



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 					snake_case__						(       tf.keras.layers.Layer     ):






     """simple docstring"""


     def __init__(       self  :  List[Any], _snake_case  :  Dict[str, int], _snake_case  :  List[str], _snake_case  :  int = None, _snake_case  :  int = None					)   ->Tuple:
            super().__init__()
            snake_case__							:	str   	=	pad_token_id
            snake_case__							:	List[Any]   	=	max_length
            snake_case__							:	str   	=	vocab
            snake_case__							:	List[Any]   	=	merges
            snake_case__							:	int   	=	BytePairTokenizer(_snake_case, _snake_case, sequence_length=_snake_case					)






     @classmethod
     def    lowercase_   (       cls  :  Optional[int], _snake_case  :  GPTaTokenizer, *_snake_case  :  Any, **_snake_case  :  Any					)   ->List[Any]:
            snake_case__							:	Tuple   	=	[' '.join(_snake_case					) for m in tokenizer.bpe_ranks.keys()]
            snake_case__							:	int   	=	tokenizer.get_vocab()
            return cls(_snake_case, _snake_case, *_snake_case, **_snake_case					)






     @classmethod
     def    lowercase_   (       cls  :  Tuple, _snake_case  :  Union[str, os.PathLike], *_snake_case  :  List[str], **_snake_case  :  Tuple					)   ->Dict:
            snake_case__							:	List[Any]   	=	GPTaTokenizer.from_pretrained(_snake_case, *_snake_case, **_snake_case					)
            return cls.from_tokenizer(_snake_case, *_snake_case, **_snake_case					)






     @classmethod
     def    lowercase_   (       cls  :  int, _snake_case  :  List[Any]					)   ->Dict:
            return cls(**_snake_case					)






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Tuple:
            return {
                "vocab": self.vocab,
                "merges": self.merges,
                "max_length": self.max_length,
                "pad_token_id": self.pad_token_id,
            }






     def    lowercase_   (       self  :  str, _snake_case  :  Any, _snake_case  :  int = None					)   ->int:
            snake_case__							:	Optional[Any]   	=	self.tf_tokenizer(_snake_case					)
            snake_case__							:	Tuple   	=	tf.ones_like(_snake_case					)

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

                   if max_length is not None:
                          snake_case__	,				snake_case__							:	Union[str, Any]   	=	pad_model_inputs(
                              _snake_case, max_seq_length=_snake_case, pad_value=self.pad_token_id					)

            return {"attention_mask": attention_mask, "input_ids": input_ids}











from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_   :Optional[int]        =      logging.get_logger(__name__)

a_   :Dict        =      {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""openai-gpt"""
     _SCREAMING_SNAKE_CASE						=	{
         """max_position_embeddings""": """n_positions""",
         """hidden_size""": """n_embd""",
         """num_attention_heads""": """n_head""",
         """num_hidden_layers""": """n_layer""",
     }






     def __init__(       self  :  Optional[int], _snake_case  :  Dict=4_0_4_7_8, _snake_case  :  str=5_1_2, _snake_case  :  int=7_6_8, _snake_case  :  Tuple=1_2, _snake_case  :  Any=1_2, _snake_case  :  str="gelu", _snake_case  :  List[str]=0.1, _snake_case  :  Any=0.1, _snake_case  :  Dict=0.1, _snake_case  :  int=1e-5, _snake_case  :  Optional[Any]=0.0_2, _snake_case  :  List[Any]="cls_index", _snake_case  :  Any=True, _snake_case  :  Any=None, _snake_case  :  int=True, _snake_case  :  Optional[Any]=0.1, **_snake_case  :  List[Any], )   ->Optional[int]:
            snake_case__							:	int   	=	vocab_size
            snake_case__							:	Dict   	=	n_positions
            snake_case__							:	str   	=	n_embd
            snake_case__							:	str   	=	n_layer
            snake_case__							:	List[Any]   	=	n_head
            snake_case__							:	List[Any]   	=	afn
            snake_case__							:	Optional[Any]   	=	resid_pdrop
            snake_case__							:	List[str]   	=	embd_pdrop
            snake_case__							:	List[Any]   	=	attn_pdrop
            snake_case__							:	Optional[int]   	=	layer_norm_epsilon
            snake_case__							:	str   	=	initializer_range
            snake_case__							:	List[str]   	=	summary_type
            snake_case__							:	Optional[int]   	=	summary_use_proj
            snake_case__							:	List[str]   	=	summary_activation
            snake_case__							:	Optional[Any]   	=	summary_first_dropout
            snake_case__							:	int   	=	summary_proj_to_labels
            super().__init__(**_snake_case					)






