
"""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


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



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							Union[str, Any]:







       """simple docstring"""
       if "xprophetnet" in prophetnet_checkpoint_path:
              UpperCamelCase					 =		XLMProphetNetForConditionalGenerationOld.from_pretrained(lowerCamelCase_				)
              UpperCamelCase					 =		XLMProphetNetForConditionalGeneration.from_pretrained(
                  lowerCamelCase_		,      output_loading_info=lowerCamelCase_				)
       else:
              UpperCamelCase					 =		ProphetNetForConditionalGenerationOld.from_pretrained(lowerCamelCase_				)
              UpperCamelCase					 =		ProphetNetForConditionalGeneration.from_pretrained(
                  lowerCamelCase_		,      output_loading_info=lowerCamelCase_				)

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

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

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

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

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

                            elif attribute == "key_proj":
                                   UpperCamelCase					 =		nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]				)
                                   UpperCamelCase					 =		nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]				)
                            elif attribute == "value_proj":
                                   UpperCamelCase					 =		nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]				)
                                   UpperCamelCase					 =		nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]				)
                            UpperCamelCase					 =		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."
                            UpperCamelCase					 =		nn.Parameter(old_model.embed_positions.weight[:5_12, :]				)
                            UpperCamelCase					 =		True
                            break

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

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

              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(lowerCamelCase_				)


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







"""simple docstring"""


import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE      =     logging.getLogger(__name__)



@dataclass(frozen=_lowerCAmelCase	)
class   __a  :
       UpperCamelCase_		:					str
       UpperCamelCase_		:					str
       UpperCamelCase_		:					Optional[str]   =			None
       UpperCamelCase_		:					Optional[str]   =			None
       UpperCamelCase_		:					Optional[str]   =			None





@dataclass(frozen=_lowerCAmelCase	)
class   __a  :
       UpperCamelCase_		:					List[int]
       UpperCamelCase_		:					Optional[List[int]]   =			None
       UpperCamelCase_		:					Optional[List[int]]   =			None
       UpperCamelCase_		:					Optional[Union[int, float]]   =			None
       UpperCamelCase_		:					Optional[int]   =			None


if is_torch_available():
      import torch
      from torch.utils.data import Dataset



      class   __a  (      _lowerCAmelCase	):
             UpperCamelCase_		:					List[InputFeatures]






             def __init__(  self :							Union[str, Any]       ,      UpperCAmelCase_ :							str       ,      UpperCAmelCase_ :							PreTrainedTokenizer       ,      UpperCAmelCase_ :							str       ,      UpperCAmelCase_ :							Optional[int] = None       ,      UpperCAmelCase_ :							Optional[Any]=False       ,      UpperCAmelCase_ :							bool = False       ,      )->  Dict:







                    """simple docstring"""
                    UpperCamelCase					 =		hans_processors[task]()

                    UpperCamelCase					 =		os.path.join(
                        UpperCAmelCase_       ,      "cached_{}_{}_{}_{}".format(
                            "dev" if evaluate else "train"       ,      tokenizer.__class__.__name__       ,      str(UpperCAmelCase_			)       ,      UpperCAmelCase_       ,      )       ,      )
                    UpperCamelCase					 =		processor.get_labels()
                    if tokenizer.__class__ in (
                        RobertaTokenizer,
                        RobertaTokenizerFast,
                        XLMRobertaTokenizer,
                        BartTokenizer,
                        BartTokenizerFast,
                    ):
                           # HACK(label indices are swapped in RoBERTa pretrained model)
                           UpperCamelCase ,       UpperCamelCase					 =		label_list[2], label_list[1]
                    UpperCamelCase					 =		label_list

                    # Make sure only the first process in distributed training processes the dataset,
                    # and the others will use the cache.
                    UpperCamelCase					 =		cached_features_file + ".lock"
                    with FileLock(UpperCAmelCase_			):
                           if os.path.exists(UpperCAmelCase_			) and not overwrite_cache:
                                  logger.info(f"Loading features from cached file {cached_features_file}"			)
                                  UpperCamelCase					 =		torch.load(UpperCAmelCase_			)
                           else:
                                  logger.info(f"Creating features from dataset file at {data_dir}"			)

                                  UpperCamelCase					 =		(
                                      processor.get_dev_examples(UpperCAmelCase_			) if evaluate else processor.get_train_examples(UpperCAmelCase_			)
                                  )

                                  logger.info("Training examples: %s"       ,      len(UpperCAmelCase_			)			)
                                  UpperCamelCase					 =		hans_convert_examples_to_features(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)
                                  logger.info("Saving features into cached file %s"       ,      UpperCAmelCase_			)
                                  torch.save(self.features       ,      UpperCAmelCase_			)






             def __len__(  self :							Optional[Any]			)->  List[Any]:







                    """simple docstring"""
                    return len(self.features			)






             def __getitem__(  self :							Optional[Any]       ,      UpperCAmelCase_ :							Any			)->  InputFeatures:







                    """simple docstring"""
                    return self.features[i]






             def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  List[Any]:







                    """simple docstring"""
                    return self.label_list


if is_tf_available():
      import tensorflow as tf



      class   __a  :
             UpperCamelCase_		:					List[InputFeatures]






             def __init__(  self :							int       ,      UpperCAmelCase_ :							str       ,      UpperCAmelCase_ :							PreTrainedTokenizer       ,      UpperCAmelCase_ :							str       ,      UpperCAmelCase_ :							Optional[int] = 128       ,      UpperCAmelCase_ :							Optional[Any]=False       ,      UpperCAmelCase_ :							bool = False       ,      )->  Union[str, Any]:







