


'''simple docstring'''




# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
       import jax
       import jaxlib

__snake_case    						=get_logger()

__snake_case    						=None

class 		UpperCAmelCase_      (  TensorFormatter[Mapping, '''jax.Array''', Mapping]		):





     def __init__( self       :      Optional[int]    ,      UpperCAmelCase__       :      Union[str, Any]=None    ,      UpperCAmelCase__       :      List[Any]=None    ,      **UpperCAmelCase__       :      Dict    )				->  int:
        super().__init__(features=UpperCAmelCase__    )
        import jax
        from jaxlib.xla_client import Device

        if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):
           raise ValueError(
               F'''Expected {device} to be a `str` not {type(UpperCAmelCase__    )}, as `jaxlib.xla_extension.Device` '''
               'is not serializable neither with `pickle` nor with `dill`. Instead you can surround '
               'the device with `str()` to get its string identifier that will be internally mapped '
               'to the actual `jaxlib.xla_extension.Device`.'    )
        lowerCAmelCase     =			device if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ) else str(jax.devices()[0]    )
        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
        global DEVICE_MAPPING
        if DEVICE_MAPPING is None:
           lowerCAmelCase     =			self._map_devices_to_str()
        if self.device not in list(DEVICE_MAPPING.keys()    ):
           logger.warning(
               F'''Device with string identifier {self.device} not listed among the available '''
               F'''devices: {list(DEVICE_MAPPING.keys()    )}, so falling back to the default '''
               F'''device: {str(jax.devices()[0]    )}.'''    )
           lowerCAmelCase     =			str(jax.devices()[0]    )
        lowerCAmelCase     =			jnp_array_kwargs





     @staticmethod
     def 							__UpperCAmelCase      ( )				->  Dict[str, "jaxlib.xla_extension.Device"]:
        import jax

        return {str(UpperCAmelCase__    ): device for device in jax.devices()}





     def 							__UpperCAmelCase      ( self       :      Tuple    ,      UpperCAmelCase__       :      Optional[Any]    )				->  List[Any]:
        import jax
        import jax.numpy as jnp

        if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ) and column:
           if all(
               isinstance(UpperCAmelCase__    ,      jax.Array    ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column    ):
              return jnp.stack(UpperCAmelCase__    ,      axis=0    )
        return column





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]    )				->  List[Any]:
        import jax
        import jax.numpy as jnp

        if isinstance(UpperCAmelCase__    ,      (str, bytes, type(UpperCAmelCase__    ))    ):
           return value
        elif isinstance(UpperCAmelCase__    ,      (np.character, np.ndarray)    ) and np.issubdtype(value.dtype    ,      np.character    ):
           return value.tolist()

        lowerCAmelCase     =			{}

        if isinstance(UpperCAmelCase__    ,      (np.number, np.ndarray)    ) and np.issubdtype(value.dtype    ,      np.integer    ):
           # the default int precision depends on the jax config
           # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
           if jax.config.jax_enable_xaa:
              lowerCAmelCase     =			{'dtype': jnp.intaa}
           else:
              lowerCAmelCase     =			{'dtype': jnp.intaa}
        elif isinstance(UpperCAmelCase__    ,      (np.number, np.ndarray)    ) and np.issubdtype(value.dtype    ,      np.floating    ):
           lowerCAmelCase     =			{'dtype': jnp.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
           import PIL.Image

           if isinstance(UpperCAmelCase__    ,      PIL.Image.Image    ):
              lowerCAmelCase     =			np.asarray(UpperCAmelCase__    )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
        global DEVICE_MAPPING
        if DEVICE_MAPPING is None:
           lowerCAmelCase     =			self._map_devices_to_str()

        with jax.default_device(DEVICE_MAPPING[self.device]    ):
           # calling jnp.array on a np.ndarray does copy the data
           # see https://github.com/google/jax/issues/4486
           return jnp.array(UpperCAmelCase__    ,      **{**default_dtype, **self.jnp_array_kwargs}    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      Optional[int]    )				->  str:
        import jax

        # support for torch, tf, jax etc.
        if config.TORCH_AVAILABLE and "torch" in sys.modules:
           import torch

           if isinstance(UpperCAmelCase__    ,      torch.Tensor    ):
              return self._tensorize(data_struct.detach().cpu().numpy()[()]    )
        if hasattr(UpperCAmelCase__    ,      '__array__'    ) and not isinstance(UpperCAmelCase__    ,      jax.Array    ):
           lowerCAmelCase     =			data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(UpperCAmelCase__    ,      np.ndarray    ):
           if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
              return self._consolidate([self.recursive_tensorize(UpperCAmelCase__    ) for substruct in data_struct]    )
        elif isinstance(UpperCAmelCase__    ,      (list, tuple)    ):
           return self._consolidate([self.recursive_tensorize(UpperCAmelCase__    ) for substruct in data_struct]    )
        return self._tensorize(UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      List[str]    ,      UpperCAmelCase__       :      dict    )				->  str:
        return map_nested(self._recursive_tensorize    ,      UpperCAmelCase__    ,      map_list=UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      pa.Table    )				->  Mapping:
        lowerCAmelCase     =			self.numpy_arrow_extractor().extract_row(UpperCAmelCase__    )
        lowerCAmelCase     =			self.python_features_decoder.decode_row(UpperCAmelCase__    )
        return self.recursive_tensorize(UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    ,      UpperCAmelCase__       :      pa.Table    )				->  "jax.Array":
        lowerCAmelCase     =			self.numpy_arrow_extractor().extract_column(UpperCAmelCase__    )
        lowerCAmelCase     =			self.python_features_decoder.decode_column(UpperCAmelCase__    ,      pa_table.column_names[0]    )
        lowerCAmelCase     =			self.recursive_tensorize(UpperCAmelCase__    )
        lowerCAmelCase     =			self._consolidate(UpperCAmelCase__    )
        return column





