





'''simple docstring'''

from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):



		if not inplace:
				__UpperCAmelCase	:							str     =     ma + ma
		else:
				ma += ma

		return ma


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							List[Any]     =     -1 * len(_UpperCAmelCase  )
		__UpperCAmelCase	:							Optional[int]     =     list(range(len(tensor.shape[:zero_index]  )  )  )
		return tensor.permute(first_inds + [zero_index + i for i in inds]  )


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase  ):
		return t.reshape(t.shape[:-no_dims] + (-1,)  )


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase = 1E-4  ):
		__UpperCAmelCase	:							Any     =     mask.expand(*value.shape  )
		return torch.sum(mask * value,       dim=_UpperCAmelCase  ) / (eps + torch.sum(_UpperCAmelCase,       dim=_UpperCAmelCase  ))


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase = 2.3_125,       _UpperCAmelCase = 21.6_875,       _UpperCAmelCase = 64  ):
		__UpperCAmelCase	:							Optional[Any]     =     torch.linspace(_UpperCAmelCase,       _UpperCAmelCase,       no_bins - 1,       device=pts.device  )
		__UpperCAmelCase	:							Union[str, Any]     =     torch.sqrt(torch.sum((pts.unsqueeze(-2  ) - pts.unsqueeze(-3  )) ** 2,       dim=-1  )  )
		return torch.bucketize(_UpperCAmelCase,       _UpperCAmelCase  )


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							Dict     =     dicts[0]
		__UpperCAmelCase	:							List[Any]     =     {}
		for k, v in first.items():
				__UpperCAmelCase	:							Optional[int]     =     [d[k] for d in dicts]
				if isinstance(_UpperCAmelCase,       _UpperCAmelCase  ):
						__UpperCAmelCase	:							Tuple     =     dict_multimap(_UpperCAmelCase,       _UpperCAmelCase  )
				else:
						__UpperCAmelCase	:							Union[str, Any]     =     fn(_UpperCAmelCase  )

		return new_dict


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							int     =     v_bins.view(((1,) * len(x.shape  )) + (len(_UpperCAmelCase  ),)  )
		__UpperCAmelCase	:							List[Any]     =     x[..., None] - reshaped_bins
		__UpperCAmelCase	:							Optional[int]     =     torch.argmin(torch.abs(_UpperCAmelCase  ),       dim=-1  )
		return nn.functional.one_hot(_UpperCAmelCase,       num_classes=len(_UpperCAmelCase  )  ).float()


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase = 0,       _UpperCAmelCase = 0  ):
		__UpperCAmelCase	:							List[Union[slice, torch.Tensor]]     =     []
		for i, s in enumerate(data.shape[:no_batch_dims]  ):
				__UpperCAmelCase	:							List[Any]     =     torch.arange(_UpperCAmelCase  )
				__UpperCAmelCase	:							Optional[Any]     =     r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape  ) - i - 1)))  )
				ranges.append(_UpperCAmelCase  )

		__UpperCAmelCase	:							List[Union[slice, torch.Tensor]]     =     [slice(_UpperCAmelCase  ) for _ in range(len(data.shape  ) - no_batch_dims  )]
		__UpperCAmelCase	:							Union[str, Any]     =     inds
		ranges.extend(_UpperCAmelCase  )
		# Matt note: Editing this to get around the behaviour of using a list as an array index changing
		# in recent Numpy versions
		return data[tuple(_UpperCAmelCase  )]


lowerCAmelCase__      :   int      	=			TypeVar("T")


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							Dict[Any, Union[dict, list, tuple, Any]]     =     {}
		for k, v in dic.items():
				if isinstance(_UpperCAmelCase,       _UpperCAmelCase  ):
						__UpperCAmelCase	:							int     =     dict_map(_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  )
				else:
						__UpperCAmelCase	:							Optional[int]     =     tree_map(_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  )

		return new_dict


@overload
def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		...


@overload
def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		...


@overload
def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		...


@overload
def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		...


