
"""simple docstring"""

import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


SCREAMING_SNAKE_CASE_         =  namedtuple('''ExampleNamedTuple''', '''a b c''')
class        _UpperCAmelCase							(						unittest.TestCase							):




      def       a_ (							self							)  ->   Optional[int]:
          UpperCAmelCase       =   torch.randn(5				,    2							)
          UpperCAmelCase       =   torch.device('cuda'							) if torch.cuda.is_available() else torch.device('cpu'							)

          UpperCAmelCase       =   send_to_device(lowercase_				,    lowercase_							)
          self.assertTrue(torch.equal(resulta.cpu()				,    lowercase_							)							)

          UpperCAmelCase       =   send_to_device((tensor, [tensor, tensor], 1)				,    lowercase_							)
          self.assertIsInstance(lowercase_				,    lowercase_							)
          self.assertTrue(torch.equal(resulta[0].cpu()				,    lowercase_							)							)
          self.assertIsInstance(resulta[1]				,    lowercase_							)
          self.assertTrue(torch.equal(resulta[1][0].cpu()				,    lowercase_							)							)
          self.assertTrue(torch.equal(resulta[1][1].cpu()				,    lowercase_							)							)
          self.assertEqual(resulta[2]				,    1							)

          UpperCAmelCase       =   send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}				,    lowercase_							)
          self.assertIsInstance(lowercase_				,    lowercase_							)
          self.assertTrue(torch.equal(resulta['a'].cpu()				,    lowercase_							)							)
          self.assertIsInstance(resulta['b']				,    lowercase_							)
          self.assertTrue(torch.equal(resulta['b'][0].cpu()				,    lowercase_							)							)
          self.assertTrue(torch.equal(resulta['b'][1].cpu()				,    lowercase_							)							)
          self.assertEqual(resulta['c']				,    1							)

          UpperCAmelCase       =   send_to_device(ExampleNamedTuple(a=lowercase_				,    b=[tensor, tensor]				,    c=1							)				,    lowercase_							)
          self.assertIsInstance(lowercase_				,    lowercase_							)
          self.assertTrue(torch.equal(resulta.a.cpu()				,    lowercase_							)							)
          self.assertIsInstance(resulta.b				,    lowercase_							)
          self.assertTrue(torch.equal(resulta.b[0].cpu()				,    lowercase_							)							)
          self.assertTrue(torch.equal(resulta.b[1].cpu()				,    lowercase_							)							)
          self.assertEqual(resulta.c				,    1							)

          UpperCAmelCase       =   send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}							)				,    lowercase_							)
          self.assertIsInstance(lowercase_				,    lowercase_							)
          self.assertTrue(torch.equal(resulta['a'].cpu()				,    lowercase_							)							)
          self.assertIsInstance(resulta['b']				,    lowercase_							)
          self.assertTrue(torch.equal(resulta['b'][0].cpu()				,    lowercase_							)							)
          self.assertTrue(torch.equal(resulta['b'][1].cpu()				,    lowercase_							)							)
          self.assertEqual(resulta['c']				,    1							)




      def       a_ (							self							)  ->   List[str]:
          with self.assertRaises(lowercase_							) as cm:
              UpperCAmelCase       =   recursively_apply(torch.tensor				,    (torch.tensor(1							), 1)				,    error_on_other_type=lowercase_							)
          self.assertEqual(
              str(cm.exception							)				,    'Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'				,    )




      def       a_ (							self							)  ->   List[str]:
          UpperCAmelCase       =   torch.tensor([1, 2, 3, 4, 5]							)
          self.assertEqual(listify(lowercase_							)				,    [1, 2, 3, 4, 5]							)

          UpperCAmelCase       =   torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]							)
          self.assertEqual(listify(lowercase_							)				,    [[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]							)

          UpperCAmelCase       =   torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]							)
          self.assertEqual(
              listify(lowercase_							)				,    [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]							)




      def       a_ (							self							)  ->   List[str]:
          with patch_environment(aa=1				,    BB=2							):
              self.assertEqual(os.environ.get('AA'							)				,    '1'							)
              self.assertEqual(os.environ.get('BB'							)				,    '2'							)

          self.assertNotIn('AA'				,    os.environ							)
          self.assertNotIn('BB'				,    os.environ							)




      def       a_ (							self							)  ->   Optional[int]:
          UpperCAmelCase       =   RegressionModel()
          UpperCAmelCase       =   model.forward
          UpperCAmelCase       =   convert_outputs_to_fpaa(model.forward							)
          UpperCAmelCase       =   extract_model_from_parallel(lowercase_				,    keep_fpaa_wrapper=lowercase_							)
          UpperCAmelCase       =   pickle.dumps(lowercase_							)




      @require_cuda
      def       a_ (							self							)  ->   List[Any]:
          UpperCAmelCase       =   RegressionModel()
          UpperCAmelCase       =   model.forward
          UpperCAmelCase       =   torch.cuda.amp.autocast(dtype=torch.floataa							)(model.forward							)
          UpperCAmelCase       =   convert_outputs_to_fpaa(model.forward							)
          UpperCAmelCase       =   extract_model_from_parallel(lowercase_				,    keep_fpaa_wrapper=lowercase_							)
          UpperCAmelCase       =   pickle.dumps(lowercase_							)




      @require_cuda
      @require_torch_min_version(version='2.0'							)
      def       a_ (							self							)  ->   str:
          UpperCAmelCase       =   RegressionModel()
          UpperCAmelCase       =   model.forward
          UpperCAmelCase       =   torch.cuda.amp.autocast(dtype=torch.floataa							)(model.forward							)
          UpperCAmelCase       =   convert_outputs_to_fpaa(model.forward							)
          UpperCAmelCase       =   torch.compile(model.forward				,    backend='inductor'							)
          UpperCAmelCase       =   torch.randn(4				,    1_0							).cuda()
          UpperCAmelCase       =   model(lowercase_							)




      def       a_ (							self							)  ->   List[str]:
          UpperCAmelCase       =   RegressionModel()
          # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
          UpperCAmelCase       =   torch.nn.parallel.DataParallel(lowercase_							)
          UpperCAmelCase       =   extract_model_from_parallel(lowercase_							)

          self.assertEqual(lowercase_				,    lowercase_							)




      @require_torch_min_version(version='2.0'							)
      def       a_ (							self							)  ->   Optional[int]:
          UpperCAmelCase       =   RegressionModel()
          UpperCAmelCase       =   torch.compile(lowercase_							)

          # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
          UpperCAmelCase       =   torch.nn.parallel.DataParallel(lowercase_							)
          UpperCAmelCase       =   torch.compile(lowercase_							)
          UpperCAmelCase       =   extract_model_from_parallel(lowercase_							)

          self.assertEqual(compiled_model._orig_mod				,    compiled_model_unwrapped._orig_mod							)




      def       a_ (							self							)  ->   Optional[int]:
          self.assertEqual(find_device([1, 'a', torch.tensor([1, 2, 3]							)]							)				,    torch.device('cpu'							)							)
          self.assertEqual(find_device({'a': 1, 'b': torch.tensor([1, 2, 3]							)}							)				,    torch.device('cpu'							)							)
          self.assertIsNone(find_device([1, 'a']							)							)





"""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       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Optional[Any]	)	-> Optional[Any]:






    """simple docstring"""




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

    UpperCAmelCase       =   restore_checkpoint(lowerCAmelCase	,			lowerCAmelCase	)
    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(lowerCAmelCase	)

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


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)