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      pa.Table    )				->  Mapping:
        lowerCAmelCase     =			self.numpy_arrow_extractor().extract_batch(UpperCAmelCase__    )
        lowerCAmelCase     =			self.python_features_decoder.decode_batch(UpperCAmelCase__    )
        lowerCAmelCase     =			self.recursive_tensorize(UpperCAmelCase__    )
        for column_name in batch:
           lowerCAmelCase     =			self._consolidate(batch[column_name]    )
        return batch







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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


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

SCREAMING_SNAKE_CASE							:Optional[int]							 =				{'vocab_file': 'sentencepiece.bpe.model'}

SCREAMING_SNAKE_CASE							:Tuple							 =				{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

SCREAMING_SNAKE_CASE							:List[Any]							 =				{
    'camembert-base': 512,
}

SCREAMING_SNAKE_CASE							:List[str]							 =				'▁'





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     VOCAB_FILES_NAMES
	snake_case_					    =     PRETRAINED_VOCAB_FILES_MAP
	snake_case_					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_					    =     ["input_ids", "attention_mask"]






	def __init__(       self					: Optional[Any]							,A					: List[str]							,A					: List[Any]="<s>"							,A					: Tuple="</s>"							,A					: Any="</s>"							,A					: Optional[Any]="<s>"							,A					: Tuple="<unk>"							,A					: str="<pad>"							,A					: int="<mask>"							,A					: Optional[int]=["<s>NOTUSED", "</s>NOTUSED"]							,A					: Optional[Dict[str, Any]] = None							,**A					: Optional[Any]							,):
					# Mask token behave like a normal word, i.e. include the space before it
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else mask_token

					__A   			= {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=A							,eos_token=A							,unk_token=A							,sep_token=A							,cls_token=A							,pad_token=A							,mask_token=A							,additional_special_tokens=A							,sp_model_kwargs=self.sp_model_kwargs							,**A							,)
					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(str(A ) )
					__A   			= vocab_file
					# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
					# sentencepiece vocabulary (this is the case for <s> and </s>
					__A   			= {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
					__A   			= len(self.fairseq_tokens_to_ids )
					__A   			= len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
					__A   			= {v: k for k, v in self.fairseq_tokens_to_ids.items()}






	def 				UpperCamelCase_  (       self					: int							,A					: List[int]							,A					: Optional[List[int]] = None ):

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






	def 				UpperCamelCase_  (       self					: Dict							,A					: List[int]							,A					: Optional[List[int]] = None							,A					: bool = False ):
					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=A							,token_ids_a=A							,already_has_special_tokens=A )

					if token_ids_a is None:
									return [1] + ([0] * len(A )) + [1]
					return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: List[int]							,A					: Optional[List[int]] = None ):
					__A   			= [self.sep_token_id]
					__A   			= [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]






	@property
	def 				UpperCamelCase_  (       self					: Dict ):
					return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab






	def 				UpperCamelCase_  (       self					: Any							,A					: str ):
					return self.sp_model.encode(A							,out_type=A )






	def 				UpperCamelCase_  (       self					: List[str]							,A					: Dict ):
					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					elif self.sp_model.PieceToId(A ) == 0:
									# Convert sentence piece unk token to fairseq unk token index
									return self.unk_token_id
					return self.fairseq_offset + self.sp_model.PieceToId(A )






	def 				UpperCamelCase_  (       self					: Dict							,A					: Tuple ):
					if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(index - self.fairseq_offset )






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: Dict ):
					__A   			= []
					__A   			= ""
					__A   			= False
					for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
													if not prev_is_special:
																	out_string += " "
													out_string += self.sp_model.decode(A ) + token
													__A   			= True
													__A   			= []
									else:
													current_sub_tokens.append(A )
													__A   			= False
					out_string += self.sp_model.decode(A )
					return out_string.strip()






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






	def __setstate__(       self					: Union[str, Any]							,A					: Any ):
					__A   			= d

					# for backward compatibility
					if not hasattr(self							,"sp_model_kwargs" ):
									__A   			= {}

					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(self.vocab_file )







	def 				UpperCamelCase_  (       self					: Any							,A					: str							,A					: Optional[str] = None ):
					if not os.path.isdir(A ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
					__A   			= 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 ) and os.path.isfile(self.vocab_file ):
									copyfile(self.vocab_file							,A )
					elif not os.path.isfile(self.vocab_file ):
									with open(A							,"wb" ) as fi:
													__A   			= self.sp_model.serialized_model_proto()
													fi.write(A )

					return (out_vocab_file,)
