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       snake_case					(      unittest.TestCase						):




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




       """simple docstring"""
       # A mock response for an HTTP head request to emulate server down
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		mock.Mock()
       SCREAMING_SNAKE_CASE__				:				int			=		500
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		{}
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		HTTPError
       SCREAMING_SNAKE_CASE__				:				List[str]			=		{}

       # Download this model to make sure it's in the cache.
       SCREAMING_SNAKE_CASE__				:				List[str]			=		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=a_				) as mock_head:
            SCREAMING_SNAKE_CASE__				:				Tuple			=		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 	__lowercase(      self     :	List[Any]				)->       int:




       """simple docstring"""
       # A mock response for an HTTP head request to emulate server down
       SCREAMING_SNAKE_CASE__				:				Tuple			=		mock.Mock()
       SCREAMING_SNAKE_CASE__				:				List[str]			=		500
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		{}
       SCREAMING_SNAKE_CASE__				:				List[str]			=		HTTPError
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		{}

       # Download this model to make sure it's in the cache.
       SCREAMING_SNAKE_CASE__				:				List[str]			=		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=a_				) as mock_head:
            SCREAMING_SNAKE_CASE__				:				Any			=		GPTaTokenizerFast.from_pretrained('gpt2'				)
            # This check we did call the fake head request
            mock_head.assert_called()




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




       """simple docstring"""
       # This test is for deprecated behavior and can be removed in v5
       try:
            SCREAMING_SNAKE_CASE__				:				str			=		tempfile.mktemp()
            with open(a_     , 'wb'				) as f:
                 http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'     , a_				)

            SCREAMING_SNAKE_CASE__				:				List[str]			=		AlbertTokenizer.from_pretrained(a_				)
       finally:
            os.remove(a_				)

       # 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'     , a_				)
            SCREAMING_SNAKE_CASE__				:				int			=		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 	__lowercase(      self     :	Any				)->       Dict:




       """simple docstring"""
       # This test is for deprecated behavior and can be removed in v5
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'				)




@is_staging_test
class       snake_case					(      unittest.TestCase						):
  lowercase_			=     ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']




  @classmethod
  def 	__lowercase(      cls     :	Tuple				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		TOKEN
       HfFolder.save_token(a_				)




  @classmethod
  def 	__lowercase(      cls     :	Any				)->       Dict:




       """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 	__lowercase(      self     :	List[Any]				)->       List[str]:




       """simple docstring"""
       with tempfile.TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		os.path.join(a_     , 'vocab.txt'				)
            with open(a_     , 'w'     , encoding='utf-8'				) as vocab_writer:
                 vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]				)				)
            SCREAMING_SNAKE_CASE__				:				Tuple			=		BertTokenizer(a_				)

       tokenizer.push_to_hub('test-tokenizer'     , use_auth_token=self._token				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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(a_     , repo_id='test-tokenizer'     , push_to_hub=a_     , use_auth_token=self._token				)

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




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




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

       tokenizer.push_to_hub('valid_org/test-tokenizer-org'     , use_auth_token=self._token				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		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(
                a_     , repo_id='valid_org/test-tokenizer-org'     , push_to_hub=a_     , use_auth_token=self._token				)

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




  @require_tokenizers
  def 	__lowercase(      self     :	Tuple				)->       List[Any]:




       """simple docstring"""
       CustomTokenizer.register_for_auto_class()
       with tempfile.TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		os.path.join(a_     , 'vocab.txt'				)
            with open(a_     , 'w'     , encoding='utf-8'				) as vocab_writer:
                 vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]				)				)
            SCREAMING_SNAKE_CASE__				:				int			=		CustomTokenizer(a_				)

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

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

            SCREAMING_SNAKE_CASE__				:				int			=		BertTokenizerFast.from_pretrained(a_				)
            bert_tokenizer.save_pretrained(a_				)
            SCREAMING_SNAKE_CASE__				:				Any			=		CustomTokenizerFast.from_pretrained(a_				)

