
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer




class 				_lowerCamelCase (      UpperCAmelCase__ ):





					"""simple docstring"""







					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




											"""simple docstring"""







											UpperCamelCase__    :   Dict					=				tempfile.mkdtemp()
											UpperCamelCase__    :   List[str]					=				5

											# Realm tok
											UpperCamelCase__    :   Dict					=				[
											    '''[UNK]''',
											    '''[CLS]''',
											    '''[SEP]''',
											    '''[PAD]''',
											    '''[MASK]''',
											    '''test''',
											    '''question''',
											    '''this''',
											    '''is''',
											    '''the''',
											    '''first''',
											    '''second''',
											    '''third''',
											    '''fourth''',
											    '''fifth''',
											    '''record''',
											    '''want''',
											    '''##want''',
											    '''##ed''',
											    '''wa''',
											    '''un''',
											    '''runn''',
											    '''##ing''',
											    ''',''',
											    '''low''',
											    '''lowest''',
											]
											UpperCamelCase__    :   Tuple					=				os.path.join(self.tmpdirname						,						'''realm_tokenizer'''  )
											os.makedirs(__lowerCAmelCase						,						exist_ok=__lowerCAmelCase  )
											UpperCamelCase__    :   List[str]					=				os.path.join(__lowerCAmelCase						,						VOCAB_FILES_NAMES['''vocab_file''']  )
											with open(self.vocab_file						,						'''w'''						,						encoding='''utf-8'''  ) as vocab_writer:
																	vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )

											UpperCamelCase__    :   Any					=				os.path.join(self.tmpdirname						,						'''realm_block_records'''  )
											os.makedirs(__lowerCAmelCase						,						exist_ok=__lowerCAmelCase  )






					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   RealmTokenizer:




											"""simple docstring"""







											return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname						,						'''realm_tokenizer'''  )  )






					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




											"""simple docstring"""







											shutil.rmtree(self.tmpdirname  )






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




											"""simple docstring"""







											UpperCamelCase__    :   List[str]					=				RealmConfig(num_block_records=self.num_block_records  )
											return config






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




											"""simple docstring"""







											UpperCamelCase__    :   Any					=				Dataset.from_dict(
											    {
											        '''id''': ['''0''', '''1'''],
											        '''question''': ['''foo''', '''bar'''],
											        '''answers''': [['''Foo''', '''Bar'''], ['''Bar''']],
											    }  )
											return dataset






					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




											"""simple docstring"""







											UpperCamelCase__    :   Tuple					=				np.array(
											    [
											        B'''This is the first record''',
											        B'''This is the second record''',
											        B'''This is the third record''',
											        B'''This is the fourth record''',
											        B'''This is the fifth record''',
											        B'''This is a longer longer longer record''',
											    ]						,						dtype=__lowerCAmelCase						,						)
											return block_records






					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




											"""simple docstring"""







											UpperCamelCase__    :   Optional[int]					=				RealmRetriever(
											    block_records=self.get_dummy_block_records()						,						tokenizer=self.get_tokenizer()						,						)
											return retriever






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




											"""simple docstring"""







											UpperCamelCase__    :   int					=				self.get_config()
											UpperCamelCase__    :   int					=				self.get_dummy_retriever()
											UpperCamelCase__    :   int					=				retriever.tokenizer

											UpperCamelCase__    :   Optional[int]					=				np.array([0, 3]						,						dtype='''long'''  )
											UpperCamelCase__    :   List[str]					=				tokenizer(['''Test question''']  ).input_ids
											UpperCamelCase__    :   Dict					=				tokenizer(
											    ['''the fourth''']						,						add_special_tokens=__lowerCAmelCase						,						return_token_type_ids=__lowerCAmelCase						,						return_attention_mask=__lowerCAmelCase						,						).input_ids
											UpperCamelCase__    :   Optional[int]					=				config.reader_seq_len

											UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__    :   Any					=				retriever(
											    __lowerCAmelCase						,						__lowerCAmelCase						,						answer_ids=__lowerCAmelCase						,						max_length=__lowerCAmelCase						,						return_tensors='''np'''  )

											self.assertEqual(len(__lowerCAmelCase  )						,						2  )
											self.assertEqual(len(__lowerCAmelCase  )						,						2  )
											self.assertEqual(len(__lowerCAmelCase  )						,						2  )
											self.assertEqual(concat_inputs.input_ids.shape						,						(2, 1_0)  )
											self.assertEqual(concat_inputs.attention_mask.shape						,						(2, 1_0)  )
											self.assertEqual(concat_inputs.token_type_ids.shape						,						(2, 1_0)  )
											self.assertEqual(concat_inputs.special_tokens_mask.shape						,						(2, 1_0)  )
											self.assertEqual(
											    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]  )						,						['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''first''', '''record''', '''[SEP]''']						,						)
											self.assertEqual(
											    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]  )						,						['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''fourth''', '''record''', '''[SEP]''']						,						)






					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




											"""simple docstring"""







