





import unittest

from diffusers import __version__
from diffusers.utils import deprecate







class     lowerCamelCase(	unittest.TestCase  ):
     '''simple docstring'''


     __magic_name__        = '.'.join([str(int(__version__.split('.'  )[0]  ) + 1  )] + __version__.split('.'  )[1:]  )
     __magic_name__        = '0.0.1'




     def 					lowerCAmelCase__		(						self       ):
         _A    =       {'deprecated_arg': 4}

         with self.assertWarns(snake_case_       ) as warning:
             _A    =       deprecate('deprecated_arg'							,    self.higher_version							,    'message'							,    take_from=snake_case_       )

         assert output == 4
         assert (
             str(warning.warning       )
             == F"The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}."
             " message"
         )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       {'deprecated_arg': 4}

         with self.assertWarns(snake_case_       ) as warning:
             _A    =       deprecate(('deprecated_arg', self.higher_version, 'message')							,    take_from=snake_case_       )

         assert output == 4
         assert (
             str(warning.warning       )
             == F"The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}."
             " message"
         )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       {'deprecated_arg_1': 4, 'deprecated_arg_2': 8}
         with self.assertWarns(snake_case_       ) as warning:
             _A, _A    =       deprecate(
                 ('deprecated_arg_1', self.higher_version, 'Hey')							,    ('deprecated_arg_2', self.higher_version, 'Hey')							,    take_from=snake_case_							,    )
         assert output_a == 4
         assert output_a == 8
         assert (
             str(warning.warnings[0].message       )
             == "The `deprecated_arg_1` argument is deprecated and will be removed in version"
             F" {self.higher_version}. Hey"
         )
         assert (
             str(warning.warnings[1].message       )
             == "The `deprecated_arg_2` argument is deprecated and will be removed in version"
             F" {self.higher_version}. Hey"
         )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       {'deprecated_arg': 4}

         with self.assertRaises(snake_case_       ) as error:
             deprecate(('wrong_arg', self.higher_version, 'message')							,    take_from=snake_case_       )

         assert "test_deprecate_function_incorrect_arg in" in str(error.exception       )
         assert "line" in str(error.exception       )
         assert "got an unexpected keyword argument `deprecated_arg`" in str(error.exception       )




     def 					lowerCAmelCase__		(						self       ):
         with self.assertWarns(snake_case_       ) as warning:
             deprecate(('deprecated_arg', self.higher_version, 'message')       )

         assert (
             str(warning.warning       )
             == F"`deprecated_arg` is deprecated and will be removed in version {self.higher_version}. message"
         )




     def 					lowerCAmelCase__		(						self       ):
         with self.assertWarns(snake_case_       ) as warning:
             deprecate(
                 ('deprecated_arg_1', self.higher_version, 'Hey')							,    ('deprecated_arg_2', self.higher_version, 'Hey')							,    )
         assert (
             str(warning.warnings[0].message       )
             == F"`deprecated_arg_1` is deprecated and will be removed in version {self.higher_version}. Hey"
         )
         assert (
             str(warning.warnings[1].message       )
             == F"`deprecated_arg_2` is deprecated and will be removed in version {self.higher_version}. Hey"
         )




     def 					lowerCAmelCase__		(						self       ):







         class     lowerCamelCase:
              '''simple docstring'''


              __magic_name__        = 5

         with self.assertWarns(snake_case_       ) as warning:
             _A    =       deprecate(('arg', self.higher_version, 'message')							,    take_from=Args()       )

         assert arg == 5
         assert (
             str(warning.warning       )
             == F"The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message"
         )




     def 					lowerCAmelCase__		(						self       ):







         class     lowerCamelCase:
              '''simple docstring'''


              __magic_name__        = 5
              __magic_name__        = 7

         with self.assertWarns(snake_case_       ) as warning:
             _A, _A    =       deprecate(
                 ('arg', self.higher_version, 'message')							,    ('foo', self.higher_version, 'message')							,    ('does not exist', self.higher_version, 'message')							,    take_from=Args()							,    )

         assert arg_a == 5
         assert arg_a == 7
         assert (
             str(warning.warning       )
             == F"The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message"
         )
         assert (
             str(warning.warnings[0].message       )
             == F"The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message"
         )
         assert (
             str(warning.warnings[1].message       )
             == F"The `foo` attribute is deprecated and will be removed in version {self.higher_version}. message"
         )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       {'deprecated_arg': 4}

         with self.assertRaises(snake_case_       ) as error:
             deprecate(('wrong_arg', self.lower_version, 'message')							,    take_from=snake_case_       )

         assert (
             str(error.exception       )
             == "The deprecation tuple ('wrong_arg', '0.0.1', 'message') should be removed since diffusers' version"
             F" {__version__} is >= {self.lower_version}"
         )




     def 					lowerCAmelCase__		(						self       ):
         with self.assertWarns(snake_case_       ) as warning:
             deprecate(('deprecated_arg', self.higher_version, 'This message is better!!!')							,    standard_warn=snake_case_       )

         assert str(warning.warning       ) == "This message is better!!!"




     def 					lowerCAmelCase__		(						self       ):
         with self.assertWarns(snake_case_       ) as warning:
             deprecate(('deprecated_arg', self.higher_version, 'This message is better!!!')							,    standard_warn=snake_case_       )
         assert str(warning.warning       ) == "This message is better!!!"
         assert "diffusers/tests/others/test_utils.py" in warning.filename












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(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[str]:




    """simple docstring"""




    _A    =       FNetConfig.from_json_file(_SCREAMING_SNAKE_CASE							)
    print(F"Building PyTorch model from configuration: {config}"							)
    _A    =       FNetForPreTraining(_SCREAMING_SNAKE_CASE							)

    _A    =       restore_checkpoint(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
    _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(_SCREAMING_SNAKE_CASE							)

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


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





