
'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__lowerCAmelCase  							= {
    'configuration_align': [
        'ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AlignConfig',
        'AlignTextConfig',
        'AlignVisionConfig',
    ],
    'processing_align': ['AlignProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase  							= [
				    'ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'AlignModel',
				    'AlignPreTrainedModel',
				    'AlignTextModel',
				    'AlignVisionModel',
				]

if TYPE_CHECKING:
				from .configuration_align import (
				    ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AlignConfig,
				    AlignTextConfig,
				    AlignVisionConfig,
				)
				from .processing_align import AlignProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_align import (
								    ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AlignModel,
								    AlignPreTrainedModel,
								    AlignTextModel,
								    AlignVisionModel,
								)

else:
				import sys

				__lowerCAmelCase  							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

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


if is_sentencepiece_available():
				from .tokenization_xglm import XGLMTokenizer
else:
				__lowerCAmelCase  							= None


__lowerCAmelCase  							= logging.get_logger(__name__)

__lowerCAmelCase  							= {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase  							= {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase  							= {
    'facebook/xglm-564M': 2_048,
}


class     _lowerCAmelCase					(					__snake_case   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		VOCAB_FILES_NAMES
							lowerCAmelCase_      		=		PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase_      		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase_      		=		["input_ids", "attention_mask"]
							lowerCAmelCase_      		=		XGLMTokenizer

							def __init__(self						,						UpperCAmelCase=None						,						UpperCAmelCase=None						,						UpperCAmelCase="<s>"						,						UpperCAmelCase="</s>"						,						UpperCAmelCase="</s>"						,						UpperCAmelCase="<s>"						,						UpperCAmelCase="<unk>"						,						UpperCAmelCase="<pad>"						,						**UpperCAmelCase						,						)  ->     List[Any]:
														# Compatibility with the original tokenizer
														_snake_case     =						7
														_snake_case     =						[f"""<madeupword{i}>""" for i in range(self.num_madeup_words						)]

														_snake_case     =						kwargs.get("""additional_special_tokens"""						,						[]						)
														kwargs["additional_special_tokens"] += [
														    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
														]

														super().__init__(
														    UpperCAmelCase						,						tokenizer_file=UpperCAmelCase						,						bos_token=UpperCAmelCase						,						eos_token=UpperCAmelCase						,						sep_token=UpperCAmelCase						,						cls_token=UpperCAmelCase						,						unk_token=UpperCAmelCase						,						pad_token=UpperCAmelCase						,						**UpperCAmelCase						,						)

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

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     List[int]:

														if token_ids_a is None:
																					return [self.sep_token_id] + token_ids_a
														_snake_case     =						[self.sep_token_id]
														return sep + token_ids_a + sep + sep + token_ids_a

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     List[int]:
														_snake_case     =						[self.sep_token_id]

														if token_ids_a is None:
																					return len(sep + token_ids_a						) * [0]
														return len(sep + token_ids_a + sep + sep + token_ids_a						) * [0]





							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     Tuple[str]:
														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(UpperCAmelCase						):
																					logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""						)
																					return
														_snake_case     =						os.path.join(
														    UpperCAmelCase						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

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

														return (out_vocab_file,)