


import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 							lowercase  (						_UpperCAmelCase    ,       unittest.TestCase			):
     lowerCamelCase			:			Union[str, Any]      = MgpstrTokenizer
     lowerCamelCase			:			Tuple      = False
     lowerCamelCase			:			str      = {}
     lowerCamelCase			:			Dict      = False







     def 							lowercase__       (						self :						Dict      ):
           super().setUp()

           # fmt: off
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
           # fmt: on
           SCREAMING_SNAKE_CASE__       :  str						     =		dict(zip(_lowercase   ,   range(len(_lowercase      )      )      )      )

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES['''vocab_file''']      )
           with open(self.vocab_file   ,   '''w'''   ,   encoding='''utf-8'''      ) as fp:
                 fp.write(json.dumps(_lowercase      ) + '''\n'''      )







     def 							lowercase__       (						self :						Any   ,   **_lowercase :						List[Any]      ):
           return MgpstrTokenizer.from_pretrained(self.tmpdirname   ,   **_lowercase      )







     def 							lowercase__       (						self :						int   ,   _lowercase :						List[str]      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		'''tester'''
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		'''tester'''
           return input_text, output_text







     @unittest.skip('''MGP-STR always lower cases letters.'''      )
     def 							lowercase__       (						self :						Dict      ):
           pass







     def 							lowercase__       (						self :						str      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.get_tokenizers(do_lower_case=_lowercase      )
           for tokenizer in tokenizers:
                 with self.subTest(f"""{tokenizer.__class__.__name__}"""      ):
                       SCREAMING_SNAKE_CASE__       :  List[str]						     =		'''[SPECIAL_TOKEN]'''

                       tokenizer.add_special_tokens({'''cls_token''': special_token}      )
                       SCREAMING_SNAKE_CASE__       :  List[Any]						     =		tokenizer.encode([special_token]   ,   add_special_tokens=_lowercase      )
                       self.assertEqual(len(_lowercase      )   ,   1      )

                       SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		tokenizer.decode(_lowercase   ,   skip_special_tokens=_lowercase      )
                       self.assertTrue(special_token not in decoded      )







     def 							lowercase__       (						self :						Dict      ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self.get_tokenizers()
           for tokenizer in tokenizers:
                 with self.subTest(f"""{tokenizer.__class__.__name__}"""      ):
                       SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		self.get_input_output_texts(_lowercase      )

                       SCREAMING_SNAKE_CASE__       :  Any						     =		tokenizer.tokenize(_lowercase      )
                       SCREAMING_SNAKE_CASE__       :  List[Any]						     =		tokenizer.convert_tokens_to_ids(_lowercase      )
                       SCREAMING_SNAKE_CASE__       :  str						     =		tokenizer.encode(_lowercase   ,   add_special_tokens=_lowercase      )
                       self.assertListEqual(_lowercase   ,   _lowercase      )

                       SCREAMING_SNAKE_CASE__       :  int						     =		tokenizer.convert_ids_to_tokens(_lowercase      )
                       self.assertNotEqual(len(_lowercase      )   ,   0      )
                       SCREAMING_SNAKE_CASE__       :  str						     =		tokenizer.decode(_lowercase      )
                       self.assertIsInstance(_lowercase   ,   _lowercase      )

                       self.assertEqual(text_a.replace(''' '''   ,   ''''''      )   ,   _lowercase      )







     @unittest.skip('''MGP-STR tokenizer only handles one sequence.'''      )
     def 							lowercase__       (						self :						Optional[int]      ):
           pass







     @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''      )
     def 							lowercase__       (						self :						int      ):
           pass











"""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)