


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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					str				):
     if "xprophetnet" in prophetnet_checkpoint_path:
          __UpperCamelCase    							=XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase							,      __UpperCamelCase    							=XLMProphetNetForConditionalGeneration.from_pretrained(
              SCREAMING_SNAKE_CASE__			,     output_loading_info=SCREAMING_SNAKE_CASE__				)
     else:
          __UpperCamelCase    							=ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase							,      __UpperCamelCase    							=ProphetNetForConditionalGeneration.from_pretrained(
              SCREAMING_SNAKE_CASE__			,     output_loading_info=SCREAMING_SNAKE_CASE__				)

     __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(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				) and len(SCREAMING_SNAKE_CASE__				) > 0:
                         __UpperCamelCase    							=attribute
               elif hasattr(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				):
                    __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(SCREAMING_SNAKE_CASE__			,     'in_proj_weight'				):
                    __UpperCamelCase    							=old_model.in_proj_weight.shape[0] // 3
                    __UpperCamelCase    							=getattr(SCREAMING_SNAKE_CASE__			,     SCREAMING_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":
                         __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(SCREAMING_SNAKE_CASE__				)]
                    __UpperCamelCase    							=old_model[int(SCREAMING_SNAKE_CASE__				)]
               else:
                    __UpperCamelCase    							=getattr(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)

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









import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class     UpperCAmelCase__       (					A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Tuple      =  TransfoXLTokenizer
    UpperCAmelCase__						:		str      =  False
    UpperCAmelCase__						:		Tuple      =  False






    def _a					(			self    )  ->       Union[str, Any]:
         super().setUp()

         __UpperCamelCase    							=[
             '<unk>',
             '[CLS]',
             '[SEP]',
             'want',
             'unwanted',
             'wa',
             'un',
             'running',
             ',',
             'low',
             'l',
         ]
         __UpperCamelCase    							=os.path.join(self.tmpdirname					,   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]    )    )






    def _a					(			self					,   **A_    )  ->       Optional[int]:
         __UpperCamelCase    							=True
         return TransfoXLTokenizer.from_pretrained(self.tmpdirname					,   **A_    )






    def _a					(			self					,   A_    )  ->       Tuple:
         __UpperCamelCase    							='<unk> UNwanted , running'
         __UpperCamelCase    							='<unk> unwanted, running'
         return input_text, output_text






    def _a					(			self    )  ->       str:
         __UpperCamelCase    							=TransfoXLTokenizer(vocab_file=self.vocab_file					,   lower_case=A_    )

         __UpperCamelCase    							=tokenizer.tokenize('<unk> UNwanted , running'    )
         self.assertListEqual(A_					,   ['<unk>', 'unwanted', ',', 'running']    )

         self.assertListEqual(tokenizer.convert_tokens_to_ids(A_    )					,   [0, 4, 8, 7]    )






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=TransfoXLTokenizer(lower_case=A_    )

         self.assertListEqual(
             tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )					,   ['hello', '!', 'how', 'are', 'you', '?']    )






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=TransfoXLTokenizer(lower_case=A_    )

         self.assertListEqual(
             tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )					,   ['HeLLo', '!', 'how', 'Are', 'yoU', '?']    )






    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=TransfoXLTokenizer(lower_case=A_    )
         __UpperCamelCase    							='Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
         __UpperCamelCase    							=[
             'Hello',
             '(',
             'bracket',
             ')',
             'and',
             'side',
             '@-@',
             'scrolled',
             '[',
             'and',
             ']',
             'Henry',
             '\'s',
             '$',
             '5',
             '@,@',
             '000',
             'with',
             '3',
             '@.@',
             '34',
             'm',
             '.',
             'What',
             '\'s',
             'up',
             '!',
             '?',
         ]

         self.assertListEqual(tokenizer.tokenize(A_    )					,   A_    )

         self.assertEqual(tokenizer.convert_tokens_to_string(A_    )					,   A_    )






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=self.get_tokenizer()
         __UpperCamelCase    							=len(A_    )

         tokenizer.add_tokens(['new1', 'new2']    )
         tokenizer.move_added_token('new1'					,   1    )

         # Check that moved token is not copied (duplicate)
         self.assertEqual(len(A_    )					,   original_len + 2    )
         # Check that token is moved to specified id
         self.assertEqual(tokenizer.encode('new1'    )					,   [1]    )
         self.assertEqual(tokenizer.decode([1]    )					,   'new1'    )





