




"""simple docstring"""







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():
  SCREAMING_SNAKE_CASE_	     =       'pt'
elif is_tf_available():
  SCREAMING_SNAKE_CASE_	     =       'tf'
else:
  SCREAMING_SNAKE_CASE_	     =       'jax'

class 				snake_case_			(					lowerCamelCase_	, unittest.TestCase			):







   """simple docstring"""




   A_				      =       PerceiverTokenizer
   A_				      =       False







   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       super().setUp()
       UpperCamelCase              =	PerceiverTokenizer()
       tokenizer.save_pretrained(self.tmpdirname)







   @cached_property
   def   UpperCAmelCase__			(		self)				->   Any:
       return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''')







   def   UpperCAmelCase__			(		self       ,					**lowerCamelCase_)				->   PerceiverTokenizer:
       return self.tokenizer_class.from_pretrained(self.tmpdirname       ,					**lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_=False       ,					lowerCamelCase_=2_0       ,					lowerCamelCase_=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.

       UpperCamelCase              =	[]
       for i in range(len(lowerCamelCase_)):
           try:
               UpperCamelCase              =	tokenizer.decode([i]       ,					clean_up_tokenization_spaces=lowerCamelCase_)
           except UnicodeDecodeError:
               pass
           toks.append((i, tok))

       UpperCamelCase              =	list(filter(lambda lowerCamelCase_: re.match(R'''^[ a-zA-Z]+$'''       ,					t[1])       ,					lowerCamelCase_))
       UpperCamelCase              =	list(filter(lambda lowerCamelCase_: [t[0]] == tokenizer.encode(t[1]       ,					add_special_tokens=lowerCamelCase_)       ,					lowerCamelCase_))
       if max_length is not None and len(lowerCamelCase_) > max_length:
           UpperCamelCase              =	toks[:max_length]
       if min_length is not None and len(lowerCamelCase_) < min_length and len(lowerCamelCase_) > 0:
           while len(lowerCamelCase_) < min_length:
               UpperCamelCase              =	toks + toks
        # toks_str = [t[1] for t in toks]
       UpperCamelCase              =	[t[0] for t in toks]

       # Ensure consistency
       UpperCamelCase              =	tokenizer.decode(lowerCamelCase_       ,					clean_up_tokenization_spaces=lowerCamelCase_)
       if " " not in output_txt and len(lowerCamelCase_) > 1:
           UpperCamelCase              =	(
               tokenizer.decode([toks_ids[0]]       ,					clean_up_tokenization_spaces=lowerCamelCase_)
               + ''' '''
               + tokenizer.decode(toks_ids[1:]       ,					clean_up_tokenization_spaces=lowerCamelCase_)
           )
       if with_prefix_space:
           UpperCamelCase              =	''' ''' + output_txt
       UpperCamelCase              =	tokenizer.encode(lowerCamelCase_       ,					add_special_tokens=lowerCamelCase_)
       return output_txt, output_ids







   def   UpperCAmelCase__			(		self)				->   int:
       UpperCamelCase              =	self.perceiver_tokenizer
       UpperCamelCase              =	'''Unicode €.'''
       UpperCamelCase              =	tokenizer(lowerCamelCase_)
       UpperCamelCase              =	[4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
       self.assertEqual(encoded['''input_ids''']       ,					lowerCamelCase_)

       # decoding
       UpperCamelCase              =	tokenizer.decode(lowerCamelCase_)
       self.assertEqual(lowerCamelCase_       ,					'''[CLS]Unicode €.[SEP]''')

