


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
lowercase_	   =     'src/diffusers'

# Matches is_xxx_available()
lowercase_	   =     re.compile(R'is\_([a-z_]*)_available\(\)')
# Matches from xxx import bla
lowercase_	   =     re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')


lowercase_	   =     '\n{0} = None\n'

lowercase_	   =     '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'


lowercase_	   =     '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							str		 =						_re_backend.findall(SCREAMING_SNAKE_CASE__						)
 if len(SCREAMING_SNAKE_CASE__						) == 0:
  return None

 return "_and_".join(SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  ):
 with open(os.path.join(SCREAMING_SNAKE_CASE__      ,	'__init__.py'						)      ,	'r'      ,	encoding='utf-8'      ,	newline='\n'						) as f:
  __lowerCamelCase		:							List[Any]		 =						f.readlines()

 # Get to the point we do the actual imports for type checking
 __lowerCamelCase		:							Dict		 =						0
 __lowerCamelCase		:							Tuple		 =						{}
 # Go through the end of the file
 while line_index < len(SCREAMING_SNAKE_CASE__						):
  # If the line contains is_backend_available, we grab all objects associated with the `else` block
  __lowerCamelCase		:							Union[str, Any]		 =						find_backend(lines[line_index]						)
  if backend is not None:
   while not lines[line_index].startswith('else:'						):
    line_index += 1
   line_index += 1
   __lowerCamelCase		:							List[str]		 =						[]
   # Until we unindent, add backend objects to the list
   while line_index < len(SCREAMING_SNAKE_CASE__						) and len(lines[line_index]						) > 1:
    __lowerCamelCase		:							Optional[Any]		 =						lines[line_index]
    __lowerCamelCase		:							List[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(' ' * 8						):
     objects.append(line[8:-2]						)
    line_index += 1

   if len(SCREAMING_SNAKE_CASE__						) > 0:
    __lowerCamelCase		:							Optional[int]		 =						objects
  else:
   line_index += 1

 return backend_specific_objects
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 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 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__=None						):
 if backend_specific_objects is None:
  __lowerCamelCase		:							int		 =						read_init()
 # For special correspondence backend to module name as used in the function requires_modulename
 __lowerCamelCase		:							List[Any]		 =						{}

 for backend, objects in backend_specific_objects.items():
  __lowerCamelCase		:							str		 =						'[' + ', '.join(f'"{b}"' for b in backend.split('_and_'						)						) + ']'
  __lowerCamelCase		:							Optional[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(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						) for o in objects]						)
  __lowerCamelCase		:							Union[str, Any]		 =						dummy_file

 return dummy_files
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__=False						):
 __lowerCamelCase		:							Tuple		 =						create_dummy_files()
 # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
 __lowerCamelCase		:							Optional[int]		 =						{'torch': 'pt'}

 # Locate actual dummy modules and read their content.
 __lowerCamelCase		:							Union[str, Any]		 =						os.path.join(SCREAMING_SNAKE_CASE__      ,	'utils'						)
 __lowerCamelCase		:							Any		 =						{
     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()
 }

 __lowerCamelCase		:							List[str]		 =						{}
 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:
    __lowerCamelCase		:							Union[str, Any]		 =						f.read()
  else:
   __lowerCamelCase		:							Tuple		 =						''

 for backend in dummy_files.keys():
  if dummy_files[backend] != actual_dummies[backend]:
   if overwrite:
    print(
        f'Updating diffusers.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'diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)}_objects.py. Run `make fix-copies` '
        'to fix this.'						)


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

       check_dummies(args.fix_and_overwrite)





import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
       from ... import PreTrainedTokenizerBase, TensorType

lowercase_	   =     logging.get_logger(__name__)


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """vision-encoder-decoder"""
      __snake_case 	=       True




      def __init__(    self:	Union[str, Any]						,				**a:	Optional[Any]    ):
       super().__init__(**a    )
       if "encoder" not in kwargs or "decoder" not in kwargs:
        raise ValueError(
            F'A configuraton of type {self.model_type} cannot be instantiated because '
            F'not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'    )

