


import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
			from test_module.custom_tokenization_fast import CustomTokenizerFast


class        _a							(			unittest.TestCase			):

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





									"""simple docstring"""







									lowercase__            =      mock.Mock()
									lowercase__            =      500
									lowercase__            =      {}
									lowercase__            =      HTTPError
									lowercase__            =      {}

									# Download this model to make sure it's in the cache.
									lowercase__            =      BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''							)

									# Under the mock environment we get a 500 error when trying to reach the tokenizer.
									with mock.patch('''requests.Session.request'''    ,						return_value=UpperCamelCase_							) as mock_head:
											lowercase__            =      BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''							)
											# This check we did call the fake head request
											mock_head.assert_called()

							@require_tokenizers
							def       lowerCamelCase_ (							self:	int							)			->   List[Any]:





									"""simple docstring"""







									lowercase__            =      mock.Mock()
									lowercase__            =      500
									lowercase__            =      {}
									lowercase__            =      HTTPError
									lowercase__            =      {}

									# Download this model to make sure it's in the cache.
									lowercase__            =      GPTaTokenizerFast.from_pretrained('''gpt2'''							)

									# Under the mock environment we get a 500 error when trying to reach the tokenizer.
									with mock.patch('''requests.Session.request'''    ,						return_value=UpperCamelCase_							) as mock_head:
											lowercase__            =      GPTaTokenizerFast.from_pretrained('''gpt2'''							)
											# This check we did call the fake head request
											mock_head.assert_called()

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





									"""simple docstring"""







									try:
											lowercase__            =      tempfile.mktemp()
											with open(UpperCamelCase_    ,						'''wb'''							) as f:
													http_get('''https://huggingface.co/albert-base-v1/resolve/main/spiece.model'''    ,						UpperCamelCase_							)

											lowercase__            =      AlbertTokenizer.from_pretrained(UpperCamelCase_							)
									finally:
											os.remove(UpperCamelCase_							)

									# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
									# the current folder and have the right name.
									if os.path.isfile('''tokenizer.json'''							):
											# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
											return
									try:
											with open('''tokenizer.json'''    ,						'''wb'''							) as f:
													http_get('''https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json'''    ,						UpperCamelCase_							)
											lowercase__            =      AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''							)
											# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
											self.assertEqual(tokenizer.vocab_size    ,						1_000							)
											# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

									finally:
											os.remove('''tokenizer.json'''							)


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





									"""simple docstring"""







									lowercase__            =      AlbertTokenizer.from_pretrained('''https://huggingface.co/albert-base-v1/resolve/main/spiece.model'''							)


@is_staging_test
class        _a							(			unittest.TestCase			):
							_lowercase	:			Optional[Any]        = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']

							@classmethod
							def       lowerCamelCase_ (							cls:	Optional[int]							)			->   str:





									"""simple docstring"""







									lowercase__            =      TOKEN
									HfFolder.save_token(UpperCamelCase_							)

							@classmethod
							def       lowerCamelCase_ (							cls:	Tuple							)			->   Any:





									"""simple docstring"""







									try:
											delete_repo(token=cls._token    ,						repo_id='''test-tokenizer'''							)
									except HTTPError:
											pass

									try:
											delete_repo(token=cls._token    ,						repo_id='''valid_org/test-tokenizer-org'''							)
									except HTTPError:
											pass

									try:
											delete_repo(token=cls._token    ,						repo_id='''test-dynamic-tokenizer'''							)
									except HTTPError:
											pass

							def       lowerCamelCase_ (							self:	Dict							)			->   str:





									"""simple docstring"""







									with tempfile.TemporaryDirectory() as tmp_dir:
											lowercase__            =      os.path.join(UpperCamelCase_    ,						'''vocab.txt'''							)
											with open(UpperCamelCase_    ,						'''w'''    ,						encoding='''utf-8'''							) as vocab_writer:
													vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]							)							)
											lowercase__            =      BertTokenizer(UpperCamelCase_							)

									tokenizer.push_to_hub('''test-tokenizer'''    ,						use_auth_token=self._token							)
									lowercase__            =      BertTokenizer.from_pretrained(f'{USER}/test-tokenizer'							)
									self.assertDictEqual(new_tokenizer.vocab    ,						tokenizer.vocab							)