                    """simple docstring"""
                    UpperCamelCase					 =		hans_processors[task]()
                    UpperCamelCase					 =		processor.get_labels()
                    if tokenizer.__class__ in (
                        RobertaTokenizer,
                        RobertaTokenizerFast,
                        XLMRobertaTokenizer,
                        BartTokenizer,
                        BartTokenizerFast,
                    ):
                           # HACK(label indices are swapped in RoBERTa pretrained model)
                           UpperCamelCase ,       UpperCamelCase					 =		label_list[2], label_list[1]
                    UpperCamelCase					 =		label_list

                    UpperCamelCase					 =		processor.get_dev_examples(UpperCAmelCase_			) if evaluate else processor.get_train_examples(UpperCAmelCase_			)
                    UpperCamelCase					 =		hans_convert_examples_to_features(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)

                    def gen():
                           for ex_index, ex in tqdm.tqdm(enumerate(self.features			)       ,      desc="convert examples to features"			):
                                  if ex_index % 10_000 == 0:
                                         logger.info("Writing example %d of %d" % (ex_index, len(UpperCAmelCase_			))			)

                                  yield (
                                      {
                                          "example_id": 0,
                                          "input_ids": ex.input_ids,
                                          "attention_mask": ex.attention_mask,
                                          "token_type_ids": ex.token_type_ids,
                                      },
                                      ex.label,
                                  )

                    UpperCamelCase					 =		tf.data.Dataset.from_generator(
                        UpperCAmelCase_       ,      (
                            {
                                "example_id": tf.intaa,
                                "input_ids": tf.intaa,
                                "attention_mask": tf.intaa,
                                "token_type_ids": tf.intaa,
                            },
                            tf.intaa,
                        )       ,      (
                            {
                                "example_id": tf.TensorShape([]			),
                                "input_ids": tf.TensorShape([None, None]			),
                                "attention_mask": tf.TensorShape([None, None]			),
                                "token_type_ids": tf.TensorShape([None, None]			),
                            },
                            tf.TensorShape([]			),
                        )       ,      )






             def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Tuple:







                    """simple docstring"""
                    return self.dataset






             def __len__(  self :							List[Any]			)->  List[Any]:







                    """simple docstring"""
                    return len(self.features			)






             def __getitem__(  self :							Tuple       ,      UpperCAmelCase_ :							List[str]			)->  InputFeatures:







                    """simple docstring"""
                    return self.features[i]






             def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  List[str]:







                    """simple docstring"""
                    return self.label_list



class   __a  (      _lowerCAmelCase	):






       def 	_SCREAMING_SNAKE_CASE       (  self :							str       ,      UpperCAmelCase_ :							Tuple			)->  Tuple:







              """simple docstring"""
              return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase_       ,      "heuristics_train_set.txt"			)			)       ,      "train"			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							List[str]			)->  Dict:







              """simple docstring"""
              return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase_       ,      "heuristics_evaluation_set.txt"			)			)       ,      "dev"			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  Optional[Any]:







              """simple docstring"""
              return ["contradiction", "entailment", "neutral"]






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							Dict       ,      UpperCAmelCase_ :							str			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		[]
              for i, line in enumerate(UpperCAmelCase_			):
                     if i == 0:
                            continue
                     UpperCamelCase					 =		"%s-%s" % (set_type, line[0])
                     UpperCamelCase					 =		line[5]
                     UpperCamelCase					 =		line[6]
                     UpperCamelCase					 =		line[7][2:] if line[7].startswith("ex"			) else line[7]
                     UpperCamelCase					 =		line[0]
                     examples.append(InputExample(guid=UpperCAmelCase_       ,      text_a=UpperCAmelCase_       ,      text_b=UpperCAmelCase_       ,      label=UpperCAmelCase_       ,      pairID=UpperCAmelCase_			)			)
              return examples





def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      )->							Union[str, Any]:







       """simple docstring"""
       UpperCamelCase					 =		{label: i for i, label in enumerate(UpperCAmelCase_				)}

       UpperCamelCase					 =		[]
       for ex_index, example in tqdm.tqdm(enumerate(UpperCAmelCase_				)		,      desc="convert examples to features"				):
              if ex_index % 1_00_00 == 0:
                     logger.info("Writing example %d" % (ex_index)				)

              UpperCamelCase					 =		tokenizer(
                  example.text_a		,      example.text_b		,      add_special_tokens=UpperCAmelCase_		,      max_length=UpperCAmelCase_		,      padding="max_length"		,      truncation=UpperCAmelCase_		,      return_overflowing_tokens=UpperCAmelCase_		,      )

              UpperCamelCase					 =		label_map[example.label] if example.label in label_map else 0

              UpperCamelCase					 =		int(example.pairID				)

              features.append(InputFeatures(**UpperCAmelCase_		,      label=UpperCAmelCase_		,      pairID=UpperCAmelCase_				)				)

       for i, example in enumerate(examples[:5]				):
              logger.info("*** Example ***"				)
              logger.info(F"guid: {example}"				)
              logger.info(F"features: {features[i]}"				)

       return features


SCREAMING_SNAKE_CASE      =     {
    """hans""": 3,
}

SCREAMING_SNAKE_CASE      =     {
    """hans""": HansProcessor,
}





