





'''simple docstring'''



import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict




@pytest.mark.parametrize(
    '''files'''		, [
        ['''full:README.md''', '''dataset_infos.json'''],
        ['''empty:README.md''', '''dataset_infos.json'''],
        ['''dataset_infos.json'''],
        ['''full:README.md'''],
    ]		, )
def    _lowerCAmelCase						(				lowerCamelCase_			:  Dict		, lowerCamelCase_			:  int				):
			__lowercase										=							tmp_path_factory.mktemp('''dset_infos_dir'''				)
			if "full:README.md" in files:
						with open(dataset_infos_dir / '''README.md'''		, '''w'''				) as f:
									f.write('''---\ndataset_info:\n  dataset_size: 42\n---'''				)
			if "empty:README.md" in files:
						with open(dataset_infos_dir / '''README.md'''		, '''w'''				) as f:
									f.write(''''''				)
    # we want to support dataset_infos.json for backward compatibility
			if "dataset_infos.json" in files:
						with open(dataset_infos_dir / '''dataset_infos.json'''		, '''w'''				) as f:
									f.write('''{"default": {"dataset_size": 42}}'''				)
			__lowercase										=							DatasetInfosDict.from_directory(lowerCamelCase_				)
			assert dataset_infos
			assert dataset_infos["default"].dataset_size == 4_2




@pytest.mark.parametrize(
    '''dataset_info'''		, [
        DatasetInfo(),
        DatasetInfo(
            description='''foo'''		, features=Features({'''a''': Value('''int32'''				)}				)		, builder_name='''builder'''		, config_name='''config'''		, version='''1.0.0'''		, splits=[{'''name''': '''train'''}]		, download_size=4_2		, ),
    ]		, )
def    _lowerCAmelCase						(				lowerCamelCase_			:  Tuple		, lowerCamelCase_			:  DatasetInfo				):
			__lowercase										=							str(lowerCamelCase_				)
			dataset_info.write_to_directory(lowerCamelCase_				)
			__lowercase										=							DatasetInfo.from_directory(lowerCamelCase_				)
			assert dataset_info == reloaded
			assert os.path.exists(os.path.join(lowerCamelCase_		, '''dataset_info.json'''				)				)




def    _lowerCAmelCase						(				):
			__lowercase										=							DatasetInfo(
			    description='''foo'''		, citation='''bar'''		, homepage='''https://foo.bar'''		, license='''CC0'''		, features=Features({'''a''': Value('''int32'''				)}				)		, post_processed={}		, supervised_keys=()		, task_templates=[]		, builder_name='''builder'''		, config_name='''config'''		, version='''1.0.0'''		, splits=[{'''name''': '''train''', '''num_examples''': 4_2}]		, download_checksums={}		, download_size=1_3_3_7		, post_processing_size=4_4_2		, dataset_size=1_2_3_4		, size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4		, )
			__lowercase										=							dataset_info._to_yaml_dict()
			assert sorted(lowerCamelCase_				) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML				)
			for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
						assert key in dataset_info_yaml_dict
						assert isinstance(dataset_info_yaml_dict[key]		, (list, dict, int, str)				)
			__lowercase										=							yaml.safe_dump(lowerCamelCase_				)
			__lowercase										=							yaml.safe_load(lowerCamelCase_				)
			assert dataset_info_yaml_dict == reloaded




def    _lowerCAmelCase						(				):
			__lowercase										=							DatasetInfo()
			__lowercase										=							dataset_info._to_yaml_dict()
			assert dataset_info_yaml_dict == {}




@pytest.mark.parametrize(
    '''dataset_infos_dict'''		, [
        DatasetInfosDict(),
        DatasetInfosDict({'''default''': DatasetInfo()}				),
        DatasetInfosDict({'''my_config_name''': DatasetInfo()}				),
        DatasetInfosDict(
            {
                '''default''': DatasetInfo(
                    description='''foo'''		, features=Features({'''a''': Value('''int32'''				)}				)		, builder_name='''builder'''		, config_name='''config'''		, version='''1.0.0'''		, splits=[{'''name''': '''train'''}]		, download_size=4_2		, )
            }				),
        DatasetInfosDict(
            {
                '''v1''': DatasetInfo(dataset_size=4_2				),
                '''v2''': DatasetInfo(dataset_size=1_3_3_7				),
            }				),
    ]		, )
def    _lowerCAmelCase						(				lowerCamelCase_			:  int		, lowerCamelCase_			:  DatasetInfosDict				):
			__lowercase										=							str(lowerCamelCase_				)
			dataset_infos_dict.write_to_directory(lowerCamelCase_				)
			__lowercase										=							DatasetInfosDict.from_directory(lowerCamelCase_				)

			# the config_name of the dataset_infos_dict take over the attribute
			for config_name, dataset_info in dataset_infos_dict.items():
						__lowercase										=							config_name
						# the yaml representation doesn't include fields like description or citation
						# so we just test that we can recover what we can from the yaml
						__lowercase										=							DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()				)
			assert dataset_infos_dict == reloaded

			if dataset_infos_dict:
						assert os.path.exists(os.path.join(lowerCamelCase_		, '''README.md'''				)				)












'''simple docstring'''



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


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					=    {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE					=    {
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

_SCREAMING_SNAKE_CASE					=    {
    '''gpt-neox-20b''': 2_0_4_8,
}






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				List[Any]											=       VOCAB_FILES_NAMES
	a    :				Optional[Any]											=       PRETRAINED_VOCAB_FILES_MAP
	a    :				Optional[int]											=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a    :				List[str]											=       ["input_ids", "attention_mask"]


	def __init__(self     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase="<|endoftext|>"     ,_lowerCamelCase="<|endoftext|>"     ,_lowerCamelCase="<|endoftext|>"     ,_lowerCamelCase=False     ,**_lowerCamelCase     ,)							->							Optional[Any]:







				'''simple docstring'''





				super().__init__(
				    _lowerCamelCase     ,_lowerCamelCase     ,tokenizer_file=_lowerCamelCase     ,unk_token=_lowerCamelCase     ,bos_token=_lowerCamelCase     ,eos_token=_lowerCamelCase     ,add_prefix_space=_lowerCamelCase     ,**_lowerCamelCase     ,)

				__lowercase										=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
				if pre_tok_state.get('''add_prefix_space'''     ,_lowerCamelCase ) != add_prefix_space:
							__lowercase										=							getattr(_lowerCamelCase     ,pre_tok_state.pop('''type''' ) )
							__lowercase										=							add_prefix_space
							__lowercase										=							pre_tok_class(**_lowerCamelCase )

				__lowercase										=							add_prefix_space


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None )							->							Tuple[str]:







				'''simple docstring'''





				__lowercase										=							self._tokenizer.model.save(_lowerCamelCase     ,name=_lowerCamelCase )
				return tuple(_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							List[int]:







				'''simple docstring'''





				__lowercase										=							[]
				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:
							__lowercase										=							input_ids[-self.model_max_length :]
				return input_ids





