norm(noise.reshape(noise.shape[0]		,		-1			)		,		dim=-1			).mean()
     UpperCamelCase_						=  (self.config.snr * noise_norm / grad_norm) ** 2 * 2
     UpperCamelCase_						=  step_size * torch.ones(sample.shape[0]			).to(sample.device			)
     # self.repeat_scalar(step_size, sample.shape[0])

     # compute corrected sample: model_output term and noise term
     UpperCamelCase_						=  step_size.flatten()
     while len(step_size.shape			) < len(sample.shape			):
        UpperCamelCase_						=  step_size.unsqueeze(-1			)
     UpperCamelCase_						=  sample + step_size * model_output
     UpperCamelCase_						=  prev_sample_mean + ((step_size * 2) ** 0.5) * noise

     if not return_dict:
        return (prev_sample,)

     return SchedulerOutput(prev_sample=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		)      ->			torch.FloatTensor:
     # Make sure sigmas and timesteps have the same device and dtype as original_samples
     UpperCamelCase_						=  timesteps.to(original_samples.device			)
     UpperCamelCase_						=  self.discrete_sigmas.to(original_samples.device			)[timesteps]
     UpperCamelCase_						=  (
         noise * sigmas[:, None, None, None]
         if noise is not None
         else torch.randn_like(_UpperCAmelCase			) * sigmas[:, None, None, None]
     )
     UpperCamelCase_						=  noise + original_samples
     return noisy_samples




  def __len__(  self			)      ->			Optional[int]:
     return self.config.num_train_timesteps





