






import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


A_       :Tuple       =						namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)






def 							A     (       a_							,a_					)     ->						Dict:
					return (abs(source - target					) / target) < 0.01






@pytest.mark.integration
def 							A     (       a_					)     ->						Dict:
					__UpperCamelCase      :					Optional[int]       		=_TestCommandArgs(dataset=a_							,all_configs=a_							,save_infos=a_					)
					__UpperCamelCase      :					Any       		=TestCommand(*a_					)
					test_command.run()
					__UpperCamelCase      :					List[str]       		=os.path.join(a_							,'README.md'					)
					assert os.path.exists(a_					)
					__UpperCamelCase      :					Any       		=DatasetInfosDict.from_directory(a_					)
					__UpperCamelCase      :					int       		=DatasetInfosDict(
					    {
					        'default': DatasetInfo(
					            features=Features(
					                {
					                    'tokens': Sequence(Value('string'					)					),
					                    'ner_tags': Sequence(
					                        ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']					)					),
					                    'langs': Sequence(Value('string'					)					),
					                    'spans': Sequence(Value('string'					)					),
					                }					)							,splits=[
					                {
					                    'name': 'train',
					                    'num_bytes': 2_351_563,
					                    'num_examples': 10_000,
					                },
					                {
					                    'name': 'validation',
					                    'num_bytes': 238_418,
					                    'num_examples': 1_000,
					                },
					            ]							,download_size=3_940_680							,dataset_size=2_589_981							,)
					    }					)
					assert dataset_infos.keys() == expected_dataset_infos.keys()
					for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
										__UpperCamelCase		,				__UpperCamelCase      :					Optional[Any]       		=getattr(dataset_infos['default']							,a_					), getattr(expected_dataset_infos['default']							,a_					)
										if key == "num_bytes":
															assert is_apercent_close(a_							,a_					)
										elif key == "splits":
															assert list(a_					) == list(a_					)
															for split in result:
																				assert result[split].name == expected[split].name
																				assert result[split].num_examples == expected[split].num_examples
																				assert is_apercent_close(result[split].num_bytes							,expected[split].num_bytes					)
										else:
															result == expected





"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


__UpperCamelCase						=      logging.get_logger(__name__)

__UpperCamelCase						=      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCamelCase						=      [
    '''small''',
    '''small-base''',
    '''medium''',
    '''medium-base''',
    '''intermediate''',
    '''intermediate-base''',
    '''large''',
    '''large-base''',
    '''xlarge''',
    '''xlarge-base''',
]

__UpperCamelCase						=      {
    '''vocab_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
        '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
        '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
        '''funnel-transformer/small-base''': (
            '''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
        '''funnel-transformer/large-base''': (
            '''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
        ),
    },
}
__UpperCamelCase						=      {f'''funnel-transformer/{name}''': 512 for name in _model_names}
__UpperCamelCase						=      {f'''funnel-transformer/{name}''': {'''do_lower_case''': True} for name in _model_names}
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     int								=     VOCAB_FILES_NAMES
		SCREAMING_SNAKE_CASE_						:     List[str]								=     PRETRAINED_VOCAB_FILES_MAP
		SCREAMING_SNAKE_CASE_						:     List[Any]								=     PRETRAINED_INIT_CONFIGURATION
		SCREAMING_SNAKE_CASE_						:     str								=     FunnelTokenizer
		SCREAMING_SNAKE_CASE_						:     Dict								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		SCREAMING_SNAKE_CASE_						:     int								=     2







		def __init__(	self						,       lowerCAmelCase__=None						,       lowerCAmelCase__=None						,       lowerCAmelCase__=True						,       lowerCAmelCase__="<unk>"						,       lowerCAmelCase__="<sep>"						,       lowerCAmelCase__="<pad>"						,       lowerCAmelCase__="<cls>"						,       lowerCAmelCase__="<mask>"						,       lowerCAmelCase__="<s>"						,       lowerCAmelCase__="</s>"						,       lowerCAmelCase__=True						,       lowerCAmelCase__=True						,       lowerCAmelCase__=None						,       lowerCAmelCase__="##"						,       **lowerCAmelCase__						,       )     ->							Tuple:
								super().__init__(
								    lowerCAmelCase__						,       tokenizer_file=lowerCAmelCase__						,       do_lower_case=lowerCAmelCase__						,       unk_token=lowerCAmelCase__						,       sep_token=lowerCAmelCase__						,       pad_token=lowerCAmelCase__						,       cls_token=lowerCAmelCase__						,       mask_token=lowerCAmelCase__						,       bos_token=lowerCAmelCase__						,       eos_token=lowerCAmelCase__						,       clean_text=lowerCAmelCase__						,       tokenize_chinese_chars=lowerCAmelCase__						,       strip_accents=lowerCAmelCase__						,       wordpieces_prefix=lowerCAmelCase__						,       **lowerCAmelCase__						,       )

								SCREAMING_SNAKE_CASE													=					json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
								if (
								    normalizer_state.get('lowercase'						,       lowerCAmelCase__				) != do_lower_case
								    or normalizer_state.get('strip_accents'						,       lowerCAmelCase__				) != strip_accents
								    or normalizer_state.get('handle_chinese_chars'						,       lowerCAmelCase__				) != tokenize_chinese_chars
								):
														SCREAMING_SNAKE_CASE													=					getattr(lowerCAmelCase__						,       normalizer_state.pop('type'				)				)
														SCREAMING_SNAKE_CASE													=					do_lower_case
														SCREAMING_SNAKE_CASE													=					strip_accents
														SCREAMING_SNAKE_CASE													=					tokenize_chinese_chars
														SCREAMING_SNAKE_CASE													=					normalizer_class(**lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					do_lower_case







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__=None				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
														output += token_ids_a + [self.sep_token_id]

								return output







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None				)     ->							List[int]:
								SCREAMING_SNAKE_CASE													=					[self.sep_token_id]
								SCREAMING_SNAKE_CASE													=					[self.cls_token_id]
								if token_ids_a is None:
														return len(cls				) * [self.cls_token_type_id] + len(token_ids_a + sep				) * [0]
								return len(cls				) * [self.cls_token_type_id] + len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None				)     ->							Tuple[str]:
								SCREAMING_SNAKE_CASE													=					self._tokenizer.model.save(lowerCAmelCase__						,       name=lowerCAmelCase__				)
								return tuple(lowerCAmelCase__				)


