


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


UpperCAmelCase    =					logging.get_logger(__name__)

UpperCAmelCase    =					{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase    =					{
    """vocab_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"""
        ),
        """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt""",
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCAmelCase    =					{
    """squeezebert/squeezebert-uncased""": 512,
    """squeezebert/squeezebert-mnli""": 512,
    """squeezebert/squeezebert-mnli-headless""": 512,
}


UpperCAmelCase    =					{
    """squeezebert/squeezebert-uncased""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli-headless""": {"""do_lower_case""": True},
}







class lowerCAmelCase_		(				_lowercase ):

	'''simple docstring'''

	__snake_case				=						VOCAB_FILES_NAMES
	__snake_case				=						PRETRAINED_VOCAB_FILES_MAP
	__snake_case				=						PRETRAINED_INIT_CONFIGURATION
	__snake_case				=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__snake_case				=						SqueezeBertTokenizer



	def __init__(			self	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   _UpperCAmelCase=True	,   _UpperCAmelCase="[UNK]"	,   _UpperCAmelCase="[SEP]"	,   _UpperCAmelCase="[PAD]"	,   _UpperCAmelCase="[CLS]"	,   _UpperCAmelCase="[MASK]"	,   _UpperCAmelCase=True	,   _UpperCAmelCase=None	,   **_UpperCAmelCase	,   ):
							super().__init__(
							    A_	,   tokenizer_file=A_	,   do_lower_case=A_	,   unk_token=A_	,   sep_token=A_	,   pad_token=A_	,   cls_token=A_	,   mask_token=A_	,   tokenize_chinese_chars=A_	,   strip_accents=A_	,   **A_	,   )

							snake_case_ =   json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
							if (
							    normalizer_state.get('''lowercase'''	,   A_      ) != do_lower_case
							    or normalizer_state.get('''strip_accents'''	,   A_      ) != strip_accents
							    or normalizer_state.get('''handle_chinese_chars'''	,   A_      ) != tokenize_chinese_chars
							):
													snake_case_ =   getattr(A_	,   normalizer_state.pop('''type'''      )      )
													snake_case_ =   do_lower_case
													snake_case_ =   strip_accents
													snake_case_ =   tokenize_chinese_chars
													snake_case_ =   normalizer_class(**A_      )

							snake_case_ =   do_lower_case



	def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase=None      ):
							snake_case_ =   [self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
													output += token_ids_a + [self.sep_token_id]

							return output



	def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None      ):
							snake_case_ =   [self.sep_token_id]
							snake_case_ =   [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 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None      ):
							snake_case_ =   self._tokenizer.model.save(A_	,   name=A_      )
							return tuple(A_      )



# 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

UpperCAmelCase    =					get_logger()

UpperCAmelCase    =					None







class lowerCAmelCase_		(				TensorFormatter[Mapping, "jax.Array", Mapping] ):

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   **_UpperCAmelCase      ):
       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`.'''      )
       snake_case_ =   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:
             snake_case_ =   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]      )}.'''      )
             snake_case_ =   str(jax.devices()[0]      )
       snake_case_ =   jnp_array_kwargs



 @staticmethod
 def 		UpperCamelCase__  (			):
       import jax

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



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       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	,   _UpperCAmelCase      ):
       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()

       snake_case_ =   {}

       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:
                   snake_case_ =   {'''dtype''': jnp.intaa}
             else:
                   snake_case_ =   {'''dtype''': jnp.intaa}
       elif isinstance(_UpperCAmelCase	,   (np.number, np.ndarray)      ) and np.issubdtype(value.dtype	,   np.floating      ):
             snake_case_ =   {'''dtype''': jnp.floataa}
       elif config.PIL_AVAILABLE and "PIL" in sys.modules:
             import PIL.Image

             if isinstance(_UpperCAmelCase	,   PIL.Image.Image      ):
                   snake_case_ =   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:
             snake_case_ =   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	,   _UpperCAmelCase      ):
       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      ):
             snake_case_ =   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	,   _UpperCAmelCase      ):
       return map_nested(self._recursive_tensorize	,   _UpperCAmelCase	,   map_list=_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       snake_case_ =   self.numpy_arrow_extractor().extract_row(_UpperCAmelCase      )
       snake_case_ =   self.python_features_decoder.decode_row(_UpperCAmelCase      )
       return self.recursive_tensorize(_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       snake_case_ =   self.numpy_arrow_extractor().extract_column(_UpperCAmelCase      )
       snake_case_ =   self.python_features_decoder.decode_column(_UpperCAmelCase	,   pa_table.column_names[0]      )
       snake_case_ =   self.recursive_tensorize(_UpperCAmelCase      )
       snake_case_ =   self._consolidate(_UpperCAmelCase      )
       return column





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       snake_case_ =   self.numpy_arrow_extractor().extract_batch(_UpperCAmelCase      )
       snake_case_ =   self.python_features_decoder.decode_batch(_UpperCAmelCase      )
       snake_case_ =   self.recursive_tensorize(_UpperCAmelCase      )
       for column_name in batch:
             snake_case_ =   self._consolidate(batch[column_name]      )
       return batch