

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







def _a					(			SCREAMING_SNAKE_CASE						:     List[Any]						)       ->       Optional[Any]:

 """simple docstring"""






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

 return orig_key







def _a					(			SCREAMING_SNAKE_CASE						:     Any       ,   SCREAMING_SNAKE_CASE						:     int						)       ->       List[str]:

 """simple docstring"""






 for key in orig_state_dict.copy().keys():
  __lowerCAmelCase:  Optional[Any]        = orig_state_dict.pop(SCREAMING_SNAKE_CASE						)

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

 __lowerCAmelCase:  Any        = orig_state_dict['cls.predictions.decoder.bias']
 __lowerCAmelCase:  Any        = torch.arange(SCREAMING_SNAKE_CASE						).expand((1, -1)						) + 2

 return orig_state_dict







def _a					(			SCREAMING_SNAKE_CASE						:     Optional[Any]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     int						)       ->       Dict:

 """simple docstring"""






 __lowerCAmelCase:  int        = torch.load(SCREAMING_SNAKE_CASE       ,   map_location='cpu'						)['model_state_dict']
 __lowerCAmelCase:  int        = MraConfig.from_json_file(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  int        = MraForMaskedLM(SCREAMING_SNAKE_CASE						)

 __lowerCAmelCase:  Dict        = convert_checkpoint_helper(config.max_position_embeddings       ,   SCREAMING_SNAKE_CASE						)

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

 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)








from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_a									=  logging.get_logger(__name__)

_a									=  {
    '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json''',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}






class 				A_					(						snake_case__   ):
     _lowercase     :	List[Any]        =      'm2m_100'
     _lowercase     :	str        =      ['past_key_values']
     _lowercase     :	List[Any]        =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


     def __init__(				self     :						int     ,    UpperCAmelCase     :						Any=1_2_8_1_1_2     ,    UpperCAmelCase     :						Optional[int]=1_0_2_4     ,    UpperCAmelCase     :						Tuple=1_2     ,    UpperCAmelCase     :						List[str]=4_0_9_6     ,    UpperCAmelCase     :						Dict=1_6     ,    UpperCAmelCase     :						int=1_2     ,    UpperCAmelCase     :						int=4_0_9_6     ,    UpperCAmelCase     :						List[str]=1_6     ,    UpperCAmelCase     :						Optional[Any]=0.05     ,    UpperCAmelCase     :						Dict=0.05     ,    UpperCAmelCase     :						Union[str, Any]=True     ,    UpperCAmelCase     :						Dict=True     ,    UpperCAmelCase     :						List[str]="relu"     ,    UpperCAmelCase     :						Dict=1_0_2_4     ,    UpperCAmelCase     :						str=0.1     ,    UpperCAmelCase     :						Dict=0.1     ,    UpperCAmelCase     :						List[str]=0.0     ,    UpperCAmelCase     :						Union[str, Any]=0.02     ,    UpperCAmelCase     :						Optional[Any]=2     ,    UpperCAmelCase     :						str=True     ,    UpperCAmelCase     :						Optional[int]=1     ,    UpperCAmelCase     :						List[Any]=0     ,    UpperCAmelCase     :						Union[str, Any]=2     ,    **UpperCAmelCase     :						Union[str, Any]     ,    )				->      Tuple:
      __lowerCAmelCase:  Optional[Any]        = vocab_size
      __lowerCAmelCase:  Dict        = max_position_embeddings
      __lowerCAmelCase:  Tuple        = d_model
      __lowerCAmelCase:  List[str]        = encoder_ffn_dim
      __lowerCAmelCase:  Any        = encoder_layers
      __lowerCAmelCase:  int        = encoder_attention_heads
      __lowerCAmelCase:  str        = decoder_ffn_dim
      __lowerCAmelCase:  Tuple        = decoder_layers
      __lowerCAmelCase:  Tuple        = decoder_attention_heads
      __lowerCAmelCase:  Any        = dropout
      __lowerCAmelCase:  Optional[int]        = attention_dropout
      __lowerCAmelCase:  Tuple        = activation_dropout
      __lowerCAmelCase:  Optional[Any]        = activation_function
      __lowerCAmelCase:  Union[str, Any]        = init_std
      __lowerCAmelCase:  List[str]        = encoder_layerdrop
      __lowerCAmelCase:  Optional[Any]        = decoder_layerdrop
      __lowerCAmelCase:  Dict        = use_cache
      __lowerCAmelCase:  Any        = encoder_layers
      __lowerCAmelCase:  Tuple        = scale_embedding  # scale factor will be sqrt(d_model) if True

      super().__init__(
          pad_token_id=UpperCAmelCase     ,    bos_token_id=UpperCAmelCase     ,    eos_token_id=UpperCAmelCase     ,    is_encoder_decoder=UpperCAmelCase     ,    decoder_start_token_id=UpperCAmelCase     ,    **UpperCAmelCase     ,    )







class 				A_					(						snake_case__   ):


