




"""simple docstring"""

import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


a         =      logging.get_logger(__name__)
logging.set_verbosity_info()






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: str							)   -> Dict:






      '''simple docstring'''


      if "xprophetnet" in prophetnet_checkpoint_path:
            _A        =       XLMProphetNetForConditionalGenerationOld.from_pretrained(_snake_case							)
            _A       ,   _A        =       XLMProphetNetForConditionalGeneration.from_pretrained(
                _snake_case    ,  output_loading_info=_snake_case							)
      else:
            _A        =       ProphetNetForConditionalGenerationOld.from_pretrained(_snake_case							)
            _A       ,   _A        =       ProphetNetForConditionalGeneration.from_pretrained(
                _snake_case    ,  output_loading_info=_snake_case							)

      _A        =       ['key_proj', 'value_proj', 'query_proj']

      _A        =       {
          'self_attn': 'ngram_self_attn',
          'cross_attn': 'encoder_attn',
          'cross_attn_layer_norm': 'encoder_attn_layer_norm',
          'feed_forward_layer_norm': 'final_layer_norm',
          'feed_forward': '',
          'intermediate': 'fc1',
          'output': 'fc2',
          'key_proj': 'k_proj',
          'query_proj': 'q_proj',
          'value_proj': 'v_proj',
          'word_embeddings': 'embed_tokens',
          'embeddings_layer_norm': 'emb_layer_norm',
          'relative_pos_embeddings': 'relative_linear',
          'ngram_embeddings': 'ngram_input_embed',
          'position_embeddings': 'embed_positions',
      }

      for key in loading_info["missing_keys"]:
            _A        =       key.split('.'							)

            if attributes[0] == "lm_head":
                  _A        =       prophet
                  _A        =       prophet_old
            else:
                  _A        =       prophet.prophetnet
                  _A        =       prophet_old.model

            _A        =       False
            for attribute in attributes:
                  if attribute in mapping:
                        _A        =       mapping[attribute]
                        if not hasattr(_snake_case    ,  _snake_case							) and len(_snake_case							) > 0:
                              _A        =       attribute
                  elif hasattr(_snake_case    ,  _snake_case							):
                        _A        =       attribute

                  if attribute == "weight":
                        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                        _A        =       old_model.weight
                        logger.info(F'''{attribute} is initialized.'''							)
                        _A        =       True
                        break
                  elif attribute == "bias":
                        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                        _A        =       old_model.bias
                        logger.info(F'''{attribute} is initialized'''							)
                        _A        =       True
                        break
                  elif attribute in special_keys and hasattr(_snake_case    ,  'in_proj_weight'							):
                        _A        =       old_model.in_proj_weight.shape[0] // 3
                        _A        =       getattr(_snake_case    ,  _snake_case							)
                        param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
                        param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
                        if attribute == "query_proj":
                              _A        =       nn.Parameter(old_model.in_proj_weight[:embed_dim, :]							)
                              _A        =       nn.Parameter(old_model.in_proj_bias[:embed_dim]							)

                        elif attribute == "key_proj":
                              _A        =       nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]							)
                              _A        =       nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]							)
                        elif attribute == "value_proj":
                              _A        =       nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]							)
                              _A        =       nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]							)
                        _A        =       True
                        break
                  elif attribute == "position_embeddings":
                        assert (
                            model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
                        ), "Hidden size has to match"
                        assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
                        _A        =       nn.Parameter(old_model.embed_positions.weight[:5_12, :]							)
                        _A        =       True
                        break

                  if attribute.isdigit():
                        _A        =       model[int(_snake_case							)]
                        _A        =       old_model[int(_snake_case							)]
                  else:
                        _A        =       getattr(_snake_case    ,  _snake_case							)

                        if old_attribute == "":
                              _A        =       old_model
                        else:
                              if not hasattr(_snake_case    ,  _snake_case							):
                                    raise ValueError(F'''{old_model} does not have {old_attribute}'''							)
                              _A        =       getattr(_snake_case    ,  _snake_case							)

            if not is_key_init:
                  raise ValueError(F'''{key} was not correctly initialized!'''							)

      print(F'''Saving model to {pytorch_dump_folder_path}'''							)
      prophet.save_pretrained(_snake_case							)


if __name__ == "__main__":
       a         =      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       a         =      parser.parse_args()
       convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""

def 						_snake_case					( _snake_case							: int							)   -> list:






      '''simple docstring'''


      _A        =       int(_snake_case							)
      if n_element < 1:
            _A        =       ValueError('a should be a positive number'							)
            raise my_error

      _A        =       [1]
      _A       ,   _A       ,   _A        =       (0, 0, 0)
      _A        =       1
      while index < n_element:
            while hamming_list[i] * 2 <= hamming_list[-1]:
                  i += 1
            while hamming_list[j] * 3 <= hamming_list[-1]:
                  j += 1
            while hamming_list[k] * 5 <= hamming_list[-1]:
                  k += 1
            hamming_list.append(
                min(hamming_list[i] * 2    ,  hamming_list[j] * 3    ,  hamming_list[k] * 5							)							)
            index += 1
      return hamming_list


if __name__ == "__main__":
       a         =      input('''Enter the last number (nth term) of the Hamming Number Series: ''')
       print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
       a         =      hamming(int(n))
       print('''-----------------------------------------------------''')
       print(F'''The list with nth numbers is: {hamming_numbers}''')
       print('''-----------------------------------------------------''')

