import argparse
import os
import re


__magic_name__								=					'''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__magic_name__								=					re.compile(r'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
__magic_name__								=					re.compile(r'''\s*\(\s*"(\S[^"]+)"''')







def  __UpperCamelCase    (						A ,							A = False							):
     with open(A ,							'''r''' ,							encoding='''utf-8'''							) as f:
          UpperCamelCase__										=      f.read()

     UpperCamelCase__										=      content.split('''\n'''							)
     UpperCamelCase__										=      []
     UpperCamelCase__										=      0
     while line_idx < len(A							):
          if _re_intro_mapping.search(lines[line_idx]							) is not None:
               UpperCamelCase__										=      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

               UpperCamelCase__										=      []
               while lines[line_idx].strip() != "]":
                    # Blocks either fit in one line or not
                    if lines[line_idx].strip() == "(":
                         UpperCamelCase__										=      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
               UpperCamelCase__										=      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  __UpperCamelCase    (						A = False							):
     UpperCamelCase__										=      [os.path.join(A ,							A							) for f in os.listdir(A							) if f.endswith('''.py'''							)]
     UpperCamelCase__										=      [sort_auto_mapping(A ,							overwrite=A							) for fname in fnames]

     if not overwrite and any(A							):
          UpperCamelCase__										=      [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__":
     __magic_name__								=					argparse.ArgumentParser()
     parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
     __magic_name__								=					parser.parse_args()

     sort_all_auto_mappings(not args.check_only)




import math

import tensorflow as tf
from packaging import version







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0							) ,							x.dtype							)							))

     return x * cdf







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(math.pi ,							x.dtype							)
     UpperCamelCase__										=      tf.cast(0.04_47_15 ,							x.dtype							)
     UpperCamelCase__										=      0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi							) * (x + coeff * tf.pow(A ,							3							))							))

     return x * cdf







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)

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







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(0.04_47_15 ,							x.dtype							)
     UpperCamelCase__										=      tf.cast(0.79_78_84_56_08 ,							x.dtype							)

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







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(1.7_02 ,							x.dtype							)
     return x * tf.math.sigmoid(coeff * x							)







def  __UpperCamelCase    (						A							):
     return tf.clip_by_value(_gelu(A							) ,							-10 ,							10							)







def  __UpperCamelCase    (						A ,							A=-1							):
     UpperCamelCase__						,       UpperCamelCase__										=      tf.split(A ,							2 ,							axis=A							)
     return a * tf.math.sigmoid(A							)


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







     def  __UpperCamelCase    (						A							):
          return tf.keras.activations.gelu(A ,							approximate=A							)

     __magic_name__								=tf.keras.activations.gelu



     __magic_name__								=approximate_gelu_wrap
else:
     __magic_name__								=_gelu
     __magic_name__								=_gelu_new


__magic_name__								={
    '''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  __UpperCamelCase    (						A							):
     if activation_string in ACTaFN:
          return ACTaFN[activation_string]
     else:
          raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()							)}"							)