									# Reset repo
									delete_repo(token=self._token    ,						repo_id='''test-tokenizer'''							)

									# Push to hub via save_pretrained
									with tempfile.TemporaryDirectory() as tmp_dir:
											tokenizer.save_pretrained(UpperCamelCase_    ,						repo_id='''test-tokenizer'''    ,						push_to_hub=UpperCamelCase_    ,						use_auth_token=self._token							)

									lowercase__            =      BertTokenizer.from_pretrained(f'{USER}/test-tokenizer'							)
									self.assertDictEqual(new_tokenizer.vocab    ,						tokenizer.vocab							)

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





									"""simple docstring"""







									with tempfile.TemporaryDirectory() as tmp_dir:
											lowercase__            =      os.path.join(UpperCamelCase_    ,						'''vocab.txt'''							)
											with open(UpperCamelCase_    ,						'''w'''    ,						encoding='''utf-8'''							) as vocab_writer:
													vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]							)							)
											lowercase__            =      BertTokenizer(UpperCamelCase_							)

									tokenizer.push_to_hub('''valid_org/test-tokenizer-org'''    ,						use_auth_token=self._token							)
									lowercase__            =      BertTokenizer.from_pretrained('''valid_org/test-tokenizer-org'''							)
									self.assertDictEqual(new_tokenizer.vocab    ,						tokenizer.vocab							)

									# Reset repo
									delete_repo(token=self._token    ,						repo_id='''valid_org/test-tokenizer-org'''							)

									# Push to hub via save_pretrained
									with tempfile.TemporaryDirectory() as tmp_dir:
											tokenizer.save_pretrained(
											    UpperCamelCase_    ,						repo_id='''valid_org/test-tokenizer-org'''    ,						push_to_hub=UpperCamelCase_    ,						use_auth_token=self._token							)

									lowercase__            =      BertTokenizer.from_pretrained('''valid_org/test-tokenizer-org'''							)
									self.assertDictEqual(new_tokenizer.vocab    ,						tokenizer.vocab							)


							@require_tokenizers
							def       lowerCamelCase_ (							self:	Any							)			->   Tuple:





									"""simple docstring"""







									CustomTokenizer.register_for_auto_class()
									with tempfile.TemporaryDirectory() as tmp_dir:
											lowercase__            =      os.path.join(UpperCamelCase_    ,						'''vocab.txt'''							)
											with open(UpperCamelCase_    ,						'''w'''    ,						encoding='''utf-8'''							) as vocab_writer:
													vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]							)							)
											lowercase__            =      CustomTokenizer(UpperCamelCase_							)

									# No fast custom tokenizer
									tokenizer.push_to_hub('''test-dynamic-tokenizer'''    ,						use_auth_token=self._token							)

									lowercase__            =      AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer'    ,						trust_remote_code=UpperCamelCase_							)
									# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
									self.assertEqual(tokenizer.__class__.__name__    ,						'''CustomTokenizer'''							)

									# Fast and slow custom tokenizer
									CustomTokenizerFast.register_for_auto_class()
									with tempfile.TemporaryDirectory() as tmp_dir:
											lowercase__            =      os.path.join(UpperCamelCase_    ,						'''vocab.txt'''							)
											with open(UpperCamelCase_    ,						'''w'''    ,						encoding='''utf-8'''							) as vocab_writer:
													vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]							)							)

											lowercase__            =      BertTokenizerFast.from_pretrained(UpperCamelCase_							)
											bert_tokenizer.save_pretrained(UpperCamelCase_							)
											lowercase__            =      CustomTokenizerFast.from_pretrained(UpperCamelCase_							)

									tokenizer.push_to_hub('''test-dynamic-tokenizer'''    ,						use_auth_token=self._token							)

									lowercase__            =      AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer'    ,						trust_remote_code=UpperCamelCase_							)
									# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
									self.assertEqual(tokenizer.__class__.__name__    ,						'''CustomTokenizerFast'''							)
									lowercase__            =      AutoTokenizer.from_pretrained(
									    f'{USER}/test-dynamic-tokenizer'    ,						use_fast=UpperCamelCase_    ,						trust_remote_code=UpperCamelCase_							)
									# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
									self.assertEqual(tokenizer.__class__.__name__    ,						'''CustomTokenizer'''							)


class        _a							(			unittest.TestCase			):

							def       lowerCamelCase_ (							self:	Tuple							)			->   Any:





