code: 
  """simple docstring"""
  
  
  
  
  
  
  
  from typing import SupportsIndex
  
  import numpy as np
  from scipy.ndimage import convolve
  def 			__UpperCAmelCase		(		a_			:		np.ndarray    ,  a_			:		np.ndarray    ,  a_			:		np.ndarray				)     ->       np.ndarray:
     _UpperCAmelCase   	=					np.stack((horizontal_flow, vertical_flow)    ,  2				)
  
     # Create a grid of all pixel coordinates and subtract the flow to get the
     # target pixels coordinates
     _UpperCAmelCase   	=					np.stack(
         np.meshgrid(np.arange(0    ,  image.shape[1]				)    ,  np.arange(0    ,  image.shape[0]				)				)    ,  2				)
     _UpperCAmelCase   	=					np.round(grid - flow				).astype(np.intaa				)
  
     # Find the locations outside of the original image
     _UpperCAmelCase   	=					(grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]				))
     _UpperCAmelCase   	=					0
  
     _UpperCAmelCase   	=					image[grid[:, :, 1], grid[:, :, 0]]
  
     # Set pixels at invalid locations to 0
     _UpperCAmelCase   	=					0
  
     return warped
  def 			__UpperCAmelCase		(		a_			:		np.ndarray    ,  a_			:		np.ndarray    ,  a_			:		SupportsIndex    ,  a_			:		float | None = None    ,  )     ->       tuple[np.ndarray, np.ndarray]:
     if alpha is None:
        _UpperCAmelCase   	=					0.1
  
     # Initialize flow
     _UpperCAmelCase   	=					np.zeros_like(a_				)
     _UpperCAmelCase   	=					np.zeros_like(a_				)
  
     # Prepare kernels for the calculation of the derivatives and the average velocity
     _UpperCAmelCase   	=					np.array([[-1, 1], [-1, 1]]				) * 0.25
     _UpperCAmelCase   	=					np.array([[-1, -1], [1, 1]]				) * 0.25
     _UpperCAmelCase   	=					np.array([[1, 1], [1, 1]]				) * 0.25
     _UpperCAmelCase   	=					np.array(
         [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]				)
  
     # Iteratively refine the flow
     for _ in range(a_				):
        _UpperCAmelCase   	=					warp(a_    ,  a_    ,  a_				)
        _UpperCAmelCase   	=					convolve(a_    ,  a_				) + convolve(a_    ,  a_				)
        _UpperCAmelCase   	=					convolve(a_    ,  a_				) + convolve(a_    ,  a_				)
        _UpperCAmelCase   	=					convolve(a_    ,  a_				) + convolve(a_    ,  -kernel_t				)
  
        _UpperCAmelCase   	=					convolve(a_    ,  a_				)
        _UpperCAmelCase   	=					convolve(a_    ,  a_				)
  
        # This updates the flow as proposed in the paper (Step 12)
        _UpperCAmelCase   	=					(
            derivative_x * avg_horizontal_velocity
            + derivative_y * avg_vertical_velocity
            + derivative_t
        )
        _UpperCAmelCase   	=					update / (alpha**2 + derivative_x**2 + derivative_y**2)
  
        _UpperCAmelCase   	=					avg_horizontal_velocity - derivative_x * update
        _UpperCAmelCase   	=					avg_vertical_velocity - derivative_y * update
  
     return horizontal_flow, vertical_flow
  
  
  if __name__ == "__main__":
    import doctest
  
    doctest.testmod()
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  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 			__UpperCAmelCase		(		a_			:		Optional[Any]    ,  a_			:		Any    ,  a_			:		List[Any]				)     ->       Optional[int]:
     with open(a_    ,  "r"    ,  encoding="utf-8"    ,  newline="\n"				) as f:
        _UpperCAmelCase   	=					f.readlines()
     # Find the start prompt.
     _UpperCAmelCase   	=					0
     while not lines[start_index].startswith(a_				):
        start_index += 1
     start_index += 1
  
     _UpperCAmelCase   	=					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 			__UpperCAmelCase		(		a_			:		List[Any]				)     ->       Dict:
     _UpperCAmelCase   	=					re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"    ,  a_				)
     return [m.group(0				) for m in matches]
  def 			__UpperCAmelCase		(		a_			:		Any    ,  a_			:		List[Any]				)     ->       Optional[int]:
     _UpperCAmelCase   	=					2 if text == "✅" or text == "❌" else len(a_				)
     _UpperCAmelCase   	=					(width - text_length) // 2
     _UpperCAmelCase   	=					width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent
  def 			__UpperCAmelCase		(		)     ->       List[Any]:
     _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(a_				)
     _UpperCAmelCase   	=					collections.defaultdict(a_				)
     _UpperCAmelCase   	=					collections.defaultdict(a_				)
     _UpperCAmelCase   	=					collections.defaultdict(a_				)
     _UpperCAmelCase   	=					collections.defaultdict(a_				)
  
     # Let's lookup through all diffusers object (once).
     for attr_name in dir(a_				):
        _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(a_				) is not None:
           _UpperCAmelCase   	=					tf_models
           _UpperCAmelCase   	=					_re_tf_models.match(a_				).groups()[0]
        elif _re_flax_models.match(a_				) is not None:
           _UpperCAmelCase   	=					flax_models
           _UpperCAmelCase   	=					_re_flax_models.match(a_				).groups()[0]
        elif _re_pt_models.match(a_				) is not None:
           _UpperCAmelCase   	=					pt_models
           _UpperCAmelCase   	=					_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   	=					True
                 break
              # Try again after removing the last word in the name
              _UpperCAmelCase   	=					"".join(camel_case_split(a_				)[:-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(a_				) + 2 for c in columns]
     _UpperCAmelCase   	=					max([len(a_				) for name in model_names]				) + 2
  
     # Build the table per se
     _UpperCAmelCase   	=					"|" + "|".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   	=					{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(a_    ,  a_				) for l, w in zip(a_    ,  a_				)]				) + "|\n"
     return table
  def 			__UpperCAmelCase		(		a_			:		Union[str, Any]=False				)     ->       int:
     _UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase   	=					_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   	=					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)
  
  