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
A     :  Tuple					     =       "src/diffusers"

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


A     :  int					     =       "\n{0} = None\n"

A     :  Dict					     =       "\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"


A     :  List[str]					     =       "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  _re_backend.findall(__UpperCamelCase				)
      if len(__UpperCamelCase				) == 0:
            return None

      return "_and_".join(__UpperCamelCase				)


def      a__ (		):
      with open(os.path.join(__UpperCamelCase     ,      "__init__.py"				)     ,      "r"     ,      encoding="utf-8"     ,      newline="\n"				) as f:
            SCREAMING_SNAKE_CASE_          =  f.readlines()

      # Get to the point we do the actual imports for type checking
      SCREAMING_SNAKE_CASE_          =  0
      SCREAMING_SNAKE_CASE_          =  {}
      # Go through the end of the file
      while line_index < len(__UpperCamelCase				):
            # If the line contains is_backend_available, we grab all objects associated with the `else` block
            SCREAMING_SNAKE_CASE_          =  find_backend(lines[line_index]				)
            if backend is not None:
                  while not lines[line_index].startswith("else:"				):
                        line_index += 1
                  line_index += 1
                  SCREAMING_SNAKE_CASE_          =  []
                  # Until we unindent, add backend objects to the list
                  while line_index < len(__UpperCamelCase				) and len(lines[line_index]				) > 1:
                        SCREAMING_SNAKE_CASE_          =  lines[line_index]
                        SCREAMING_SNAKE_CASE_          =  _re_single_line_import.search(__UpperCamelCase				)
                        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(__UpperCamelCase				) > 0:
                        SCREAMING_SNAKE_CASE_          =  objects
            else:
                  line_index += 1

      return backend_specific_objects


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


def      a__ (		__UpperCamelCase=None				):
      if backend_specific_objects is None:
            SCREAMING_SNAKE_CASE_          =  read_init()
      # For special correspondence backend to module name as used in the function requires_modulename
      SCREAMING_SNAKE_CASE_          =  {}

      for backend, objects in backend_specific_objects.items():
            SCREAMING_SNAKE_CASE_          =  "[" + ", ".join(F'''"{b}"''' for b in backend.split("_and_"				)				) + "]"
            SCREAMING_SNAKE_CASE_          =  "# 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(__UpperCamelCase     ,      __UpperCamelCase				) for o in objects]				)
            SCREAMING_SNAKE_CASE_          =  dummy_file

      return dummy_files


def      a__ (		__UpperCamelCase=False				):
      SCREAMING_SNAKE_CASE_          =  create_dummy_files()
      # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
      SCREAMING_SNAKE_CASE_          =  {"torch": "pt"}

      # Locate actual dummy modules and read their content.
      SCREAMING_SNAKE_CASE_          =  os.path.join(__UpperCamelCase     ,      "utils"				)
      SCREAMING_SNAKE_CASE_          =  {
          backend: os.path.join(__UpperCamelCase     ,      F'''dummy_{short_names.get(__UpperCamelCase     ,      __UpperCamelCase				)}_objects.py'''				)
          for backend in dummy_files.keys()
      }

      SCREAMING_SNAKE_CASE_          =  {}
      for backend, file_path in dummy_file_paths.items():
            if os.path.isfile(__UpperCamelCase				):
                  with open(__UpperCamelCase     ,      "r"     ,      encoding="utf-8"     ,      newline="\n"				) as f:
                        SCREAMING_SNAKE_CASE_          =  f.read()
            else:
                  SCREAMING_SNAKE_CASE_          =  ""

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


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

      check_dummies(args.fix_and_overwrite)



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :  str					     =       {
    "configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig", "RemBertOnnxConfig"]
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       ["RemBertTokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       ["RemBertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RemBertForCausalLM",
          "RemBertForMaskedLM",
          "RemBertForMultipleChoice",
          "RemBertForQuestionAnswering",
          "RemBertForSequenceClassification",
          "RemBertForTokenClassification",
          "RemBertLayer",
          "RemBertModel",
          "RemBertPreTrainedModel",
          "load_tf_weights_in_rembert",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  List[str]					     =       [
          "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRemBertForCausalLM",
          "TFRemBertForMaskedLM",
          "TFRemBertForMultipleChoice",
          "TFRemBertForQuestionAnswering",
          "TFRemBertForSequenceClassification",
          "TFRemBertForTokenClassification",
          "TFRemBertLayer",
          "TFRemBertModel",
          "TFRemBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert import RemBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert_fast import RemBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rembert import (
                REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RemBertForCausalLM,
                RemBertForMaskedLM,
                RemBertForMultipleChoice,
                RemBertForQuestionAnswering,
                RemBertForSequenceClassification,
                RemBertForTokenClassification,
                RemBertLayer,
                RemBertModel,
                RemBertPreTrainedModel,
                load_tf_weights_in_rembert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rembert import (
                TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRemBertForCausalLM,
                TFRemBertForMaskedLM,
                TFRemBertForMultipleChoice,
                TFRemBertForQuestionAnswering,
                TFRemBertForSequenceClassification,
                TFRemBertForTokenClassification,
                TFRemBertLayer,
                TFRemBertModel,
                TFRemBertPreTrainedModel,
            )


else:
      import sys

      A     :  Tuple					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