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

       SCREAMING_SNAKE_CASE__				:				Tuple			=		AutoTokenizer.from_pretrained(F'''{USER}/test-dynamic-tokenizer'''     , trust_remote_code=a_				)
       # 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'				)
       SCREAMING_SNAKE_CASE__				:				Any			=		AutoTokenizer.from_pretrained(
           F'''{USER}/test-dynamic-tokenizer'''     , use_fast=a_     , trust_remote_code=a_				)
       # 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       snake_case					(      unittest.TestCase						):




  def 	__lowercase(      self     :	List[str]				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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 	__lowercase(      self     :	Optional[int]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		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 	__lowercase(      self     :	int				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		Trie()
       trie.add('A'				)
       self.assertEqual(trie.split('ABC'				)     , ['A', 'BC']				)
       self.assertEqual(trie.split('BCA'				)     , ['BC', 'A']				)




  def 	__lowercase(      self     :	Tuple				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		Trie()
       trie.add('TOKEN]'				)
       trie.add('[SPECIAL_TOKEN]'				)
       self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'				)     , ['This is something ', '[SPECIAL_TOKEN]']				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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 	__lowercase(      self     :	Optional[Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		Trie()
       trie.add('AB'				)
       trie.add('B'				)
       trie.add('C'				)
       self.assertEqual(trie.split('ABC'				)     , ['AB', 'C']				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		Trie()
       trie.add('ABC'				)
       trie.add('B'				)
       trie.add('CD'				)
       self.assertEqual(trie.split('ABCD'				)     , ['ABC', 'D']				)




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




       """simple docstring"""
       # Even if the offsets are wrong, we necessarily output correct string
       # parts.
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		Trie()
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		trie.cut_text('ABC'     , [0, 0, 2, 1, 2, 3]				)
       self.assertEqual(a_     , ['AB', 'C']				)




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


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

@require_sentencepiece
@require_tokenizers
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PegasusTokenizer
  lowercase_			=     PegasusTokenizerFast
  lowercase_			=     True
  lowercase_			=     True




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




       """simple docstring"""
       super().setUp()

       # We have a SentencePiece fixture for testing
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		PegasusTokenizer(a_				)
       tokenizer.save_pretrained(self.tmpdirname				)




  @cached_property
  def 	__lowercase(      self     :	Optional[Any]				)->       Optional[int]:




       """simple docstring"""
       return PegasusTokenizer.from_pretrained('google/pegasus-large'				)




  def 	__lowercase(      self     :	Any     , **a_     :	Optional[Any]				)->       PegasusTokenizer:




       """simple docstring"""
       return PegasusTokenizer.from_pretrained(self.tmpdirname     , **a_				)




  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	List[Any]				)->       Optional[int]:




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




  def 	__lowercase(      self     :	Optional[int]				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		'</s>'
       SCREAMING_SNAKE_CASE__				:				Any			=		1

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




  def 	__lowercase(      self     :	Dict				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]     , '<pad>'				)
       self.assertEqual(vocab_keys[1]     , '</s>'				)
       self.assertEqual(vocab_keys[-1]     , 'v'				)
       self.assertEqual(len(a_				)     , 1103				)




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




       """simple docstring"""
       self.assertEqual(self.get_tokenizer().vocab_size     , 1103				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.tokenizer_class.from_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		(
           'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
           ' </s> <pad> <pad> <pad>'
       )
       SCREAMING_SNAKE_CASE__				:				List[str]			=		rust_tokenizer([raw_input_str]     , return_tensors=a_     , add_special_tokens=a_				).input_ids[0]
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		py_tokenizer([raw_input_str]     , return_tensors=a_     , add_special_tokens=a_				).input_ids[0]
       self.assertListEqual(a_     , a_				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self._large_tokenizer
       # <mask_1> masks whole sentence while <mask_2> masks single word
       SCREAMING_SNAKE_CASE__				:				Any			=		'<mask_1> To ensure a <mask_2> flow of bank resolutions.'
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		tokenizer([raw_input_str]     , return_tensors=a_				).input_ids[0]
       self.assertListEqual(a_     , a_				)




