



'''simple docstring'''



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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_camembert import CamembertTokenizer
else:
							lowerCAmelCase_  :							List[Any]             =    None


lowerCAmelCase_  :							Any             =    logging.get_logger(__name__)

lowerCAmelCase_  :							int             =    {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase_  :							Union[str, Any]             =    {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase_  :							Dict             =    {
    "camembert-base": 512,
}

lowerCAmelCase_  :							List[str]             =    "▁"
class 						lowerCamelCase_				(       __a      ):
						_lowerCAmelCase  : Dict						  =					VOCAB_FILES_NAMES
						_lowerCAmelCase  : Optional[int]						  =					PRETRAINED_VOCAB_FILES_MAP
						_lowerCAmelCase  : int						  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCAmelCase  : Tuple						  =					['''input_ids''', '''attention_mask''']
						_lowerCAmelCase  : str						  =					CamembertTokenizer







						def __init__(	self :   List[str]					,     lowerCAmelCase__ :   Tuple=None					,     lowerCAmelCase__ :   Union[str, Any]=None					,     lowerCAmelCase__ :   int="<s>"					,     lowerCAmelCase__ :   Optional[Any]="</s>"					,     lowerCAmelCase__ :   Optional[Any]="</s>"					,     lowerCAmelCase__ :   Dict="<s>"					,     lowerCAmelCase__ :   Any="<unk>"					,     lowerCAmelCase__ :   Dict="<pad>"					,     lowerCAmelCase__ :   Tuple="<mask>"					,     lowerCAmelCase__ :   Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"]					,     **lowerCAmelCase__ :   str					,     ):

													"""simple docstring"""
													# Mask token behave like a normal word, i.e. include the space before it
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      AddedToken(__UpperCamelCase					,     lstrip=__UpperCamelCase					,     rstrip=__UpperCamelCase					) if isinstance(__UpperCamelCase					,     __UpperCamelCase					) else mask_token

													super().__init__(
													    __UpperCamelCase					,     tokenizer_file=__UpperCamelCase					,     bos_token=__UpperCamelCase					,     eos_token=__UpperCamelCase					,     sep_token=__UpperCamelCase					,     cls_token=__UpperCamelCase					,     unk_token=__UpperCamelCase					,     pad_token=__UpperCamelCase					,     mask_token=__UpperCamelCase					,     additional_special_tokens=__UpperCamelCase					,     **__UpperCamelCase					,     )

													SCREAMING_SNAKE_CASE       :   Tuple         =      vocab_file
													SCREAMING_SNAKE_CASE       :   List[Any]         =      False if not self.vocab_file else True







						def        __lowercase     (	self :   Union[str, Any]					,     lowerCAmelCase__ :   List[str]					,     lowerCAmelCase__ :   Optional[Any] = None					):

													"""simple docstring"""

													if token_ids_a is None:
																				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													SCREAMING_SNAKE_CASE       :   str         =      [self.cls_token_id]
													SCREAMING_SNAKE_CASE       :   List[str]         =      [self.sep_token_id]
													return cls + token_ids_a + sep + sep + token_ids_a + sep







						def        __lowercase     (	self :   str					,     lowerCAmelCase__ :   str					,     lowerCAmelCase__ :   Optional[Any] = None					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      [self.sep_token_id]
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      [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 + sep + token_ids_a + sep					) * [0]







						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   Optional[Any]					,     lowerCAmelCase__ :   Optional[int] = None					):

													"""simple docstring"""
													if not self.can_save_slow_tokenizer:
																				raise ValueError(
																				    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																				    '''tokenizer.'''					)

													if not os.path.isdir(__UpperCamelCase					):
																				logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
																				return
													SCREAMING_SNAKE_CASE       :   Dict         =      os.path.join(
													    __UpperCamelCase					,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

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

													return (out_vocab_file,)





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

from packaging import version


if TYPE_CHECKING:
	from ... import PreTrainedTokenizer, TensorType

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


SCREAMING_SNAKE_CASE		:     List[str]         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     str         =	{
    "bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
    "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
    "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
    "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
    "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
    "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}

class        UpperCamelCase						(				__a				):
						a__		:Any      =			'''bloom'''
						a__		:Optional[int]      =			['''past_key_values''']
						a__		:Tuple      =			{
						    '''num_hidden_layers''': '''n_layer''',
						    '''num_attention_heads''': '''n_head''',
						}





