





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

       UpperCamelCase_      				=     start_index
       while not lines[end_index].startswith(UpperCamelCase_     ):
              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        lowerCAmelCase_       (							UpperCamelCase_     )   ->						Any:
       UpperCamelCase_      				=     re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"    ,						UpperCamelCase_     )
       return [m.group(0     ) for m in matches]




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_     )   ->						Optional[Any]:
       UpperCamelCase_      				=     2 if text == "✅" or text == "❌" else len(UpperCamelCase_     )
       UpperCamelCase_      				=     (width - text_length) // 2
       UpperCamelCase_      				=     width - text_length - left_indent
       return " " * left_indent + text + " " * right_indent




def        lowerCAmelCase_       (							)   ->						Tuple:
       UpperCamelCase_      				=     diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       UpperCamelCase_      				=     {
           name: config_mapping_names[code]
           for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
           if code in config_mapping_names
       }
       UpperCamelCase_      				=     {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_      				=     collections.defaultdict(UpperCamelCase_     )
       UpperCamelCase_      				=     collections.defaultdict(UpperCamelCase_     )
       UpperCamelCase_      				=     collections.defaultdict(UpperCamelCase_     )
       UpperCamelCase_      				=     collections.defaultdict(UpperCamelCase_     )
       UpperCamelCase_      				=     collections.defaultdict(UpperCamelCase_     )

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

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

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

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

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




def        lowerCAmelCase_       (							UpperCamelCase_=False     )   ->						Optional[Any]:
       UpperCamelCase_				,       UpperCamelCase_				,       UpperCamelCase_				,       UpperCamelCase_      				=     _find_text_in_file(
           filename=os.path.join(UpperCamelCase_    ,						"index.mdx"     )    ,						start_prompt="<!--This table is updated automatically from the auto modules"    ,						end_prompt="<!-- End table-->"    ,						)
       UpperCamelCase_      				=     get_model_table_from_auto_modules()

       if current_table != new_table:
              if overwrite:
                     with open(os.path.join(UpperCamelCase_    ,						"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)








from typing import TYPE_CHECKING

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


_UpperCAmelCase				       = {
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = ['MPNetTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = [
         'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MPNetForMaskedLM',
         'MPNetForMultipleChoice',
         'MPNetForQuestionAnswering',
         'MPNetForSequenceClassification',
         'MPNetForTokenClassification',
         'MPNetLayer',
         'MPNetModel',
         'MPNetPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = [
         'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFMPNetEmbeddings',
         'TFMPNetForMaskedLM',
         'TFMPNetForMultipleChoice',
         'TFMPNetForQuestionAnswering',
         'TFMPNetForSequenceClassification',
         'TFMPNetForTokenClassification',
         'TFMPNetMainLayer',
         'TFMPNetModel',
         'TFMPNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
     from .tokenization_mpnet import MPNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mpnet_fast import MPNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mpnet import (
              MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              MPNetForMaskedLM,
              MPNetForMultipleChoice,
              MPNetForQuestionAnswering,
              MPNetForSequenceClassification,
              MPNetForTokenClassification,
              MPNetLayer,
              MPNetModel,
              MPNetPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mpnet import (
              TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMPNetEmbeddings,
              TFMPNetForMaskedLM,
              TFMPNetForMultipleChoice,
              TFMPNetForQuestionAnswering,
              TFMPNetForSequenceClassification,
              TFMPNetForTokenClassification,
              TFMPNetMainLayer,
              TFMPNetModel,
              TFMPNetPreTrainedModel,
          )

else:
     import sys

     _UpperCAmelCase				       = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