       UpperCamelCase              =	tokenizer('''e è é ê ë''')
       UpperCamelCase              =	[4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
       self.assertEqual(encoded['''input_ids''']       ,					lowerCamelCase_)
       # decoding
       UpperCamelCase              =	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   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	self.perceiver_tokenizer
       UpperCamelCase              =	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       # fmt: off
       UpperCamelCase              =	[4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
       # fmt: on
       UpperCamelCase              =	tokenizer(lowerCamelCase_       ,					padding=lowerCamelCase_       ,					return_tensors=lowerCamelCase_)
       self.assertIsInstance(lowerCamelCase_       ,					lowerCamelCase_)

       if FRAMEWORK != "jax":
           UpperCamelCase              =	list(batch.input_ids.numpy()[0])
       else:
           UpperCamelCase              =	list(batch.input_ids.tolist()[0])

       self.assertListEqual(lowerCamelCase_       ,					lowerCamelCase_)

       self.assertEqual((2, 3_8)       ,					batch.input_ids.shape)
       self.assertEqual((2, 3_8)       ,					batch.attention_mask.shape)







   def   UpperCAmelCase__			(		self)				->   List[str]:
       UpperCamelCase              =	self.perceiver_tokenizer
       UpperCamelCase              =	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       UpperCamelCase              =	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   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	self.perceiver_tokenizer
       UpperCamelCase              =	[
           '''Summary of the text.''',
           '''Another summary.''',
       ]
       UpperCamelCase              =	tokenizer(
           text_target=lowerCamelCase_       ,					max_length=3_2       ,					padding='''max_length'''       ,					truncation=lowerCamelCase_       ,					return_tensors=lowerCamelCase_)
       self.assertEqual(3_2       ,					targets['''input_ids'''].shape[1])







   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       # safety check on max_len default value so we are sure the test works
       UpperCamelCase              =	self.get_tokenizers()
       for tokenizer in tokenizers:
           with self.subTest(F'{tokenizer.__class__.__name__}'):
               self.assertNotEqual(tokenizer.model_max_length       ,					4_2)

        # Now let's start the test
       UpperCamelCase              =	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
               UpperCamelCase              =	tempfile.mkdtemp()

               UpperCamelCase              =	''' He is very happy, UNwant\u00E9d,running'''
               UpperCamelCase              =	tokenizer.encode(lowerCamelCase_       ,					add_special_tokens=lowerCamelCase_)
               tokenizer.save_pretrained(lowerCamelCase_)

               UpperCamelCase              =	tokenizer.__class__.from_pretrained(lowerCamelCase_)
               UpperCamelCase              =	after_tokenizer.encode(lowerCamelCase_       ,					add_special_tokens=lowerCamelCase_)
               self.assertListEqual(lowerCamelCase_       ,					lowerCamelCase_)

               shutil.rmtree(lowerCamelCase_)

       UpperCamelCase              =	self.get_tokenizers(model_max_length=4_2)
       for tokenizer in tokenizers:
           with self.subTest(F'{tokenizer.__class__.__name__}'):
               # Isolate this from the other tests because we save additional tokens/etc
               UpperCamelCase              =	tempfile.mkdtemp()

               UpperCamelCase              =	''' He is very happy, UNwant\u00E9d,running'''
               tokenizer.add_tokens(['''bim''', '''bambam'''])
               UpperCamelCase              =	tokenizer.additional_special_tokens
               additional_special_tokens.append('''new_additional_special_token''')
               tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens})
               UpperCamelCase              =	tokenizer.encode(lowerCamelCase_       ,					add_special_tokens=lowerCamelCase_)
               tokenizer.save_pretrained(lowerCamelCase_)

               UpperCamelCase              =	tokenizer.__class__.from_pretrained(lowerCamelCase_)
               UpperCamelCase              =	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       ,					4_2)

               UpperCamelCase              =	tokenizer.__class__.from_pretrained(lowerCamelCase_       ,					model_max_length=4_3)
               self.assertEqual(tokenizer.model_max_length       ,					4_3)

               shutil.rmtree(lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   str:
       UpperCamelCase              =	[]
       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:
                   UpperCamelCase              =	json.load(lowerCamelCase_)

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

               UpperCamelCase              =	[F'<extra_id_{i}>' for i in range(1_2_5)]

