

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__						=							logging.get_logger(__name__)

A__						=							{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

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

A__						=							{
    "gpt-neox-20b": 2048,
}

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   int       				=  VOCAB_FILES_NAMES
	_lowercase  :   Union[str, Any]       				=  PRETRAINED_VOCAB_FILES_MAP
	_lowercase  :   int       				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowercase  :   Optional[int]       				=  ["input_ids", "attention_mask"]







	def __init__(   self: Tuple  , __UpperCamelCase: str=None  , __UpperCamelCase: Tuple=None  , __UpperCamelCase: Any=None  , __UpperCamelCase: Optional[int]="<|endoftext|>"  , __UpperCamelCase: Dict="<|endoftext|>"  , __UpperCamelCase: Union[str, Any]="<|endoftext|>"  , __UpperCamelCase: List[str]=False  , **__UpperCamelCase: Dict  , ):

						'''simple docstring'''





						super().__init__(
						    __UpperCamelCase  , __UpperCamelCase  , tokenizer_file=__UpperCamelCase  , unk_token=__UpperCamelCase  , bos_token=__UpperCamelCase  , eos_token=__UpperCamelCase  , add_prefix_space=__UpperCamelCase  , **__UpperCamelCase  , )

						__magic_name__							=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
						if pre_tok_state.get('add_prefix_space'  , __UpperCamelCase      ) != add_prefix_space:
											__magic_name__							=    getattr(__UpperCamelCase  , pre_tok_state.pop('type'      )      )
											__magic_name__							=    add_prefix_space
											__magic_name__							=    pre_tok_class(**__UpperCamelCase      )

						__magic_name__							=    add_prefix_space







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[str] = None      ):

						'''simple docstring'''





						__magic_name__							=    self._tokenizer.model.save(__UpperCamelCase  , name=__UpperCamelCase      )
						return tuple(__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: "Conversation"      ):

						'''simple docstring'''





						__magic_name__							=    []
						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:
											__magic_name__							=    input_ids[-self.model_max_length :]
						return input_ids







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__						=							logging.get_logger(__name__)

A__						=							{
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[int]       				=  "camembert"







	def __init__(   self: Optional[Any]  , __UpperCamelCase: Any=3_05_22  , __UpperCamelCase: Tuple=7_68  , __UpperCamelCase: str=12  , __UpperCamelCase: Optional[int]=12  , __UpperCamelCase: List[str]=30_72  , __UpperCamelCase: Any="gelu"  , __UpperCamelCase: Optional[int]=0.1  , __UpperCamelCase: Any=0.1  , __UpperCamelCase: str=5_12  , __UpperCamelCase: Dict=2  , __UpperCamelCase: str=0.02  , __UpperCamelCase: List[Any]=1E-12  , __UpperCamelCase: List[Any]=1  , __UpperCamelCase: Any=0  , __UpperCamelCase: Union[str, Any]=2  , __UpperCamelCase: Dict="absolute"  , __UpperCamelCase: Any=True  , __UpperCamelCase: Any=None  , **__UpperCamelCase: Union[str, Any]  , ):

						'''simple docstring'''





						super().__init__(pad_token_id=__UpperCamelCase  , bos_token_id=__UpperCamelCase  , eos_token_id=__UpperCamelCase  , **__UpperCamelCase      )

						__magic_name__							=    vocab_size
						__magic_name__							=    hidden_size
						__magic_name__							=    num_hidden_layers
						__magic_name__							=    num_attention_heads
						__magic_name__							=    hidden_act
						__magic_name__							=    intermediate_size
						__magic_name__							=    hidden_dropout_prob
						__magic_name__							=    attention_probs_dropout_prob
						__magic_name__							=    max_position_embeddings
						__magic_name__							=    type_vocab_size
						__magic_name__							=    initializer_range
						__magic_name__							=    layer_norm_eps
						__magic_name__							=    position_embedding_type
						__magic_name__							=    use_cache
						__magic_name__							=    classifier_dropout




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						if self.task == "multiple-choice":
											__magic_name__							=    {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											__magic_name__							=    {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]      )




