

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


lowercase_									=   logging.get_logger(__name__)
logging.set_verbosity_info()
def      a      (			A__	:   str      ,   A__	:   str     )    ->      Any:





      """simple docstring"""



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

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

      _lowercase	       ={
          '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"]:
            _lowercase	       =key.split('.'     )

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

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

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

                        elif attribute == "key_proj":
                              _lowercase	       =nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]     )
                              _lowercase	       =nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]     )
                        elif attribute == "value_proj":
                              _lowercase	       =nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]     )
                              _lowercase	       =nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]     )
                        _lowercase	       =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] == 512, "We want 512 position_embeddings."
                        _lowercase	       =nn.Parameter(old_model.embed_positions.weight[:512, :]     )
                        _lowercase	       =True
                        break

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

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

            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(A__     )


if __name__ == "__main__":
  lowercase_									=   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.'
  )
  lowercase_									=   parser.parse_args()
  convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


lowercase_									=   logging.get_logger(__name__)





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   **lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =feature_size
        _lowercase	       =sampling_rate
        _lowercase	       =padding_value

        _lowercase	       =kwargs.pop('padding_side'							,   'right'					)
        _lowercase	       =kwargs.pop('return_attention_mask'							,   lowerCAmelCase					)

        super().__init__(**lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = True							,   lowerCAmelCase = None							,   lowerCAmelCase = False							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   )  -> BatchFeature:
        '''simple docstring'''






        if isinstance(lowerCAmelCase							,   (list, tuple)					) and isinstance(processed_features[0]							,   (dict, BatchFeature)					):
              _lowercase	       ={
                  key: [example[key] for example in processed_features] for key in processed_features[0].keys()
              }

        # The model's main input name, usually `input_values`, has be passed for padding
        if self.model_input_names[0] not in processed_features:
              raise ValueError(
                  'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
                  F''' to this method that includes {self.model_input_names[0]}, but you provided'''
                  F''' {list(processed_features.keys()					)}'''					)

        _lowercase	       =processed_features[self.model_input_names[0]]
        _lowercase	       =(
            return_attention_mask if return_attention_mask is not None else self.return_attention_mask
        )

        if len(lowerCAmelCase					) == 0:
              if return_attention_mask:
                    _lowercase	       =[]
              return processed_features

        # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
        # and rebuild them afterwards if no return_tensors is specified
        # Note that we lose the specific device the tensor may be on for PyTorch

        _lowercase	       =required_input[0]
        if isinstance(lowerCAmelCase							,   (list, tuple)					):
              # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
              _lowercase	       =0
              while len(required_input[index]					) == 0:
                    index += 1
              if index < len(lowerCAmelCase					):
                    _lowercase	       =required_input[index][0]

        if return_tensors is None:
              if is_tf_tensor(lowerCAmelCase					):
                    _lowercase	       ='tf'
              elif is_torch_tensor(lowerCAmelCase					):
                    _lowercase	       ='pt'
              elif isinstance(lowerCAmelCase							,   (int, float, list, tuple, np.ndarray)					):
                    _lowercase	       ='np'
              else:
                    raise ValueError(
                        F'''type of {first_element} unknown: {type(lowerCAmelCase					)}. '''
                        'Should be one of a python, numpy, pytorch or tensorflow object.'					)

        for key, value in processed_features.items():
              if isinstance(value[0]							,   (int, float)					):
                    _lowercase	       =to_numpy(lowerCAmelCase					)
              else:
                    _lowercase	       =[to_numpy(lowerCAmelCase					) for v in value]

        # Convert padding_strategy in PaddingStrategy
        _lowercase	       =self._get_padding_strategies(padding=lowerCAmelCase							,   max_length=lowerCAmelCase					)

        _lowercase	       =processed_features[self.model_input_names[0]]

        _lowercase	       =len(lowerCAmelCase					)
        if not all(len(lowerCAmelCase					) == batch_size for v in processed_features.values()					):
              raise ValueError('Some items in the output dictionary have a different batch size than others.'					)

