






import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase			:		Optional[Any]   				=    logging.get_logger(__name__)

_UpperCAmelCase			:		Union[str, Any]   				=    {"vocab_file": "sentencepiece.model"}

_UpperCAmelCase			:		Any   				=    {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

_UpperCAmelCase			:		Optional[Any]   				=    {
    "google/rembert": 256,
}
class  lowerCAmelCase_       (				_lowercase					):
							UpperCamelCase_		:Optional[int]		      =    VOCAB_FILES_NAMES
							UpperCamelCase_		:List[Any]		      =    PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase_		:Optional[int]		      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




							def __init__(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Any=False ,							SCREAMING_SNAKE_CASE_     :   List[str]=True ,							SCREAMING_SNAKE_CASE_     :   Tuple=True ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]="[CLS]" ,							SCREAMING_SNAKE_CASE_     :   Any="[SEP]" ,							SCREAMING_SNAKE_CASE_     :   Any="[UNK]" ,							SCREAMING_SNAKE_CASE_     :   str="[SEP]" ,							SCREAMING_SNAKE_CASE_     :   int="[PAD]" ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]="[CLS]" ,							SCREAMING_SNAKE_CASE_     :   Dict="[MASK]" ,							**SCREAMING_SNAKE_CASE_     :   Any ,							):
											super().__init__(
											    do_lower_case=A_ ,							remove_space=A_ ,							keep_accents=A_ ,							bos_token=A_ ,							eos_token=A_ ,							unk_token=A_ ,							sep_token=A_ ,							pad_token=A_ ,							cls_token=A_ ,							mask_token=A_ ,							**A_ ,							)

											lowerCAmelCase__      			=						do_lower_case
											lowerCAmelCase__      			=						remove_space
											lowerCAmelCase__      			=						keep_accents
											lowerCAmelCase__      			=						vocab_file

											lowerCAmelCase__      			=						spm.SentencePieceProcessor()
											self.sp_model.Load(A_	)




							@property
							def 						__snake_case					(   self     :   Union[str, Any]	):
											return len(self.sp_model	)




							def 						__snake_case					(   self     :   Any	):
											lowerCAmelCase__      			=						{self.convert_ids_to_tokens(A_	): i for i in range(self.vocab_size	)}
											vocab.update(self.added_tokens_encoder	)
											return vocab




							def __getstate__(   self     :   Dict	):
											lowerCAmelCase__      			=						self.__dict__.copy()
											lowerCAmelCase__      			=						None
											return state




							def __setstate__(   self     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   str	):
											lowerCAmelCase__      			=						d
											lowerCAmelCase__      			=						spm.SentencePieceProcessor()
											self.sp_model.Load(self.vocab_file	)




							def 						__snake_case					(   self     :   int ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=False	):
											lowerCAmelCase__      			=						self.sp_model.EncodeAsPieces(A_	)
											return pieces




							def 						__snake_case					(   self     :   str ,							SCREAMING_SNAKE_CASE_     :   List[Any]	):
											return self.sp_model.PieceToId(A_	)




							def 						__snake_case					(   self     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   Any	):
											return self.sp_model.IdToPiece(A_	)




							def 						__snake_case					(   self     :   int ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]	):
											lowerCAmelCase__      			=						self.sp_model.decode_pieces(A_	)
											return out_string




							def 						__snake_case					(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   List[int] ,							SCREAMING_SNAKE_CASE_     :   Optional[List[int]] = None	):
											lowerCAmelCase__      			=						[self.sep_token_id]
											lowerCAmelCase__      			=						[self.cls_token_id]
											if token_ids_a is None:
															return cls + token_ids_a + sep
											return cls + token_ids_a + sep + token_ids_a + sep




							def 						__snake_case					(   self     :   str ,							SCREAMING_SNAKE_CASE_     :   List[int] ,							SCREAMING_SNAKE_CASE_     :   Optional[List[int]] = None ,							SCREAMING_SNAKE_CASE_     :   bool = False	):

											if already_has_special_tokens:
															if token_ids_a is not None:
																			raise ValueError(
																			    '''You should not supply a second sequence if the provided sequence of '''
																			    '''ids is already formatted with special tokens for the model.'''	)
															return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

											if token_ids_a is not None:
															return [1] + ([0] * len(A_	)) + [1] + ([0] * len(A_	)) + [1]
											return [1] + ([0] * len(A_	)) + [1]




							def 						__snake_case					(   self     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   List[int] ,							SCREAMING_SNAKE_CASE_     :   Optional[List[int]] = None	):
											lowerCAmelCase__      			=						[self.sep_token_id]
											lowerCAmelCase__      			=						[self.cls_token_id]

											if token_ids_a is None:
															return len(cls + token_ids_a + sep	) * [0]
											return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]




							def 						__snake_case					(   self     :   str ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   Optional[str] = None	):
											if not os.path.isdir(A_	):
															logger.error('''Vocabulary path ({}) should be a directory'''.format(A_	)	)
															return
											lowerCAmelCase__      			=						os.path.join(
											    A_ ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

											if os.path.abspath(self.vocab_file	) != os.path.abspath(A_	):
															copyfile(self.vocab_file ,							A_	)

											return (out_vocab_file,)













from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_UpperCAmelCase			:		Tuple   				=    logging.get_logger(__name__)


