
"""simple docstring"""




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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)





@flax.struct.dataclass
class 							A__		:
      _UpperCAmelCase    :   CommonSchedulerState

      # setable values
      _UpperCAmelCase    :   jnp.ndarray
      _UpperCAmelCase    :   jnp.ndarray
      _UpperCAmelCase    :   jnp.ndarray
      _UpperCAmelCase    :   Optional[int]   	=      None

      # running values
      _UpperCAmelCase    :   Optional[jnp.ndarray]   	=      None
      @classmethod
      def 						UpperCamelCase__							(				cls   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       return cls(common=__magic_name__   ,  init_noise_sigma=__magic_name__   ,  timesteps=__magic_name__   ,  sigmas=__magic_name__				)





@dataclass
class 							A__		(     snake_case_):
      _UpperCAmelCase    :   LMSDiscreteSchedulerState





class 							A__		(     snake_case_  ,       snake_case_):
      _UpperCAmelCase    :   List[Any]   	=      [e.name for e in FlaxKarrasDiffusionSchedulers]

      _UpperCAmelCase    :   jnp.dtype
      @property
      def 						UpperCamelCase__							(				self				):
       return True
      @register_to_config
      def __init__(				self   ,  __magic_name__ = 1_0_0_0   ,  __magic_name__ = 0.0_001   ,  __magic_name__ = 0.02   ,  __magic_name__ = "linear"   ,  __magic_name__ = None   ,  __magic_name__ = "epsilon"   ,  __magic_name__ = jnp.floataa   ,  ):
       lowerCamelCase		:     Union[str, Any]          =				dtype
      def 						UpperCamelCase__							(				self   ,  __magic_name__ = None				):


       if common is None:
        lowerCamelCase		:     Union[str, Any]          =				CommonSchedulerState.create(self				)

       lowerCamelCase		:     Any          =				jnp.arange(0   ,  self.config.num_train_timesteps				).round()[::-1]
       lowerCamelCase		:     int          =				((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

       # standard deviation of the initial noise distribution
       lowerCamelCase		:     Optional[int]          =				sigmas.max()

       return LMSDiscreteSchedulerState.create(
           common=__magic_name__   ,  init_noise_sigma=__magic_name__   ,  timesteps=__magic_name__   ,  sigmas=__magic_name__   ,  )
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       (lowerCamelCase       )		:     Tuple          =				jnp.where(state.timesteps == timestep   ,  size=1				)
       lowerCamelCase		:     Union[str, Any]          =				step_index[0]

       lowerCamelCase		:     List[str]          =				state.sigmas[step_index]
       lowerCamelCase		:     Any          =				sample / ((sigma**2 + 1) ** 0.5)
       return sample
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):


       def lms_derivative(__magic_name__				):
        lowerCamelCase		:     Union[str, Any]          =				1.0
        for k in range(__magic_name__				):
         if current_order == k:
          continue
         prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
        return prod

       lowerCamelCase		:     List[str]          =				integrate.quad(__magic_name__   ,  state.sigmas[t]   ,  state.sigmas[t + 1]   ,  epsrel=1e-4				)[0]

       return integrated_coeff
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__ = ()				):
       lowerCamelCase		:     Union[str, Any]          =				jnp.linspace(self.config.num_train_timesteps - 1   ,  0   ,  __magic_name__   ,  dtype=self.dtype				)

       lowerCamelCase		:     int          =				jnp.floor(__magic_name__				).astype(jnp.intaa				)
       lowerCamelCase		:     int          =				jnp.ceil(__magic_name__				).astype(jnp.intaa				)

       lowerCamelCase		:     Dict          =				jnp.mod(__magic_name__   ,  1.0				)

       lowerCamelCase		:     Union[str, Any]          =				((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
       lowerCamelCase		:     Any          =				(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
       lowerCamelCase		:     List[Any]          =				jnp.concatenate([sigmas, jnp.array([0.0]   ,  dtype=self.dtype				)]				)

       lowerCamelCase		:     int          =				timesteps.astype(jnp.intaa				)

       # initial running values
       lowerCamelCase		:     Optional[Any]          =				jnp.zeros((0,) + shape   ,  dtype=self.dtype				)

       return state.replace(
           timesteps=__magic_name__   ,  sigmas=__magic_name__   ,  num_inference_steps=__magic_name__   ,  derivatives=__magic_name__   ,  )
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__ = 4   ,  __magic_name__ = True   ,  ):


       if state.num_inference_steps is None:
        raise ValueError(
            """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""				)

       lowerCamelCase		:     Any          =				state.sigmas[timestep]

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
        lowerCamelCase		:     str          =				sample - sigma * model_output
       elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        lowerCamelCase		:     Tuple          =				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
       else:
        raise ValueError(
            F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''				)

       # 2. Convert to an ODE derivative
       lowerCamelCase		:     Dict          =				(sample - pred_original_sample) / sigma
       lowerCamelCase		:     Dict          =				state.replace(derivatives=jnp.append(state.derivatives   ,  __magic_name__				)				)
       if len(state.derivatives				) > order:
        lowerCamelCase		:     Optional[int]          =				state.replace(derivatives=jnp.delete(state.derivatives   ,  0				)				)

