


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 					_UpperCamelCase (      lowerCamelCase__	):







							'''simple docstring'''
							_A  				=  VOCAB_FILES_NAMES
							_A  				=  PRETRAINED_VOCAB_FILES_MAP
							_A  				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_A  				=  ["input_ids", "attention_mask"]
							_A  				=  XLMRobertaTokenizer







							def __init__(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				Dict=None		,				SCREAMING_SNAKE_CASE_  :				int=None		,				SCREAMING_SNAKE_CASE_  :				Tuple="<s>"		,				SCREAMING_SNAKE_CASE_  :				List[Any]="</s>"		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]="</s>"		,				SCREAMING_SNAKE_CASE_  :				List[str]="<s>"		,				SCREAMING_SNAKE_CASE_  :				Any="<unk>"		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]="<pad>"		,				SCREAMING_SNAKE_CASE_  :				Any="<mask>"		,				**SCREAMING_SNAKE_CASE_  :				Tuple		,				):
													# Mask token behave like a normal word, i.e. include the space before it
													_a     =			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_		,				)

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







							def 		_UpperCAmelCase	(		self  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				List[int]		,				SCREAMING_SNAKE_CASE_  :				Optional[List[int]] = None		):

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







							def 		_UpperCAmelCase	(		self  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				List[int]		,				SCREAMING_SNAKE_CASE_  :				Optional[List[int]] = None		):
													_a     =			[self.sep_token_id]
													_a     =			[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 		_UpperCAmelCase	(		self  :				Dict		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				Optional[str] = None		):
													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
													_a     =			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,)




from typing import TYPE_CHECKING

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


lowercase_								=    {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_								=    [
		    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'CodeGenForCausalLM',
		    'CodeGenModel',
		    'CodeGenPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
		from .tokenization_codegen import CodeGenTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_codegen_fast import CodeGenTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_codegen import (
				    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CodeGenForCausalLM,
				    CodeGenModel,
				    CodeGenPreTrainedModel,
				)

else:
		import sys

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


