


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
__UpperCAmelCase			:			int			      =					"src/diffusers"
__UpperCAmelCase			:			int			      =					"docs/source/en"
__UpperCAmelCase			:			str			      =					"."


def   a						(				SCREAMING_SNAKE_CASE_   :		Any				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]							):






       """simple docstring"""





       with open(SCREAMING_SNAKE_CASE_				,      '''r'''				,      encoding='''utf-8'''				,      newline='''\n'''							) as f:
              UpperCamelCase     :						List[Any] 						=		f.readlines()
       # Find the start prompt.
       UpperCamelCase     :						Dict 						=		0
       while not lines[start_index].startswith(SCREAMING_SNAKE_CASE_							):
              start_index += 1
       start_index += 1

       UpperCamelCase     :						List[Any] 						=		start_index
       while not lines[end_index].startswith(SCREAMING_SNAKE_CASE_							):
              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 |
__UpperCAmelCase			:			List[Any]			      =					"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
__UpperCAmelCase			:			List[Any]			      =					re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
__UpperCAmelCase			:			List[str]			      =					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.
__UpperCAmelCase			:			Dict			      =					re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


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


def   a						(				SCREAMING_SNAKE_CASE_   :		str							):






       """simple docstring"""





       UpperCamelCase     :						Union[str, Any] 						=		re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''				,      SCREAMING_SNAKE_CASE_							)
       return [m.group(0							) for m in matches]


def   a						(				SCREAMING_SNAKE_CASE_   :		Any				,      SCREAMING_SNAKE_CASE_   :		List[Any]							):






       """simple docstring"""





       UpperCamelCase     :						Union[str, Any] 						=		2 if text == '''✅''' or text == '''❌''' else len(SCREAMING_SNAKE_CASE_							)
       UpperCamelCase     :						str 						=		(width - text_length) // 2
       UpperCamelCase     :						Union[str, Any] 						=		width - text_length - left_indent
       return " " * left_indent + text + " " * right_indent


def   a						(				):






       """simple docstring"""





       UpperCamelCase     :						List[Any] 						=		diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       UpperCamelCase     :						str 						=		{
           name: config_mapping_names[code]
           for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
           if code in config_mapping_names
       }
       UpperCamelCase     :						Any 						=		{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.
       UpperCamelCase     :						Optional[int] 						=		collections.defaultdict(SCREAMING_SNAKE_CASE_							)
       UpperCamelCase     :						Optional[Any] 						=		collections.defaultdict(SCREAMING_SNAKE_CASE_							)
       UpperCamelCase     :						int 						=		collections.defaultdict(SCREAMING_SNAKE_CASE_							)
       UpperCamelCase     :						List[str] 						=		collections.defaultdict(SCREAMING_SNAKE_CASE_							)
       UpperCamelCase     :						Union[str, Any] 						=		collections.defaultdict(SCREAMING_SNAKE_CASE_							)

       # Let's lookup through all diffusers object (once).
       for attr_name in dir(SCREAMING_SNAKE_CASE_							):
              UpperCamelCase     :						str 						=		None
              if attr_name.endswith('''Tokenizer'''							):
                     UpperCamelCase     :						Optional[int] 						=		slow_tokenizers
                     UpperCamelCase     :						Tuple 						=		attr_name[:-9]
              elif attr_name.endswith('''TokenizerFast'''							):
                     UpperCamelCase     :						Optional[Any] 						=		fast_tokenizers
                     UpperCamelCase     :						str 						=		attr_name[:-1_3]
              elif _re_tf_models.match(SCREAMING_SNAKE_CASE_							) is not None:
                     UpperCamelCase     :						Any 						=		tf_models
                     UpperCamelCase     :						Optional[int] 						=		_re_tf_models.match(SCREAMING_SNAKE_CASE_							).groups()[0]
              elif _re_flax_models.match(SCREAMING_SNAKE_CASE_							) is not None:
                     UpperCamelCase     :						Optional[Any] 						=		flax_models
                     UpperCamelCase     :						List[Any] 						=		_re_flax_models.match(SCREAMING_SNAKE_CASE_							).groups()[0]
              elif _re_pt_models.match(SCREAMING_SNAKE_CASE_							) is not None:
                     UpperCamelCase     :						Tuple 						=		pt_models
                     UpperCamelCase     :						Optional[int] 						=		_re_pt_models.match(SCREAMING_SNAKE_CASE_							).groups()[0]

