




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 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		Any:





  """simple docstring"""





  _lowercase:			List[str]      =     FNetConfig.from_json_file(UpperCamelCase				)
  print(F'Building PyTorch model from configuration: {config}'				)
  _lowercase:			Optional[int]      =     FNetForPreTraining(UpperCamelCase				)

  _lowercase:			Dict      =     restore_checkpoint(UpperCamelCase       ,  UpperCamelCase				)
  _lowercase:			List[str]      =     checkpoint_dict['target']

  # Embeddings
  # Position IDs
  _lowercase:			int      =     fnet_pretraining_model.state_dict()

  _lowercase:			Dict      =     state_dict['fnet.embeddings.position_ids']
  _lowercase:			Optional[int]      =     {'fnet.embeddings.position_ids': position_ids}
  # Embedding Layers
  _lowercase:			Tuple      =     torch.tensor(
      pretrained_model_params['encoder']['embedder']['word']['embedding']				)
  _lowercase:			List[Any]      =     torch.tensor(
      pretrained_model_params['encoder']['embedder']['position']['embedding'][0]				)
  _lowercase:			int      =     torch.tensor(
      pretrained_model_params['encoder']['embedder']['type']['embedding']				)
  _lowercase:			Tuple      =     torch.tensor(
      pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']				).T
  _lowercase:			List[Any]      =     torch.tensor(
      pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']				)
  _lowercase:			Optional[int]      =     torch.tensor(
      pretrained_model_params['encoder']['embedder']['layer_norm']['scale']				)
  _lowercase:			List[Any]      =     torch.tensor(
      pretrained_model_params['encoder']['embedder']['layer_norm']['bias']				)

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

    _lowercase:			Tuple      =     torch.tensor(
        pretrained_model_params['encoder'][F'feed_forward_{layer}']['intermediate']['kernel']				).T
    _lowercase:			str      =     torch.tensor(
        pretrained_model_params['encoder'][F'feed_forward_{layer}']['intermediate']['bias']				)

    _lowercase:			Any      =     torch.tensor(
        pretrained_model_params['encoder'][F'feed_forward_{layer}']['output']['kernel']				).T
    _lowercase:			Tuple      =     torch.tensor(
        pretrained_model_params['encoder'][F'feed_forward_{layer}']['output']['bias']				)

    _lowercase:			Optional[Any]      =     torch.tensor(
        pretrained_model_params['encoder'][F'encoder_{layer}']['output_layer_norm']['scale']				)
    _lowercase:			Dict      =     torch.tensor(
        pretrained_model_params['encoder'][F'encoder_{layer}']['output_layer_norm']['bias']				)

  # Pooler Layers
  _lowercase:			Dict      =     torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']				).T
  _lowercase:			str      =     torch.tensor(pretrained_model_params['encoder']['pooler']['bias']				)

  # Masked LM Layers
  _lowercase:			int      =     torch.tensor(
      pretrained_model_params['predictions_dense']['kernel']				).T
  _lowercase:			Any      =     torch.tensor(
      pretrained_model_params['predictions_dense']['bias']				)
  _lowercase:			str      =     torch.tensor(
      pretrained_model_params['predictions_layer_norm']['scale']				)
  _lowercase:			List[str]      =     torch.tensor(
      pretrained_model_params['predictions_layer_norm']['bias']				)
  _lowercase:			str      =     torch.tensor(
      pretrained_model_params['encoder']['embedder']['word']['embedding']				)
  _lowercase:			Union[str, Any]      =     torch.tensor(
      pretrained_model_params['predictions_output']['output_bias']				)
  _lowercase:			int      =     torch.tensor(pretrained_model_params['predictions_output']['output_bias']				)

  # Seq Relationship Layers
  _lowercase:			Any      =     torch.tensor(
      pretrained_model_params['classification']['output_kernel']				)
  _lowercase:			Optional[Any]      =     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__":
   UpperCAmelCase__    :							Any       		=     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.')
   UpperCAmelCase__    :							Union[str, Any]       		=     parser.parse_args()
   convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)






from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCAmelCase__    :							Tuple       		=     [
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

UpperCAmelCase__    :							Tuple       		=     get_logger(__name__)


UpperCAmelCase__    :							List[Any]       		=     {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		Optional[TaskTemplate]:





  """simple docstring"""





  _lowercase:			Union[str, Any]      =     task_template_dict.get('task'				)
  if task_name is None:
    logger.warning(F'Couldn\'t find template for task \'{task_name}\'. Available templates: {list(UpperCamelCase				)}'				)
    return None
  _lowercase:			int      =     NAMEaTEMPLATE.get(UpperCamelCase				)
  return template.from_dict(UpperCamelCase				)
