


import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_SCREAMING_SNAKE_CASE          = '''src/transformers'''

# Matches is_xxx_available()
_SCREAMING_SNAKE_CASE          = re.compile(r'''is\_([a-z_]*)_available()''')
# Matches from xxx import bla
_SCREAMING_SNAKE_CASE          = re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
_SCREAMING_SNAKE_CASE          = re.compile(r'''^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)''')


_SCREAMING_SNAKE_CASE          = '''
{0} = None
'''

_SCREAMING_SNAKE_CASE          = '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


_SCREAMING_SNAKE_CASE          = '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''





def    _snake_case		(snake_case_				:    Union[str, Any]     )       ->		List[Any]:
  if _re_test_backend.search(snake_case_     ) is None:
    return None
  __UpperCAmelCase  :     List[Any]		=    [b[0] for b in _re_backend.findall(snake_case_     )]
  backends.sort()
  return "_and_".join(snake_case_     )





def    _snake_case		()       ->		str:
  with open(os.path.join(snake_case_						,					'__init__.py'     )						,					'r'						,					encoding='utf-8'						,					newline='\n'     ) as f:
    __UpperCAmelCase  :     int		=    f.readlines()

  # Get to the point we do the actual imports for type checking
  __UpperCAmelCase  :     Optional[Any]		=    0
  while not lines[line_index].startswith('if TYPE_CHECKING'     ):
    line_index += 1

  __UpperCAmelCase  :     Dict		=    {}
  # Go through the end of the file
  while line_index < len(snake_case_     ):
    # If the line is an if is_backend_available, we grab all objects associated.
    __UpperCAmelCase  :     Tuple		=    find_backend(lines[line_index]     )
    if backend is not None:
      while not lines[line_index].startswith('    else:'     ):
        line_index += 1
      line_index += 1

      __UpperCAmelCase  :     int		=    []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]     ) <= 1 or lines[line_index].startswith(' ' * 8     ):
        __UpperCAmelCase  :     int		=    lines[line_index]
        __UpperCAmelCase  :     Union[str, Any]		=    _re_single_line_import.search(snake_case_     )
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(', '     )     )
        elif line.startswith(' ' * 12     ):
          objects.append(line[12:-2]     )
        line_index += 1

      __UpperCAmelCase  :     Tuple		=    objects
    else:
      line_index += 1

  return backend_specific_objects





def    _snake_case		(snake_case_				:    str						,					snake_case_				:    Any     )       ->		str:
  if name.isupper():
    return DUMMY_CONSTANT.format(snake_case_     )
  elif name.islower():
    return DUMMY_FUNCTION.format(snake_case_						,					snake_case_     )
  else:
    return DUMMY_CLASS.format(snake_case_						,					snake_case_     )





def    _snake_case		(snake_case_				:    List[str]=None     )       ->		List[Any]:
  if backend_specific_objects is None:
    __UpperCAmelCase  :     Tuple		=    read_init()
  # For special correspondence backend to module name as used in the function requires_modulename
  __UpperCAmelCase  :     List[str]		=    {}

  for backend, objects in backend_specific_objects.items():
    __UpperCAmelCase  :     Dict		=    '[' + ', '.join(F"\"{b}\"" for b in backend.split('_and_'     )     ) + ']'
    __UpperCAmelCase  :     int		=    '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
    dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
    dummy_file += "\n".join([create_dummy_object(snake_case_						,					snake_case_     ) for o in objects]     )
    __UpperCAmelCase  :     Optional[Any]		=    dummy_file

  return dummy_files





def    _snake_case		(snake_case_				:    List[Any]=False     )       ->		Dict:
  __UpperCAmelCase  :     Optional[int]		=    create_dummy_files()
  # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  __UpperCAmelCase  :     Any		=    {'torch': 'pt'}

  # Locate actual dummy modules and read their content.
  __UpperCAmelCase  :     Optional[int]		=    os.path.join(snake_case_						,					'utils'     )
  __UpperCAmelCase  :     List[str]		=    {
      backend: os.path.join(snake_case_						,					F"dummy_{short_names.get(snake_case_						,					snake_case_     )}_objects.py"     )
      for backend in dummy_files.keys()
  }

