

import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def      _lowerCAmelCase(	__A   ,       __A			):
  # Load checkpoint
  UpperCAmelCase					      =  torch.load(__A   ,       map_location="cpu"			)

  UpperCAmelCase					      =  chkpt["model"]

  # We have the base model one level deeper than the original XLM repository
  UpperCAmelCase					      =  {}
  for k, v in state_dict.items():
    if "pred_layer" in k:
      UpperCAmelCase					      =  v
    else:
      UpperCAmelCase					      =  v

  UpperCAmelCase					      =  chkpt["params"]
  UpperCAmelCase					      =  {n: v for n, v in config.items() if not isinstance(__A   ,       (torch.FloatTensor, numpy.ndarray)			)}

  UpperCAmelCase					      =  chkpt["dico_word2id"]
  UpperCAmelCase					      =  {s + "</w>" if s.find("@@"			) == -1 and i > 13 else s.replace("@@"   ,       ""			): i for s, i in vocab.items()}

  # Save pytorch-model
  UpperCAmelCase					      =  pytorch_dump_folder_path + "/" + WEIGHTS_NAME
  UpperCAmelCase					      =  pytorch_dump_folder_path + "/" + CONFIG_NAME
  UpperCAmelCase					      =  pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]

  print(F"Save PyTorch model to {pytorch_weights_dump_path}"			)
  torch.save(__A   ,       __A			)

  print(F"Save configuration file to {pytorch_config_dump_path}"			)
  with open(__A   ,       "w"   ,       encoding="utf-8"			) as f:
    f.write(json.dumps(__A   ,       indent=2			) + "\n"			)

  print(F"Save vocab file to {pytorch_config_dump_path}"			)
  with open(__A   ,       "w"   ,       encoding="utf-8"			) as f:
    f.write(json.dumps(__A   ,       indent=2			) + "\n"			)


if __name__ == "__main__":
      lowerCAmelCase__       = argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--xlm_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_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)






import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						__magic_name__    (       _snake_case	,						unittest.TestCase    ):
 UpperCAmelCase							    =						LEDTokenizer
 UpperCAmelCase							    =						LEDTokenizerFast
 UpperCAmelCase							    =						True


 def _UpperCamelCase	(						self		: Tuple    )    ->      Union[str, Any]:
   super().setUp()
   UpperCAmelCase					      =  [
       "l",
       "o",
       "w",
       "e",
       "r",
       "s",
       "t",
       "i",
       "d",
       "n",
       "\u0120",
       "\u0120l",
       "\u0120n",
       "\u0120lo",
       "\u0120low",
       "er",
       "\u0120lowest",
       "\u0120newer",
       "\u0120wider",
       "<unk>",
   ]
   UpperCAmelCase					      =  dict(zip(lowerCAmelCase__			,						range(len(lowerCAmelCase__    )    )    )    )
   UpperCAmelCase					      =  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
   UpperCAmelCase					      =  {"unk_token": "<unk>"}

   UpperCAmelCase					      =  os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES["vocab_file"]    )
   UpperCAmelCase					      =  os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES["merges_file"]    )
   with open(self.vocab_file			,						"w"			,						encoding="utf-8"    ) as fp:
     fp.write(json.dumps(lowerCAmelCase__    ) + "\n"    )
   with open(self.merges_file			,						"w"			,						encoding="utf-8"    ) as fp:
     fp.write("\n".join(lowerCAmelCase__    )    )


 def _UpperCamelCase	(						self		: Union[str, Any]			,						**lowerCAmelCase__		: Optional[int]    )    ->      Optional[int]:
   kwargs.update(self.special_tokens_map    )
   return self.tokenizer_class.from_pretrained(self.tmpdirname			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: str			,						**lowerCAmelCase__		: str    )    ->      Optional[int]:
   kwargs.update(self.special_tokens_map    )
   return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: List[str]			,						lowerCAmelCase__		: List[Any]    )    ->      List[Any]:
   return "lower newer", "lower newer"


 @cached_property
 def _UpperCamelCase	(						self		: Dict    )    ->      str:
   return LEDTokenizer.from_pretrained("allenai/led-base-16384"    )


 @cached_property
 def _UpperCamelCase	(						self		: int    )    ->      Tuple:
   return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"    )