       # 3. Compute linear multistep coefficients
       lowerCamelCase		:     int          =				min(timestep + 1   ,  __magic_name__				)
       lowerCamelCase		:     Any          =				[self.get_lms_coefficient(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				) for curr_order in range(__magic_name__				)]

       # 4. Compute previous sample based on the derivatives path
       lowerCamelCase		:     Any          =				sample + sum(
           coeff * derivative for coeff, derivative in zip(__magic_name__   ,  reversed(state.derivatives				)				)				)

       if not return_dict:
        return (prev_sample, state)

       return FlaxLMSSchedulerOutput(prev_sample=__magic_name__   ,  state=__magic_name__				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  ):
       lowerCamelCase		:     int          =				state.sigmas[timesteps].flatten()
       lowerCamelCase		:     Dict          =				broadcast_to_shape_from_left(__magic_name__   ,  noise.shape				)

       lowerCamelCase		:     Dict          =				original_samples + noise * sigma

       return noisy_samples






      def __len__(				self				):
       return self.config.num_train_timesteps






import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 # Initialise PyTorch model
 lowerCamelCase		:     List[str]          =				FNetConfig.from_json_file(lowerCamelCase							)
 print(F'''Building PyTorch model from configuration: {config}'''							)
 lowerCamelCase		:     Any          =				FNetForPreTraining(lowerCamelCase							)

 lowerCamelCase		:     List[Any]          =				restore_checkpoint(lowerCamelCase,     lowerCamelCase							)
 lowerCamelCase		:     List[str]          =				checkpoint_dict["""target"""]

 # Embeddings
 # Position IDs
 lowerCamelCase		:     Optional[Any]          =				fnet_pretraining_model.state_dict()

 lowerCamelCase		:     str          =				state_dict["""fnet.embeddings.position_ids"""]
 lowerCamelCase		:     List[str]          =				{"""fnet.embeddings.position_ids""": position_ids}
 # Embedding Layers
 lowerCamelCase		:     Tuple          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]							)
 lowerCamelCase		:     List[Any]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]							)
 lowerCamelCase		:     Optional[Any]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]							)
 lowerCamelCase		:     Optional[int]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]							).T
 lowerCamelCase		:     int          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]							)
 lowerCamelCase		:     str          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]							)
 lowerCamelCase		:     Optional[int]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]							)

 # Encoder Layers
 for layer in range(config.num_hidden_layers							):
  lowerCamelCase		:     List[Any]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""mixing_layer_norm"""]["""scale"""]							)
  lowerCamelCase		:     Any          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""mixing_layer_norm"""]["""bias"""]							)

  lowerCamelCase		:     int          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]							).T
  lowerCamelCase		:     List[str]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]							)

  lowerCamelCase		:     List[Any]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""kernel"""]							).T
  lowerCamelCase		:     str          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""bias"""]							)

  lowerCamelCase		:     Tuple          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]							)
  lowerCamelCase		:     Optional[int]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]							)

 # Pooler Layers
 lowerCamelCase		:     Tuple          =				torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]							).T
 lowerCamelCase		:     List[Any]          =				torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]							)

 # Masked LM Layers
 lowerCamelCase		:     Optional[Any]          =				torch.tensor(
     pretrained_model_params["""predictions_dense"""]["""kernel"""]							).T
 lowerCamelCase		:     Tuple          =				torch.tensor(
     pretrained_model_params["""predictions_dense"""]["""bias"""]							)
 lowerCamelCase		:     Tuple          =				torch.tensor(
     pretrained_model_params["""predictions_layer_norm"""]["""scale"""]							)
 lowerCamelCase		:     int          =				torch.tensor(
     pretrained_model_params["""predictions_layer_norm"""]["""bias"""]							)
 lowerCamelCase		:     Dict          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]							)
 lowerCamelCase		:     Dict          =				torch.tensor(
     pretrained_model_params["""predictions_output"""]["""output_bias"""]							)
 lowerCamelCase		:     Any          =				torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]							)

 # Seq Relationship Layers
 lowerCamelCase		:     int          =				torch.tensor(
     pretrained_model_params["""classification"""]["""output_kernel"""]							)
 lowerCamelCase		:     Optional[Any]          =				torch.tensor(
     pretrained_model_params["""classification"""]["""output_bias"""]							)

 # Load State Dict
 fnet_pretraining_model.load_state_dict(lowerCamelCase							)

 # Save PreTrained
 print(F'''Saving pretrained model to {save_path}'''							)
 fnet_pretraining_model.save_pretrained(lowerCamelCase							)


if __name__ == "__main__":
      _lowerCamelCase        =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--flax_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--fnet_config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained FNet model. \n"""
              """This specifies the model architecture."""
          ),
      )
      parser.add_argument("""--save_path""", default=None, type=str, required=True, help="""Path to the output model.""")
      _lowerCamelCase        =parser.parse_args()
      convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




