
"""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
__A       						=       'src/diffusers'
__A       						=       'docs/source/en'
__A       						=       '.'



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

	_lowerCAmelCase								=start_index
	while not lines[end_index].startswith(__UpperCamelCase       ):
		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 |
__A       						=       'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__A       						=       re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__A       						=       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.
__A       						=       re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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



def     _lowerCamelCase(__UpperCamelCase       )				-> Dict:
	_lowerCAmelCase								=re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""    , __UpperCamelCase       )
	return [m.group(0       ) for m in matches]



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase       )				-> List[str]:
	_lowerCAmelCase								=2 if text == """✅""" or text == """❌""" else len(__UpperCamelCase       )
	_lowerCAmelCase								=(width - text_length) // 2
	_lowerCAmelCase								=width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent



def     _lowerCamelCase()				-> Tuple:
	_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(__UpperCamelCase       )
	_lowerCAmelCase								=collections.defaultdict(__UpperCamelCase       )
	_lowerCAmelCase								=collections.defaultdict(__UpperCamelCase       )
	_lowerCAmelCase								=collections.defaultdict(__UpperCamelCase       )
	_lowerCAmelCase								=collections.defaultdict(__UpperCamelCase       )

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

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

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



def     _lowerCamelCase(__UpperCamelCase=False       )				-> List[Any]:
	_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase								=_find_text_in_file(
	    filename=os.path.join(__UpperCamelCase    , """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(__UpperCamelCase    , """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__":
			__A       						=       argparse.ArgumentParser()
			parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
			__A       						=       parser.parse_args()

			check_model_table(args.fix_and_overwrite)





"""simple docstring"""


def     _lowerCamelCase(__UpperCamelCase       )				-> list:
	_lowerCAmelCase								=[True for i in range(n + 1       )]
	_lowerCAmelCase								=[i - 1 for i in range(n + 1       )]
	_lowerCAmelCase								=[]
	for i in range(2    , n + 1       ):
		if is_prime[i]:
			primes.append(__UpperCamelCase       )
		for j in range(0    , len(__UpperCamelCase       )       ):
			if i * primes[j] >= n:
				break
			_lowerCAmelCase								=False

			if i % primes[j] == 0:
				_lowerCAmelCase								=totients[i] * primes[j]
				break

			_lowerCAmelCase								=totients[i] * (primes[j] - 1)

	return totients


if __name__ == "__main__":
			import doctest

			doctest.testmod()



