






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    _lowerCamelCase		(  __A			:							Tuple						,						__A			:							int						,						__A			:							List[str]       )					->    Optional[Any]:

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

    _UpperCAmelCase :					List[str]	     = 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    _lowerCamelCase		(  __A			:							Union[str, Any]       )					->    Optional[Any]:
    _UpperCAmelCase :					List[Any]	     = re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''						,						A_       )
    return [m.group(0       ) for m in matches]

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

def    _lowerCamelCase		(  )					->    Optional[int]:
    _UpperCAmelCase :					Any	     = diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    _UpperCAmelCase :					List[Any]	     = {
        name: config_mapping_names[code]
        for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
        if code in config_mapping_names
    }
    _UpperCAmelCase :					Optional[int]	     = {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 :					List[str]	     = collections.defaultdict(A_       )
    _UpperCAmelCase :					List[Any]	     = collections.defaultdict(A_       )
    _UpperCAmelCase :					Optional[Any]	     = collections.defaultdict(A_       )
    _UpperCAmelCase :					Any	     = collections.defaultdict(A_       )
    _UpperCAmelCase :					Dict	     = collections.defaultdict(A_       )

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

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

    # Build the table per se
    _UpperCAmelCase :					Tuple	     = '|' + '|'.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"

    _UpperCAmelCase :					Tuple	     = {True: '✅', False: '❌'}
    for name in model_names:
        _UpperCAmelCase :					List[str]	     = model_name_to_prefix[name]
        _UpperCAmelCase :					Any	     = [
            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    _lowerCamelCase		(  __A			:							List[str]=False       )					->    int:
    _UpperCAmelCase :					Any	     = _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-->'''						,						)
    _UpperCAmelCase :					Any	     = 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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase				:    List[Any]     =						{
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    Any     =						['OpenAIGPTTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    int     =						[
       'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'OpenAIGPTDoubleHeadsModel',
       'OpenAIGPTForSequenceClassification',
       'OpenAIGPTLMHeadModel',
       'OpenAIGPTModel',
       'OpenAIGPTPreTrainedModel',
       'load_tf_weights_in_openai_gpt',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    List[str]     =						[
       'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFOpenAIGPTDoubleHeadsModel',
       'TFOpenAIGPTForSequenceClassification',
       'TFOpenAIGPTLMHeadModel',
       'TFOpenAIGPTMainLayer',
       'TFOpenAIGPTModel',
       'TFOpenAIGPTPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
   from .tokenization_openai import OpenAIGPTTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_openai_fast import OpenAIGPTTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_openai import (
          OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OpenAIGPTDoubleHeadsModel,
          OpenAIGPTForSequenceClassification,
          OpenAIGPTLMHeadModel,
          OpenAIGPTModel,
          OpenAIGPTPreTrainedModel,
          load_tf_weights_in_openai_gpt,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_openai import (
          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFOpenAIGPTDoubleHeadsModel,
          TFOpenAIGPTForSequenceClassification,
          TFOpenAIGPTLMHeadModel,
          TFOpenAIGPTMainLayer,
          TFOpenAIGPTModel,
          TFOpenAIGPTPreTrainedModel,
      )

else:
   import sys

   _UpperCamelCase				:    Union[str, Any]     =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




