



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():
   _lowercase    	=		"pt"
elif is_tf_available():
   _lowercase    	=		"tf"
else:
   _lowercase    	=		"jax"



class      _UpperCAmelCase      (							A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					PerceiverTokenizer
     UpperCamelCase__		     =					False




     def 		snake_case_		(					self):
         super().setUp()
         A__					=       PerceiverTokenizer()
         tokenizer.save_pretrained(self.tmpdirname)




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




     def 		snake_case_		(					self			,    **a__):
         return self.tokenizer_class.from_pretrained(self.tmpdirname			,    **a__)




     def 		snake_case_		(					self			,    a__			,    a__=False			,    a__=2_0			,    a__=5):
         # 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.

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

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

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




     def 		snake_case_		(					self):
         A__					=       self.perceiver_tokenizer
         A__					=       '''Unicode €.'''
         A__					=       tokenizer(a__)
         A__					=       [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''']			,    a__)

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

         A__					=       tokenizer('''e è é ê ë''')
         A__					=       [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''']			,    a__)
         # decoding
         A__					=       tokenizer.decode(a__)
         self.assertEqual(a__			,    '''[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 		snake_case_		(					self):
         A__					=       self.perceiver_tokenizer
         A__					=       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         # fmt: off
         A__					=       [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
         A__					=       tokenizer(a__			,    padding=a__			,    return_tensors=a__)
         self.assertIsInstance(a__			,    a__)

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

         self.assertListEqual(a__			,    a__)

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




     def 		snake_case_		(					self):
         A__					=       self.perceiver_tokenizer
         A__					=       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         A__					=       tokenizer(a__			,    padding=a__			,    return_tensors=a__)
         # check if input_ids are returned and no decoder_input_ids
         self.assertIn('''input_ids'''			,    a__)
         self.assertIn('''attention_mask'''			,    a__)
         self.assertNotIn('''decoder_input_ids'''			,    a__)
         self.assertNotIn('''decoder_attention_mask'''			,    a__)




     def 		snake_case_		(					self):
         A__					=       self.perceiver_tokenizer
         A__					=       [
             '''Summary of the text.''',
             '''Another summary.''',
         ]
         A__					=       tokenizer(
             text_target=a__			,    max_length=3_2			,    padding='''max_length'''			,    truncation=a__			,    return_tensors=a__)
         self.assertEqual(3_2			,    targets['''input_ids'''].shape[1])




     def 		snake_case_		(					self):
         # safety check on max_len default value so we are sure the test works
         A__					=       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
         A__					=       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
                 A__					=       tempfile.mkdtemp()

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

                 A__					=       tokenizer.__class__.from_pretrained(a__)
                 A__					=       after_tokenizer.encode(a__			,    add_special_tokens=a__)
                 self.assertListEqual(a__			,    a__)

                 shutil.rmtree(a__)

         A__					=       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
                 A__					=       tempfile.mkdtemp()

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

                 A__					=       tokenizer.__class__.from_pretrained(a__)
                 A__					=       after_tokenizer.encode(a__			,    add_special_tokens=a__)
                 self.assertListEqual(a__			,    a__)
                 self.assertIn('''new_additional_special_token'''			,    after_tokenizer.additional_special_tokens)
                 self.assertEqual(after_tokenizer.model_max_length			,    4_2)

                 A__					=       tokenizer.__class__.from_pretrained(a__			,    model_max_length=4_3)
                 self.assertEqual(tokenizer.model_max_length			,    4_3)

                 shutil.rmtree(a__)




     def 		snake_case_		(					self):
         A__					=       []
         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(a__)

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

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

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

                 A__					=       added_tokens_extra_ids + [
                     '''an_additional_special_token'''
                 ]
                 A__					=       added_tokens_extra_ids + [
                     '''an_additional_special_token'''
                 ]

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

                 # 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
                 A__					=       tokenizer_class.from_pretrained(
                     a__			,    )
                 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
                 A__					=       added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''			,    lstrip=a__)]
                 A__					=       tokenizer_class.from_pretrained(
                     a__			,    additional_special_tokens=a__			,    )

                 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 		snake_case_		(					self):
         A__					=       self.perceiver_tokenizer
         self.assertEqual(tokenizer.decode([1_7_8])			,    '''�''')




     def 		snake_case_		(					self):
         pass




     def 		snake_case_		(					self):
         pass




     def 		snake_case_		(					self):
         pass




     def 		snake_case_		(					self):
         pass




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

                 self.assertIsInstance(a__			,    a__)





# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

_lowercase    	=		"2.13.1"

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("3.7"):
   raise ImportWarning(
       "To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."
   )

if version.parse(pyarrow.__version__).major < 8:
   raise ImportWarning(
       "To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
       "If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
   )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

_lowercase    	=		concatenate_datasets
_lowercase    	=		DownloadConfig
_lowercase    	=		DownloadManager
_lowercase    	=		DownloadMode
_lowercase    	=		DownloadConfig
_lowercase    	=		DownloadMode
_lowercase    	=		DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager
