




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
SCREAMING_SNAKE_CASE       = "src/diffusers"
SCREAMING_SNAKE_CASE       = "docs/source/en"
SCREAMING_SNAKE_CASE       = "."







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Optional[int]:
 with open(a_    ,				"r"    ,				encoding="utf-8"    ,				newline="\n"		) as f:
  A__ =       f.readlines()
 # Find the start prompt.
 A__ =       0
 while not lines[start_index].startswith(a_		):
  start_index += 1
 start_index += 1

 A__ =       start_index
 while not lines[end_index].startswith(a_		):
  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 |
SCREAMING_SNAKE_CASE       = "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE       = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
SCREAMING_SNAKE_CASE       = 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.
SCREAMING_SNAKE_CASE       = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the diffusers module imported is the one in the repo.
SCREAMING_SNAKE_CASE       = importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(TRANSFORMERS_PATH, "__init__.py"),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
SCREAMING_SNAKE_CASE       = spec.loader.load_module()







def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Dict:
 A__ =       re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"    ,				a_		)
 return [m.group(0		) for m in matches]







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	Optional[int]:
 A__ =       2 if text == "✅" or text == "❌" else len(a_		)
 A__ =       (width - text_length) // 2
 A__ =       width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent







def 						_SCREAMING_SNAKE_CASE     (							)		->	List[Any]:
 A__ =       diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 A__ =       {
     name: config_mapping_names[code]
     for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
     if code in config_mapping_names
 }
 A__ =       {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.
 A__ =       collections.defaultdict(a_		)
 A__ =       collections.defaultdict(a_		)
 A__ =       collections.defaultdict(a_		)
 A__ =       collections.defaultdict(a_		)
 A__ =       collections.defaultdict(a_		)

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

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

    # Let's build that table!
 A__ =       list(model_name_to_config.keys()		)
 model_names.sort(key=str.lower		)
 A__ =       ["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).
 A__ =       [len(a_		) + 2 for c in columns]
 A__ =       max([len(a_		) for name in model_names]		) + 2

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

 A__ =       {True: "✅", False: "❌"}
 for name in model_names:
  A__ =       model_name_to_prefix[name]
  A__ =       [
      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(a_    ,				a_		) for l, w in zip(a_    ,				a_		)]		) + "|\n"
 return table







def 						_SCREAMING_SNAKE_CASE     (							lowercase_=False		)		->	int:
 A__,   A__,   A__,   A__ =       _find_text_in_file(
     filename=os.path.join(a_    ,				"index.mdx"		)    ,				start_prompt="<!--This table is updated automatically from the auto modules"    ,				end_prompt="<!-- End table-->"    ,				)
 A__ =       get_model_table_from_auto_modules()

 if current_table != new_table:
  if overwrite:
   with open(os.path.join(a_    ,				"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__":
      SCREAMING_SNAKE_CASE       = argparse.ArgumentParser()
      parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
      SCREAMING_SNAKE_CASE       = 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


SCREAMING_SNAKE_CASE       = {
    "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE       = ["DetaImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE       = [
          "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DetaForObjectDetection",
          "DetaModel",
          "DetaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_deta import DetaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_deta import (
                DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                DetaForObjectDetection,
                DetaModel,
                DetaPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE       = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





