





import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
    _UpperCAmelCase				:       str	   =				'pt'
elif is_tf_available():
    _UpperCAmelCase				:       Dict	   =				'tf'
else:
    _UpperCAmelCase				:       Dict	   =				'jax'

class    lowerCAmelCase							(					A__,  unittest.TestCase			):
 UpperCAmelCase__													=		PerceiverTokenizer
 UpperCAmelCase__													=		False
 def      A_	(      self  :      Any	)			->	str:
     super().setUp()
     lowerCamelCase__    :      int					 =		PerceiverTokenizer()
     tokenizer.save_pretrained(self.tmpdirname	)
 @cached_property
 def      A_	(      self  :      str	)			->	List[Any]:
     return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'	)
 def      A_	(      self  :      List[Any]			,     **UpperCAmelCase  :      Optional[int]	)			->	PerceiverTokenizer:
     return self.tokenizer_class.from_pretrained(self.tmpdirname			,     **lowerCAmelCase__	)
 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      Optional[int]			,     UpperCAmelCase  :      Union[str, Any]=False			,     UpperCAmelCase  :      List[Any]=20			,     UpperCAmelCase  :      str=5	)			->	Tuple[str, list]:
     # XXX The default common tokenizer tests assume that every ID is decodable on its own.
     # This assumption is invalid for Perceiver because single bytes might not be
     # valid utf-8 (byte 128 for instance).
     # Here we're overriding the smallest possible method to provide
     # a clean sequence without making the same assumption.

     lowerCamelCase__    :      Union[str, Any]					 =		[]
     for i in range(len(lowerCAmelCase__	)	):
         try:
             lowerCamelCase__    :      List[Any]					 =		tokenizer.decode([i]			,     clean_up_tokenization_spaces=lowerCAmelCase__	)
         except UnicodeDecodeError:
             pass
         toks.append((i, tok)	)

     lowerCamelCase__    :      Optional[int]					 =		list(filter(lambda UpperCAmelCase	: re.match(R'^[ a-zA-Z]+$'			,     t[1]	)			,     lowerCAmelCase__	)	)
     lowerCamelCase__    :      Union[str, Any]					 =		list(filter(lambda UpperCAmelCase	: [t[0]] == tokenizer.encode(t[1]			,     add_special_tokens=lowerCAmelCase__	)			,     lowerCAmelCase__	)	)
     if max_length is not None and len(lowerCAmelCase__	) > max_length:
         lowerCamelCase__    :      Dict					 =		toks[:max_length]
     if min_length is not None and len(lowerCAmelCase__	) < min_length and len(lowerCAmelCase__	) > 0:
         while len(lowerCAmelCase__	) < min_length:
             lowerCamelCase__    :      List[Any]					 =		toks + toks
        # toks_str = [t[1] for t in toks]
     lowerCamelCase__    :      Optional[int]					 =		[t[0] for t in toks]

     # Ensure consistency
     lowerCamelCase__    :      Dict					 =		tokenizer.decode(lowerCAmelCase__			,     clean_up_tokenization_spaces=lowerCAmelCase__	)
     if " " not in output_txt and len(lowerCAmelCase__	) > 1:
         lowerCamelCase__    :      Any					 =		(
             tokenizer.decode([toks_ids[0]]			,     clean_up_tokenization_spaces=lowerCAmelCase__	)
             + ''' '''
             + tokenizer.decode(toks_ids[1:]			,     clean_up_tokenization_spaces=lowerCAmelCase__	)
         )
     if with_prefix_space:
         lowerCamelCase__    :      List[str]					 =		''' ''' + output_txt
     lowerCamelCase__    :      List[Any]					 =		tokenizer.encode(lowerCAmelCase__			,     add_special_tokens=lowerCAmelCase__	)
     return output_txt, output_ids
 def      A_	(      self  :      Union[str, Any]	)			->	Tuple:
     lowerCamelCase__    :      Any					 =		self.perceiver_tokenizer
     lowerCamelCase__    :      str					 =		'''Unicode €.'''
     lowerCamelCase__    :      Any					 =		tokenizer(lowerCAmelCase__	)
     lowerCamelCase__    :      Tuple					 =		[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
     self.assertEqual(encoded['input_ids']			,     lowerCAmelCase__	)

