





import math

import tensorflow as tf
from packaging import version


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						tf.convert_to_tensor(lowerCAmelCase					)
       _UpperCamelCase              =						0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0					)    ,       x.dtype					)					))

       return x * cdf


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						tf.convert_to_tensor(lowerCAmelCase					)
       _UpperCamelCase              =						tf.cast(math.pi    ,       x.dtype					)
       _UpperCamelCase              =						tf.cast(0.0_4_4_7_1_5    ,       x.dtype					)
       _UpperCamelCase              =						0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi					) * (x + coeff * tf.pow(lowerCAmelCase    ,       3					))					))

       return x * cdf


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						tf.convert_to_tensor(lowerCAmelCase					)

       return x * tf.tanh(tf.math.softplus(lowerCAmelCase					)					)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						tf.convert_to_tensor(lowerCAmelCase					)
       _UpperCamelCase              =						tf.cast(0.0_4_4_7_1_5    ,       x.dtype					)
       _UpperCamelCase              =						tf.cast(0.7_9_7_8_8_4_5_6_0_8    ,       x.dtype					)

       return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)					))


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						tf.convert_to_tensor(lowerCAmelCase					)
       _UpperCamelCase              =						tf.cast(1.7_0_2    ,       x.dtype					)
       return x * tf.math.sigmoid(coeff * x					)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       return tf.clip_by_value(_gelu(lowerCAmelCase					)    ,       -10    ,       10					)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase=-1					):
       _UpperCamelCase       ,		_UpperCamelCase              =						tf.split(lowerCAmelCase    ,       2    ,       axis=lowerCAmelCase					)
       return a * tf.math.sigmoid(lowerCAmelCase					)


if version.parse(tf.version.VERSION) >= version.parse("""2.4"""):


       def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
              return tf.keras.activations.gelu(lowerCAmelCase    ,       approximate=lowerCAmelCase					)

       lowercase    : Tuple    	=  tf.keras.activations.gelu

       lowercase    : Dict    	=  approximate_gelu_wrap
else:
       lowercase    : Optional[int]    	=  _gelu
       lowercase    : Optional[int]    	=  _gelu_new


lowercase    : Union[str, Any]    	=  {
    """gelu""": gelu,
    """gelu_10""": gelu_aa,
    """gelu_fast""": gelu_fast,
    """gelu_new""": gelu_new,
    """glu""": glu,
    """mish""": mish,
    """quick_gelu""": quick_gelu,
    """relu""": tf.keras.activations.relu,
    """sigmoid""": tf.keras.activations.sigmoid,
    """silu""": tf.keras.activations.swish,
    """swish""": tf.keras.activations.swish,
    """tanh""": tf.keras.activations.tanh,
}


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       if activation_string in ACTaFN:
              return ACTaFN[activation_string]
       else:
              raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()					)}'''					)













import json
import os
from typing import Optional, Tuple

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


lowercase    : str    	=  logging.get_logger(__name__)

lowercase    : Tuple    	=  {"""vocab_file""": """vocab.json"""}

lowercase    : int    	=  {
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

lowercase    : int    	=  {"""mgp-str""": 27}

class  __A(						__UpperCAmelCase ):
     __A	  =       VOCAB_FILES_NAMES
     __A	  =       PRETRAINED_VOCAB_FILES_MAP
     __A	  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

     def __init__(      self,       A,       A="[GO]",       A="[GO]",       A="[s]",       A="[GO]",       **A      ):





            """simple docstring"""

            super().__init__(
                unk_token=A,       bos_token=A,       eos_token=A,       pad_token=A,       **A,       )

            with open(A,       encoding='''utf-8'''      ) as vocab_handle:
                   _UpperCamelCase              =						json.load(A      )
            _UpperCamelCase              =						{v: k for k, v in self.vocab.items()}

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return len(self.vocab      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return dict(self.vocab,       **self.added_tokens_encoder      )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						[]
            for s in text:
                   char_tokens.extend(A      )
            return char_tokens

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            return self.vocab.get(A,       self.vocab.get(self.unk_token      )      )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            return self.decoder.get(A      )



     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

            if not os.path.isdir(A      ):
                   logger.error('''Vocabulary path ({}) should be a directory'''.format(A      )      )
                   return
            _UpperCamelCase              =						os.path.join(
                A,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

            with open(A,       '''w''',       encoding='''utf-8'''      ) as f:
                   f.write(json.dumps(self.vocab,       indent=2,       sort_keys=A,       ensure_ascii=A      ) + '''\n'''      )

            return (vocab_file,)






