
"""simple docstring"""
import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any],						lowerCamelCase__							:							int,						lowerCamelCase__							:							Any				)  ->    List[str]:
  # Initialise PyTorch model
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = MobileBertConfig.from_json_file(lowerCamelCase__				)
  print(f'''Building PyTorch model from configuration: {config}'''				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = MobileBertForPreTraining(lowerCamelCase__				)
  # Load weights from tf checkpoint
  _SCREAMING_SNAKE_CASE   :				Tuple							    = load_tf_weights_in_mobilebert(lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)
  # Save pytorch-model
  print(f'''Save PyTorch model to {pytorch_dump_path}'''				)
  torch.save(model.state_dict(),						lowerCamelCase__				)


if __name__ == "__main__":
      lowercase_       :						List[str]     					=	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
      )
      parser.add_argument(
          '''--mobilebert_config_file''',
          default=None,
          type=str,
          required=True,
          help=(
              '''The config json file corresponding to the pre-trained MobileBERT model. \n'''
              '''This specifies the model architecture.'''
          ),
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      lowercase_       :						List[str]     					=	parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase      (							unittest.TestCase  ):
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = TFCamembertModel.from_pretrained("jplu/tf-camembert-base"				)

       _SCREAMING_SNAKE_CASE   :				str							    = tf.convert_to_tensor(
           [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]  ,     dtype=tf.intaa  ,     )  # J'aime le camembert !"

       _SCREAMING_SNAKE_CASE   :				str							    = model(snake_case__				)["last_hidden_state"]
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tf.TensorShape((1, 10, 768)				)
       self.assertEqual(output.shape  ,     snake_case__				)
       # compare the actual values for a slice.
       _SCREAMING_SNAKE_CASE   :				int							    = tf.convert_to_tensor(
           [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]  ,     dtype=tf.floataa  ,     )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,     expected_slice.numpy()  ,     atol=1E-4				)				)