     @property
     def     UpperCAmelCase    (				self     :						List[Any]   )				->      Mapping[str, Mapping[int, str]]:
      __lowerCAmelCase:  Union[str, Any]        = OrderedDict(
          [
              ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
              ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
          ]   )

      if self.use_past:
       __lowerCAmelCase:  str        = {0: 'batch'}
       __lowerCAmelCase:  Union[str, Any]        = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
      else:
       __lowerCAmelCase:  Any        = {0: 'batch', 1: 'decoder_sequence'}
       __lowerCAmelCase:  Dict        = {0: 'batch', 1: 'decoder_sequence'}

      if self.use_past:
       self.fill_with_past_key_values_(UpperCAmelCase     ,    direction='inputs'   )
      return common_inputs


     def     UpperCAmelCase    (				self     :						Tuple     ,    UpperCAmelCase     :						PreTrainedTokenizer     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						bool = False     ,    UpperCAmelCase     :						Optional[TensorType] = None     ,    )				->      Mapping[str, Any]:
      # Copied from OnnxConfig.generate_dummy_inputs
      # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
      # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
      __lowerCAmelCase:  List[str]        = compute_effective_axis_dimension(
          UpperCAmelCase     ,    fixed_dimension=OnnxConfig.default_fixed_batch     ,    num_token_to_add=0   )

      # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
      __lowerCAmelCase:  Tuple        = tokenizer.num_special_tokens_to_add(UpperCAmelCase   )
      __lowerCAmelCase:  List[str]        = compute_effective_axis_dimension(
          UpperCAmelCase     ,    fixed_dimension=OnnxConfig.default_fixed_sequence     ,    num_token_to_add=UpperCAmelCase   )

      # Generate dummy inputs according to compute batch and sequence
      __lowerCAmelCase:  int        = [' '.join([tokenizer.unk_token]   ) * seq_length] * batch_size
      __lowerCAmelCase:  Optional[int]        = dict(tokenizer(UpperCAmelCase     ,    return_tensors=UpperCAmelCase   )   )
      return common_inputs


     def     UpperCAmelCase    (				self     :						str     ,    UpperCAmelCase     :						PreTrainedTokenizer     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						bool = False     ,    UpperCAmelCase     :						Optional[TensorType] = None     ,    )				->      Mapping[str, Any]:
      __lowerCAmelCase:  Optional[int]        = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   )

      # Generate decoder inputs
      __lowerCAmelCase:  Any        = seq_length if not self.use_past else 1
      __lowerCAmelCase:  Union[str, Any]        = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   )
      __lowerCAmelCase:  List[Any]        = {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
      __lowerCAmelCase:  List[str]        = dict(**UpperCAmelCase     ,    **UpperCAmelCase   )

      if self.use_past:
       if not is_torch_available():
        raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
       else:
        import torch
       __lowerCAmelCase      ,				__lowerCAmelCase:  List[Any]        = common_inputs['input_ids'].shape
       __lowerCAmelCase:  int        = common_inputs['decoder_input_ids'].shape[1]
       __lowerCAmelCase      ,				__lowerCAmelCase:  str        = self.num_attention_heads
       __lowerCAmelCase:  str        = (
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       __lowerCAmelCase:  Optional[int]        = decoder_seq_length + 3
       __lowerCAmelCase:  List[str]        = (
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )

       __lowerCAmelCase:  Dict        = torch.cat(
           [common_inputs['decoder_attention_mask'], torch.ones(UpperCAmelCase     ,    UpperCAmelCase   )]     ,    dim=1   )

       __lowerCAmelCase:  str        = []
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       __lowerCAmelCase      ,				__lowerCAmelCase:  str        = self.num_layers
       __lowerCAmelCase:  Tuple        = min(UpperCAmelCase     ,    UpperCAmelCase   )
       __lowerCAmelCase:  List[Any]        = max(UpperCAmelCase     ,    UpperCAmelCase   ) - min_num_layers
       __lowerCAmelCase:  Any        = 'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

       for _ in range(UpperCAmelCase   ):
        common_inputs["past_key_values"].append(
            (
                torch.zeros(UpperCAmelCase   ),
                torch.zeros(UpperCAmelCase   ),
                torch.zeros(UpperCAmelCase   ),
                torch.zeros(UpperCAmelCase   ),
            )   )
       # TODO: test this.
       __lowerCAmelCase:  List[Any]        = encoder_shape if remaining_side_name == 'encoder' else decoder_shape
       for _ in range(UpperCAmelCase     ,    UpperCAmelCase   ):
        common_inputs["past_key_values"].append((torch.zeros(UpperCAmelCase   ), torch.zeros(UpperCAmelCase   ))   )
      return common_inputs


     _lowercase     :	int        =      _generate_dummy_inputs_for_default_and_seqaseq_lm





