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						:    Tuple     = 'src/diffusers'
__SCREAMING_SNAKE_CASE						:    List[Any]     = 'docs/source/en'
__SCREAMING_SNAKE_CASE						:    str     = '.'
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase						)       ->       Optional[int]:

						'''simple docstring'''



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

						_snake_case						:     List[str]				=    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 |
__SCREAMING_SNAKE_CASE						:    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						:    List[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						:    str     = 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						:    Optional[Any]     = importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__SCREAMING_SNAKE_CASE						:    List[Any]     = spec.loader.load_module()
def 					snake_case						(__lowercase						)       ->       Tuple:

						'''simple docstring'''



						_snake_case						:     Optional[int]				=    re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"					,			__lowercase						)
						return [m.group(0						) for m in matches]
def 					snake_case						(__lowercase					,			__lowercase						)       ->       Optional[int]:

						'''simple docstring'''



						_snake_case						:     int				=    2 if text == "✅" or text == "❌" else len(__lowercase						)
						_snake_case						:     Union[str, Any]				=    (width - text_length) // 2
						_snake_case						:     List[Any]				=    width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent
def 					snake_case						()       ->       str:

						'''simple docstring'''



						_snake_case						:     Any				=    diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						_snake_case						:     int				=    {
						    name: config_mapping_names[code]
						    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
						    if code in config_mapping_names
						}
						_snake_case						:     List[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.
						_snake_case						:     Union[str, Any]				=    collections.defaultdict(__lowercase						)
						_snake_case						:     Dict				=    collections.defaultdict(__lowercase						)
						_snake_case						:     List[str]				=    collections.defaultdict(__lowercase						)
						_snake_case						:     Optional[int]				=    collections.defaultdict(__lowercase						)
						_snake_case						:     Any				=    collections.defaultdict(__lowercase						)

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

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

						# Build the table per se
						_snake_case						:     str				=    "|" + "|".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"

						_snake_case						:     int				=    {True: "✅", False: "❌"}
						for name in model_names:
												_snake_case						:     int				=    model_name_to_prefix[name]
												_snake_case						:     Tuple				=    [
												    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 					snake_case						(__lowercase=False						)       ->       Dict:

						'''simple docstring'''



						_snake_case				,_snake_case				,_snake_case				,_snake_case						:     int				=    _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-->"					,			)
						_snake_case						:     str				=    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__":
				__SCREAMING_SNAKE_CASE						:    List[str]     = argparse.ArgumentParser()
				parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
				__SCREAMING_SNAKE_CASE						:    Optional[int]     = parser.parse_args()

				check_model_table(args.fix_and_overwrite)





'''simple docstring'''







def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		return "".join(chr(ord(lowercase_  ) + 32  ) if "A" <= char <= "Z" else char for char in word  )


if __name__ == "__main__":
						from doctest import testmod

						testmod()





