


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   import torch

   from transformers import CamembertModel



@require_torch
@require_sentencepiece
@require_tokenizers
class 							a       (      unittest.TestCase ):





     @slow
     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any				):
        __lowerCamelCase:					Tuple	 =						CamembertModel.from_pretrained("""camembert-base"""				)
        model.to(SCREAMING_SNAKE_CASE_				)

        __lowerCamelCase:					Tuple	 =						torch.tensor(
            [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]     ,						device=SCREAMING_SNAKE_CASE_     ,						dtype=torch.long     ,						)  # J'aime le camembert !
        with torch.no_grad():
           __lowerCamelCase:					Optional[Any]	 =						model(SCREAMING_SNAKE_CASE_				)["""last_hidden_state"""]
        __lowerCamelCase:					Optional[Any]	 =						torch.Size((1, 10, 768)				)
        self.assertEqual(output.shape     ,						SCREAMING_SNAKE_CASE_				)
        # compare the actual values for a slice.
        __lowerCamelCase:					Dict	 =						torch.tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]     ,						device=SCREAMING_SNAKE_CASE_     ,						dtype=torch.float     ,						)
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(torch.allclose(output[:, :3, :3]     ,						SCREAMING_SNAKE_CASE_     ,						atol=1E-4				)				)






import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]    ,   snake_case						:     Optional[int]			)							->				List[Any]:
   # Initialize PyTorch model
   __lowerCamelCase:					str	 =						CanineConfig()
   __lowerCamelCase:					Union[str, Any]	 =						CanineModel(snake_case			)
   model.eval()

   print(f'Building PyTorch model from configuration: {config}'			)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(snake_case    ,   snake_case    ,   snake_case			)

   # Save pytorch-model (weights and configuration)
   print(f'Save PyTorch model to {pytorch_dump_path}'			)
   model.save_pretrained(snake_case			)

   # Save tokenizer files
   __lowerCamelCase:					Optional[int]	 =						CanineTokenizer()
   print(f'Save tokenizer files to {pytorch_dump_path}'			)
   tokenizer.save_pretrained(snake_case			)


if __name__ == "__main__":
   _A		:					int          =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to a folder where the PyTorch model will be placed.''',
   )
   _A		:					int          =							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


