





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


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Dict  ,				__lowercase:		List[Any]  ,				__lowercase:		Dict):



  '''simple docstring'''






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

  __lowerCAmelCase								=					start_index
  while not lines[end_index].startswith(__lowercase):
    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				    =      """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
__lowercase				    =      re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__lowercase				    =      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				    =      re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Dict):



  '''simple docstring'''






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


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[Any]  ,				__lowercase:		Tuple):



  '''simple docstring'''






  __lowerCAmelCase								=					2 if text == '''✅''' or text == '''❌''' else len(__lowercase)
  __lowerCAmelCase								=					(width - text_length) // 2
  __lowerCAmelCase								=					width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent


def        __SCREAMING_SNAKE_CASE	(				):



  '''simple docstring'''






  __lowerCAmelCase								=					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  __lowerCAmelCase								=					{
      name: config_mapping_names[code]
      for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
      if code in config_mapping_names
  }
  __lowerCAmelCase								=					{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.
  __lowerCAmelCase								=					collections.defaultdict(__lowercase)
  __lowerCAmelCase								=					collections.defaultdict(__lowercase)
  __lowerCAmelCase								=					collections.defaultdict(__lowercase)
  __lowerCAmelCase								=					collections.defaultdict(__lowercase)
  __lowerCAmelCase								=					collections.defaultdict(__lowercase)

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

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

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

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

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


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any=False):



  '''simple docstring'''






  __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase								=					_find_text_in_file(
      filename=os.path.join(__lowercase  ,				'''index.mdx''')  ,				start_prompt='''<!--This table is updated automatically from the auto modules'''  ,				end_prompt='''<!-- End table-->'''  ,				)
  __lowerCAmelCase								=					get_model_table_from_auto_modules()

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

      check_model_table(args.fix_and_overwrite)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE     : str      =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     : Any      ={
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}






class       A      (		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:						str  ="tvlt"




     def __init__(     self	: List[Any]						,	A__	: List[str]=2_2_4						,	A__	: Optional[int]=2_0_4_8						,	A__	: str=1_2_8						,	A__	: Union[str, Any]=[1_6, 1_6]						,	A__	: List[Any]=[1_6, 1_6]						,	A__	: int=3						,	A__	: Optional[int]=1						,	A__	: Tuple=8						,	A__	: Tuple=7_6_8						,	A__	: List[str]=1_2						,	A__	: Tuple=1_2						,	A__	: int=3_0_7_2						,	A__	: Tuple="gelu"						,	A__	: str=0.0						,	A__	: Dict=0.0						,	A__	: Any=0.02						,	A__	: int=1E-6						,	A__	: List[str]=True						,	A__	: List[str]=False						,	A__	: Optional[Any]=1_6						,	A__	: Optional[Any]=5_1_2						,	A__	: List[Any]=8						,	A__	: Any=2_0_4_8						,	A__	: List[Any]=0.75						,	A__	: List[Any]=0.15						,	A__	: str="frame-level"						,	A__	: Union[str, Any]=True						,	A__	: Optional[int]=True						,	A__	: Optional[Any]="classification"						,	**A__	: int						,	)					->							Tuple:
           super().__init__(**A__      )

           if audio_mask_type not in ("frame-level", "patch_level"):
                 raise ValueError(
                     'audio_mask_type must be one of two acceptable strategies - {\'frame_level\', \'patch-level\') '
                     F"""got {audio_mask_type}"""      )

           lowerCamelCase__   	=					image_size
           lowerCamelCase__   	=					spectrogram_length
           lowerCamelCase__   	=					frequency_length
           lowerCamelCase__   	=					image_patch_size
           lowerCamelCase__   	=					audio_patch_size
           lowerCamelCase__   	=					num_image_channels
           lowerCamelCase__   	=					num_audio_channels
           lowerCamelCase__   	=					num_frames

           lowerCamelCase__   	=					hidden_size
           lowerCamelCase__   	=					num_hidden_layers
           lowerCamelCase__   	=					num_attention_heads
           lowerCamelCase__   	=					intermediate_size
           lowerCamelCase__   	=					hidden_act
           lowerCamelCase__   	=					hidden_dropout_prob
           lowerCamelCase__   	=					attention_probs_dropout_prob
           lowerCamelCase__   	=					initializer_range
           lowerCamelCase__   	=					layer_norm_eps
           lowerCamelCase__   	=					qkv_bias
           lowerCamelCase__   	=					use_mean_pooling

           lowerCamelCase__   	=					decoder_num_attention_heads
           lowerCamelCase__   	=					decoder_hidden_size
           lowerCamelCase__   	=					decoder_num_hidden_layers
           lowerCamelCase__   	=					decoder_intermediate_size
           lowerCamelCase__   	=					pixel_mask_ratio
           lowerCamelCase__   	=					audio_mask_ratio
           lowerCamelCase__   	=					audio_mask_type

           lowerCamelCase__   	=					task_matching
           lowerCamelCase__   	=					task_mae
           lowerCamelCase__   	=					loss_type