


"""simple docstring"""
import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_reformer import ReformerTokenizer
else:
				__SCREAMING_SNAKE_CASE				:     Union[str, Any]		=     None


__SCREAMING_SNAKE_CASE				:     int		=     logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE				:     Union[str, Any]		=     """▁"""

__SCREAMING_SNAKE_CASE				:     Optional[int]		=     {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE				:     Tuple		=     {
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    },
    """tokenizer_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"""
        )
    },
}

__SCREAMING_SNAKE_CASE				:     List[Any]		=     {
    """google/reformer-crime-and-punishment""": 524_288,
}





class   __A  (__snake_case):





							'''simple docstring'''

							__lowercase:		str				   =     VOCAB_FILES_NAMES
							__lowercase:		Tuple				   =     PRETRAINED_VOCAB_FILES_MAP
							__lowercase:		Optional[int]				   =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowercase:		Dict				   =     ["""input_ids""", """attention_mask"""]
							__lowercase:		Optional[int]				   =     ReformerTokenizer



							def __init__(					self				:   Dict		,							UpperCAmelCase_				:   List[str]=None		,							UpperCAmelCase_				:   str=None		,							UpperCAmelCase_				:   str="</s>"		,							UpperCAmelCase_				:   Dict="<unk>"		,							UpperCAmelCase_				:   List[str]=[]		,							**UpperCAmelCase_				:   Optional[Any]		,							)       ->Optional[int]:



														"""simple docstring"""



														super().__init__(
														    a_		,							tokenizer_file=a_		,							eos_token=a_		,							unk_token=a_		,							additional_special_tokens=a_		,							**a_		,							)

														snake_case_     							=		vocab_file
														snake_case_     							=		False if not self.vocab_file else True



							def 		lowerCAmelCase							(					self				:   Dict		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   int = None							)       ->int:



														"""simple docstring"""



														if not self.can_save_slow_tokenizer:
																					raise ValueError(
																					    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																					    """tokenizer."""							)

														if not os.path.isdir(a_							):
																					logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""							)
																					return
														snake_case_     							=		os.path.join(
														    a_		,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

														if os.path.abspath(self.vocab_file							) != os.path.abspath(a_							):
																					copyfile(self.vocab_file		,							a_							)

														return (out_vocab_file,)







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE					:       Tuple							   =							{
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							["""RealmTokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Dict							   =							[
			    """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """RealmEmbedder""",
			    """RealmForOpenQA""",
			    """RealmKnowledgeAugEncoder""",
			    """RealmPreTrainedModel""",
			    """RealmReader""",
			    """RealmScorer""",
			    """load_tf_weights_in_realm""",
			]
			SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							["""RealmRetriever"""]


if TYPE_CHECKING:
			from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
			from .tokenization_realm import RealmTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_realm import RealmTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_realm import (
						    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RealmEmbedder,
						    RealmForOpenQA,
						    RealmKnowledgeAugEncoder,
						    RealmPreTrainedModel,
						    RealmReader,
						    RealmScorer,
						    load_tf_weights_in_realm,
						)
						from .retrieval_realm import RealmRetriever


else:
			import sys

			SCREAMING_SNAKE_CASE					:       str							   =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





