






"""simple docstring"""




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

import numpy as np
import torch

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


lowerCamelCase__			=   logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class 					_UpperCamelCase		( __snake_case):
    __lowerCamelCase         =  4_2







class 					_UpperCamelCase		( __snake_case						, __snake_case):
    __lowerCamelCase         =  1






    @register_to_config
    def __init__(self						,      lowerCamelCase__ = 4_0						,      lowerCamelCase__ = 0.0_0_2						,      lowerCamelCase__ = 8_0.0						,      lowerCamelCase__ = 0.5						,      lowerCamelCase__ = 1.0						,      lowerCamelCase__ = 7.0						,      lowerCamelCase__ = True						,      ):


      """simple docstring"""
      A__											=							sigma_max

      A__											=							np.linspace(0						,      1						,      lowerCamelCase__   )
      A__											=							self._convert_to_karras(lowerCamelCase__   )
      A__											=							self.sigma_to_t(lowerCamelCase__   )

      # setable values
      A__											=							None
      A__											=							torch.from_numpy(lowerCamelCase__   )
      A__											=							torch.from_numpy(lowerCamelCase__   )
      A__											=							False
      A__											=							False






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__=None   ):


      """simple docstring"""
      if schedule_timesteps is None:
        A__											=							self.timesteps

      A__											=							(schedule_timesteps == timestep).nonzero()
      return indices.item()






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      # Get sigma corresponding to timestep
      if isinstance(lowerCamelCase__						,      torch.Tensor   ):
        A__											=							timestep.to(self.timesteps.device   )
      A__											=							self.index_for_timestep(lowerCamelCase__   )
      A__											=							self.sigmas[step_idx]

      A__											=							sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

      A__											=							True
      return sample






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      if not isinstance(lowerCamelCase__						,      np.ndarray   ):
        A__											=							np.array(lowerCamelCase__						,      dtype=np.floataa   )

      A__											=							1_0_0_0 * 0.2_5 * np.log(sigmas + 1E-44   )

      return timesteps






    def  A    (self						,      lowerCamelCase__ = None						,      lowerCamelCase__ = None						,      lowerCamelCase__ = None						,      ):


      """simple docstring"""
      if num_inference_steps is None and timesteps is None:
        raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""   )

      if num_inference_steps is not None and timesteps is not None:
        raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""   )

      # Follow DDPMScheduler custom timesteps logic
      if timesteps is not None:
        for i in range(1						,      len(lowerCamelCase__   )   ):
          if timesteps[i] >= timesteps[i - 1]:
            raise ValueError("""`timesteps` must be in descending order."""   )

        if timesteps[0] >= self.config.num_train_timesteps:
          raise ValueError(
              F"""`timesteps` must start before `self.config.train_timesteps`:"""
              F""" {self.config.num_train_timesteps}."""   )

        A__											=							np.array(lowerCamelCase__						,      dtype=np.intaa   )
        A__											=							True
      else:
        if num_inference_steps > self.config.num_train_timesteps:
          raise ValueError(
              F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
              F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
              F""" maximal {self.config.num_train_timesteps} timesteps."""   )

        A__											=							num_inference_steps

        A__											=							self.config.num_train_timesteps // self.num_inference_steps
        A__											=							(np.arange(0						,      lowerCamelCase__   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
        A__											=							False

      # Map timesteps to Karras sigmas directly for multistep sampling
      # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
      A__											=							self.config.num_train_timesteps
      A__											=							timesteps[::-1].copy()
      A__											=							ramp / (num_train_timesteps - 1)
      A__											=							self._convert_to_karras(lowerCamelCase__   )
      A__											=							self.sigma_to_t(lowerCamelCase__   )

      A__											=							np.concatenate([sigmas, [self.sigma_min]]   ).astype(np.floataa   )
      A__											=							torch.from_numpy(lowerCamelCase__   ).to(device=lowerCamelCase__   )

      if str(lowerCamelCase__   ).startswith("""mps"""   ):
        # mps does not support float64
        A__											=							torch.from_numpy(lowerCamelCase__   ).to(lowerCamelCase__						,      dtype=torch.floataa   )
      else:
        A__											=							torch.from_numpy(lowerCamelCase__   ).to(device=lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							self.config.sigma_min
      A__											=							self.config.sigma_max

      A__											=							self.config.rho
      A__											=							sigma_min ** (1 / rho)
      A__											=							sigma_max ** (1 / rho)
      A__											=							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return sigmas






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							self.config.sigma_data

      A__											=							sigma_data**2 / (sigma**2 + sigma_data**2)
      A__											=							sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
      return c_skip, c_out






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							self.config.sigma_min
      A__											=							self.config.sigma_data

      A__											=							sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
      A__											=							(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
      return c_skip, c_out






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__ = None						,      lowerCamelCase__ = True						,      ):


