





import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class       A__     ( __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:Tuple							=     LayoutLMTokenizer
      _UpperCAmelCase			:Any							=     LayoutLMTokenizerFast
      _UpperCAmelCase			:Optional[int]							=     True
      _UpperCAmelCase			:List[Any]							=     True





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       super().setUp()

       UpperCamelCase			:      Union[str, Any] 			=     [
           "[UNK]",
           "[CLS]",
           "[SEP]",
           "want",
           "##want",
           "##ed",
           "wa",
           "un",
           "runn",
           "##ing",
           ",",
           "low",
           "lowest",
       ]
       UpperCamelCase			:      Optional[int] 			=     os.path.join(self.tmpdirname ,							VOCAB_FILES_NAMES["vocab_file"]     )
       with open(self.vocab_file ,							"w" ,							encoding="utf-8"     ) as vocab_writer:
        vocab_writer.write("".join([x + "\n" for x in vocab_tokens]     )     )





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       return LayoutLMTokenizer.from_pretrained(self.tmpdirname ,							**A_     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     "UNwant\u00E9d,running"
       UpperCamelCase			:      Union[str, Any] 			=     "unwanted, running"
       return input_text, output_text





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     self.tokenizer_class(self.vocab_file     )

       UpperCamelCase			:      Union[str, Any] 			=     tokenizer.tokenize("UNwant\u00E9d,running"     )
       self.assertListEqual(A_ ,							["un", "##want", "##ed", ",", "runn", "##ing"]     )
       self.assertListEqual(tokenizer.convert_tokens_to_ids(A_     ) ,							[7, 4, 5, 10, 8, 9]     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       pass







import math

import tensorflow as tf
from packaging import version



def 	A_       (  _lowerCAmelCase  )		->				Any:
 UpperCamelCase			:      List[Any] 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      Any 			=     0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0  )							,     x.dtype  )  ))

 return x * cdf



def 	A_       (  _lowerCAmelCase  )		->				Dict:
 UpperCamelCase			:      Union[str, Any] 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(math.pi							,     x.dtype  )
 UpperCamelCase			:      Optional[Any] 			=     tf.cast(0.044_715							,     x.dtype  )
 UpperCamelCase			:      int 			=     0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi  ) * (x + coeff * tf.pow(_lowerCAmelCase							,     3  ))  ))

 return x * cdf



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      str 			=     tf.convert_to_tensor(_lowerCAmelCase  )

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



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      Tuple 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(0.044_715							,     x.dtype  )
 UpperCamelCase			:      Optional[Any] 			=     tf.cast(0.7_978_845_608							,     x.dtype  )

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



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:
 UpperCamelCase			:      Any 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(1.702							,     x.dtype  )
 return x * tf.math.sigmoid(coeff * x  )



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 return tf.clip_by_value(_gelu(_lowerCAmelCase  )							,     -10							,     10  )



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase=-1  )		->				str:
 UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     tf.split(_lowerCAmelCase							,     2							,     axis=_lowerCAmelCase  )
 return a * tf.math.sigmoid(_lowerCAmelCase  )


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



    def 	A_       (  _lowerCAmelCase  )		->				Any:
     return tf.keras.activations.gelu(_lowerCAmelCase							,     approximate=_lowerCAmelCase  )

    __lowerCamelCase :  Optional[int]										=  tf.keras.activations.gelu


    __lowerCamelCase :  int										=  approximate_gelu_wrap
else:
    __lowerCamelCase :  List[Any]										=  _gelu
    __lowerCamelCase :  Optional[Any]										=  _gelu_new


__lowerCamelCase :  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 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:
 if activation_string in ACTaFN:
  return ACTaFN[activation_string]
 else:
  raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()  )}"""  )
