


"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

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


if is_sentencepiece_available():
		from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
		lowercase_							 =       None


lowercase_							 =       logging.get_logger(__name__)

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

lowercase_							 =       {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
        ),
    },
}

lowercase_							 =       {
    'xlm-roberta-base': 5_12,
    'xlm-roberta-large': 5_12,
    'xlm-roberta-large-finetuned-conll02-dutch': 5_12,
    'xlm-roberta-large-finetuned-conll02-spanish': 5_12,
    'xlm-roberta-large-finetuned-conll03-english': 5_12,
    'xlm-roberta-large-finetuned-conll03-german': 5_12,
}







class       __a	(   __snake_case       ):
				lowerCamelCase    :					Optional[int] 					=VOCAB_FILES_NAMES
				lowerCamelCase    :					Dict 					=PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase    :					str 					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase    :					Union[str, Any] 					=['input_ids', 'attention_mask']
				lowerCamelCase    :					Tuple 					=XLMRobertaTokenizer







				def __init__(   self			,  UpperCAmelCase=None			,  UpperCAmelCase=None			,  UpperCAmelCase="<s>"			,  UpperCAmelCase="</s>"			,  UpperCAmelCase="</s>"			,  UpperCAmelCase="<s>"			,  UpperCAmelCase="<unk>"			,  UpperCAmelCase="<pad>"			,  UpperCAmelCase="<mask>"			,  **UpperCAmelCase			,  ):

					'''simple docstring'''






					lowerCAmelCase_			       = AddedToken(UpperCAmelCase			,  lstrip=UpperCAmelCase			,  rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase			,  UpperCAmelCase      ) else mask_token

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

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







				def     lowerCamelCase_    (   self			,  UpperCAmelCase			,  UpperCAmelCase = None      ):

					'''simple docstring'''






					if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					lowerCAmelCase_			       = [self.cls_token_id]
					lowerCAmelCase_			       = [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep







				def     lowerCamelCase_    (   self			,  UpperCAmelCase			,  UpperCAmelCase = None      ):

					'''simple docstring'''






					lowerCAmelCase_			       = [self.sep_token_id]
					lowerCAmelCase_			       = [self.cls_token_id]

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







				def     lowerCamelCase_    (   self			,  UpperCAmelCase			,  UpperCAmelCase = None      ):

					'''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(UpperCAmelCase      ):
						logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""      )
						return
					lowerCAmelCase_			       = 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,)



"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_							 =       {'configuration_pegasus': ['PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusConfig']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							 =       ['PegasusTokenizer']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							 =       ['PegasusTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							 =       [
		    'PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'PegasusForCausalLM',
		    'PegasusForConditionalGeneration',
		    'PegasusModel',
		    'PegasusPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							 =       [
		    'TFPegasusForConditionalGeneration',
		    'TFPegasusModel',
		    'TFPegasusPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							 =       [
		    'FlaxPegasusForConditionalGeneration',
		    'FlaxPegasusModel',
		    'FlaxPegasusPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus import PegasusTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus_fast import PegasusTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_pegasus import (
				    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PegasusForCausalLM,
				    PegasusForConditionalGeneration,
				    PegasusModel,
				    PegasusPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_pegasus import (
				    FlaxPegasusForConditionalGeneration,
				    FlaxPegasusModel,
				    FlaxPegasusPreTrainedModel,
				)

else:
		import sys

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