




'''simple docstring'''





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM





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

    return orig_key





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_						):
    for key in orig_state_dict.copy().keys():
        _a				:							List[Any]							       =      orig_state_dict.pop(UpperCAmelCase_						)

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            _a				:							List[Any]							       =      val

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

    return orig_state_dict





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						):
    _a				:							List[Any]							       =      torch.load(UpperCAmelCase_   ,	map_location="""cpu"""						)["""model_state_dict"""]
    _a				:							Dict							       =      MraConfig.from_json_file(UpperCAmelCase_						)
    _a				:							Optional[int]							       =      MraForMaskedLM(UpperCAmelCase_						)

    _a				:							List[str]							       =      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__":
  a		:	Optional[int]       =      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		:	Any       =      parser.parse_args()
  convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





import unittest

from cer import CER


lowerCAmelCase__             =		CER()







class 						lowercase     (					unittest.TestCase				):


		"""simple docstring"""




		def 		A__       (					self):
			_UpperCamelCase :		Any								=    ['White House']
			_UpperCamelCase :		Tuple								=    ['white house']
			# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
			_UpperCamelCase :		Tuple								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.1_8_1_8_1_8_1_8_1_8) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    ['were wolf']
			_UpperCamelCase :		List[Any]								=    ['werewolf']
			# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
			_UpperCamelCase :		Dict								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.1_1_1_1_1_1_1) < 1e-6)

			_UpperCamelCase :		int								=    ['werewolf']
			_UpperCamelCase :		Optional[Any]								=    ['weae     wolf']
			# S = 1, D = 1, I = 0, N = 8, CER = 0.25
			_UpperCamelCase :		str								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.2_5) < 1e-6)

			# consecutive whitespaces case 1
			_UpperCamelCase :		Tuple								=    ['were wolf']
			_UpperCamelCase :		Dict								=    ['were               wolf']
			# S = 0, D = 0, I = 0, N = 9, CER = 0
			_UpperCamelCase :		List[str]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.0) < 1e-6)

			# consecutive whitespaces case 2
			_UpperCamelCase :		Dict								=    ['were   wolf']
			_UpperCamelCase :		Dict								=    ['were               wolf']
			# S = 0, D = 0, I = 0, N = 9, CER = 0
			_UpperCamelCase :		Dict								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.0) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    ['werewolf']
			_UpperCamelCase :		Optional[Any]								=    ['weaewolf']
			# S = 1, D = 0, I = 0, N = 8, CER = 0.125
			_UpperCamelCase :		List[str]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.1_2_5) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		int								=    ['werewolf']
			_UpperCamelCase :		Any								=    ['wereawolf']
			# S = 0, D = 1, I = 0, N = 8, CER = 0.125
			_UpperCamelCase :		Optional[int]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.1_2_5) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		str								=    ['werewolf']
			_UpperCamelCase :		Optional[Any]								=    ['wereolf']
			# S = 0, D = 0, I = 1, N = 8, CER = 0.125
			_UpperCamelCase :		List[Any]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.1_2_5) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		Optional[Any]								=    ['werewolf']
			_UpperCamelCase :		Optional[Any]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertEqual(__snake_case						,							0.0)


		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    ['werewolf', 'I am your father']
			_UpperCamelCase :		Dict								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertEqual(__snake_case						,							0.0)

			_UpperCamelCase :		List[Any]								=    ['werewolf', 'I am your father', 'doge']
			_UpperCamelCase :		Optional[int]								=    ['werxwolf', 'I       am your father', 'doge']
			# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
			_UpperCamelCase :		Any								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.0_3_5_7_1_4_2_8) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    ['My hovercraft', 'is full of eels']
			_UpperCamelCase :		List[Any]								=    ['My hovercraft is full', ' of eels']
			# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
			# whitespace at the front of " of eels" will be strip during preporcessing
			# so need to insert 2 whitespaces
			_UpperCamelCase :		Dict								=    cer.compute(predictions=__snake_case						,							references=__snake_case						,							concatenate_texts=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.0_7_1_4_2_8) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    ['我能吞下玻璃而不伤身体']
			_UpperCamelCase :		Optional[Any]								=    [' 能吞虾玻璃而 不霜身体啦']
			# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
			_UpperCamelCase :		Any								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5) < 1e-6)

			_UpperCamelCase :		Union[str, Any]								=    ['我能吞下玻璃', '而不伤身体']
			_UpperCamelCase :		List[str]								=    ['我    能 吞 下 玻 璃', '而不伤身体']
			# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
			_UpperCamelCase :		Union[str, Any]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5_4_5) < 1e-6)

			_UpperCamelCase :		Optional[int]								=    ['我能吞下玻璃而不伤身体']
			_UpperCamelCase :		int								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertFalse(__snake_case						,							0.0)






		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    ['']
			_UpperCamelCase :		Dict								=    ['Hypothesis']
			with self.assertRaises(__snake_case):
				cer.compute(predictions=__snake_case						,							references=__snake_case)


if __name__ == "__main__":
							unittest.main()