						def __init__(self			,     __UpperCamelCase=250_880			,     __UpperCamelCase=64			,     __UpperCamelCase=2			,     __UpperCamelCase=8			,     __UpperCamelCase=1E-5			,     __UpperCamelCase=0.02			,     __UpperCamelCase=True			,     __UpperCamelCase=1			,     __UpperCamelCase=2			,     __UpperCamelCase=False			,     __UpperCamelCase=0.0			,     __UpperCamelCase=0.0			,     __UpperCamelCase=1			,     __UpperCamelCase=False			,     **__UpperCamelCase			,     )				->				List[Any]:
												UpperCamelCase_		:							Dict  =		vocab_size
												# Backward compatibility with n_embed kwarg
												UpperCamelCase_		:							Optional[Any]  =		kwargs.pop("""n_embed"""			,     __UpperCamelCase						)
												UpperCamelCase_		:							List[Any]  =		hidden_size if n_embed is None else n_embed
												UpperCamelCase_		:							List[Any]  =		n_layer
												UpperCamelCase_		:							Optional[Any]  =		n_head
												UpperCamelCase_		:							List[str]  =		layer_norm_epsilon
												UpperCamelCase_		:							int  =		initializer_range
												UpperCamelCase_		:							List[str]  =		use_cache
												UpperCamelCase_		:							List[Any]  =		pretraining_tp
												UpperCamelCase_		:							Optional[int]  =		apply_residual_connection_post_layernorm
												UpperCamelCase_		:							Any  =		hidden_dropout
												UpperCamelCase_		:							Optional[Any]  =		attention_dropout

												UpperCamelCase_		:							List[str]  =		bos_token_id
												UpperCamelCase_		:							Optional[Any]  =		eos_token_id
												UpperCamelCase_		:							str  =		slow_but_exact

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


class        UpperCamelCase						(				__a				):
						a__		:Dict      =			version.parse('''1.12'''				)





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase = "default"			,     __UpperCamelCase = None			,     __UpperCamelCase = False			,     )				->				List[str]:
												super().__init__(__UpperCamelCase			,     task=__UpperCamelCase			,     patching_specs=__UpperCamelCase			,     use_past=__UpperCamelCase						)
												if not getattr(self._config			,     """pad_token_id"""			,     __UpperCamelCase						):
																		# TODO: how to do that better?
																		UpperCamelCase_		:							Optional[Any]  =		0





						@property
						def        A_							(self						)				->				Mapping[str, Mapping[int, str]]:
												UpperCamelCase_		:							str  =		OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}						)
												if self.use_past:
																		# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
																		self.fill_with_past_key_values_(__UpperCamelCase			,     direction="""inputs"""			,     inverted_values_shape=__UpperCamelCase						)
																		UpperCamelCase_		:							int  =		{0: """batch""", 1: """past_sequence + sequence"""}
												else:
																		UpperCamelCase_		:							Dict  =		{0: """batch""", 1: """sequence"""}

												return common_inputs





						@property
						def        A_							(self						)				->				int:
												return self._config.n_layer





						@property
						def        A_							(self						)				->				int:
												return self._config.n_head





						@property
						def        A_							(self						)				->				float:
												return 1E-3





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = -1			,     __UpperCamelCase = -1			,     __UpperCamelCase = False			,     __UpperCamelCase = None			,     )				->				Mapping[str, Any]:
												UpperCamelCase_		:							Union[str, Any]  =		super(__UpperCamelCase			,     self						).generate_dummy_inputs(
												    __UpperCamelCase			,     batch_size=__UpperCamelCase			,     seq_length=__UpperCamelCase			,     is_pair=__UpperCamelCase			,     framework=__UpperCamelCase						)

												# We need to order the input in the way they appears in the forward()
												UpperCamelCase_		:							List[Any]  =		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

																								UpperCamelCase_,UpperCamelCase_		:							List[str]  =		common_inputs["""input_ids"""].shape
																								# Not using the same length for past_key_values
																								UpperCamelCase_		:							List[str]  =		seqlen + 2
																								UpperCamelCase_		:							Tuple  =		self._config.hidden_size // self.num_attention_heads
																								UpperCamelCase_		:							List[Any]  =		(
																								    batch * self.num_attention_heads,
																								    head_dim,
																								    past_key_values_length,
																								)
																								UpperCamelCase_		:							str  =		(
																								    batch * self.num_attention_heads,
																								    past_key_values_length,
																								    head_dim,
																								)
																								UpperCamelCase_		:							Any  =		[
																								    (torch.zeros(__UpperCamelCase						), torch.zeros(__UpperCamelCase						)) for _ in range(self.num_layers						)
																								]

												UpperCamelCase_		:							int  =		common_inputs["""attention_mask"""]
												if self.use_past:
																		UpperCamelCase_		:							List[str]  =		ordered_inputs["""attention_mask"""].dtype
																		UpperCamelCase_		:							Any  =		torch.cat(
																		    [ordered_inputs["""attention_mask"""], torch.ones(__UpperCamelCase			,     __UpperCamelCase			,     dtype=__UpperCamelCase						)]			,     dim=1						)

												return ordered_inputs





						@property
						def        A_							(self						)				->				int:
												return 13






