





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


A   : Dict			=				get_tests_dir('''fixtures/test_sentencepiece.model''')

A   : Dict			=				{'''target_lang''': '''fi''', '''source_lang''': '''en'''}
A   : Tuple			=				'''>>zh<<'''
A   : Optional[int]			=				'''Helsinki-NLP/'''

if is_torch_available():
					A   : Optional[Any]			=				'''pt'''
elif is_tf_available():
					A   : Any			=				'''tf'''
else:
					A   : Tuple			=				'''jax'''


@require_sentencepiece
class   A      (SCREAMING_SNAKE_CASE       ,					unittest.TestCase   ):
			'''simple docstring'''




			__lowerCamelCase     :	List[str]          =   MarianTokenizer
			__lowerCamelCase     :	str          =   False
			__lowerCamelCase     :	Union[str, Any]          =   True






			def        a_	(      self       :   Tuple       )     ->     Optional[Any]:







					"""simple docstring"""






					super().setUp()
					A__  				=       ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
					A__  				=       dict(zip(__lowerCAmelCase							,     range(len(__lowerCAmelCase       )       )       )       )
					A__  				=       Path(self.tmpdirname       )
					save_json(__lowerCAmelCase							,     save_dir / VOCAB_FILES_NAMES["""vocab"""]       )
					save_json(__lowerCAmelCase							,     save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]       )
					if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
							copyfile(__lowerCAmelCase							,     save_dir / VOCAB_FILES_NAMES["""source_spm"""]       )
							copyfile(__lowerCAmelCase							,     save_dir / VOCAB_FILES_NAMES["""target_spm"""]       )

					A__  				=       MarianTokenizer.from_pretrained(self.tmpdirname       )
					tokenizer.save_pretrained(self.tmpdirname       )






			def        a_	(      self       :   Tuple							,     **__lowerCAmelCase       :   Optional[int]       )     ->     MarianTokenizer:







					"""simple docstring"""






					return MarianTokenizer.from_pretrained(self.tmpdirname							,     **__lowerCAmelCase       )






			def        a_	(      self       :   Tuple							,     __lowerCAmelCase       :   Optional[Any]       )     ->     Optional[Any]:







					"""simple docstring"""






					return (
					    "This is a test",
					    "This is a test",
					)






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







					"""simple docstring"""






					A__  				=       """</s>"""
					A__  				=       0

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






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







					"""simple docstring"""






					A__  				=       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(__lowerCAmelCase       )							,     9       )






			def        a_	(      self       :   Tuple       )     ->     Optional[int]:







					"""simple docstring"""






					self.assertEqual(self.get_tokenizer().vocab_size							,     9       )






			def        a_	(      self       :   Optional[int]       )     ->     Tuple:







					"""simple docstring"""






					A__  				=       MarianTokenizer.from_pretrained(f'{ORG_NAME}opus-mt-en-de'       )
					A__  				=       en_de_tokenizer(["""I am a small frog"""]							,     return_tensors=__lowerCAmelCase       )
					self.assertIsInstance(__lowerCAmelCase							,     __lowerCAmelCase       )
					A__  				=       [38, 1_21, 14, 6_97, 3_88_48, 0]
					self.assertListEqual(__lowerCAmelCase							,     batch.input_ids[0]       )

					A__  				=       tempfile.mkdtemp()
					en_de_tokenizer.save_pretrained(__lowerCAmelCase       )
					A__  				=       [x.name for x in Path(__lowerCAmelCase       ).glob("""*"""       )]
					self.assertIn("""source.spm"""							,     __lowerCAmelCase       )
					MarianTokenizer.from_pretrained(__lowerCAmelCase       )






			def        a_	(      self       :   str       )     ->     Tuple:







					"""simple docstring"""






					A__  				=       self.get_tokenizer()

					A__  				=       tok(
					    ["""I am a small frog""" * 10_00, """I am a small frog"""]							,     padding=__lowerCAmelCase							,     truncation=__lowerCAmelCase							,     return_tensors=__lowerCAmelCase       )
					self.assertIsInstance(__lowerCAmelCase							,     __lowerCAmelCase       )
					self.assertEqual(batch.input_ids.shape							,     (2, 5_12)       )






			def        a_	(      self       :   Any       )     ->     Dict:







					"""simple docstring"""






					A__  				=       self.get_tokenizer()
					A__  				=       tok(["""I am a tiny frog""", """I am a small frog"""]							,     padding=__lowerCAmelCase							,     return_tensors=__lowerCAmelCase       )
					self.assertIsInstance(__lowerCAmelCase							,     __lowerCAmelCase       )
					self.assertEqual(batch_smaller.input_ids.shape							,     (2, 10)       )