     # decoding
     lowerCamelCase__    :      List[Any]					 =		tokenizer.decode(lowerCAmelCase__	)
     self.assertEqual(lowerCAmelCase__			,     '[CLS]Unicode €.[SEP]'	)

     lowerCamelCase__    :      List[str]					 =		tokenizer('e è é ê ë'	)
     lowerCamelCase__    :      Tuple					 =		[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
     self.assertEqual(encoded['input_ids']			,     lowerCAmelCase__	)
     # decoding
     lowerCamelCase__    :      Any					 =		tokenizer.decode(lowerCAmelCase__	)
     self.assertEqual(lowerCAmelCase__			,     '[CLS]e è é ê ë[SEP]'	)

     # encode/decode, but with `encode` instead of `__call__`
     self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'	)	)			,     '[CLS]e è é ê ë[SEP]'	)
 def      A_	(      self  :      List[Any]	)			->	int:
     lowerCamelCase__    :      List[Any]					 =		self.perceiver_tokenizer
     lowerCamelCase__    :      Dict					 =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     # fmt: off
     lowerCamelCase__    :      Any					 =		[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
     # fmt: on
     lowerCamelCase__    :      Tuple					 =		tokenizer(lowerCAmelCase__			,     padding=lowerCAmelCase__			,     return_tensors=lowerCAmelCase__	)
     self.assertIsInstance(lowerCAmelCase__			,     lowerCAmelCase__	)

     if FRAMEWORK != "jax":
         lowerCamelCase__    :      Union[str, Any]					 =		list(batch.input_ids.numpy()[0]	)
     else:
         lowerCamelCase__    :      str					 =		list(batch.input_ids.tolist()[0]	)

     self.assertListEqual(lowerCAmelCase__			,     lowerCAmelCase__	)

     self.assertEqual((2, 38)			,     batch.input_ids.shape	)
     self.assertEqual((2, 38)			,     batch.attention_mask.shape	)
 def      A_	(      self  :      Union[str, Any]	)			->	List[Any]:
     lowerCamelCase__    :      List[str]					 =		self.perceiver_tokenizer
     lowerCamelCase__    :      Optional[Any]					 =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     lowerCamelCase__    :      List[Any]					 =		tokenizer(lowerCAmelCase__			,     padding=lowerCAmelCase__			,     return_tensors=lowerCAmelCase__	)
     # check if input_ids are returned and no decoder_input_ids
     self.assertIn('input_ids'			,     lowerCAmelCase__	)
     self.assertIn('attention_mask'			,     lowerCAmelCase__	)
     self.assertNotIn('decoder_input_ids'			,     lowerCAmelCase__	)
     self.assertNotIn('decoder_attention_mask'			,     lowerCAmelCase__	)
 def      A_	(      self  :      List[Any]	)			->	List[str]:
     lowerCamelCase__    :      Dict					 =		self.perceiver_tokenizer
     lowerCamelCase__    :      int					 =		[
         '''Summary of the text.''',
         '''Another summary.''',
     ]
     lowerCamelCase__    :      Optional[int]					 =		tokenizer(
         text_target=lowerCAmelCase__			,     max_length=32			,     padding='max_length'			,     truncation=lowerCAmelCase__			,     return_tensors=lowerCAmelCase__	)
     self.assertEqual(32			,     targets['input_ids'].shape[1]	)
 def      A_	(      self  :      Union[str, Any]	)			->	Dict:
     # safety check on max_len default value so we are sure the test works
     lowerCamelCase__    :      Union[str, Any]					 =		self.get_tokenizers()
     for tokenizer in tokenizers:
         with self.subTest(F"""{tokenizer.__class__.__name__}"""	):
             self.assertNotEqual(tokenizer.model_max_length			,     42	)

        # Now let's start the test
     lowerCamelCase__    :      List[str]					 =		self.get_tokenizers()
     for tokenizer in tokenizers:
         with self.subTest(F"""{tokenizer.__class__.__name__}"""	):
             # Isolate this from the other tests because we save additional tokens/etc
             lowerCamelCase__    :      Dict					 =		tempfile.mkdtemp()

             lowerCamelCase__    :      Tuple					 =		''' He is very happy, UNwant\u00E9d,running'''
             lowerCamelCase__    :      Any					 =		tokenizer.encode(lowerCAmelCase__			,     add_special_tokens=lowerCAmelCase__	)
             tokenizer.save_pretrained(lowerCAmelCase__	)

