




'''simple docstring'''







import argparse

import torch

from transformers import MraConfig, MraForMaskedLM





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

    return orig_key





def 						snake_case_							(_a     :							int   ,       _a     :							List[str]					):
    for key in orig_state_dict.copy().keys():
        UpperCAmelCase      	=		orig_state_dict.pop(_a					)

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

    UpperCAmelCase      	=		orig_state_dict['''cls.predictions.decoder.bias''']
    UpperCAmelCase      	=		torch.arange(_a					).expand((1, -1)					) + 2

    return orig_state_dict





def 						snake_case_							(_a     :							Optional[int]   ,       _a     :							List[str]   ,       _a     :							Tuple					):
    UpperCAmelCase      	=		torch.load(_a   ,       map_location='''cpu'''					)['''model_state_dict''']
    UpperCAmelCase      	=		MraConfig.from_json_file(_a					)
    UpperCAmelCase      	=		MraForMaskedLM(_a					)

    UpperCAmelCase      	=		convert_checkpoint_helper(config.max_position_embeddings   ,       _a					)

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

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


if __name__ == "__main__":
   A			   =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.'
   )
   A			   =parser.parse_args()
   convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










'''simple docstring'''







import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
   import torch


class       _a  (     unittest.TestCase							,							__a			):
       def    A				(     self						:     str    ):



           '''simple docstring'''

           UpperCAmelCase      	=		load_tool('''speech-to-text'''    )
           self.tool.setup()
       def    A				(     self						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tool(torch.ones(3_000    )    )
           self.assertEqual(lowercase       ,    ''' you'''    )







       def    A				(     self						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tool(audio=torch.ones(3_000    )    )
           self.assertEqual(lowercase       ,    ''' you'''    )




