





'''simple docstring'''






import argparse
import collections
import importlib.util
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_table.py
A__       :     Union[str, Any]          ='''src/diffusers'''
A__       :     int          ='''docs/source/en'''
A__       :     int          ='''.'''
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      with open(lowerCAmelCase							,			"""r"""							,			encoding="""utf-8"""							,			newline="""\n"""						) as f:
            _lowerCAmelCase							  =							f.readlines()
      # Find the start prompt.
      _lowerCAmelCase							  =							0
      while not lines[start_index].startswith(lowerCAmelCase						):
            start_index += 1
      start_index += 1

      _lowerCAmelCase							  =							start_index
      while not lines[end_index].startswith(lowerCAmelCase						):
            end_index += 1
      end_index -= 1

      while len(lines[start_index]						) <= 1:
            start_index += 1
      while len(lines[end_index]						) <= 1:
            end_index -= 1
      end_index += 1
      return "".join(lines[start_index:end_index]						), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
A__       :     Union[str, Any]          ='''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
A__       :     Any          =re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
A__       :     Tuple          =re.compile(r'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
A__       :     str          =re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the diffusers module imported is the one in the repo.
A__       :     Dict          =importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(TRANSFORMERS_PATH, '''__init__.py'''),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
A__       :     Any          =spec.loader.load_module()
def      UpperCamelCase__   (	lowerCAmelCase						):





      """simple docstring"""


      _lowerCAmelCase							  =							re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""							,			lowerCAmelCase						)
      return [m.group(0						) for m in matches]
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      _lowerCAmelCase							  =							2 if text == """✅""" or text == """❌""" else len(lowerCAmelCase						)
      _lowerCAmelCase							  =							(width - text_length) // 2
      _lowerCAmelCase							  =							width - text_length - left_indent
      return " " * left_indent + text + " " * right_indent
def      UpperCamelCase__   (	):





      """simple docstring"""


      _lowerCAmelCase							  =							diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      _lowerCAmelCase							  =							{
          name: config_mapping_names[code]
          for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
          if code in config_mapping_names
      }
      _lowerCAmelCase							  =							{name: config.replace("""ConfigMixin"""							,			""""""						) for name, config in model_name_to_config.items()}

      # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
      _lowerCAmelCase							  =							collections.defaultdict(lowerCAmelCase						)
      _lowerCAmelCase							  =							collections.defaultdict(lowerCAmelCase						)
      _lowerCAmelCase							  =							collections.defaultdict(lowerCAmelCase						)
      _lowerCAmelCase							  =							collections.defaultdict(lowerCAmelCase						)
      _lowerCAmelCase							  =							collections.defaultdict(lowerCAmelCase						)

      # Let's lookup through all diffusers object (once).
      for attr_name in dir(lowerCAmelCase						):
            _lowerCAmelCase							  =							None
            if attr_name.endswith("""Tokenizer"""						):
                  _lowerCAmelCase							  =							slow_tokenizers
                  _lowerCAmelCase							  =							attr_name[:-9]
            elif attr_name.endswith("""TokenizerFast"""						):
                  _lowerCAmelCase							  =							fast_tokenizers
                  _lowerCAmelCase							  =							attr_name[:-13]
            elif _re_tf_models.match(lowerCAmelCase						) is not None:
                  _lowerCAmelCase							  =							tf_models
                  _lowerCAmelCase							  =							_re_tf_models.match(lowerCAmelCase						).groups()[0]
            elif _re_flax_models.match(lowerCAmelCase						) is not None:
                  _lowerCAmelCase							  =							flax_models
                  _lowerCAmelCase							  =							_re_flax_models.match(lowerCAmelCase						).groups()[0]
            elif _re_pt_models.match(lowerCAmelCase						) is not None:
                  _lowerCAmelCase							  =							pt_models
                  _lowerCAmelCase							  =							_re_pt_models.match(lowerCAmelCase						).groups()[0]

            if lookup_dict is not None:
                  while len(lowerCAmelCase						) > 0:
                        if attr_name in model_name_to_prefix.values():
                              _lowerCAmelCase							  =							True
                              break
                        # Try again after removing the last word in the name
                        _lowerCAmelCase							  =							"""""".join(camel_case_split(lowerCAmelCase						)[:-1]						)

    # Let's build that table!
      _lowerCAmelCase							  =							list(model_name_to_config.keys()						)
      model_names.sort(key=str.lower						)
      _lowerCAmelCase							  =							["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
      # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
      _lowerCAmelCase							  =							[len(lowerCAmelCase						) + 2 for c in columns]
      _lowerCAmelCase							  =							max([len(lowerCAmelCase						) for name in model_names]						) + 2

      # Build the table per se
      _lowerCAmelCase							  =							"""|""" + """|""".join([_center_text(lowerCAmelCase							,			lowerCAmelCase						) for c, w in zip(lowerCAmelCase							,			lowerCAmelCase						)]						) + """|\n"""
      # Use ":-----:" format to center-aligned table cell texts
      table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths]						) + "|\n"

      _lowerCAmelCase							  =							{True: """✅""", False: """❌"""}
      for name in model_names:
            _lowerCAmelCase							  =							model_name_to_prefix[name]
            _lowerCAmelCase							  =							[
                name,
                check[slow_tokenizers[prefix]],
                check[fast_tokenizers[prefix]],
                check[pt_models[prefix]],
                check[tf_models[prefix]],
                check[flax_models[prefix]],
            ]
            table += "|" + "|".join([_center_text(lowerCAmelCase							,			lowerCAmelCase						) for l, w in zip(lowerCAmelCase							,			lowerCAmelCase						)]						) + "|\n"
      return table
def      UpperCamelCase__   (	lowerCAmelCase=False						):





      """simple docstring"""


      _lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase							  =							_find_text_in_file(
          filename=os.path.join(lowerCAmelCase							,			"""index.mdx"""						)							,			start_prompt="""<!--This table is updated automatically from the auto modules"""							,			end_prompt="""<!-- End table-->"""							,			)
      _lowerCAmelCase							  =							get_model_table_from_auto_modules()

      if current_table != new_table:
            if overwrite:
                  with open(os.path.join(lowerCAmelCase							,			"""index.mdx"""						)							,			"""w"""							,			encoding="""utf-8"""							,			newline="""\n"""						) as f:
                        f.writelines(lines[:start_index] + [new_table] + lines[end_index:]						)
            else:
                  raise ValueError(
                      """The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."""						)


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

      check_model_table(args.fix_and_overwrite)













'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__       :     Tuple          ={
    '''configuration_oneformer''': ['''ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OneFormerConfig'''],
    '''processing_oneformer''': ['''OneFormerProcessor'''],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     List[str]          =['''OneFormerImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     Dict          =[
          '''ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''OneFormerForUniversalSegmentation''',
          '''OneFormerModel''',
          '''OneFormerPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
      from .processing_oneformer import OneFormerProcessor

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_oneformer import OneFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_oneformer import (
                ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                OneFormerForUniversalSegmentation,
                OneFormerModel,
                OneFormerPreTrainedModel,
            )


else:
      import sys

      A__       :     Optional[Any]          =_LazyModule(__name__, globals()['''__file__'''], _import_structure)






