

import argparse
import os
import re


lowercase_             =     'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowercase_             =     re.compile(R'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
lowercase_             =     re.compile(R'\s*\(\s*"(\S[^"]+)"')
def   a   (    A__			:     List[str]   ,     A__			:     bool = False  )					->   int:





      """simple docstring"""



      with open(A__   ,     'r'   ,     encoding='utf-8'  ) as f:
            _lowercase			 =f.read()

      _lowercase			 =content.split('\n'  )
      _lowercase			 =[]
      _lowercase			 =0
      while line_idx < len(A__  ):
            if _re_intro_mapping.search(lines[line_idx]  ) is not None:
                  _lowercase			 =len(re.search(r'^(\s*)\S'   ,     lines[line_idx]  ).groups()[0]  ) + 8
                  # Start of a new mapping!
                  while not lines[line_idx].startswith(' ' * indent + '('  ):
                        new_lines.append(lines[line_idx]  )
                        line_idx += 1

                  _lowercase			 =[]
                  while lines[line_idx].strip() != "]":
                        # Blocks either fit in one line or not
                        if lines[line_idx].strip() == "(":
                              _lowercase			 =line_idx
                              while not lines[line_idx].startswith(' ' * indent + ')'  ):
                                    line_idx += 1
                              blocks.append('\n'.join(lines[start_idx : line_idx + 1]  )  )
                        else:
                              blocks.append(lines[line_idx]  )
                        line_idx += 1

                  # Sort blocks by their identifiers
                  _lowercase			 =sorted(A__   ,     key=lambda A__  : _re_identifier.search(A__  ).groups()[0]  )
                  new_lines += blocks
            else:
                  new_lines.append(lines[line_idx]  )
                  line_idx += 1

      if overwrite:
            with open(A__   ,     'w'   ,     encoding='utf-8'  ) as f:
                  f.write('\n'.join(A__  )  )
      elif "\n".join(A__  ) != content:
            return True
def   a   (    A__			:     bool = False  )					->   int:





      """simple docstring"""



      _lowercase			 =[os.path.join(A__   ,     A__  ) for f in os.listdir(A__  ) if f.endswith('.py'  )]
      _lowercase			 =[sort_auto_mapping(A__   ,     overwrite=A__  ) for fname in fnames]

      if not overwrite and any(A__  ):
            _lowercase			 =[f for f, d in zip(A__   ,     A__  ) if d]
            raise ValueError(
                F'''The following files have auto mappings that need sorting: {", ".join(A__  )}. Run `make style` to fix'''
                ' this.'  )


if __name__ == "__main__":
  lowercase_             =     argparse.ArgumentParser()
  parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
  lowercase_             =     parser.parse_args()

  sort_all_auto_mappings(not args.check_only)





import math

import tensorflow as tf
from packaging import version
def   a   (    A__			:     Tuple  )					->   List[Any]:





      """simple docstring"""



      _lowercase			 =tf.convert_to_tensor(A__  )
      _lowercase			 =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0  )   ,     x.dtype  )  ))

      return x * cdf
def   a   (    A__			:     Any  )					->   int:





      """simple docstring"""



      _lowercase			 =tf.convert_to_tensor(A__  )
      _lowercase			 =tf.cast(math.pi   ,     x.dtype  )
      _lowercase			 =tf.cast(0.044715   ,     x.dtype  )
      _lowercase			 =0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi  ) * (x + coeff * tf.pow(A__   ,     3  ))  ))

      return x * cdf
def   a   (    A__			:     Optional[int]  )					->   int:





      """simple docstring"""



      _lowercase			 =tf.convert_to_tensor(A__  )

      return x * tf.tanh(tf.math.softplus(A__  )  )
def   a   (    A__			:     Tuple  )					->   List[str]:





      """simple docstring"""



      _lowercase			 =tf.convert_to_tensor(A__  )
      _lowercase			 =tf.cast(0.044715   ,     x.dtype  )
      _lowercase			 =tf.cast(0.7978845608   ,     x.dtype  )

      return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)  ))
def   a   (    A__			:     int  )					->   Any:





      """simple docstring"""



      _lowercase			 =tf.convert_to_tensor(A__  )
      _lowercase			 =tf.cast(1.702   ,     x.dtype  )
      return x * tf.math.sigmoid(coeff * x  )
def   a   (    A__			:     int  )					->   Dict:





      """simple docstring"""



      return tf.clip_by_value(_gelu(A__  )   ,     -10   ,     10  )
def   a   (    A__			:     Optional[int]   ,     A__			:     int=-1  )					->   Optional[Any]:





      """simple docstring"""



      _lowercase , _lowercase			 =tf.split(A__   ,     2   ,     axis=A__  )
      return a * tf.math.sigmoid(A__  )


if version.parse(tf.version.VERSION) >= version.parse('2.4'):
  def   a   (    A__			:     Any  )					->   Dict:





        """simple docstring"""



        return tf.keras.activations.gelu(A__   ,     approximate=A__  )

  lowercase_             =     tf.keras.activations.gelu







  lowercase_             =     approximate_gelu_wrap
else:
  lowercase_             =     _gelu
  lowercase_             =     _gelu_new


lowercase_             =     {
    '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   (    A__			:     Optional[int]  )					->   str:





      """simple docstring"""



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


