

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			):
			"""simple docstring"""







			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






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

							# Download this model to make sure it's in the cache.
							A__        =			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=lowercase       ) as mock_head:
											A__        =			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      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






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

							# Download this model to make sure it's in the cache.
							A__        =			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=lowercase       ) as mock_head:
											A__        =			GPTaTokenizerFast.from_pretrained("gpt2"       )
											# This check we did call the fake head request
											mock_head.assert_called()


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






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

											A__        =			AlbertTokenizer.from_pretrained(lowercase       )
							finally:
											os.remove(lowercase       )

							# 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"      ,      lowercase       )
											A__        =			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      ,      1000       )
											# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

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


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






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

@is_staging_test
class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			__lowerCamelCase        =       ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']


			@classmethod
			def      UpperCamelCase		(    cls       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			TOKEN
							HfFolder.save_token(lowercase       )


			@classmethod
			def      UpperCamelCase		(    cls       )  ->						Union[str, 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      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






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

							tokenizer.push_to_hub("test-tokenizer"      ,      use_auth_token=self._token       )
							A__        =			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(lowercase      ,      repo_id="test-tokenizer"      ,      push_to_hub=lowercase      ,      use_auth_token=self._token       )

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


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






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

							tokenizer.push_to_hub("valid_org/test-tokenizer-org"      ,      use_auth_token=self._token       )
							A__        =			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(
											    lowercase      ,      repo_id="valid_org/test-tokenizer-org"      ,      push_to_hub=lowercase      ,      use_auth_token=self._token       )

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


			@require_tokenizers
			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






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

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

							A__        =			AutoTokenizer.from_pretrained(F'{USER}/test-dynamic-tokenizer'      ,      trust_remote_code=lowercase       )
							# 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:
											A__        =			os.path.join(lowercase      ,      "vocab.txt"       )
											with open(lowercase      ,      "w"      ,      encoding="utf-8"       ) as vocab_writer:
															vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]       )       )

											A__        =			BertTokenizerFast.from_pretrained(lowercase       )
											bert_tokenizer.save_pretrained(lowercase       )
											A__        =			CustomTokenizerFast.from_pretrained(lowercase       )

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

							A__        =			AutoTokenizer.from_pretrained(F'{USER}/test-dynamic-tokenizer'      ,      trust_remote_code=lowercase       )
							# 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"       )
							A__        =			AutoTokenizer.from_pretrained(
							    F'{USER}/test-dynamic-tokenizer'      ,      use_fast=lowercase      ,      trust_remote_code=lowercase       )
							# 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			):
			"""simple docstring"""







			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__        =			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      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							A__        =			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      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__        =			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      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			Trie()
							A__        =			trie.cut_text("ABC"      ,      [0, 0, 2, 1, 2, 3]       )
							self.assertEqual(lowercase      ,      ["AB", "C"]       )




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							str=1_0_2_4      )			->      Any:




				'''simple docstring'''





				A__    ,				A__        =			[], []

				A__        =			list(zip(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )      )
				A__    ,				A__        =			sorted_examples[0]

				def is_too_big(SCREAMING_SNAKE_CASE_:							List[str]      ):
								return tok(SCREAMING_SNAKE_CASE_	,       return_tensors="pt"      ).input_ids.shape[1] > max_tokens

				for src, tgt in tqdm(sorted_examples[1:]      ):
								A__        =			new_src + " " + src
								A__        =			new_tgt + " " + tgt
								if is_too_big(SCREAMING_SNAKE_CASE_      ) or is_too_big(SCREAMING_SNAKE_CASE_      ):  # cant fit, finalize example
												finished_src.append(SCREAMING_SNAKE_CASE_      )
												finished_tgt.append(SCREAMING_SNAKE_CASE_      )
												A__    ,				A__        =			src, tgt
								else:  # can fit, keep adding
												A__    ,				A__        =			cand_src, cand_tgt

    # cleanup
				if new_src:
								assert new_tgt
								finished_src.append(SCREAMING_SNAKE_CASE_      )
								finished_tgt.append(SCREAMING_SNAKE_CASE_      )
				return finished_src, finished_tgt



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[int]	,       SCREAMING_SNAKE_CASE_:							Path	,       SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							Tuple      )			->      Union[str, Any]:




				'''simple docstring'''





				A__        =			Path(SCREAMING_SNAKE_CASE_      )
				save_path.mkdir(exist_ok=SCREAMING_SNAKE_CASE_      )
				for split in ["train"]:
								A__    ,				A__        =			data_dir / F'{split}.source', data_dir / F'{split}.target'
								A__        =			[x.rstrip() for x in Path(SCREAMING_SNAKE_CASE_      ).open().readlines()]
								A__        =			[x.rstrip() for x in Path(SCREAMING_SNAKE_CASE_      ).open().readlines()]
								A__    ,				A__        =			pack_examples(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
								print(F'packed {split} split from {len(SCREAMING_SNAKE_CASE_      )} examples -> {len(SCREAMING_SNAKE_CASE_      )}.'      )
								Path(save_path / F'{split}.source'      ).open("w"      ).write("\n".join(SCREAMING_SNAKE_CASE_      )      )
								Path(save_path / F'{split}.target'      ).open("w"      ).write("\n".join(SCREAMING_SNAKE_CASE_      )      )
				for split in ["val", "test"]:
								A__    ,				A__        =			data_dir / F'{split}.source', data_dir / F'{split}.target'
								shutil.copyfile(SCREAMING_SNAKE_CASE_	,       save_path / F'{split}.source'      )
								shutil.copyfile(SCREAMING_SNAKE_CASE_	,       save_path / F'{split}.target'      )



def 					lowerCAmelCase__							(	)			->      Optional[int]:




				'''simple docstring'''





				A__        =			argparse.ArgumentParser()
				parser.add_argument("--tok_name"	,       type=SCREAMING_SNAKE_CASE_	,       help="like facebook/bart-large-cnn,t5-base, etc."      )
				parser.add_argument("--max_seq_len"	,       type=SCREAMING_SNAKE_CASE_	,       default=1_2_8      )
				parser.add_argument("--data_dir"	,       type=SCREAMING_SNAKE_CASE_      )
				parser.add_argument("--save_path"	,       type=SCREAMING_SNAKE_CASE_      )
				A__        =			parser.parse_args()
				A__        =			AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(SCREAMING_SNAKE_CASE_	,       Path(args.data_dir      )	,       args.max_seq_len	,       args.save_path      )


if __name__ == "__main__":
	packer_cli()

