






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     : List[Any]   	=      get_tests_dir("""fixtures/test_sentencepiece.model""")

_a     : Union[str, Any]   	=      {'target_lang': 'fi', 'source_lang': 'en'}
_a     : Union[str, Any]   	=      '>>zh<<'
_a     : List[str]   	=      'Helsinki-NLP/'

if is_torch_available():
 _a     : Optional[int]   	=      'pt'
elif is_tf_available():
 _a     : List[Any]   	=      'tf'
else:
 _a     : Union[str, Any]   	=      'jax'







@require_sentencepiece
class 						_UpperCAmelCase	(	__SCREAMING_SNAKE_CASE					,				unittest.TestCase		):




      """simple docstring"""



      A    =     MarianTokenizer
      A    =     False
      A    =     True
      def       snake_case_   ( self		):



          '''simple docstring'''
          super().setUp()
          lowerCAmelCase__ :Dict   				=					["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
          lowerCAmelCase__ :Union[str, Any]   				=					dict(zip(_SCREAMING_SNAKE_CASE     ,						range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
          lowerCAmelCase__ :int   				=					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"]		)

          lowerCAmelCase__ :Tuple   				=					MarianTokenizer.from_pretrained(self.tmpdirname		)
          tokenizer.save_pretrained(self.tmpdirname		)
      def       snake_case_   ( self     ,						**_lowerCAmelCase		):



          '''simple docstring'''
          return MarianTokenizer.from_pretrained(self.tmpdirname     ,						**_SCREAMING_SNAKE_CASE		)
      def       snake_case_   ( self     ,						_lowerCAmelCase		):



          '''simple docstring'''
          return (
              "This is a test",
              "This is a test",
          )
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :Union[str, Any]   				=					"</s>"
          lowerCAmelCase__ :int   				=					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'''
          lowerCAmelCase__ :List[str]   				=					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'''
          lowerCAmelCase__ :Optional[Any]   				=					MarianTokenizer.from_pretrained(f'''{ORG_NAME}opus-mt-en-de'''		)
          lowerCAmelCase__ :int   				=					en_de_tokenizer(["I am a small frog"]     ,						return_tensors=_SCREAMING_SNAKE_CASE		)
          self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE		)
          lowerCAmelCase__ :Optional[int]   				=					[38, 121, 14, 697, 38_848, 0]
          self.assertListEqual(_SCREAMING_SNAKE_CASE     ,						batch.input_ids[0]		)

          lowerCAmelCase__ :Tuple   				=					tempfile.mkdtemp()
          en_de_tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE		)
          lowerCAmelCase__ :List[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'''
          lowerCAmelCase__ :List[str]   				=					self.get_tokenizer()

          lowerCAmelCase__ :int   				=					tok(
              ["I am a small frog" * 1_000, "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'''
          lowerCAmelCase__ :Any   				=					self.get_tokenizer()
          lowerCAmelCase__ :str   				=					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'''
          lowerCAmelCase__ :Optional[int]   				=					{"input_ids": [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], "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'''
          lowerCAmelCase__ :List[Any]   				=					MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"		)

          lowerCAmelCase__ :List[str]   				=					"Tämä on testi"
          lowerCAmelCase__ :int   				=					"This is a test"

          lowerCAmelCase__ :Union[str, Any]   				=					[76, 7, 2_047, 2]
          lowerCAmelCase__ :Dict   				=					[69, 12, 11, 940, 2]

          lowerCAmelCase__ :List[str]   				=					tokenizer(_SCREAMING_SNAKE_CASE		).input_ids
          self.assertListEqual(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE		)

          lowerCAmelCase__ :str   				=					tokenizer(text_target=_SCREAMING_SNAKE_CASE		).input_ids
          self.assertListEqual(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE		)

          lowerCAmelCase__ :Dict   				=					tokenizer.decode(_SCREAMING_SNAKE_CASE     ,						skip_special_tokens=_SCREAMING_SNAKE_CASE		)
          self.assertEqual(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE		)








"""simple docstring"""






import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
	__snake_case				:  Optional[Any] =       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')
	__snake_case				:  List[Any] =       parser.parse_args()

	if args.model_type == "bert":
		__snake_case				:  int =       BertForMaskedLM.from_pretrained(args.model_name)
		__snake_case				:  Tuple =       'bert'
	else:
		raise ValueError('args.model_type should be "bert".')

	__snake_case				:  List[Any] =       model.state_dict()
	__snake_case				:  Any =       {}

	for w in ["word_embeddings", "position_embeddings"]:
		__snake_case				:  List[str] =       state_dict[F"""{prefix}.embeddings.{w}.weight"""]
	for w in ["weight", "bias"]:
		__snake_case				:  Optional[int] =       state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

	__snake_case				:  List[str] =       0
	for teacher_idx in [0, 2, 4, 7, 9, 11]:
		for w in ["weight", "bias"]:
			__snake_case				:  str =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
			]
			__snake_case				:  Tuple =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
			]
			__snake_case				:  str =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
			]

			__snake_case				:  Any =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
			]
			__snake_case				:  Union[str, Any] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
			]

			__snake_case				:  Union[str, Any] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
			]
			__snake_case				:  Any =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
			]
			__snake_case				:  Optional[int] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
			]
		std_idx += 1

	__snake_case				:  Union[str, Any] =       state_dict['cls.predictions.decoder.weight']
	__snake_case				:  Union[str, Any] =       state_dict['cls.predictions.bias']
	if args.vocab_transform:
		for w in ["weight", "bias"]:
			__snake_case				:  Union[str, Any] =       state_dict[F"""cls.predictions.transform.dense.{w}"""]
			__snake_case				:  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)