             lowerCamelCase__    :      int					 =		tokenizer.__class__.from_pretrained(lowerCAmelCase__	)
             lowerCamelCase__    :      Optional[int]					 =		after_tokenizer.encode(lowerCAmelCase__			,     add_special_tokens=lowerCAmelCase__	)
             self.assertListEqual(lowerCAmelCase__			,     lowerCAmelCase__	)

             shutil.rmtree(lowerCAmelCase__	)

     lowerCamelCase__    :      Union[str, Any]					 =		self.get_tokenizers(model_max_length=42	)
     for tokenizer in tokenizers:
         with self.subTest(F"""{tokenizer.__class__.__name__}"""	):
             # Isolate this from the other tests because we save additional tokens/etc
             lowerCamelCase__    :      int					 =		tempfile.mkdtemp()

             lowerCamelCase__    :      List[str]					 =		''' He is very happy, UNwant\u00E9d,running'''
             tokenizer.add_tokens(['bim', 'bambam']	)
             lowerCamelCase__    :      Dict					 =		tokenizer.additional_special_tokens
             additional_special_tokens.append('new_additional_special_token'	)
             tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}	)
             lowerCamelCase__    :      Optional[int]					 =		tokenizer.encode(lowerCAmelCase__			,     add_special_tokens=lowerCAmelCase__	)
             tokenizer.save_pretrained(lowerCAmelCase__	)

             lowerCamelCase__    :      Tuple					 =		tokenizer.__class__.from_pretrained(lowerCAmelCase__	)
             lowerCamelCase__    :      List[Any]					 =		after_tokenizer.encode(lowerCAmelCase__			,     add_special_tokens=lowerCAmelCase__	)
             self.assertListEqual(lowerCAmelCase__			,     lowerCAmelCase__	)
             self.assertIn('new_additional_special_token'			,     after_tokenizer.additional_special_tokens	)
             self.assertEqual(after_tokenizer.model_max_length			,     42	)

             lowerCamelCase__    :      Optional[int]					 =		tokenizer.__class__.from_pretrained(lowerCAmelCase__			,     model_max_length=43	)
             self.assertEqual(tokenizer.model_max_length			,     43	)

             shutil.rmtree(lowerCAmelCase__	)
 def      A_	(      self  :      List[Any]	)			->	Optional[int]:
     lowerCamelCase__    :      Tuple					 =		[]
     if self.test_slow_tokenizer:
         tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())	)

     if self.test_rust_tokenizer:
         tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())	)

     for tokenizer_class, tokenizer_utils in tokenizer_list:
         with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer_utils.save_pretrained(lowerCAmelCase__	)

             with open(os.path.join(lowerCAmelCase__			,     'special_tokens_map.json'	)			,     encoding='utf-8'	) as json_file:
                 lowerCamelCase__    :      Tuple					 =		json.load(lowerCAmelCase__	)

             with open(os.path.join(lowerCAmelCase__			,     'tokenizer_config.json'	)			,     encoding='utf-8'	) as json_file:
                 lowerCamelCase__    :      int					 =		json.load(lowerCAmelCase__	)

             lowerCamelCase__    :      Union[str, Any]					 =		[F"""<extra_id_{i}>""" for i in range(125	)]

             lowerCamelCase__    :      List[Any]					 =		added_tokens_extra_ids + [
                 '''an_additional_special_token'''
             ]
             lowerCamelCase__    :      Tuple					 =		added_tokens_extra_ids + [
                 '''an_additional_special_token'''
             ]

             with open(os.path.join(lowerCAmelCase__			,     'special_tokens_map.json'	)			,     'w'			,     encoding='utf-8'	) as outfile:
                 json.dump(lowerCAmelCase__			,     lowerCAmelCase__	)
             with open(os.path.join(lowerCAmelCase__			,     'tokenizer_config.json'	)			,     'w'			,     encoding='utf-8'	) as outfile:
                 json.dump(lowerCAmelCase__			,     lowerCAmelCase__	)

             # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
             # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
             # "special_tokens_map.json" files
             lowerCamelCase__    :      Optional[int]					 =		tokenizer_class.from_pretrained(
                 lowerCAmelCase__			,     )
             self.assertIn(
                 'an_additional_special_token'			,     tokenizer_without_change_in_init.additional_special_tokens	)
             self.assertEqual(
                 ['an_additional_special_token']			,     tokenizer_without_change_in_init.convert_ids_to_tokens(
                     tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']	)	)			,     )