_UpperCAmelCase			:		int   				=    {
    "Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
    "Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
    "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
    "Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
    "Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
    "Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
    "Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
    "Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
    "Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
    "Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
    "Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
    "Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:int		      =    'codegen'
							UpperCamelCase_		:int		      =    {
							    'max_position_embeddings': 'n_positions',
							    'hidden_size': 'n_embd',
							    'num_attention_heads': 'n_head',
							    'num_hidden_layers': 'n_layer',
							}




							def __init__(   self     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   str=50_400 ,							SCREAMING_SNAKE_CASE_     :   str=2_048 ,							SCREAMING_SNAKE_CASE_     :   int=2_048 ,							SCREAMING_SNAKE_CASE_     :   Any=4_096 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=28 ,							SCREAMING_SNAKE_CASE_     :   str=16 ,							SCREAMING_SNAKE_CASE_     :   str=64 ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							SCREAMING_SNAKE_CASE_     :   Dict="gelu_new" ,							SCREAMING_SNAKE_CASE_     :   str=0.0 ,							SCREAMING_SNAKE_CASE_     :   Any=0.0 ,							SCREAMING_SNAKE_CASE_     :   Any=0.0 ,							SCREAMING_SNAKE_CASE_     :   Any=1e-5 ,							SCREAMING_SNAKE_CASE_     :   Any=0.02 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=True ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=50_256 ,							SCREAMING_SNAKE_CASE_     :   Any=50_256 ,							SCREAMING_SNAKE_CASE_     :   List[str]=False ,							**SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							):
											lowerCAmelCase__      			=						vocab_size
											lowerCAmelCase__      			=						n_ctx
											lowerCAmelCase__      			=						n_positions
											lowerCAmelCase__      			=						n_embd
											lowerCAmelCase__      			=						n_layer
											lowerCAmelCase__      			=						n_head
											lowerCAmelCase__      			=						n_inner
											lowerCAmelCase__      			=						rotary_dim
											lowerCAmelCase__      			=						activation_function
											lowerCAmelCase__      			=						resid_pdrop
											lowerCAmelCase__      			=						embd_pdrop
											lowerCAmelCase__      			=						attn_pdrop
											lowerCAmelCase__      			=						layer_norm_epsilon
											lowerCAmelCase__      			=						initializer_range
											lowerCAmelCase__      			=						use_cache

											lowerCAmelCase__      			=						bos_token_id
											lowerCAmelCase__      			=						eos_token_id

											super().__init__(
											    bos_token_id=SCREAMING_SNAKE_CASE_ ,							eos_token_id=SCREAMING_SNAKE_CASE_ ,							tie_word_embeddings=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)



class  lowerCAmelCase_       (				snake_case__					):




							def __init__(   self     :   str ,							SCREAMING_SNAKE_CASE_     :   PretrainedConfig ,							SCREAMING_SNAKE_CASE_     :   str = "default" ,							SCREAMING_SNAKE_CASE_     :   List[PatchingSpec] = None ,							SCREAMING_SNAKE_CASE_     :   bool = False ,							):
											super().__init__(SCREAMING_SNAKE_CASE_ ,							task=SCREAMING_SNAKE_CASE_ ,							patching_specs=SCREAMING_SNAKE_CASE_ ,							use_past=SCREAMING_SNAKE_CASE_	)
											if not getattr(self._config ,							'''pad_token_id''' ,							SCREAMING_SNAKE_CASE_	):
															# TODO: how to do that better?
															lowerCAmelCase__      			=						0




							@property
							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
											if self.use_past:
															self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_ ,							direction='''inputs'''	)
															lowerCAmelCase__      			=						{0: '''batch''', 1: '''past_sequence + sequence'''}
											else:
															lowerCAmelCase__      			=						{0: '''batch''', 1: '''sequence'''}

											return common_inputs




							@property
							def 						__snake_case					(   self     :   Dict	):
											return self._config.n_layer




							@property
							def 						__snake_case					(   self     :   Union[str, Any]	):
											return self._config.n_head




							def 						__snake_case					(   self     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   PreTrainedTokenizer ,							SCREAMING_SNAKE_CASE_     :   int = -1 ,							SCREAMING_SNAKE_CASE_     :   int = -1 ,							SCREAMING_SNAKE_CASE_     :   bool = False ,							SCREAMING_SNAKE_CASE_     :   Optional[TensorType] = None ,							):
											lowerCAmelCase__      			=						super(SCREAMING_SNAKE_CASE_ ,							self	).generate_dummy_inputs(
											    SCREAMING_SNAKE_CASE_ ,							batch_size=SCREAMING_SNAKE_CASE_ ,							seq_length=SCREAMING_SNAKE_CASE_ ,							is_pair=SCREAMING_SNAKE_CASE_ ,							framework=SCREAMING_SNAKE_CASE_	)

											# We need to order the input in the way they appears in the forward()
											lowerCAmelCase__      			=						OrderedDict({'''input_ids''': common_inputs['''input_ids''']}	)

											# Need to add the past_keys
											if self.use_past:
															if not is_torch_available():
																			raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
															else:
																			import torch

																			lowerCAmelCase__			,		lowerCAmelCase__      			=						common_inputs['''input_ids'''].shape
																			# Not using the same length for past_key_values
																			lowerCAmelCase__      			=						seqlen + 2
																			lowerCAmelCase__      			=						(
																			    batch,
																			    self.num_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // self.num_attention_heads,
																			)
																			lowerCAmelCase__      			=						[
																			    (torch.zeros(SCREAMING_SNAKE_CASE_	), torch.zeros(SCREAMING_SNAKE_CASE_	)) for _ in range(self.num_layers	)
																			]

											lowerCAmelCase__      			=						common_inputs['''attention_mask''']
											if self.use_past:
															lowerCAmelCase__      			=						ordered_inputs['''attention_mask'''].dtype
															lowerCAmelCase__      			=						torch.cat(
															    [ordered_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							dtype=SCREAMING_SNAKE_CASE_	)] ,							dim=1	)

											return ordered_inputs




							@property
							def 						__snake_case					(   self     :   Optional[int]	):
											return 13





