




import argparse

import torch

from transformers import MraConfig, MraForMaskedLM
def        __lowerCAmelCase (UpperCAmelCase_		):
    if "model" in orig_key:
        a__      =       orig_key.replace("""model."""   ,    """"""		)
    if "norm1" in orig_key:
        a__      =       orig_key.replace("""norm1"""   ,    """attention.output.LayerNorm"""		)
    if "norm2" in orig_key:
        a__      =       orig_key.replace("""norm2"""   ,    """output.LayerNorm"""		)
    if "norm" in orig_key:
        a__      =       orig_key.replace("""norm"""   ,    """LayerNorm"""		)
    if "transformer" in orig_key:
        a__      =       orig_key.split("""."""		)[0].split("""_"""		)[-1]
        a__      =       orig_key.replace(F"""transformer_{layer_num}"""   ,    F"""encoder.layer.{layer_num}"""		)
    if "mha.attn" in orig_key:
        a__      =       orig_key.replace("""mha.attn"""   ,    """attention.self"""		)
    if "mha" in orig_key:
        a__      =       orig_key.replace("""mha"""   ,    """attention"""		)
    if "W_q" in orig_key:
        a__      =       orig_key.replace("""W_q"""   ,    """self.query"""		)
    if "W_k" in orig_key:
        a__      =       orig_key.replace("""W_k"""   ,    """self.key"""		)
    if "W_v" in orig_key:
        a__      =       orig_key.replace("""W_v"""   ,    """self.value"""		)
    if "ff.0" in orig_key:
        a__      =       orig_key.replace("""ff.0"""   ,    """intermediate.dense"""		)
    if "ff.2" in orig_key:
        a__      =       orig_key.replace("""ff.2"""   ,    """output.dense"""		)
    if "ff" in orig_key:
        a__      =       orig_key.replace("""ff"""   ,    """output.dense"""		)
    if "mlm_class" in orig_key:
        a__      =       orig_key.replace("""mlm.mlm_class"""   ,    """cls.predictions.decoder"""		)
    if "mlm" in orig_key:
        a__      =       orig_key.replace("""mlm"""   ,    """cls.predictions.transform"""		)
    if "backbone.backbone.encoders" in orig_key:
        a__      =       orig_key.replace("""backbone.backbone.encoders"""   ,    """encoder.layer"""		)
    if "cls" not in orig_key:
        a__      =       """mra.""" + orig_key

    return orig_key
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_		):
    for key in orig_state_dict.copy().keys():
        a__      =       orig_state_dict.pop(UpperCAmelCase_		)

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            a__      =       val

    a__      =       orig_state_dict["""cls.predictions.decoder.bias"""]
    a__      =       torch.arange(UpperCAmelCase_		).expand((1, -1)		) + 2

    return orig_state_dict
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_		):
    a__      =       torch.load(UpperCAmelCase_   ,    map_location="""cpu"""		)["""model_state_dict"""]
    a__      =       MraConfig.from_json_file(UpperCAmelCase_		)
    a__      =       MraForMaskedLM(UpperCAmelCase_		)

    a__      =       convert_checkpoint_helper(config.max_position_embeddings   ,    UpperCAmelCase_		)

    print(model.load_state_dict(UpperCAmelCase_		)		)
    model.eval()
    model.save_pretrained(UpperCAmelCase_		)

    print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""		)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_						:					Any			   =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Mra pytorch checkpoint.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The json file for Mra model config.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)












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 		__UpperCAmelCase(		unittest.TestCase ):





     """simple docstring"""



     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			List[Any]:






         """simple docstring"""
         a__      =       CamembertModel.from_pretrained("""camembert-base"""    )
         model.to(A    )

         a__      =       torch.tensor(
             [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]						,				device=A						,				dtype=torch.long						,				)  # J'aime le camembert !
         with torch.no_grad():
             a__      =       model(A    )["""last_hidden_state"""]
         a__      =       torch.Size((1, 10, 7_68)    )
         self.assertEqual(output.shape						,				A    )
         # compare the actual values for a slice.
         a__      =       torch.tensor(
             [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,				device=A						,				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]						,				A						,				atol=1E-4    )    )