             # Now we test that we can change the value of additional_special_tokens in the from_pretrained
             lowerCamelCase__    :      Any					 =		added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'			,     lstrip=lowerCAmelCase__	)]
             lowerCamelCase__    :      Any					 =		tokenizer_class.from_pretrained(
                 lowerCAmelCase__			,     additional_special_tokens=lowerCAmelCase__			,     )

             self.assertIn('a_new_additional_special_token'			,     tokenizer.additional_special_tokens	)
             self.assertEqual(
                 ['a_new_additional_special_token']			,     tokenizer.convert_ids_to_tokens(
                     tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']	)	)			,     )
 def      A_	(      self  :      Optional[Any]	)			->	List[Any]:
     lowerCamelCase__    :      Optional[Any]					 =		self.perceiver_tokenizer
     self.assertEqual(tokenizer.decode([178]	)			,     '�'	)
 def      A_	(      self  :      Any	)			->	Dict:
     pass
 def      A_	(      self  :      Optional[int]	)			->	List[Any]:
     pass
 def      A_	(      self  :      Any	)			->	str:
     pass
 def      A_	(      self  :      Optional[int]	)			->	Union[str, Any]:
     pass







 def      A_	(      self  :      Union[str, Any]	)			->	str:
     # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
     # strings and special added tokens as tokens
     lowerCamelCase__    :      Tuple					 =		self.get_tokenizers(fast=lowerCAmelCase__			,     do_lower_case=lowerCAmelCase__	)
     for tokenizer in tokenizers:
         with self.subTest(F"""{tokenizer.__class__.__name__}"""	):
             lowerCamelCase__    :      int					 =		['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
             lowerCamelCase__    :      Any					 =		tokenizer.convert_tokens_to_string(lowerCAmelCase__	)

             self.assertIsInstance(lowerCAmelCase__			,     lowerCAmelCase__	)









import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


__lowerCAmelCase	: Optional[int]  				=logging.get_logger(__name__)

__lowerCAmelCase	: Optional[Any]  				={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase	: List[str]  				={
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase	: Optional[int]  				={
    'gpt-neox-20b': 2_0_4_8,
}
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			str						=		VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__				:			Dict						=		PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__				:			Any						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__				:			Optional[Any]						=		['''input_ids''', '''attention_mask''']


				def __init__(       self			:int			,			lowerCAmelCase__			:Any=None			,			lowerCAmelCase__			:Optional[Any]=None			,			lowerCAmelCase__			:List[Any]=None			,			lowerCAmelCase__			:str="<|endoftext|>"			,			lowerCAmelCase__			:str="<|endoftext|>"			,			lowerCAmelCase__			:Dict="<|endoftext|>"			,			lowerCAmelCase__			:Union[str, Any]=False			,			**lowerCAmelCase__			:List[str]			,			)		->			Any:
										super().__init__(
										    lowerCAmelCase__			,			lowerCAmelCase__			,			tokenizer_file=lowerCAmelCase__			,			unk_token=lowerCAmelCase__			,			bos_token=lowerCAmelCase__			,			eos_token=lowerCAmelCase__			,			add_prefix_space=lowerCAmelCase__			,			**lowerCAmelCase__			,			)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
										if pre_tok_state.get('''add_prefix_space'''			,			lowerCAmelCase__	) != add_prefix_space:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		getattr(lowerCAmelCase__			,			pre_tok_state.pop('''type'''	)	)
																__SCREAMING_SNAKE_CASE						:     str					=		add_prefix_space
																__SCREAMING_SNAKE_CASE						:     Any					=		pre_tok_class(**lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		add_prefix_space


				def 		__magic_name__(       self			:Union[str, Any]			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Optional[str] = None	)		->			Tuple[str]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self._tokenizer.model.save(lowerCAmelCase__			,			name=lowerCAmelCase__	)
										return tuple(lowerCAmelCase__	)



				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:"Conversation"	)		->			List[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[]
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(lowerCAmelCase__			,			add_special_tokens=lowerCAmelCase__	) + [self.eos_token_id]	)

										if len(lowerCAmelCase__	) > self.model_max_length:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		input_ids[-self.model_max_length :]
										return input_ids