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		self._large_tokenizer
       # The tracebacks for the following asserts are **better** without messages or self.assertEqual
       assert tokenizer.vocab_size == 9_6103
       assert tokenizer.pad_token_id == 0
       assert tokenizer.eos_token_id == 1
       assert tokenizer.offset == 103
       assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
       assert tokenizer.unk_token == "<unk>"
       assert tokenizer.model_max_length == 1024
       SCREAMING_SNAKE_CASE__				:				int			=		'To ensure a smooth flow of bank resolutions.'
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		tokenizer([raw_input_str]     , return_tensors=a_				).input_ids[0]
       self.assertListEqual(a_     , a_				)
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]				) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




  @require_torch
  def 	__lowercase(      self     :	Union[str, Any]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		['This is going to be way too long.' * 150, 'short example']
       SCREAMING_SNAKE_CASE__				:				int			=		['not super long but more than 5 tokens', 'tiny']
       SCREAMING_SNAKE_CASE__				:				int			=		self._large_tokenizer(a_     , padding=a_     , truncation=a_     , return_tensors='pt'				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self._large_tokenizer(
           text_target=a_     , max_length=5     , padding=a_     , truncation=a_     , return_tensors='pt'				)

       assert batch.input_ids.shape == (2, 1024)
       assert batch.attention_mask.shape == (2, 1024)
       assert targets["input_ids"].shape == (2, 5)
       assert len(a_				) == 2  # input_ids, attention_mask.




  @slow
  def 	__lowercase(      self     :	Any				)->       str:




       """simple docstring"""
       # fmt: off
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		{'input_ids': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 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]], '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, 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, 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=a_     , model_name='google/bigbird-pegasus-large-arxiv'     , revision='ba85d0851d708441f91440d509690f1ab6353415'     , )




@require_sentencepiece
@require_tokenizers
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PegasusTokenizer
  lowercase_			=     PegasusTokenizerFast
  lowercase_			=     True
  lowercase_			=     True




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




       """simple docstring"""
       super().setUp()

       # We have a SentencePiece fixture for testing
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		PegasusTokenizer(a_     , offset=0     , mask_token_sent=a_     , mask_token='[MASK]'				)
       tokenizer.save_pretrained(self.tmpdirname				)




  @cached_property
  def 	__lowercase(      self     :	Optional[Any]				)->       List[str]:




       """simple docstring"""
       return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'				)




  def 	__lowercase(      self     :	List[str]     , **a_     :	Optional[Any]				)->       PegasusTokenizer:




       """simple docstring"""
       return PegasusTokenizer.from_pretrained(self.tmpdirname     , **a_				)




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




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




  def 	__lowercase(      self     :	str				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.tokenizer_class.from_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		(
           'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
           ' <pad> <pad> <pad>'
       )
       SCREAMING_SNAKE_CASE__				:				str			=		rust_tokenizer([raw_input_str]     , return_tensors=a_     , add_special_tokens=a_				).input_ids[0]
       SCREAMING_SNAKE_CASE__				:				str			=		py_tokenizer([raw_input_str]     , return_tensors=a_     , add_special_tokens=a_				).input_ids[0]
       self.assertListEqual(a_     , a_				)




  @require_torch
  def 	__lowercase(      self     :	List[str]				)->       Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		['This is going to be way too long.' * 1000, 'short example']
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		['not super long but more than 5 tokens', 'tiny']
       SCREAMING_SNAKE_CASE__				:				str			=		self._large_tokenizer(a_     , padding=a_     , truncation=a_     , return_tensors='pt'				)
       SCREAMING_SNAKE_CASE__				:				int			=		self._large_tokenizer(
           text_target=a_     , max_length=5     , padding=a_     , truncation=a_     , return_tensors='pt'				)

       assert batch.input_ids.shape == (2, 4096)
       assert batch.attention_mask.shape == (2, 4096)
       assert targets["input_ids"].shape == (2, 5)
       assert len(a_				) == 2  # input_ids, attention_mask.




  def 	__lowercase(      self     :	Dict				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		(
           'This is an example string that is used to test the original TF implementation against the HF'
           ' implementation'
       )

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self._large_tokenizer(a_				).input_ids

       self.assertListEqual(
           a_     , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]     , )