               UpperCamelCase              =	added_tokens_extra_ids + [
                   '''an_additional_special_token'''
               ]
               UpperCamelCase              =	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
               UpperCamelCase              =	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
               UpperCamelCase              =	added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''       ,					lstrip=lowerCamelCase_)]
               UpperCamelCase              =	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   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	self.perceiver_tokenizer
       self.assertEqual(tokenizer.decode([1_7_8])       ,					'''�''')







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       pass







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







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       pass







   def   UpperCAmelCase__			(		self)				->   Tuple:
       pass







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

               self.assertIsInstance(lowerCamelCase_       ,					lowerCamelCase_)



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case   							=						logging.get_logger(__name__)

__snake_case   							=						{
    '''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/config.json''',
    '''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/config.json''',
    '''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/config.json''',
    '''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/config.json''',
    '''bert-base-multilingual-uncased''': '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json''',
    '''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json''',
    '''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/config.json''',
    '''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/config.json''',
    '''bert-large-uncased-whole-word-masking''': (
        '''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'''
    ),
    '''bert-large-cased-whole-word-masking''': (
        '''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'''
    ),
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
        '''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
    ),
    '''bert-large-cased-whole-word-masking-finetuned-squad''': (
        '''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
    ),
    '''bert-base-cased-finetuned-mrpc''': '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json''',
    '''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json''',
    '''bert-base-german-dbmdz-uncased''': '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json''',
    '''cl-tohoku/bert-base-japanese''': '''https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json''',
    '''cl-tohoku/bert-base-japanese-whole-word-masking''': (
        '''https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'''
    ),
    '''cl-tohoku/bert-base-japanese-char''': (
        '''https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'''
    ),
    '''cl-tohoku/bert-base-japanese-char-whole-word-masking''': (
        '''https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'''
    ),
    '''TurkuNLP/bert-base-finnish-cased-v1''': (
        '''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'''
    ),
    '''TurkuNLP/bert-base-finnish-uncased-v1''': (
        '''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'''
    ),
    '''wietsedv/bert-base-dutch-cased''': '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json''',
    # See all BERT models at https://huggingface.co/models?filter=bert
}



class 	__lowerCamelCase			(_a						):
       _lowercase											=     """bert"""







       def __init__(			self: Any,A_: Dict=3_0522,A_: Optional[Any]=768,A_: Union[str, Any]=12,A_: List[Any]=12,A_: Optional[int]=3072,A_: Union[str, Any]="gelu",A_: List[str]=0.1,A_: Dict=0.1,A_: Optional[int]=512,A_: Optional[Any]=2,A_: Union[str, Any]=0.0_2,A_: List[Any]=1E-12,A_: Optional[int]=0,A_: List[Any]="absolute",A_: str=True,A_: Union[str, Any]=None,**A_: int,):






            '''simple docstring'''




            super().__init__(pad_token_id=A_,**A_					)

            __UpperCamelCase =			vocab_size
            __UpperCamelCase =			hidden_size
            __UpperCamelCase =			num_hidden_layers
            __UpperCamelCase =			num_attention_heads
            __UpperCamelCase =			hidden_act
            __UpperCamelCase =			intermediate_size
            __UpperCamelCase =			hidden_dropout_prob
            __UpperCamelCase =			attention_probs_dropout_prob
            __UpperCamelCase =			max_position_embeddings
            __UpperCamelCase =			type_vocab_size
            __UpperCamelCase =			initializer_range
            __UpperCamelCase =			layer_norm_eps
            __UpperCamelCase =			position_embedding_type
            __UpperCamelCase =			use_cache
            __UpperCamelCase =			classifier_dropout



class 	__lowerCamelCase			(_a						):







       @property
       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            if self.task == "multiple-choice":
                 __UpperCamelCase =			{0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                 __UpperCamelCase =			{0: 'batch', 1: 'sequence'}
            return OrderedDict(
                [
                    ('input_ids', dynamic_axis),
                    ('attention_mask', dynamic_axis),
                    ('token_type_ids', dynamic_axis),
                ]					)


