

import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


A__        =			logging.get_logger(__name__)

if is_vision_available():
  import PIL.Image
  from PIL import Image
  from PIL.Image import Image as ImageType
else:
  A__        =			object

if is_torch_available():
  import torch

if is_soundfile_availble():
  import soundfile as sf







class    UpperCamelCase_			:


  """simple docstring"""



  def __init__(	self    :				List[str]     ,  A_    :				Optional[Any]	)					-> Tuple:
      SCREAMING_SNAKE_CASE__					     =						value



  def __str__(	self    :				List[Any]	)					-> Any:
      return self.to_string()



  def      _UpperCamelCase    (	self    :				int	)					-> Any:
      logger.error(
          "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"	)
      return self._value







  def      _UpperCamelCase    (	self    :				List[str]	)					-> str:
      logger.error(
          "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"	)
      return str(self._value	)







class    UpperCamelCase_			(  _snake_case		,     _snake_case			):


  """simple docstring"""



  def      _UpperCamelCase    (	self    :				List[Any]	)					-> Tuple:
      return self._value







  def      _UpperCamelCase    (	self    :				Dict	)					-> Any:
      return self._value







class    UpperCamelCase_			(  _snake_case		,     _snake_case			):


  """simple docstring"""



  def __init__(	self    :				int     ,  A_    :				Tuple	)					-> List[str]:
      super().__init__(A_	)

      if not is_vision_available():
          raise ImportError("PIL must be installed in order to handle images."	)

      SCREAMING_SNAKE_CASE__					     =						None
      SCREAMING_SNAKE_CASE__					     =						None
      SCREAMING_SNAKE_CASE__					     =						None

      if isinstance(A_     ,  A_	):
          SCREAMING_SNAKE_CASE__					     =						value
      elif isinstance(A_     ,  (str, pathlib.Path)	):
          SCREAMING_SNAKE_CASE__					     =						value
      elif isinstance(A_     ,  torch.Tensor	):
          SCREAMING_SNAKE_CASE__					     =						value
      else:
          raise ValueError(f"Unsupported type for {self.__class__.__name__}: {type(A_	)}"	)



  def      _UpperCamelCase    (	self    :				Dict     ,  A_    :				Optional[int]=None     ,  A_    :				List[str]=None	)					-> str:
      from IPython.display import Image, display

      display(Image(self.to_string()	)	)



  def      _UpperCamelCase    (	self    :				List[str]	)					-> List[Any]:
      if self._raw is not None:
          return self._raw

      if self._path is not None:
          SCREAMING_SNAKE_CASE__					     =						Image.open(self._path	)
          return self._raw







  def      _UpperCamelCase    (	self    :				List[Any]	)					-> List[str]:
      if self._path is not None:
          return self._path

      if self._raw is not None:
          SCREAMING_SNAKE_CASE__					     =						tempfile.mkdtemp()
          SCREAMING_SNAKE_CASE__					     =						os.path.join(A_     ,  str(uuid.uuida()	) + ".png"	)
          self._raw.save(self._path	)

          return self._path

      if self._tensor is not None:
          SCREAMING_SNAKE_CASE__					     =						self._tensor.cpu().detach().numpy()

          # There is likely simpler than load into image into save
          SCREAMING_SNAKE_CASE__					     =						Image.fromarray((array * 255).astype(np.uinta	)	)

          SCREAMING_SNAKE_CASE__					     =						tempfile.mkdtemp()
          SCREAMING_SNAKE_CASE__					     =						os.path.join(A_     ,  str(uuid.uuida()	) + ".png"	)

          img.save(self._path	)

          return self._path







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""



  def __init__(	self    :				int     ,  A_    :				Optional[Any]     ,  A_    :				Optional[int]=1_6000	)					-> Optional[int]:
      super().__init__(A_	)

      if not is_soundfile_availble():
          raise ImportError("soundfile must be installed in order to handle audio."	)

      SCREAMING_SNAKE_CASE__					     =						None
      SCREAMING_SNAKE_CASE__					     =						None

      SCREAMING_SNAKE_CASE__					     =						samplerate

      if isinstance(A_     ,  (str, pathlib.Path)	):
          SCREAMING_SNAKE_CASE__					     =						value
      elif isinstance(A_     ,  torch.Tensor	):
          SCREAMING_SNAKE_CASE__					     =						value
      else:
          raise ValueError(f"Unsupported audio type: {type(A_	)}"	)



  def      _UpperCamelCase    (	self    :				Optional[int]     ,  A_    :				List[str]=None     ,  A_    :				int=None	)					-> Any:
      from IPython.display import Audio, display

