




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
_snake_case     :   List[Any]            = "src/diffusers"

# Matches is_xxx_available()
_snake_case     :   List[Any]            = re.compile(r"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
_snake_case     :   List[str]            = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")


_snake_case     :   Optional[int]            = "\n{0} = None\n"

_snake_case     :   str            = "\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"


_snake_case     :   Dict            = "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  List[Any]    =		_re_backend.findall(snake_case_  )
      if len(snake_case_  ) == 0:
            return None

      return "_and_".join(snake_case_  )







def        lowerCAmelCase_			(  ):
      with open(os.path.join(snake_case_,"""__init__.py"""  ),"""r""",encoding="""utf-8""",newline="""\n"""  ) as f:
            _A							:  Optional[int]    =		f.readlines()

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

                  if len(snake_case_  ) > 0:
                        _A							:  Any    =		objects
            else:
                  line_index += 1

      return backend_specific_objects







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      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        lowerCAmelCase_			(  snake_case_=None  ):
      if backend_specific_objects is None:
            _A							:  Dict    =		read_init()
      # For special correspondence backend to module name as used in the function requires_modulename
      _A							:  List[Any]    =		{}

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

      return dummy_files







def        lowerCAmelCase_			(  snake_case_=False  ):
      _A							:  Dict    =		create_dummy_files()
      # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
      _A							:  Optional[int]    =		{"""torch""": """pt"""}

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

      _A							:  List[str]    =		{}
      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:
                        _A							:  Optional[Any]    =		f.read()
            else:
                  _A							:  int    =		""""""

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


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

  check_dummies(args.fix_and_overwrite)







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class 		lowercase							(     tf.keras.layers.Layer	):

     def __init__(						self	,  _a	,  _a	,  _a = None	,  _a = None       )		->							Any:
           super().__init__()
           _A							:  Dict    =		pad_token_id
           _A							:  List[Any]    =		max_length
           _A							:  Optional[int]    =		vocab
           _A							:  Optional[int]    =		merges
           _A							:  Optional[int]    =		BytePairTokenizer(_a	,  _a	,  sequence_length=_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							str:
           _A							:  Any    =		[""" """.join(_a       ) for m in tokenizer.bpe_ranks.keys()]
           _A							:  str    =		tokenizer.get_vocab()
           return cls(_a	,  _a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							List[Any]:
           _A							:  Union[str, Any]    =		GPTaTokenizer.from_pretrained(_a	,  *_a	,  **_a       )
           return cls.from_tokenizer(_a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a       )		->							Union[str, Any]:
           return cls(**_a       )

     def       a__			(						self       )		->							Union[str, Any]:
           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }



     def       a__			(						self	,  _a	,  _a = None       )		->							int:
           _A							:  Optional[int]    =		self.tf_tokenizer(_a       )
           _A							:  Tuple    =		tf.ones_like(_a       )

           if self.pad_token_id is not None:
                 # pad the tokens up to max length
                 _A							:  Dict    =		max_length if max_length is not None else self.max_length

                 if max_length is not None:
                       _A    ,     _A							:  Dict    =		pad_model_inputs(
                           _a	,  max_seq_length=_a	,  pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}