									"""simple docstring"""







									lowercase__            =      Trie()
									trie.add('''Hello 友達'''							)
									self.assertEqual(trie.data    ,						{'''H''': {'''e''': {'''l''': {'''l''': {'''o''': {''' ''': {'''友''': {'''達''': {'''''': 1}}}}}}}}}							)
									trie.add('''Hello'''							)
									trie.data
									self.assertEqual(trie.data    ,						{'''H''': {'''e''': {'''l''': {'''l''': {'''o''': {'''''': 1, ''' ''': {'''友''': {'''達''': {'''''': 1}}}}}}}}}							)

							def       lowerCamelCase_ (							self:	Dict							)			->   int:





									"""simple docstring"""







									lowercase__            =      Trie()
									self.assertEqual(trie.split('''[CLS] This is a extra_id_100'''							)    ,						['''[CLS] This is a extra_id_100''']							)
									trie.add('''[CLS]'''							)
									trie.add('''extra_id_1'''							)
									trie.add('''extra_id_100'''							)
									self.assertEqual(trie.split('''[CLS] This is a extra_id_100'''							)    ,						['''[CLS]''', ''' This is a ''', '''extra_id_100''']							)

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





									"""simple docstring"""







									lowercase__            =      Trie()
									trie.add('''A'''							)
									self.assertEqual(trie.split('''ABC'''							)    ,						['''A''', '''BC''']							)
									self.assertEqual(trie.split('''BCA'''							)    ,						['''BC''', '''A''']							)

							def       lowerCamelCase_ (							self:	str							)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      Trie()
									trie.add('''TOKEN]'''							)
									trie.add('''[SPECIAL_TOKEN]'''							)
									self.assertEqual(trie.split('''This is something [SPECIAL_TOKEN]'''							)    ,						['''This is something ''', '''[SPECIAL_TOKEN]''']							)

							def       lowerCamelCase_ (							self:	List[str]							)			->   Dict:





									"""simple docstring"""







									lowercase__            =      Trie()
									trie.add('''A'''							)
									trie.add('''P'''							)
									trie.add('''[SPECIAL_TOKEN]'''							)
									self.assertEqual(trie.split('''This is something [SPECIAL_TOKEN]'''							)    ,						['''This is something ''', '''[SPECIAL_TOKEN]''']							)

							def       lowerCamelCase_ (							self:	Dict							)			->   Dict:





									"""simple docstring"""







									lowercase__            =      Trie()
									trie.add('''AB'''							)
									trie.add('''B'''							)
									trie.add('''C'''							)
									self.assertEqual(trie.split('''ABC'''							)    ,						['''AB''', '''C''']							)

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





									"""simple docstring"""







									lowercase__            =      Trie()
									trie.add('''ABC'''							)
									trie.add('''B'''							)
									trie.add('''CD'''							)
									self.assertEqual(trie.split('''ABCD'''							)    ,						['''ABC''', '''D''']							)


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





									"""simple docstring"""







									lowercase__            =      Trie()
									lowercase__            =      trie.cut_text('''ABC'''    ,						[0, 0, 2, 1, 2, 3]							)
									self.assertEqual(UpperCamelCase_    ,						['''AB''', '''C''']							)






import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      RobertaPreLayerNormConfig.from_pretrained(
		    SCREAMING_SNAKE_CASE						,				architectures=['''RobertaPreLayerNormForMaskedLM''']     )

		# convert state_dict
		lowercase__            =      torch.load(hf_hub_download(repo_id=SCREAMING_SNAKE_CASE						,				filename='''pytorch_model.bin'''     )     )
		lowercase__            =      {}
		for tensor_key, tensor_value in original_state_dict.items():
				# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
				if tensor_key.startswith('''roberta.'''     ):
						lowercase__            =      '''roberta_prelayernorm.''' + tensor_key[len('''roberta.'''     ) :]

				# The original implementation contains weights which are not used, remove them from the state_dict
				if tensor_key.endswith('''.self.LayerNorm.weight'''     ) or tensor_key.endswith('''.self.LayerNorm.bias'''     ):
						continue

				lowercase__            =      tensor_value

		lowercase__            =      RobertaPreLayerNormForMaskedLM.from_pretrained(
		    pretrained_model_name_or_path=SCREAMING_SNAKE_CASE						,				config=SCREAMING_SNAKE_CASE						,				state_dict=SCREAMING_SNAKE_CASE     )
		model.save_pretrained(SCREAMING_SNAKE_CASE     )

		# convert tokenizer
		lowercase__            =      AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE     )
		tokenizer.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
			lowerCAmelCase       =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--checkpoint-repo',
			    default=None,
			    type=str,
			    required=True,
			    help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
			)
			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_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