  __UpperCAmelCase  :     Dict		=    {}
  for backend, file_path in dummy_file_paths.items():
    if os.path.isfile(snake_case_     ):
      with open(snake_case_						,					'r'						,					encoding='utf-8'						,					newline='\n'     ) as f:
        __UpperCAmelCase  :     Optional[Any]		=    f.read()
    else:
      __UpperCAmelCase  :     Optional[Any]		=    ''

  for backend in dummy_files.keys():
    if dummy_files[backend] != actual_dummies[backend]:
      if overwrite:
        print(
            F"Updating transformers.utils.dummy_{short_names.get(snake_case_						,					snake_case_     )}_objects.py as the main "
            '__init__ has new objects.'     )
        with open(dummy_file_paths[backend]						,					'w'						,					encoding='utf-8'						,					newline='\n'     ) as f:
          f.write(dummy_files[backend]     )
      else:
        raise ValueError(
            'The main __init__ has objects that are not present in '
            F"transformers.utils.dummy_{short_names.get(snake_case_						,					snake_case_     )}_objects.py. Run `make fix-copies` "
            'to fix this.'     )


if __name__ == "__main__":
      _SCREAMING_SNAKE_CASE          = argparse.ArgumentParser()
      parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
      _SCREAMING_SNAKE_CASE          = parser.parse_args()

      check_dummies(args.fix_and_overwrite)








from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_SCREAMING_SNAKE_CASE          = logging.get_logger(__name__)


class     lowerCamelCase_   ( _lowerCAmelCase):
 _lowerCAmelCase       		=  '''mt5'''
 _lowerCAmelCase       		=  ['''past_key_values''']




 def __init__( self     ,     _UpperCAmelCase=250_112     ,     _UpperCAmelCase=512     ,     _UpperCAmelCase=64     ,     _UpperCAmelCase=1_024     ,     _UpperCAmelCase=8     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=6     ,     _UpperCAmelCase=32     ,     _UpperCAmelCase=128     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=1E-6     ,     _UpperCAmelCase=1.0     ,     _UpperCAmelCase="gated-gelu"     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase="T5Tokenizer"     ,     _UpperCAmelCase=False     ,     _UpperCAmelCase=0     ,     _UpperCAmelCase=1     ,     _UpperCAmelCase=0     ,     **_UpperCAmelCase     ,     ):
   super().__init__(
       is_encoder_decoder=_UpperCAmelCase     ,     tokenizer_class=_UpperCAmelCase     ,     tie_word_embeddings=_UpperCAmelCase     ,     pad_token_id=_UpperCAmelCase     ,     eos_token_id=_UpperCAmelCase     ,     decoder_start_token_id=_UpperCAmelCase     ,     **_UpperCAmelCase     ,     )
   __UpperCAmelCase  :     Dict		=    vocab_size
   __UpperCAmelCase  :     List[str]		=    d_model
   __UpperCAmelCase  :     str		=    d_kv
   __UpperCAmelCase  :     List[str]		=    d_ff
   __UpperCAmelCase  :     int		=    num_layers
   __UpperCAmelCase  :     List[Any]		=    (
       num_decoder_layers if num_decoder_layers is not None else self.num_layers
   )  # default = symmetry
   __UpperCAmelCase  :     List[Any]		=    num_heads
   __UpperCAmelCase  :     Tuple		=    relative_attention_num_buckets
   __UpperCAmelCase  :     Optional[int]		=    relative_attention_max_distance
   __UpperCAmelCase  :     Dict		=    dropout_rate
   __UpperCAmelCase  :     Union[str, Any]		=    layer_norm_epsilon
   __UpperCAmelCase  :     Tuple		=    initializer_factor
   __UpperCAmelCase  :     Union[str, Any]		=    feed_forward_proj
   __UpperCAmelCase  :     Optional[Any]		=    use_cache

   __UpperCAmelCase  :     Any		=    self.feed_forward_proj.split('-'       )
   __UpperCAmelCase  :     Dict		=    act_info[-1]
   __UpperCAmelCase  :     Any		=    act_info[0] == 'gated'

   if len(_UpperCAmelCase       ) > 1 and act_info[0] != "gated" or len(_UpperCAmelCase       ) > 2:
     raise ValueError(
         F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
         'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
         '\'gated-gelu\' or \'relu\''       )

   # for backwards compatibility
   if feed_forward_proj == "gated-gelu":
     __UpperCAmelCase  :     List[str]		=    'gelu_new'




 @property
 def 						a					( self       ):
   return self.d_model




 @property
 def 						a					( self       ):
   return self.num_heads




 @property
 def 						a					( self       ):
   return self.num_layers



class     lowerCamelCase_   ( _lowerCAmelCase):




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[int]		=    {
       'input_ids': {0: 'batch', 1: 'encoder_sequence'},
       'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
   }
   if self.use_past:
     __UpperCAmelCase  :     Optional[Any]		=    'past_encoder_sequence + sequence'
     __UpperCAmelCase  :     Tuple		=    {0: 'batch'}
     __UpperCAmelCase  :     List[Any]		=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
   else:
     __UpperCAmelCase  :     Tuple		=    {0: 'batch', 1: 'decoder_sequence'}
     __UpperCAmelCase  :     int		=    {0: 'batch', 1: 'decoder_sequence'}

   if self.use_past:
     self.fill_with_past_key_values_(_UpperCAmelCase     ,     direction='inputs'       )

   return common_inputs




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def 						a					( self       ):
   return 13




 @property
 def 						a					( self       ):
   return 5E-4