       __lowerCamelCase		:							Dict		 =						kwargs.pop('encoder'    )
       __lowerCamelCase		:							int		 =						encoder_config.pop('model_type'    )
       __lowerCamelCase		:							Any		 =						kwargs.pop('decoder'    )
       __lowerCamelCase		:							Union[str, Any]		 =						decoder_config.pop('model_type'    )

       __lowerCamelCase		:							Optional[Any]		 =						AutoConfig.for_model(a						,				**a    )
       __lowerCamelCase		:							List[Any]		 =						AutoConfig.for_model(a						,				**a    )
       __lowerCamelCase		:							Tuple		 =						True




      @classmethod
      def 					_snake_case     (    cls:	Optional[Any]						,				a:	PretrainedConfig						,				a:	PretrainedConfig						,				**a:	Dict    ):
       logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'    )
       __lowerCamelCase		:							Optional[Any]		 =						True
       __lowerCamelCase		:							Optional[Any]		 =						True

       return cls(encoder=encoder_config.to_dict()						,				decoder=decoder_config.to_dict()						,				**a    )




      def 					_snake_case     (    self:	str    ):
       __lowerCamelCase		:							int		 =						copy.deepcopy(self.__dict__    )
       __lowerCamelCase		:							Dict		 =						self.encoder.to_dict()
       __lowerCamelCase		:							Union[str, Any]		 =						self.decoder.to_dict()
       __lowerCamelCase		:							Optional[int]		 =						self.__class__.model_type
       return output



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       version.parse("""1.11"""   )




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       return OrderedDict(
           [
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
           ]    )




      @property
      def 					_snake_case     (    self:	Optional[Any]    ):
       return 1e-4




      @property
      def 					_snake_case     (    self:	List[str]    ):
       return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}    )



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      @property
      def 					_snake_case     (    self:	Tuple    ):
       __lowerCamelCase		:							Union[str, Any]		 =						OrderedDict()
       __lowerCamelCase		:							Union[str, Any]		 =						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
       __lowerCamelCase		:							Optional[Any]		 =						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
       __lowerCamelCase		:							Dict		 =						{0: 'batch', 1: 'encoder_sequence'}

       return common_inputs




      def 					_snake_case     (    self:	List[Any]						,				a:	"PreTrainedTokenizerBase"						,				a:	int = -1						,				a:	int = -1						,				a:	bool = False						,				a:	Optional["TensorType"] = None						,				):
       import torch

       __lowerCamelCase		:							str		 =						OrderedDict()

       __lowerCamelCase		:							List[Any]		 =						super().generate_dummy_inputs(
           a						,				batch_size=a						,				seq_length=a						,				is_pair=a						,				framework=a    )

       __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						dummy_input['input_ids'].shape
       __lowerCamelCase		:							str		 =						(batch, encoder_sequence, self._config.encoder_hidden_size)
       __lowerCamelCase		:							str		 =						dummy_input.pop('input_ids'    )
       __lowerCamelCase		:							Union[str, Any]		 =						dummy_input.pop('attention_mask'    )
       __lowerCamelCase		:							str		 =						torch.zeros(a    )

       return common_inputs



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      @property
      def 					_snake_case     (    self:	List[Any]    ):
       pass




      def 					_snake_case     (    self:	List[str]						,				a:	PretrainedConfig    ):
       return VisionEncoderDecoderEncoderOnnxConfig(a    )




      def 					_snake_case     (    self:	Optional[int]						,				a:	PretrainedConfig						,				a:	PretrainedConfig						,				a:	str = "default"    ):
       __lowerCamelCase		:							List[Any]		 =						encoder_config.hidden_size
       return VisionEncoderDecoderDecoderOnnxConfig(a						,				a    )

