'''simple docstring'''





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM






def SCREAMING_SNAKE_CASE__   (							snake_case    :							Optional[int]   )					->							Optional[int]:







     """simple docstring"""


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

     return orig_key






def SCREAMING_SNAKE_CASE__   (							snake_case    :							Dict							,     snake_case    :							List[str]   )					->							List[Any]:







     """simple docstring"""


     for key in orig_state_dict.copy().keys():
          a :				Tuple				=      orig_state_dict.pop(snake_case   )

          if ("pooler" in key) or ("sen_class" in key):
               continue
          else:
               a :				Optional[int]				=      val

     a :				Optional[int]				=      orig_state_dict['cls.predictions.decoder.bias']
     a :				List[Any]				=      torch.arange(snake_case   ).expand((1, -1)   ) + 2

     return orig_state_dict






def SCREAMING_SNAKE_CASE__   (							snake_case    :							Optional[int]							,     snake_case    :							Any							,     snake_case    :							Optional[Any]   )					->							List[Any]:







     """simple docstring"""
     a :				str				=      torch.load(snake_case							,     map_location='cpu'   )['model_state_dict']
     a :				Optional[Any]				=      MraConfig.from_json_file(snake_case   )
     a :				List[str]				=      MraForMaskedLM(snake_case   )

     a :				Dict				=      convert_checkpoint_helper(config.max_position_embeddings							,     snake_case   )

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

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


if __name__ == "__main__":
      UpperCamelCase    :			List[str]						     =  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."""
      )
      UpperCamelCase    :			str						     =  parser.parse_args()
      convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					@classmethod
					def 	lowercase_							(				cls					):







						'''simple docstring'''




						return cls





					@slow
					@require_torch_gpu
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							self.get_auto_remove_tmp_dir()
						lowerCamelCase_							:		Tuple							   =							f"\n            --model_name Helsinki-NLP/opus-mt-en-ro\n            --save_dir {output_dir}\n            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro\n            --num_beams 2\n            --task translation\n        ".split()

						# we want this test to run even if there is only one GPU, but if there are more we use them all
						lowerCamelCase_							:		List[Any]							   =							get_gpu_count()
						lowerCamelCase_							:		Union[str, Any]							   =							f"\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        ".split()
						lowerCamelCase_							:		int							   =							[sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase ,       env=self.get_env()					)

						lowerCamelCase_							:		Any							   =							os.path.join(__lowercase ,       'test_bleu.json'					)
						lowerCamelCase_							:		List[Any]							   =							load_json(__lowercase					)
						# print(metrics)
						self.assertGreaterEqual(metrics['bleu'] ,       25					)