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):



		if isinstance(_UpperCAmelCase,       _UpperCAmelCase  ):
				return dict_map(_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  )
		elif isinstance(_UpperCAmelCase,       _UpperCAmelCase  ):
				return [tree_map(_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ) for x in tree]
		elif isinstance(_UpperCAmelCase,       _UpperCAmelCase  ):
				return tuple(tree_map(_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ) for x in tree  )
		elif isinstance(_UpperCAmelCase,       _UpperCAmelCase  ):
				return fn(_UpperCAmelCase  )
		else:
				print(type(_UpperCAmelCase  )  )
				raise ValueError("Not supported"  )






lowerCAmelCase__      :   Optional[int]      	=			partial(tree_map, leaf_type=torch.Tensor)








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
__UpperCamelCase							:							Optional[Any]    							=					"src/transformers"

# Matches is_xxx_available()
__UpperCamelCase							:							str    							=					re.compile(r"is\_([a-z_]*)_available()")
# Matches from xxx import bla
__UpperCamelCase							:							Union[str, Any]    							=					re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
__UpperCamelCase							:							Dict    							=					re.compile(r"^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)")


__UpperCamelCase							:							Any    							=					"\n{0} = None\n"

__UpperCamelCase							:							Tuple    							=					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


__UpperCamelCase							:							Optional[Any]    							=					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"

def       _a	(					SCREAMING_SNAKE_CASE						:					str  ):







 """simple docstring"""



 if _re_test_backend.search(SCREAMING_SNAKE_CASE  ) is None:
  return None
 UpperCamelCase__       :     List[str] 							= [b[0] for b in _re_backend.findall(SCREAMING_SNAKE_CASE  )]
 backends.sort()
 return "_and_".join(SCREAMING_SNAKE_CASE  )

def       _a	(					):







 """simple docstring"""



 with open(os.path.join(SCREAMING_SNAKE_CASE     ,			'''__init__.py'''  )     ,			'''r'''     ,			encoding='''utf-8'''     ,			newline='''\n'''  ) as f:
  UpperCamelCase__       :     Union[str, Any] 							= f.readlines()

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

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

   UpperCamelCase__       :     List[str] 							= []
   # Until we unindent, add backend objects to the list
   while len(lines[line_index]  ) <= 1 or lines[line_index].startswith(''' ''' * 8  ):
    UpperCamelCase__       :     str 							= lines[line_index]
    UpperCamelCase__       :     Union[str, Any] 							= _re_single_line_import.search(SCREAMING_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__       :     Optional[int] 							= objects
  else:
   line_index += 1

 return backend_specific_objects

def       _a	(					SCREAMING_SNAKE_CASE						:					str     ,			SCREAMING_SNAKE_CASE						:					List[str]  ):







 """simple docstring"""



 if name.isupper():
  return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE  )
 elif name.islower():
  return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )
 else:
  return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )

def       _a	(					SCREAMING_SNAKE_CASE						:					Dict=None  ):







 """simple docstring"""



 if backend_specific_objects is None:
  UpperCamelCase__       :     Any 							= read_init()
 # For special correspondence backend to module name as used in the function requires_modulename
 UpperCamelCase__       :     Optional[Any] 							= {}

 for backend, objects in backend_specific_objects.items():
  UpperCamelCase__       :     int 							= '''[''' + ''', '''.join(F"\"{b}\"" for b in backend.split('''_and_'''  )  ) + ''']'''
  UpperCamelCase__       :     Any 							= '''# 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(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  ) for o in objects]  )
  UpperCamelCase__       :     List[str] 							= dummy_file

 return dummy_files

def       _a	(					SCREAMING_SNAKE_CASE						:					Union[str, Any]=False  ):







 """simple docstring"""



 UpperCamelCase__       :     Dict 							= create_dummy_files()
 # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
 UpperCamelCase__       :     Tuple 							= {'''torch''': '''pt'''}

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

 UpperCamelCase__       :     Optional[Any] 							= {}
 for backend, file_path in dummy_file_paths.items():
  if os.path.isfile(SCREAMING_SNAKE_CASE  ):
   with open(SCREAMING_SNAKE_CASE     ,			'''r'''     ,			encoding='''utf-8'''     ,			newline='''\n'''  ) as f:
    UpperCamelCase__       :     List[Any] 							= f.read()
  else:
   UpperCamelCase__       :     List[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(SCREAMING_SNAKE_CASE     ,			SCREAMING_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(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )}_objects.py. Run `make fix-copies` "
        '''to fix this.'''  )


if __name__ == "__main__":
       __UpperCamelCase							:							Optional[int]    							=					argparse.ArgumentParser()
       parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
       __UpperCamelCase							:							str    							=					parser.parse_args()

       check_dummies(args.fix_and_overwrite)