			@slow
			def        a_	(      self       :   List[Any]       )     ->     Dict:







					"""simple docstring"""






					A__  				=       {"""input_ids""": [[4_34_95, 4_62, 20, 4_21_64, 13_69, 52, 4_64, 1_32, 17_03, 4_92, 13, 74_91, 3_89_99, 6, 8, 4_64, 1_32, 17_03, 4_92, 13, 46_69, 3_78_67, 13, 75_25, 27, 15_93, 9_88, 13, 3_39_72, 70_29, 6, 20, 82_51, 3_83, 2, 2_70, 58_66, 37_88, 2, 23_53, 82_51, 1_23_38, 2, 1_39_58, 3_87, 2, 36_29, 69_53, 1_88, 29_00, 2, 1_39_58, 80_11, 1_15_01, 23, 84_60, 40_73, 3_40_09, 20, 4_35, 1_14_39, 27, 8, 84_60, 40_73, 60_04, 20, 99_88, 3_75, 27, 33, 2_66, 19_45, 10_76, 13_50, 3_78_67, 32_88, 5, 5_77, 10_76, 43_74, 8, 50_82, 5, 2_64_53, 2_57, 5_56, 4_03, 2, 2_42, 1_32, 3_83, 3_16, 4_92, 8, 1_07_67, 6, 3_16, 3_04, 42_39, 3, 0], [1_48, 1_57_22, 19, 18_39, 12, 13_50, 13, 2_23_27, 50_82, 54_18, 4_75_67, 3_59_38, 59, 3_18, 1_95_52, 1_08, 21_83, 54, 1_49_76, 48_35, 32, 5_47, 11_14, 8, 3_15, 24_17, 5, 92, 1_90_88, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00], [36, 63_95, 1_25_70, 3_91_47, 1_15_97, 6, 2_66, 4, 4_54_05, 72_96, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00]], """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=__lowerCAmelCase							,     model_name="""Helsinki-NLP/opus-mt-en-de"""							,     revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""							,     decode_kwargs={"""use_source_tokenizer""": True}							,     )







			def        a_	(      self       :   Optional[int]       )     ->     List[str]:







					"""simple docstring"""






					A__  				=       MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""       )

					A__  				=       """Tämä on testi"""
					A__  				=       """This is a test"""

					A__  				=       [76, 7, 20_47, 2]
					A__  				=       [69, 12, 11, 9_40, 2]

					A__  				=       tokenizer(__lowerCAmelCase       ).input_ids
					self.assertListEqual(__lowerCAmelCase							,     __lowerCAmelCase       )

					A__  				=       tokenizer(text_target=__lowerCAmelCase       ).input_ids
					self.assertListEqual(__lowerCAmelCase							,     __lowerCAmelCase       )

					A__  				=       tokenizer.decode(__lowerCAmelCase							,     skip_special_tokens=__lowerCAmelCase       )
					self.assertEqual(__lowerCAmelCase							,     __lowerCAmelCase       )










import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
					A   : List[str]			=				argparse.ArgumentParser(
					    description=(
					        '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
					        ''' Distillation'''
					    )
					)
					parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
					parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
					parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
					parser.add_argument('''--vocab_transform''', action='''store_true''')
					A   : Tuple			=				parser.parse_args()

					if args.model_type == "bert":
										A   : Dict			=				BertForMaskedLM.from_pretrained(args.model_name)
										A   : List[str]			=				'''bert'''
					else:
										raise ValueError('''args.model_type should be "bert".''')

					A   : Optional[Any]			=				model.state_dict()
					A   : int			=				{}

					for w in ["word_embeddings", "position_embeddings"]:
										A   : str			=				state_dict[F'''{prefix}.embeddings.{w}.weight''']
					for w in ["weight", "bias"]:
										A   : Any			=				state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

					A   : Tuple			=				0
					for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
										for w in ["weight", "bias"]:
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
															]

															A   : int			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
															]
															A   : List[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
															]

															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
															]
															A   : Union[str, Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
															]
															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
															]
										std_idx += 1

					A   : int			=				state_dict['''cls.predictions.decoder.weight''']
					A   : str			=				state_dict['''cls.predictions.bias''']
					if args.vocab_transform:
										for w in ["weight", "bias"]:
															A   : List[Any]			=				state_dict[F'''cls.predictions.transform.dense.{w}''']
															A   : List[str]			=				state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']

					print(F'''N layers selected for distillation: {std_idx}''')
					print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

					print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
					torch.save(compressed_sd, args.dump_checkpoint)