              if lookup_dict is not None:
                     while len(SCREAMING_SNAKE_CASE_							) > 0:
                            if attr_name in model_name_to_prefix.values():
                                   UpperCamelCase     :						str 						=		True
                                   break
                            # Try again after removing the last word in the name
                            UpperCamelCase     :						Union[str, Any] 						=		''''''.join(camel_case_split(SCREAMING_SNAKE_CASE_							)[:-1]							)

    # Let's build that table!
       UpperCamelCase     :						Tuple 						=		list(model_name_to_config.keys()							)
       model_names.sort(key=str.lower							)
       UpperCamelCase     :						Tuple 						=		['''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).
       UpperCamelCase     :						str 						=		[len(SCREAMING_SNAKE_CASE_							) + 2 for c in columns]
       UpperCamelCase     :						Dict 						=		max([len(SCREAMING_SNAKE_CASE_							) for name in model_names]							) + 2

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

       UpperCamelCase     :						List[str] 						=		{True: '''✅''', False: '''❌'''}
       for name in model_names:
              UpperCamelCase     :						Any 						=		model_name_to_prefix[name]
              UpperCamelCase     :						Tuple 						=		[
                  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(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							) for l, w in zip(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)]							) + "|\n"
       return table


def   a						(				SCREAMING_SNAKE_CASE_   :		int=False							):






       """simple docstring"""





       UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase     :						Optional[Any] 						=		_find_text_in_file(
           filename=os.path.join(SCREAMING_SNAKE_CASE_				,      '''index.mdx'''							)				,      start_prompt='''<!--This table is updated automatically from the auto modules'''				,      end_prompt='''<!-- End table-->'''				,      )
       UpperCamelCase     :						List[str] 						=		get_model_table_from_auto_modules()

       if current_table != new_table:
              if overwrite:
                     with open(os.path.join(SCREAMING_SNAKE_CASE_				,      '''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__":
  __UpperCAmelCase			:			Union[str, Any]			      =					argparse.ArgumentParser()
  parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
  __UpperCAmelCase			:			str			      =					parser.parse_args()

  check_model_table(args.fix_and_overwrite)





from typing import TYPE_CHECKING

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


__UpperCAmelCase			:			int			      =					{"configuration_vilt": ["VILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViltConfig"]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase			:			Optional[Any]			      =					["ViltFeatureExtractor"]
  __UpperCAmelCase			:			Union[str, Any]			      =					["ViltImageProcessor"]
  __UpperCAmelCase			:			Dict			      =					["ViltProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase			:			List[Any]			      =					[
      "VILT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ViltForImageAndTextRetrieval",
      "ViltForImagesAndTextClassification",
      "ViltForTokenClassification",
      "ViltForMaskedLM",
      "ViltForQuestionAnswering",
      "ViltLayer",
      "ViltModel",
      "ViltPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_vilt import ViltFeatureExtractor
    from .image_processing_vilt import ViltImageProcessor
    from .processing_vilt import ViltProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vilt import (
        VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViltForImageAndTextRetrieval,
        ViltForImagesAndTextClassification,
        ViltForMaskedLM,
        ViltForQuestionAnswering,
        ViltForTokenClassification,
        ViltLayer,
        ViltModel,
        ViltPreTrainedModel,
    )


else:
  import sys

  __UpperCAmelCase			:			Any			      =					_LazyModule(__name__, globals()["__file__"], _import_structure)