      display(Audio(self.to_string()     ,  rate=self.samplerate	)	)



  def      _UpperCamelCase    (	self    :				Dict	)					-> Tuple:
      if self._tensor is not None:
          return self._tensor

      if self._path is not None:
          SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						sf.read(self._path	)
          SCREAMING_SNAKE_CASE__					     =						torch.tensor(A_	)
          return self._tensor







  def      _UpperCamelCase    (	self    :				Dict	)					-> List[str]:
      if self._path is not None:
          return self._path

      if self._tensor is not None:
          SCREAMING_SNAKE_CASE__					     =						tempfile.mkdtemp()
          SCREAMING_SNAKE_CASE__					     =						os.path.join(A_     ,  str(uuid.uuida()	) + ".wav"	)
          sf.write(self._path     ,  self._tensor     ,  samplerate=self.samplerate	)
          return self._path




A__        =			{"text": AgentText, "image": AgentImage, "audio": AgentAudio}
A__        =			{str: AgentText}

if is_vision_available():
  A__        =			AgentImage





def 							__a       (					*__A ,       **__A	)     ->   Optional[int]:
    SCREAMING_SNAKE_CASE__					     =						[(arg.to_raw() if isinstance(__A ,       __A	) else arg) for arg in args]
    SCREAMING_SNAKE_CASE__					     =						{k: (v.to_raw() if isinstance(__A ,       __A	) else v) for k, v in kwargs.items()}
    return args, kwargs





def 							__a       (					__A ,       __A=None	)     ->   List[str]:
    if isinstance(__A ,       __A	):
        SCREAMING_SNAKE_CASE__					     =						{}
        for i, (k, v) in enumerate(outputs.items()	):
            if output_types is not None:
                # If the class has defined outputs, we can map directly according to the class definition
                if output_types[i] in AGENT_TYPE_MAPPING:
                    SCREAMING_SNAKE_CASE__					     =						AGENT_TYPE_MAPPING[output_types[i]](__A	)
                else:
                    SCREAMING_SNAKE_CASE__					     =						AgentType(__A	)

            else:
                # If the class does not have defined output, then we map according to the type
                for _k, _v in INSTANCE_TYPE_MAPPING.items():
                    if isinstance(__A ,       _k	):
                        SCREAMING_SNAKE_CASE__					     =						_v(__A	)
                if k not in decoded_outputs:
                    SCREAMING_SNAKE_CASE__					     =						AgentType[v]

    elif isinstance(__A ,       (list, tuple)	):
        SCREAMING_SNAKE_CASE__					     =						type(__A	)()
        for i, v in enumerate(__A	):
            if output_types is not None:
                # If the class has defined outputs, we can map directly according to the class definition
                if output_types[i] in AGENT_TYPE_MAPPING:
                    decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](__A	)	)
                else:
                    decoded_outputs.append(AgentType(__A	)	)
            else:
                # If the class does not have defined output, then we map according to the type
                SCREAMING_SNAKE_CASE__					     =						False
                for _k, _v in INSTANCE_TYPE_MAPPING.items():
                    if isinstance(__A ,       _k	):
                        decoded_outputs.append(_v(__A	)	)
                        SCREAMING_SNAKE_CASE__					     =						True

                if not found:
                    decoded_outputs.append(AgentType(__A	)	)

    else:
        if output_types[0] in AGENT_TYPE_MAPPING:
            # If the class has defined outputs, we can map directly according to the class definition
            SCREAMING_SNAKE_CASE__					     =						AGENT_TYPE_MAPPING[output_types[0]](__A	)

        else:
            # If the class does not have defined output, then we map according to the type
            for _k, _v in INSTANCE_TYPE_MAPPING.items():
                if isinstance(__A ,       _k	):
                    return _v(__A	)
            return AgentType(__A	)

    return decoded_outputs





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       (					__A ,       __A ,       __A	):
    # Initialise PyTorch model
    SCREAMING_SNAKE_CASE__					     =						FNetConfig.from_json_file(__A	)
    print(f"Building PyTorch model from configuration: {config}"	)
    SCREAMING_SNAKE_CASE__					     =						FNetForPreTraining(__A	)

    SCREAMING_SNAKE_CASE__					     =						restore_checkpoint(__A ,       __A	)
    SCREAMING_SNAKE_CASE__					     =						checkpoint_dict["target"]

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

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

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

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

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

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

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

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

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

    # Load State Dict
    fnet_pretraining_model.load_state_dict(__A	)

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


if __name__ == "__main__":
  A__        =			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.")
  A__        =			parser.parse_args()
  convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)


