

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



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__      ) ->	Optional[Any]:
 with open(lowercase__				,				"r"				,				encoding="utf-8"				,				newline="\n"      ) as f:
  lowerCAmelCase__      :							List[Any]    =							f.readlines()
 # Find the start prompt.
 lowerCAmelCase__      :							Union[str, Any]    =							0
 while not lines[start_index].startswith(lowercase__      ):
  start_index += 1
 start_index += 1

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


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



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	str:
 lowerCAmelCase__      :							Dict    =							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__				,				lowercase__      ) ->	str:
 lowerCAmelCase__      :							Dict    =							2 if text == "✅" or text == "❌" else len(lowercase__      )
 lowerCAmelCase__      :							List[Any]    =							(width - text_length) // 2
 lowerCAmelCase__      :							Optional[int]    =							width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent



def 			SCREAMING_SNAKE_CASE       (   ) ->	List[Any]:
 lowerCAmelCase__      :							Any    =							diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 lowerCAmelCase__      :							Tuple    =							{
     name: config_mapping_names[code]
     for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
     if code in config_mapping_names
 }
 lowerCAmelCase__      :							Any    =							{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__      :							Dict    =							collections.defaultdict(lowercase__      )
 lowerCAmelCase__      :							Dict    =							collections.defaultdict(lowercase__      )
 lowerCAmelCase__      :							Optional[Any]    =							collections.defaultdict(lowercase__      )
 lowerCAmelCase__      :							Any    =							collections.defaultdict(lowercase__      )
 lowerCAmelCase__      :							Tuple    =							collections.defaultdict(lowercase__      )

 # Let's lookup through all diffusers object (once).
 for attr_name in dir(lowercase__      ):
  lowerCAmelCase__      :							Any    =							None
  if attr_name.endswith("Tokenizer"      ):
   lowerCAmelCase__      :							Optional[Any]    =							slow_tokenizers
   lowerCAmelCase__      :							int    =							attr_name[:-9]
  elif attr_name.endswith("TokenizerFast"      ):
   lowerCAmelCase__      :							str    =							fast_tokenizers
   lowerCAmelCase__      :							int    =							attr_name[:-1_3]
  elif _re_tf_models.match(lowercase__      ) is not None:
   lowerCAmelCase__      :							Optional[Any]    =							tf_models
   lowerCAmelCase__      :							List[str]    =							_re_tf_models.match(lowercase__      ).groups()[0]
  elif _re_flax_models.match(lowercase__      ) is not None:
   lowerCAmelCase__      :							Any    =							flax_models
   lowerCAmelCase__      :							Any    =							_re_flax_models.match(lowercase__      ).groups()[0]
  elif _re_pt_models.match(lowercase__      ) is not None:
   lowerCAmelCase__      :							Optional[Any]    =							pt_models
   lowerCAmelCase__      :							Tuple    =							_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__      :							Tuple    =							True
     break
    # Try again after removing the last word in the name
    lowerCAmelCase__      :							Dict    =							"".join(camel_case_split(lowercase__      )[:-1]      )

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

 # Build the table per se
 lowerCAmelCase__      :							Union[str, Any]    =							"|" + "|".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__      :							List[str]    =							{True: "✅", False: "❌"}
 for name in model_names:
  lowerCAmelCase__      :							Optional[int]    =							model_name_to_prefix[name]
  lowerCAmelCase__      :							Dict    =							[
      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__=False      ) ->	int:
 lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Tuple    =							_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__      :							List[Any]    =							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__":
    _UpperCamelCase		    =						argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    _UpperCamelCase		    =						parser.parse_args()

    check_model_table(args.fix_and_overwrite)









"""simple docstring"""

# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	List[str]:
 lowerCAmelCase__      :							Tuple    =							Queue()
 lowerCAmelCase__      :							Optional[int]    =							[False] * len(lowercase__      )
 lowerCAmelCase__      :							Dict    =							[-1] * len(lowercase__      )

 def bfs():
  while not queue.empty():
   lowerCAmelCase__      :							Optional[Any]    =							queue.get()
   lowerCAmelCase__      :							Union[str, Any]    =							True

   for neighbour in graph[u]:
    if neighbour == u:
     return False

    if color[neighbour] == -1:
     lowerCAmelCase__      :							Any    =							1 - color[u]
     queue.put(lowercase__      )

    elif color[neighbour] == color[u]:
     return False

  return True

 for i in range(len(lowercase__      )      ):
  if not visited[i]:
   queue.put(lowercase__      )
   lowerCAmelCase__      :							int    =							0
   if bfs() is False:
    return False

 return True


if __name__ == "__main__":
    # Adjacency List of graph
    print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))