											UpperCamelCase__    :   Optional[Any]					=				self.get_config()
											UpperCamelCase__    :   List[Any]					=				self.get_dummy_retriever()
											UpperCamelCase__    :   Union[str, Any]					=				retriever.tokenizer

											UpperCamelCase__    :   List[str]					=				np.array([0, 3, 5]						,						dtype='''long'''  )
											UpperCamelCase__    :   List[str]					=				tokenizer(['''Test question''']  ).input_ids
											UpperCamelCase__    :   Any					=				tokenizer(
											    ['''the fourth''', '''longer longer''']						,						add_special_tokens=__lowerCAmelCase						,						return_token_type_ids=__lowerCAmelCase						,						return_attention_mask=__lowerCAmelCase						,						).input_ids
											UpperCamelCase__    :   Tuple					=				config.reader_seq_len

											UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__    :   Union[str, Any]					=				retriever(
											    __lowerCAmelCase						,						__lowerCAmelCase						,						answer_ids=__lowerCAmelCase						,						max_length=__lowerCAmelCase						,						return_tensors='''np'''  )

											self.assertEqual([False, True, True]						,						__lowerCAmelCase  )
											self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]						,						__lowerCAmelCase  )
											self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]						,						__lowerCAmelCase  )






					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




											"""simple docstring"""







											UpperCamelCase__    :   str					=				self.get_dummy_retriever()
											retriever.save_pretrained(os.path.join(self.tmpdirname						,						'''realm_block_records'''  )  )

											# Test local path
											UpperCamelCase__    :   Optional[int]					=				retriever.from_pretrained(os.path.join(self.tmpdirname						,						'''realm_block_records'''  )  )
											self.assertEqual(retriever.block_records[0]						,						B'''This is the first record'''  )

											# Test mocked remote path
											with patch('''transformers.models.realm.retrieval_realm.hf_hub_download'''  ) as mock_hf_hub_download:
																	UpperCamelCase__    :   Optional[Any]					=				os.path.join(
																	    os.path.join(self.tmpdirname						,						'''realm_block_records'''  )						,						_REALM_BLOCK_RECORDS_FILENAME  )
																	UpperCamelCase__    :   Tuple					=				RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa'''  )

											self.assertEqual(retriever.block_records[0]						,						B'''This is the first record'''  )




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


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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      if "xprophetnet" in prophetnet_checkpoint_path:
            UpperCamelCase__    :   int					=				XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__		)
            UpperCamelCase__				,UpperCamelCase__    :   Union[str, Any]					=				XLMProphetNetForConditionalGeneration.from_pretrained(
                UpperCamelCase__ ,		output_loading_info=UpperCamelCase__		)
      else:
            UpperCamelCase__    :   Tuple					=				ProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__		)
            UpperCamelCase__				,UpperCamelCase__    :   Union[str, Any]					=				ProphetNetForConditionalGeneration.from_pretrained(
                UpperCamelCase__ ,		output_loading_info=UpperCamelCase__		)

      UpperCamelCase__    :   Optional[Any]					=				['''key_proj''', '''value_proj''', '''query_proj''']

      UpperCamelCase__    :   Tuple					=				{
          '''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__    :   Tuple					=				key.split('''.'''		)

            if attributes[0] == "lm_head":
                  UpperCamelCase__    :   Union[str, Any]					=				prophet
                  UpperCamelCase__    :   int					=				prophet_old
            else:
                  UpperCamelCase__    :   int					=				prophet.prophetnet
                  UpperCamelCase__    :   str					=				prophet_old.model

            UpperCamelCase__    :   Tuple					=				False
            for attribute in attributes:
                  if attribute in mapping:
                        UpperCamelCase__    :   List[str]					=				mapping[attribute]
                        if not hasattr(UpperCamelCase__ ,		UpperCamelCase__		) and len(UpperCamelCase__		) > 0:
                              UpperCamelCase__    :   Tuple					=				attribute
                  elif hasattr(UpperCamelCase__ ,		UpperCamelCase__		):
                        UpperCamelCase__    :   Tuple					=				attribute

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

                        elif attribute == "key_proj":
                              UpperCamelCase__    :   Union[str, Any]					=				nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]		)
                              UpperCamelCase__    :   Optional[int]					=				nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]		)
                        elif attribute == "value_proj":
                              UpperCamelCase__    :   List[str]					=				nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]		)
                              UpperCamelCase__    :   Tuple					=				nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]		)
                        UpperCamelCase__    :   Tuple					=				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_1_2, "We want 512 position_embeddings."
                        UpperCamelCase__    :   Optional[Any]					=				nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]		)
                        UpperCamelCase__    :   List[Any]					=				True
                        break

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

                        if old_attribute == "":
                              UpperCamelCase__    :   List[str]					=				old_model
                        else:
                              if not hasattr(UpperCamelCase__ ,		UpperCamelCase__		):
                                    raise ValueError(f'''{old_model} does not have {old_attribute}'''		)
                              UpperCamelCase__    :   List[Any]					=				getattr(UpperCamelCase__ ,		UpperCamelCase__		)

            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(UpperCamelCase__		)


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