        _lowercase	       =[]
        for i in range(lowerCAmelCase					):
              _lowercase	       ={k: v[i] for k, v in processed_features.items()}
              # truncation
              _lowercase	       =self._truncate(
                  lowerCAmelCase							,   max_length=lowerCAmelCase							,   pad_to_multiple_of=lowerCAmelCase							,   truncation=lowerCAmelCase							,   )
              truncated_inputs.append(lowerCAmelCase					)

        if padding_strategy == PaddingStrategy.LONGEST:
              # make sure that `max_length` cannot be longer than the longest truncated length
              _lowercase	       =max(len(input_slice[self.model_input_names[0]]					) for input_slice in truncated_inputs					)
              _lowercase	       =PaddingStrategy.MAX_LENGTH

        _lowercase	       ={}
        for i in range(lowerCAmelCase					):
              # padding
              _lowercase	       =self._pad(
                  truncated_inputs[i]							,   max_length=lowerCAmelCase							,   padding_strategy=lowerCAmelCase							,   pad_to_multiple_of=lowerCAmelCase							,   return_attention_mask=lowerCAmelCase							,   )

              for key, value in outputs.items():
                    if key not in batch_outputs:
                          _lowercase	       =[]
                    if value.dtype is np.dtype(np.floataa					):
                          _lowercase	       =value.astype(np.floataa					)
                    batch_outputs[key].append(lowerCAmelCase					)

        return BatchFeature(lowerCAmelCase							,   tensor_type=lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = None							,   lowerCAmelCase = PaddingStrategy.DO_NOT_PAD							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   )  -> dict:
        '''simple docstring'''






        _lowercase	       =processed_features[self.model_input_names[0]]

        if padding_strategy == PaddingStrategy.LONGEST:
              _lowercase	       =len(lowerCAmelCase					)

        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
              _lowercase	       =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        _lowercase	       =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowerCAmelCase					) < max_length

        if return_attention_mask and "attention_mask" not in processed_features:
              _lowercase	       =np.ones(len(lowerCAmelCase					)							,   dtype=np.intaa					)

        if needs_to_be_padded:
              _lowercase	       =max_length - len(lowerCAmelCase					)
              if self.padding_side == "right":
                    if return_attention_mask:
                          _lowercase	       =np.pad(
                              processed_features['attention_mask']							,   (0, difference)					)
                    _lowercase	       =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                    _lowercase	       =np.pad(
                        lowerCAmelCase							,   lowerCAmelCase							,   'constant'							,   constant_values=self.padding_value					)
              elif self.padding_side == "left":
                    if return_attention_mask:
                          _lowercase	       =np.pad(
                              processed_features['attention_mask']							,   (difference, 0)					)
                    _lowercase	       =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                    _lowercase	       =np.pad(
                        lowerCAmelCase							,   lowerCAmelCase							,   'constant'							,   constant_values=self.padding_value					)
              else:
                    raise ValueError('Invalid padding strategy:' + str(self.padding_side					)					)

        return processed_features




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   )  -> Any:
        '''simple docstring'''






        if not truncation:
              return processed_features
        elif truncation and max_length is None:
              raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'					)

        _lowercase	       =processed_features[self.model_input_names[0]]

        # find `max_length` that fits `pad_to_multiple_of`
        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
              _lowercase	       =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        _lowercase	       =len(lowerCAmelCase					) > max_length

        if needs_to_be_truncated:
              _lowercase	       =processed_features[self.model_input_names[0]][:max_length]
              if "attention_mask" in processed_features:
                    _lowercase	       =processed_features['attention_mask'][:max_length]

        return processed_features




  def   A__     (				self							,   lowerCAmelCase=False							,   lowerCAmelCase=None					)  -> Optional[int]:
        '''simple docstring'''






        if padding is not False:
              if padding is True:
                    _lowercase	       =PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
              elif not isinstance(lowerCAmelCase							,   lowerCAmelCase					):
                    _lowercase	       =PaddingStrategy(lowerCAmelCase					)
              elif isinstance(lowerCAmelCase							,   lowerCAmelCase					):
                    _lowercase	       =padding
        else:
              _lowercase	       =PaddingStrategy.DO_NOT_PAD

        # Set max length if needed
        if max_length is None:
              if padding_strategy == PaddingStrategy.MAX_LENGTH:
                    raise ValueError(
                        F'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''					)

        # Test if we have a padding value
        if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
              raise ValueError(
                  'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
                  ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'					)

        return padding_strategy


