
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


A     :	Dict    = logging.get_logger(__name__)

A     :	List[Any]    = {'tokenizer_file': 'tokenizer.json'}

A     :	Tuple    = {
    'tokenizer_file': {
        'bigscience/tokenizer': 'https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json',
        'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json',
        'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json',
        'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json',
        'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json',
        'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json',
        'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json',
    },
}







class 			__A( a			):
				snake_case_						   =    VOCAB_FILES_NAMES
				snake_case_						   =    PRETRAINED_VOCAB_FILES_MAP
				snake_case_						   =    ['''input_ids''', '''attention_mask''']
				snake_case_						   =    None




				def __init__(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     _snake_case="<unk>"		,     _snake_case="<s>"		,     _snake_case="</s>"		,     _snake_case="<pad>"		,     _snake_case=False		,     _snake_case=False		,     **_snake_case		,     )    ->      Tuple:


								'''simple docstring'''




								super().__init__(
								    _snake_case		,     _snake_case		,     tokenizer_file=_snake_case		,     unk_token=_snake_case		,     bos_token=_snake_case		,     eos_token=_snake_case		,     pad_token=_snake_case		,     add_prefix_space=_snake_case		,     clean_up_tokenization_spaces=_snake_case		,     **_snake_case		,     )
								__a										=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
								if pre_tok_state.get('''add_prefix_space'''		,     _snake_case				) != add_prefix_space:
												__a										=    getattr(_snake_case		,     pre_tok_state.pop('''type'''				)				)
												__a										=    add_prefix_space
												__a										=    pre_tok_class(**_snake_case				)

								__a										=    add_prefix_space




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      BatchEncoding:


								'''simple docstring'''




								__a										=    kwargs.get('''is_split_into_words'''		,     _snake_case				)
								if not (self.add_prefix_space or not is_split_into_words):
												raise Exception(
												    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
												    ''' pretokenized inputs.'''				)

								return super()._batch_encode_plus(*_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      BatchEncoding:


								'''simple docstring'''




								__a										=    kwargs.get('''is_split_into_words'''		,     _snake_case				)

								if not (self.add_prefix_space or not is_split_into_words):
												raise Exception(
												    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
												    ''' pretokenized inputs.'''				)

								return super()._encode_plus(*_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      Tuple[str]:


								'''simple docstring'''




								__a										=    self._tokenizer.model.save(_snake_case		,     name=_snake_case				)
								return tuple(_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      List[int]:


								'''simple docstring'''




								__a										=    []
								for is_user, text in conversation.iter_texts():
												input_ids.extend(self.encode(_snake_case		,     add_special_tokens=_snake_case				) + [self.eos_token_id]				)

								if len(_snake_case				) > self.model_max_length:
												__a										=    input_ids[-self.model_max_length :]
								return input_ids

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,
)


A     :	Dict    = {
    'configuration_xlm_roberta': [
        'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaConfig',
        'XLMRobertaOnnxConfig',
    ],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Union[str, Any]    = ['XLMRobertaTokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = ['XLMRobertaTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	List[Any]    = [
					    'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'XLMRobertaForCausalLM',
					    'XLMRobertaForMaskedLM',
					    'XLMRobertaForMultipleChoice',
					    'XLMRobertaForQuestionAnswering',
					    'XLMRobertaForSequenceClassification',
					    'XLMRobertaForTokenClassification',
					    'XLMRobertaModel',
					    'XLMRobertaPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = [
					    'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFXLMRobertaForCausalLM',
					    'TFXLMRobertaForMaskedLM',
					    'TFXLMRobertaForMultipleChoice',
					    'TFXLMRobertaForQuestionAnswering',
					    'TFXLMRobertaForSequenceClassification',
					    'TFXLMRobertaForTokenClassification',
					    'TFXLMRobertaModel',
					    'TFXLMRobertaPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Tuple    = [
					    'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaxXLMRobertaForMaskedLM',
					    'FlaxXLMRobertaForCausalLM',
					    'FlaxXLMRobertaForMultipleChoice',
					    'FlaxXLMRobertaForQuestionAnswering',
					    'FlaxXLMRobertaForSequenceClassification',
					    'FlaxXLMRobertaForTokenClassification',
					    'FlaxXLMRobertaModel',
					    'FlaxXLMRobertaPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_xlm_roberta import (
					    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    XLMRobertaConfig,
					    XLMRobertaOnnxConfig,
					)

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlm_roberta import XLMRobertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm_roberta import (
										    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMRobertaForCausalLM,
										    XLMRobertaForMaskedLM,
										    XLMRobertaForMultipleChoice,
										    XLMRobertaForQuestionAnswering,
										    XLMRobertaForSequenceClassification,
										    XLMRobertaForTokenClassification,
										    XLMRobertaModel,
										    XLMRobertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm_roberta import (
										    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMRobertaForCausalLM,
										    TFXLMRobertaForMaskedLM,
										    TFXLMRobertaForMultipleChoice,
										    TFXLMRobertaForQuestionAnswering,
										    TFXLMRobertaForSequenceClassification,
										    TFXLMRobertaForTokenClassification,
										    TFXLMRobertaModel,
										    TFXLMRobertaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_xlm_roberta import (
										    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaxXLMRobertaForCausalLM,
										    FlaxXLMRobertaForMaskedLM,
										    FlaxXLMRobertaForMultipleChoice,
										    FlaxXLMRobertaForQuestionAnswering,
										    FlaxXLMRobertaForSequenceClassification,
										    FlaxXLMRobertaForTokenClassification,
										    FlaxXLMRobertaModel,
										    FlaxXLMRobertaPreTrainedModel,
										)

else:
					import sys

					A     :	Tuple    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)