


"""simple docstring"""







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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


a__				:							Union[str, Any]						=     logging.get_logger(__name__)

a__				:							str						=     {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a__				:							Dict						=     {
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

a__				:							Dict						=     {
    '''gpt-neox-20b''': 2_0_4_8,
}




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  List[Any]    					=			VOCAB_FILES_NAMES
	snake_case__				:  Dict    					=			PRETRAINED_VOCAB_FILES_MAP
	snake_case__				:  Union[str, Any]    					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__				:  Union[str, Any]    					=			["input_ids", "attention_mask"]




	def __init__( self			:				Union[str, Any]			,			UpperCAmelCase__			:				int=None			,			UpperCAmelCase__			:				List[str]=None			,			UpperCAmelCase__			:				str=None			,			UpperCAmelCase__			:				Union[str, Any]="<|endoftext|>"			,			UpperCAmelCase__			:				Optional[Any]="<|endoftext|>"			,			UpperCAmelCase__			:				List[str]="<|endoftext|>"			,			UpperCAmelCase__			:				List[Any]=False			,			**UpperCAmelCase__			:				Optional[int]			,			)       ->	Optional[Any]:
						super().__init__(
						    UpperCAmelCase__			,			UpperCAmelCase__			,			tokenizer_file=UpperCAmelCase__			,			unk_token=UpperCAmelCase__			,			bos_token=UpperCAmelCase__			,			eos_token=UpperCAmelCase__			,			add_prefix_space=UpperCAmelCase__			,			**UpperCAmelCase__			,			)

						__SCREAMING_SNAKE_CASE						   =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
						if pre_tok_state.get("add_prefix_space"			,			UpperCAmelCase__   ) != add_prefix_space:
											__SCREAMING_SNAKE_CASE						   =				getattr(UpperCAmelCase__			,			pre_tok_state.pop("type"   )   )
											__SCREAMING_SNAKE_CASE						   =				add_prefix_space
											__SCREAMING_SNAKE_CASE						   =				pre_tok_class(**UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				add_prefix_space




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				str			,			UpperCAmelCase__			:				Optional[str] = None   )       ->	Tuple[str]:
						__SCREAMING_SNAKE_CASE						   =				self._tokenizer.model.save(UpperCAmelCase__			,			name=UpperCAmelCase__   )
						return tuple(UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				"Conversation"   )       ->	List[int]:
						__SCREAMING_SNAKE_CASE						   =				[]
						for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(UpperCAmelCase__			,			add_special_tokens=UpperCAmelCase__   ) + [self.eos_token_id]   )

						if len(UpperCAmelCase__   ) > self.model_max_length:
											__SCREAMING_SNAKE_CASE						   =				input_ids[-self.model_max_length :]
						return input_ids




"""simple docstring"""







from typing import TYPE_CHECKING

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


a__				:							str						=     {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     [
		    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RoFormerForCausalLM''',
		    '''RoFormerForMaskedLM''',
		    '''RoFormerForMultipleChoice''',
		    '''RoFormerForQuestionAnswering''',
		    '''RoFormerForSequenceClassification''',
		    '''RoFormerForTokenClassification''',
		    '''RoFormerLayer''',
		    '''RoFormerModel''',
		    '''RoFormerPreTrainedModel''',
		    '''load_tf_weights_in_roformer''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							List[Any]						=     [
		    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFRoFormerForCausalLM''',
		    '''TFRoFormerForMaskedLM''',
		    '''TFRoFormerForMultipleChoice''',
		    '''TFRoFormerForQuestionAnswering''',
		    '''TFRoFormerForSequenceClassification''',
		    '''TFRoFormerForTokenClassification''',
		    '''TFRoFormerLayer''',
		    '''TFRoFormerModel''',
		    '''TFRoFormerPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							Tuple						=     [
		    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''FlaxRoFormerForMaskedLM''',
		    '''FlaxRoFormerForMultipleChoice''',
		    '''FlaxRoFormerForQuestionAnswering''',
		    '''FlaxRoFormerForSequenceClassification''',
		    '''FlaxRoFormerForTokenClassification''',
		    '''FlaxRoFormerModel''',
		    '''FlaxRoFormerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		a__				:							int						=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
