



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

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

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__      )			->				str:
							A    =       2 if text == '✅' or text == '❌' else len(A__      )
							A    =       (width - text_length) // 2
							A    =       width - text_length - left_indent
							return " " * left_indent + text + " " * right_indent

def 	lowerCAmelCase__      (  )			->				List[Any]:
							A    =       diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
							A    =       {
							    name: config_mapping_names[code]
							    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
							    if code in config_mapping_names
							}
							A    =       {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.
							A    =       collections.defaultdict(A__      )
							A    =       collections.defaultdict(A__      )
							A    =       collections.defaultdict(A__      )
							A    =       collections.defaultdict(A__      )
							A    =       collections.defaultdict(A__      )

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

    # Let's build that table!
							A    =       list(model_name_to_config.keys()      )
							model_names.sort(key=str.lower      )
							A    =       ['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).
							A    =       [len(A__      ) + 2 for c in columns]
							A    =       max([len(A__      ) for name in model_names]      ) + 2

							# Build the table per se
							A    =       '|' + '|'.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"

							A    =       {True: '✅', False: '❌'}
							for name in model_names:
														A    =       model_name_to_prefix[name]
														A    =       [
														    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 	lowerCAmelCase__      (  lowerCamelCase__=False      )			->				Dict:
							A   ,	A   ,	A   ,	A    =       _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-->'					,   )
							A    =       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__":
							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"""




from __future__ import annotations

import math
from itertools import permutations



def 						UpperCAmelCase__ (					A__ )   ->   bool:




   """simple docstring"""

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5						,     int(math.sqrt(A__ ) + 1 )						,     6 ):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True



def 						UpperCAmelCase__ (					A__ = 7 )   ->   int:




   """simple docstring"""
   lowerCamelCase__						       =      "".join(str(A__ ) for i in range(1						,     n + 1 ) )
   lowerCamelCase__						       =      [int("".join(A__ ) ) for i in permutations(A__						,     A__ )]
   lowerCamelCase__						       =      [num for num in perm_list if is_prime(A__ )]
   return max(A__ ) if pandigitals else 0


if __name__ == "__main__":
     print(F'''{solution() = }''')




