

import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer

class 				__UpperCamelCase				(					tf.keras.layers.Layer			):







	def __init__(   self: Union[str, Any]  , __UpperCamelCase: Dict[str, int]  , __UpperCamelCase: List[str]  , __UpperCamelCase: int = None  , __UpperCamelCase: int = None      ):

						'''simple docstring'''





						super().__init__()
						__magic_name__							=    pad_token_id
						__magic_name__							=    max_length
						__magic_name__							=    vocab
						__magic_name__							=    merges
						__magic_name__							=    BytePairTokenizer(__UpperCamelCase  , __UpperCamelCase  , sequence_length=__UpperCamelCase      )







	@classmethod
	def 	_SCREAMING_SNAKE_CASE							(   cls: List[str]  , __UpperCamelCase: GPTaTokenizer  , *__UpperCamelCase: Union[str, Any]  , **__UpperCamelCase: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    [' '.join(__UpperCamelCase      ) for m in tokenizer.bpe_ranks.keys()]
						__magic_name__							=    tokenizer.get_vocab()
						return cls(__UpperCamelCase  , __UpperCamelCase  , *__UpperCamelCase  , **__UpperCamelCase      )







	@classmethod
	def 	_SCREAMING_SNAKE_CASE							(   cls: Any  , __UpperCamelCase: Union[str, os.PathLike]  , *__UpperCamelCase: Optional[Any]  , **__UpperCamelCase: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    GPTaTokenizer.from_pretrained(__UpperCamelCase  , *__UpperCamelCase  , **__UpperCamelCase      )
						return cls.from_tokenizer(__UpperCamelCase  , *__UpperCamelCase  , **__UpperCamelCase      )







	@classmethod
	def 	_SCREAMING_SNAKE_CASE							(   cls: Tuple  , __UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						return cls(**__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						return {
						    "vocab": self.vocab,
						    "merges": self.merges,
						    "max_length": self.max_length,
						    "pad_token_id": self.pad_token_id,
						}







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: List[Any]  , __UpperCamelCase: int = None      ):

						'''simple docstring'''





						__magic_name__							=    self.tf_tokenizer(__UpperCamelCase      )
						__magic_name__							=    tf.ones_like(__UpperCamelCase      )

						if self.pad_token_id is not None:
											# pad the tokens up to max length
											__magic_name__							=    max_length if max_length is not None else self.max_length

											if max_length is not None:
																__magic_name__,	__magic_name__							=    pad_model_inputs(
																    __UpperCamelCase  , max_seq_length=__UpperCamelCase  , pad_value=self.pad_token_id      )

						return {"attention_mask": attention_mask, "input_ids": 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),
						    ]      )




