




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     : int      ="""src/diffusers"""
__SCREAMING_SNAKE_CASE     : List[Any]      ="""docs/source/en"""
__SCREAMING_SNAKE_CASE     : Optional[Any]      ="""."""

def       lowerCAmelCase_      (	lowerCamelCase:							Dict  ,					lowerCamelCase:							List[Any]  ,					lowerCamelCase:							Dict    )       ->      Optional[Any]:


      '''simple docstring'''




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

      lowerCamelCase__   	=					start_index
      while not lines[end_index].startswith(lowerCamelCase    ):
            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     : List[Any]      ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
__SCREAMING_SNAKE_CASE     : Optional[Any]      =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__SCREAMING_SNAKE_CASE     : Any      =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     : List[Any]      =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     : Any      =importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__SCREAMING_SNAKE_CASE     : List[str]      =spec.loader.load_module()

def       lowerCAmelCase_      (	lowerCamelCase:							Dict    )       ->      Optional[Any]:


      '''simple docstring'''




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

def       lowerCAmelCase_      (	lowerCamelCase:							List[Any]  ,					lowerCamelCase:							Tuple    )       ->      List[Any]:


      '''simple docstring'''




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

def       lowerCAmelCase_      (	)       ->      Optional[int]:


      '''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(lowerCamelCase    )
      lowerCamelCase__   	=					collections.defaultdict(lowerCamelCase    )
      lowerCamelCase__   	=					collections.defaultdict(lowerCamelCase    )
      lowerCamelCase__   	=					collections.defaultdict(lowerCamelCase    )
      lowerCamelCase__   	=					collections.defaultdict(lowerCamelCase    )

      # Let's lookup through all diffusers object (once).
      for attr_name in dir(lowerCamelCase    ):
            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(lowerCamelCase    ) is not None:
                  lowerCamelCase__   	=					tf_models
                  lowerCamelCase__   	=					_re_tf_models.match(lowerCamelCase    ).groups()[0]
            elif _re_flax_models.match(lowerCamelCase    ) is not None:
                  lowerCamelCase__   	=					flax_models
                  lowerCamelCase__   	=					_re_flax_models.match(lowerCamelCase    ).groups()[0]
            elif _re_pt_models.match(lowerCamelCase    ) is not None:
                  lowerCamelCase__   	=					pt_models
                  lowerCamelCase__   	=					_re_pt_models.match(lowerCamelCase    ).groups()[0]

            if lookup_dict is not None:
                  while len(lowerCamelCase    ) > 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(lowerCamelCase    )[:-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(lowerCamelCase    ) + 2 for c in columns]
      lowerCamelCase__   	=					max([len(lowerCamelCase    ) for name in model_names]    ) + 2

      # Build the table per se
      lowerCamelCase__   	=					'|' + '|'.join([_center_text(lowerCamelCase  ,					lowerCamelCase    ) for c, w in zip(lowerCamelCase  ,					lowerCamelCase    )]    ) + '|\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(lowerCamelCase  ,					lowerCamelCase    ) for l, w in zip(lowerCamelCase  ,					lowerCamelCase    )]    ) + "|\n"
      return table

def       lowerCAmelCase_      (	lowerCamelCase:							Any=False    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__	,		lowerCamelCase__	,		lowerCamelCase__	,		lowerCamelCase__   	=					_find_text_in_file(
          filename=os.path.join(lowerCamelCase  ,					'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(lowerCamelCase  ,					'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     : Union[str, Any]      =argparse.ArgumentParser()
      parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
      __SCREAMING_SNAKE_CASE     : Optional[Any]      =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