
"""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       __UpperCAmelCase		(		a_      :		str     ,	a_      :		Union[str, Any]     ,	a_      :		List[str]					)  ->							Optional[Any]:
   _UpperCAmelCase 						=       FNetConfig.from_json_file(UpperCamelCase_					)
   print(F'''Building PyTorch model from configuration: {config}'''					)
   _UpperCAmelCase 						=       FNetForPreTraining(UpperCamelCase_					)

   _UpperCAmelCase 						=       restore_checkpoint(UpperCamelCase_     ,	UpperCamelCase_					)
   _UpperCAmelCase 						=       checkpoint_dict["target"]

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

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

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

      _UpperCAmelCase 						=       torch.tensor(
          pretrained_model_params["encoder"][F'''feed_forward_{layer}''']["intermediate"]["kernel"]					).T
      _UpperCAmelCase 						=       torch.tensor(
          pretrained_model_params["encoder"][F'''feed_forward_{layer}''']["intermediate"]["bias"]					)

      _UpperCAmelCase 						=       torch.tensor(
          pretrained_model_params["encoder"][F'''feed_forward_{layer}''']["output"]["kernel"]					).T
      _UpperCAmelCase 						=       torch.tensor(
          pretrained_model_params["encoder"][F'''feed_forward_{layer}''']["output"]["bias"]					)

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

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

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

   # Seq Relationship Layers
   _UpperCAmelCase 						=       torch.tensor(
       pretrained_model_params["classification"]["output_kernel"]					)
   _UpperCAmelCase 						=       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__":
  SCREAMING_SNAKE_CASE_  					= 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.')
  SCREAMING_SNAKE_CASE_  					= parser.parse_args()
  convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)









"""simple docstring"""




import math






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				list:







  '''simple docstring'''







  if n <= 0:
    raise ValueError("Only positive integers have prime factors"       )
  _a					      =						[]
  while n % 2 == 0:
    pf.append(2       )
    _a					      =						int(n / 2       )
  for i in range(3  ,			int(math.sqrt(UpperCamelCase_       )       ) + 1  ,			2       ):
    while n % i == 0:
      pf.append(UpperCamelCase_       )
      _a					      =						int(n / i       )
  if n > 2:
    pf.append(UpperCamelCase_       )
  return pf






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  if n <= 0:
    raise ValueError("Only positive numbers are accepted"       )
  _a					      =						1
  _a					      =						1
  while n % 2 == 0:
    temp += 1
    _a					      =						int(n / 2       )
  div *= temp
  for i in range(3  ,			int(math.sqrt(UpperCamelCase_       )       ) + 1  ,			2       ):
    _a					      =						1
    while n % i == 0:
      temp += 1
      _a					      =						int(n / i       )
    div *= temp
  if n > 1:
    div *= 2
  return div






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  if n <= 0:
    raise ValueError("Only positive numbers are accepted"       )
  _a					      =						1
  _a					      =						1
  while n % 2 == 0:
    temp += 1
    _a					      =						int(n / 2       )
  if temp > 1:
    s *= (2**temp - 1) / (2 - 1)
  for i in range(3  ,			int(math.sqrt(UpperCamelCase_       )       ) + 1  ,			2       ):
    _a					      =						1
    while n % i == 0:
      temp += 1
      _a					      =						int(n / i       )
    if temp > 1:
      s *= (i**temp - 1) / (i - 1)
  return int(UpperCamelCase_       )






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  _a					      =						n
  for x in set(prime_factors(UpperCamelCase_       )       ):
    s *= (x - 1) / x
  return int(UpperCamelCase_       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()


