

import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
     from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


__lowercase     : int       =						get_tests_dir('''fixtures/test_sentencepiece.model''')

__lowercase     : Dict       =						{"""target_lang""": """fi""", """source_lang""": """en"""}
__lowercase     : List[Any]       =						""">>zh<<"""
__lowercase     : int       =						"""Helsinki-NLP/"""

if is_torch_available():
     __lowercase     : int       =						"""pt"""
elif is_tf_available():
     __lowercase     : Optional[Any]       =						"""tf"""
else:
     __lowercase     : Any       =						"""jax"""



@require_sentencepiece
class 						_A     (				_UpperCamelCase						,    unittest.TestCase				):





  '''simple docstring'''



  __lowerCamelCase			:      List[Any]					=   MarianTokenizer
  __lowerCamelCase			:      List[Any]					=   False
  __lowerCamelCase			:      Dict					=   True





  def    snake_case_       (  self    ):

      '''simple docstring'''
      super().setUp()
      snake_case							:		Union[str, Any]        =		["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
      snake_case							:		List[Any]        =		dict(zip(SCREAMING_SNAKE_CASE_   ,range(len(SCREAMING_SNAKE_CASE_    )    )    )    )
      snake_case							:		Any        =		Path(self.tmpdirname    )
      save_json(SCREAMING_SNAKE_CASE_   ,save_dir / VOCAB_FILES_NAMES["""vocab"""]    )
      save_json(SCREAMING_SNAKE_CASE_   ,save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]    )
      if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
          copyfile(SCREAMING_SNAKE_CASE_   ,save_dir / VOCAB_FILES_NAMES["""source_spm"""]    )
          copyfile(SCREAMING_SNAKE_CASE_   ,save_dir / VOCAB_FILES_NAMES["""target_spm"""]    )

      snake_case							:		Dict        =		MarianTokenizer.from_pretrained(self.tmpdirname    )
      tokenizer.save_pretrained(self.tmpdirname    )





  def    snake_case_       (  self   ,**SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      return MarianTokenizer.from_pretrained(self.tmpdirname   ,**SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      return (
          "This is a test",
          "This is a test",
      )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		int        =		"</s>"
      snake_case							:		Union[str, Any]        =		0

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_    )   ,SCREAMING_SNAKE_CASE_    )
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_    )   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Any        =		list(self.get_tokenizer().get_vocab().keys()    )

      self.assertEqual(vocab_keys[0]   ,"""</s>"""    )
      self.assertEqual(vocab_keys[1]   ,"""<unk>"""    )
      self.assertEqual(vocab_keys[-1]   ,"""<pad>"""    )
      self.assertEqual(len(SCREAMING_SNAKE_CASE_    )   ,9    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      self.assertEqual(self.get_tokenizer().vocab_size   ,9    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Optional[int]        =		MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de"""    )
      snake_case							:		int        =		en_de_tokenizer(["""I am a small frog"""]   ,return_tensors=SCREAMING_SNAKE_CASE_    )
      self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[str]        =		[38, 121, 14, 697, 38848, 0]
      self.assertListEqual(SCREAMING_SNAKE_CASE_   ,batch.input_ids[0]    )

      snake_case							:		List[str]        =		tempfile.mkdtemp()
      en_de_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[Any]        =		[x.name for x in Path(SCREAMING_SNAKE_CASE_    ).glob("""*"""    )]
      self.assertIn("""source.spm"""   ,SCREAMING_SNAKE_CASE_    )
      MarianTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Union[str, Any]        =		self.get_tokenizer()

      snake_case							:		Tuple        =		tok(
          ["""I am a small frog""" * 1000, """I am a small frog"""]   ,padding=SCREAMING_SNAKE_CASE_   ,truncation=SCREAMING_SNAKE_CASE_   ,return_tensors=SCREAMING_SNAKE_CASE_    )
      self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
      self.assertEqual(batch.input_ids.shape   ,(2, 512)    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		str        =		self.get_tokenizer()
      snake_case							:		Dict        =		tok(["""I am a tiny frog""", """I am a small frog"""]   ,padding=SCREAMING_SNAKE_CASE_   ,return_tensors=SCREAMING_SNAKE_CASE_    )
      self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
      self.assertEqual(batch_smaller.input_ids.shape   ,(2, 10)    )





  @slow
  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		{"input_ids": [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=SCREAMING_SNAKE_CASE_   ,model_name="""Helsinki-NLP/opus-mt-en-de"""   ,revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""   ,decode_kwargs={"""use_source_tokenizer""": True}   ,)





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Optional[Any]        =		MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""    )

      snake_case							:		Union[str, Any]        =		"Tämä on testi"
      snake_case							:		List[Any]        =		"This is a test"

      snake_case							:		Dict        =		[76, 7, 2047, 2]
      snake_case							:		str        =		[69, 12, 11, 940, 2]

      snake_case							:		List[Any]        =		tokenizer(SCREAMING_SNAKE_CASE_    ).input_ids
      self.assertListEqual(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )

      snake_case							:		Optional[Any]        =		tokenizer(text_target=SCREAMING_SNAKE_CASE_    ).input_ids
      self.assertListEqual(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )

      snake_case							:		Optional[Any]        =		tokenizer.decode(SCREAMING_SNAKE_CASE_   ,skip_special_tokens=SCREAMING_SNAKE_CASE_    )
      self.assertEqual(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )










import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel







class 	__lowerCAmelCase      (       pl.LightningModule	):





   def __init__(			self       ,      snake_case   )					->	Dict:
      """simple docstring"""





      super().__init__()
      a__						:    int   		=	model
      a__						:    Any   		=	2
      a__						:    List[str]   		=	nn.Linear(self.model.config.hidden_size       ,      self.num_labels   )





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      pass







def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # load longformer model from model identifier
   a__						:    List[str]   		=	LongformerModel.from_pretrained(lowerCamelCase )
   a__						:    Dict   		=	LightningModel(lowerCamelCase )

   a__						:    str   		=	torch.load(lowerCamelCase				,			map_location=torch.device("cpu" ) )
   lightning_model.load_state_dict(ckpt["state_dict"] )

   # init longformer question answering model
   a__						:    Dict   		=	LongformerForQuestionAnswering.from_pretrained(lowerCamelCase )

   # transfer weights
   longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
   longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
   longformer_for_qa.eval()

   # save model
   longformer_for_qa.save_pretrained(lowerCamelCase )

   print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Any         =    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--longformer_model""",
      default=None,
      type=str,
      required=True,
      help="""model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.""",
  )
  parser.add_argument(
      """--longformer_question_answering_ckpt_path""",
      default=None,
      type=str,
      required=True,
      help="""Path the official PyTorch Lightning Checkpoint.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

