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







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     with open(SCREAMING_SNAKE_CASE__		,   'r'		,   encoding='utf-8'		,   newline='\n'   ) as f:
          snake_case_   			=   f.readlines()
     # Find the start prompt.
     snake_case_   			=   0
     while not lines[start_index].startswith(SCREAMING_SNAKE_CASE__   ):
          start_index += 1
     start_index += 1

     snake_case_   			=   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										=     "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
__UpperCamelCase										=     re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
__UpperCamelCase										=     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										=     re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


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







def    UpperCAmelCase       (						UpperCAmelCase   )							-> Optional[Any]:
     snake_case_   			=   re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'		,   SCREAMING_SNAKE_CASE__   )
     return [m.group(0   ) for m in matches]







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Tuple:
     snake_case_   			=   2 if text == '''✅''' or text == '''❌''' else len(SCREAMING_SNAKE_CASE__   )
     snake_case_   			=   (width - text_length) // 2
     snake_case_   			=   width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent







def    UpperCAmelCase       (						)							-> str:
     snake_case_   			=   diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     snake_case_   			=   {
         name: config_mapping_names[code]
         for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
         if code in config_mapping_names
     }
     snake_case_   			=   {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.
     snake_case_   			=   collections.defaultdict(SCREAMING_SNAKE_CASE__   )
     snake_case_   			=   collections.defaultdict(SCREAMING_SNAKE_CASE__   )
     snake_case_   			=   collections.defaultdict(SCREAMING_SNAKE_CASE__   )
     snake_case_   			=   collections.defaultdict(SCREAMING_SNAKE_CASE__   )
     snake_case_   			=   collections.defaultdict(SCREAMING_SNAKE_CASE__   )

     # Let's lookup through all diffusers object (once).
     for attr_name in dir(SCREAMING_SNAKE_CASE__   ):
          snake_case_   			=   None
          if attr_name.endswith('Tokenizer'   ):
               snake_case_   			=   slow_tokenizers
               snake_case_   			=   attr_name[:-9]
          elif attr_name.endswith('TokenizerFast'   ):
               snake_case_   			=   fast_tokenizers
               snake_case_   			=   attr_name[:-13]
          elif _re_tf_models.match(SCREAMING_SNAKE_CASE__   ) is not None:
               snake_case_   			=   tf_models
               snake_case_   			=   _re_tf_models.match(SCREAMING_SNAKE_CASE__   ).groups()[0]
          elif _re_flax_models.match(SCREAMING_SNAKE_CASE__   ) is not None:
               snake_case_   			=   flax_models
               snake_case_   			=   _re_flax_models.match(SCREAMING_SNAKE_CASE__   ).groups()[0]
          elif _re_pt_models.match(SCREAMING_SNAKE_CASE__   ) is not None:
               snake_case_   			=   pt_models
               snake_case_   			=   _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():
                         snake_case_   			=   True
                         break
                    # Try again after removing the last word in the name
                    snake_case_   			=   ''''''.join(camel_case_split(SCREAMING_SNAKE_CASE__   )[:-1]   )

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

     # Build the table per se
     snake_case_   			=   '''|''' + '''|'''.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"

     snake_case_   			=   {True: '''✅''', False: '''❌'''}
     for name in model_names:
          snake_case_   			=   model_name_to_prefix[name]
          snake_case_   			=   [
              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    UpperCAmelCase       (						UpperCAmelCase=False   )							-> str:
     snake_case_   			=   _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-->'		,   )
     snake_case_   			=   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										=     argparse.ArgumentParser()
    parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
    __UpperCamelCase										=     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


snake_case_  :							Any            =	{
    "configuration_flava": [
        "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "FlavaConfig",
        "FlavaImageCodebookConfig",
        "FlavaImageConfig",
        "FlavaMultimodalConfig",
        "FlavaTextConfig",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							List[str]            =	["FlavaFeatureExtractor"]
					snake_case_  :							int            =	["FlavaImageProcessor"]
					snake_case_  :							Tuple            =	["FlavaProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							Optional[int]            =	[
					    "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "FlavaForPreTraining",
					    "FlavaImageCodebook",
					    "FlavaImageModel",
					    "FlavaModel",
					    "FlavaMultimodalModel",
					    "FlavaPreTrainedModel",
					    "FlavaTextModel",
					]

if TYPE_CHECKING:
					from .configuration_flava import (
					    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    FlavaConfig,
					    FlavaImageCodebookConfig,
					    FlavaImageConfig,
					    FlavaMultimodalConfig,
					    FlavaTextConfig,
					)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_flava import FlavaFeatureExtractor
										from .image_processing_flava import FlavaImageProcessor
										from .processing_flava import FlavaProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flava import (
										    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlavaForPreTraining,
										    FlavaImageCodebook,
										    FlavaImageModel,
										    FlavaModel,
										    FlavaMultimodalModel,
										    FlavaPreTrainedModel,
										    FlavaTextModel,
										)

else:
					import sys

					snake_case_  :							Any            =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





