





import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# 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
lowercase				:	Tuple      						=  """src/transformers"""
lowercase				:	Any      						=  """docs/source/en"""
lowercase				:	Any      						=  """."""



def A_     (		A__		,				A__		,				A__    )  ->		Dict:
       with open(A__		,				'r'		,				encoding='utf-8'		,				newline='\n'    ) as f:
              a__						:     Union[str, Any]			=    f.readlines()
       # Find the start prompt.
       a__						:     Union[str, Any]			=    0
       while not lines[start_index].startswith(A__    ):
              start_index += 1
       start_index += 1

       a__						:     int			=    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 |
lowercase				:	Dict      						=  """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
lowercase				:	Optional[Any]      						=  re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
lowercase				:	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.
lowercase				:	List[Any]      						=  re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
lowercase				:	List[Any]      						=  direct_transformers_import(TRANSFORMERS_PATH)



def A_     (		A__    )  ->		str:
       a__						:     Dict			=    re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'		,				A__    )
       return [m.group(0    ) for m in matches]



def A_     (		A__		,				A__    )  ->		Union[str, Any]:
       a__						:     List[Any]			=    2 if text == '✅' or text == '❌' else len(A__    )
       a__						:     str			=    (width - text_length) // 2
       a__						:     int			=    width - text_length - left_indent
       return " " * left_indent + text + " " * right_indent



def A_     (		)  ->		List[Any]:
       a__						:     Optional[Any]			=    transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       a__						:     Union[str, Any]			=    {
           name: config_maping_names[code]
           for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
           if code in config_maping_names
       }
       a__						:     List[str]			=    {name: config.replace('Config'		,				''    ) 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__						:     int			=    collections.defaultdict(A__    )
       a__						:     Any			=    collections.defaultdict(A__    )
       a__						:     List[str]			=    collections.defaultdict(A__    )
       a__						:     Any			=    collections.defaultdict(A__    )
       a__						:     int			=    collections.defaultdict(A__    )

       # Let's lookup through all transformers object (once).
       for attr_name in dir(A__    ):
              a__						:     Optional[Any]			=    None
              if attr_name.endswith('Tokenizer'    ):
                     a__						:     Tuple			=    slow_tokenizers
                     a__						:     Dict			=    attr_name[:-9]
              elif attr_name.endswith('TokenizerFast'    ):
                     a__						:     Union[str, Any]			=    fast_tokenizers
                     a__						:     Optional[int]			=    attr_name[:-13]
              elif _re_tf_models.match(A__    ) is not None:
                     a__						:     Union[str, Any]			=    tf_models
                     a__						:     int			=    _re_tf_models.match(A__    ).groups()[0]
              elif _re_flax_models.match(A__    ) is not None:
                     a__						:     str			=    flax_models
                     a__						:     Union[str, Any]			=    _re_flax_models.match(A__    ).groups()[0]
              elif _re_pt_models.match(A__    ) is not None:
                     a__						:     Any			=    pt_models
                     a__						:     Dict			=    _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__						:     List[str]			=    True
                                   break
                            # Try again after removing the last word in the name
                            a__						:     List[Any]			=    ''.join(camel_case_split(A__    )[:-1]    )

    # Let's build that table!
       a__						:     Optional[Any]			=    list(model_name_to_config.keys()    )
       model_names.sort(key=str.lower    )
       a__						:     str			=    ['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__						:     Tuple			=    [len(A__    ) + 2 for c in columns]
       a__						:     Union[str, Any]			=    max([len(A__    ) for name in model_names]    ) + 2

       # Build the table per se
       a__						:     Optional[int]			=    '|' + '|'.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__						:     Optional[Any]			=    {True: '✅', False: '❌'}
       for name in model_names:
              a__						:     Union[str, Any]			=    model_name_to_prefix[name]
              a__						:     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(A__		,				A__    ) for l, w in zip(A__		,				A__    )]    ) + "|\n"
       return table



def A_     (		A__=False    )  ->		Dict:
       a__			,							a__			,							a__			,							a__						:     Optional[Any]			=    _find_text_in_file(
           filename=os.path.join(A__		,				'index.md'    )		,				start_prompt='<!--This table is updated automatically from the auto modules'		,				end_prompt='<!-- End table-->'		,				)
       a__						:     Optional[int]			=    get_model_table_from_auto_modules()

       if current_table != new_table:
              if overwrite:
                     with open(os.path.join(A__		,				'index.md'    )		,				'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.md` has not been updated. Run `make fix-copies` to fix this.'    )


if __name__ == "__main__":
    lowercase				:	Dict      						=  argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    lowercase				:	List[str]      						=  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,
    is_vision_available,
)


lowercase				:	List[str]      						=  {
    """configuration_clip""": [
        """CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """CLIPConfig""",
        """CLIPOnnxConfig""",
        """CLIPTextConfig""",
        """CLIPVisionConfig""",
    ],
    """processing_clip""": ["""CLIPProcessor"""],
    """tokenization_clip""": ["""CLIPTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Tuple      						=  ["""CLIPTokenizerFast"""]

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Optional[int]      						=  ["""CLIPFeatureExtractor"""]
    lowercase				:	Union[str, Any]      						=  ["""CLIPImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Dict      						=  [
        """CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CLIPModel""",
        """CLIPPreTrainedModel""",
        """CLIPTextModel""",
        """CLIPTextModelWithProjection""",
        """CLIPVisionModel""",
        """CLIPVisionModelWithProjection""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	List[str]      						=  [
        """TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFCLIPModel""",
        """TFCLIPPreTrainedModel""",
        """TFCLIPTextModel""",
        """TFCLIPVisionModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Any      						=  [
        """FlaxCLIPModel""",
        """FlaxCLIPPreTrainedModel""",
        """FlaxCLIPTextModel""",
        """FlaxCLIPTextPreTrainedModel""",
        """FlaxCLIPVisionModel""",
        """FlaxCLIPVisionPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_clip import (
        CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        CLIPConfig,
        CLIPOnnxConfig,
        CLIPTextConfig,
        CLIPVisionConfig,
    )
    from .processing_clip import CLIPProcessor
    from .tokenization_clip import CLIPTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_clip_fast import CLIPTokenizerFast

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_clip import CLIPFeatureExtractor
        from .image_processing_clip import CLIPImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_clip import (
            CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            CLIPModel,
            CLIPPreTrainedModel,
            CLIPTextModel,
            CLIPTextModelWithProjection,
            CLIPVisionModel,
            CLIPVisionModelWithProjection,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_clip import (
            TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCLIPModel,
            TFCLIPPreTrainedModel,
            TFCLIPTextModel,
            TFCLIPVisionModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_clip import (
            FlaxCLIPModel,
            FlaxCLIPPreTrainedModel,
            FlaxCLIPTextModel,
            FlaxCLIPTextPreTrainedModel,
            FlaxCLIPVisionModel,
            FlaxCLIPVisionPreTrainedModel,
        )


else:
    import sys

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