 @require_torch
 def _UpperCamelCase	(						self		: Tuple    )    ->      List[str]:
   UpperCAmelCase					      =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
   UpperCAmelCase					      =  [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  tokenizer(lowerCAmelCase__			,						max_length=len(lowerCAmelCase__    )			,						padding=lowerCAmelCase__			,						return_tensors="pt"    )
     self.assertIsInstance(lowerCAmelCase__			,						lowerCAmelCase__    )

     self.assertEqual((2, 9)			,						batch.input_ids.shape    )
     self.assertEqual((2, 9)			,						batch.attention_mask.shape    )
     UpperCAmelCase					      =  batch.input_ids.tolist()[0]
     self.assertListEqual(lowerCAmelCase__			,						lowerCAmelCase__    )


 @require_torch
 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      List[Any]:
   UpperCAmelCase					      =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  tokenizer(lowerCAmelCase__			,						padding=lowerCAmelCase__			,						return_tensors="pt"    )
     self.assertIn("input_ids"			,						lowerCAmelCase__    )
     self.assertIn("attention_mask"			,						lowerCAmelCase__    )
     self.assertNotIn("labels"			,						lowerCAmelCase__    )
     self.assertNotIn("decoder_attention_mask"			,						lowerCAmelCase__    )


 @require_torch
 def _UpperCamelCase	(						self		: int    )    ->      int:
   UpperCAmelCase					      =  [
       "Summary of the text.",
       "Another summary.",
   ]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  tokenizer(text_target=lowerCAmelCase__			,						max_length=3_2			,						padding="max_length"			,						return_tensors="pt"    )
     self.assertEqual(3_2			,						targets["input_ids"].shape[1]    )


 @require_torch
 def _UpperCamelCase	(						self		: Any    )    ->      int:
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  tokenizer(
         ["I am a small frog" * 1_0_2_4, "I am a small frog"]			,						padding=lowerCAmelCase__			,						truncation=lowerCAmelCase__			,						return_tensors="pt"    )
     self.assertIsInstance(lowerCAmelCase__			,						lowerCAmelCase__    )
     self.assertEqual(batch.input_ids.shape			,						(2, 5_1_2_2)    )


 @require_torch
 def _UpperCamelCase	(						self		: Dict    )    ->      Tuple:
   UpperCAmelCase					      =  ["A long paragraph for summarization."]
   UpperCAmelCase					      =  [
       "Summary of the text.",
   ]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  tokenizer(lowerCAmelCase__			,						return_tensors="pt"    )
     UpperCAmelCase					      =  tokenizer(text_target=lowerCAmelCase__			,						return_tensors="pt"    )
     UpperCAmelCase					      =  inputs["input_ids"]
     UpperCAmelCase					      =  targets["input_ids"]
     self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
     self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
     self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
     self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )


 @require_torch
 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Optional[Any]:
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  ["Summary of the text.", "Another summary."]
     UpperCAmelCase					      =  [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

     UpperCAmelCase					      =  tokenizer(lowerCAmelCase__			,						padding=lowerCAmelCase__    )
     UpperCAmelCase					      =  [[0] * len(lowerCAmelCase__    ) for x in encoded_output["input_ids"]]
     UpperCAmelCase					      =  tokenizer.pad(lowerCAmelCase__    )
     self.assertSequenceEqual(outputs["global_attention_mask"]			,						lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: List[str]    )    ->      int:
   pass


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Union[str, Any]:
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"    ):
       UpperCAmelCase					      =  self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__			,						**lowerCAmelCase__    )
       UpperCAmelCase					      =  self.tokenizer_class.from_pretrained(lowerCAmelCase__			,						**lowerCAmelCase__    )
       UpperCAmelCase					      =  "A, <mask> AllenNLP sentence."
       UpperCAmelCase					      =  tokenizer_r.encode_plus(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__    )
       UpperCAmelCase					      =  tokenizer_p.encode_plus(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__    )
       self.assertEqual(sum(tokens_r["token_type_ids"]    )			,						sum(tokens_p["token_type_ids"]    )    )
       self.assertEqual(
           sum(tokens_r["attention_mask"]    ) / len(tokens_r["attention_mask"]    )			,						sum(tokens_p["attention_mask"]    ) / len(tokens_p["attention_mask"]    )			,						)

       UpperCAmelCase					      =  tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]    )
       UpperCAmelCase					      =  tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]    )
       self.assertSequenceEqual(tokens_p["input_ids"]			,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]    )
       self.assertSequenceEqual(tokens_r["input_ids"]			,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]    )

       self.assertSequenceEqual(
           lowerCAmelCase__			,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]    )
       self.assertSequenceEqual(
           lowerCAmelCase__			,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]    )