      """simple docstring"""

      if (
          isinstance(lowerCamelCase__						,      lowerCamelCase__   )
          or isinstance(lowerCamelCase__						,      torch.IntTensor   )
          or isinstance(lowerCamelCase__						,      torch.LongTensor   )
      ):
        raise ValueError(
            (
                """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
                F""" `{self.__class__}.step()` is not supported. Make sure to pass"""
                """ one of the `scheduler.timesteps` as a timestep."""
            )						,      )

      if not self.is_scale_input_called:
        logger.warning(
            """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
            """See `StableDiffusionPipeline` for a usage example."""   )

      if isinstance(lowerCamelCase__						,      torch.Tensor   ):
        A__											=							timestep.to(self.timesteps.device   )

      A__											=							self.config.sigma_min
      A__											=							self.config.sigma_max

      A__											=							self.index_for_timestep(lowerCamelCase__   )

      # sigma_next corresponds to next_t in original implementation
      A__											=							self.sigmas[step_index]
      if step_index + 1 < self.config.num_train_timesteps:
        A__											=							self.sigmas[step_index + 1]
      else:
        # Set sigma_next to sigma_min
        A__											=							self.sigmas[-1]

      # Get scalings for boundary conditions
      A__						,A__											=							self.get_scalings_for_boundary_condition(lowerCamelCase__   )

      # 1. Denoise model output using boundary conditions
      A__											=							c_out * model_output + c_skip * sample
      if self.config.clip_denoised:
        A__											=							denoised.clamp(-1						,      1   )

      # 2. Sample z ~ N(0, s_noise^2 * I)
      # Noise is not used for onestep sampling.
      if len(self.timesteps   ) > 1:
        A__											=							randn_tensor(
            model_output.shape						,      dtype=model_output.dtype						,      device=model_output.device						,      generator=lowerCamelCase__   )
      else:
        A__											=							torch.zeros_like(lowerCamelCase__   )
      A__											=							noise * self.config.s_noise

      A__											=							sigma_next.clamp(min=lowerCamelCase__						,      max=lowerCamelCase__   )

      # 3. Return noisy sample
      # tau = sigma_hat, eps = sigma_min
      A__											=							denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

      if not return_dict:
        return (prev_sample,)

      return CMStochasticIterativeSchedulerOutput(prev_sample=lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      ):


      """simple docstring"""
      A__											=							self.sigmas.to(device=original_samples.device						,      dtype=original_samples.dtype   )
      if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase__   ):
        # mps does not support float64
        A__											=							self.timesteps.to(original_samples.device						,      dtype=torch.floataa   )
        A__											=							timesteps.to(original_samples.device						,      dtype=torch.floataa   )
      else:
        A__											=							self.timesteps.to(original_samples.device   )
        A__											=							timesteps.to(original_samples.device   )

      A__											=							[(schedule_timesteps == t).nonzero().item() for t in timesteps]

      A__											=							sigmas[step_indices].flatten()
      while len(sigma.shape   ) < len(original_samples.shape   ):
        A__											=							sigma.unsqueeze(-1   )

      A__											=							original_samples + noise * sigma
      return noisy_samples






    def __len__(self   ):


      """simple docstring"""
      return self.config.num_train_timesteps












"""simple docstring"""




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 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Any		,				UpperCamelCase     :	Optional[Any]		,				UpperCamelCase     :	Dict    ):
  # Initialise PyTorch model
  A__											=							FNetConfig.from_json_file(UpperCamelCase    )
  print(F"""Building PyTorch model from configuration: {config}"""    )
  A__											=							FNetForPreTraining(UpperCamelCase    )

  A__											=							restore_checkpoint(UpperCamelCase		,				UpperCamelCase    )
  A__											=							checkpoint_dict["""target"""]

  # Embeddings
  # Position IDs
  A__											=							fnet_pretraining_model.state_dict()

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

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

    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""intermediate"""]["""kernel"""]    ).T
    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""intermediate"""]["""bias"""]    )

    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""output"""]["""kernel"""]    ).T
    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""output"""]["""bias"""]    )

    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""encoder_{layer}"""]["""output_layer_norm"""]["""scale"""]    )
    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""encoder_{layer}"""]["""output_layer_norm"""]["""bias"""]    )

  # Pooler Layers
  A__											=							torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]    ).T
  A__											=							torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]    )

  # Masked LM Layers
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_dense"""]["""kernel"""]    ).T
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_dense"""]["""bias"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_layer_norm"""]["""scale"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_layer_norm"""]["""bias"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_output"""]["""output_bias"""]    )
  A__											=							torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]    )

  # Seq Relationship Layers
  A__											=							torch.tensor(
      pretrained_model_params["""classification"""]["""output_kernel"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""classification"""]["""output_bias"""]    )

  # Load State Dict
  fnet_pretraining_model.load_state_dict(UpperCamelCase    )

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


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)




