


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 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     List[Any]:







 """simple docstring"""


 with open(_lowerCamelCase	,		'''r'''	,		encoding='''utf-8'''	,		newline='''\n'''		) as f:
  _lowercase				 =f.readlines()
 # Find the start prompt.
 _lowercase				 =0
 while not lines[start_index].startswith(_lowerCamelCase		):
  start_index += 1
 start_index += 1

 _lowercase				 =start_index
 while not lines[end_index].startswith(_lowerCamelCase		):
  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 							UpperCAmelCase_   (				__snake_case		)	->     Union[str, Any]:







 """simple docstring"""


 _lowercase				 =re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''	,		_lowerCamelCase		)
 return [m.group(0		) for m in matches]
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case		)	->     List[str]:







 """simple docstring"""


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







 """simple docstring"""


 _lowercase				 =diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 _lowercase				 ={
     name: config_mapping_names[code]
     for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
     if code in config_mapping_names
 }
 _lowercase				 ={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.
 _lowercase				 =collections.defaultdict(_lowerCamelCase		)
 _lowercase				 =collections.defaultdict(_lowerCamelCase		)
 _lowercase				 =collections.defaultdict(_lowerCamelCase		)
 _lowercase				 =collections.defaultdict(_lowerCamelCase		)
 _lowercase				 =collections.defaultdict(_lowerCamelCase		)

 # Let's lookup through all diffusers object (once).
 for attr_name in dir(_lowerCamelCase		):
  _lowercase				 =None
  if attr_name.endswith('''Tokenizer'''		):
   _lowercase				 =slow_tokenizers
   _lowercase				 =attr_name[:-9]
  elif attr_name.endswith('''TokenizerFast'''		):
   _lowercase				 =fast_tokenizers
   _lowercase				 =attr_name[:-13]
  elif _re_tf_models.match(_lowerCamelCase		) is not None:
   _lowercase				 =tf_models
   _lowercase				 =_re_tf_models.match(_lowerCamelCase		).groups()[0]
  elif _re_flax_models.match(_lowerCamelCase		) is not None:
   _lowercase				 =flax_models
   _lowercase				 =_re_flax_models.match(_lowerCamelCase		).groups()[0]
  elif _re_pt_models.match(_lowerCamelCase		) is not None:
   _lowercase				 =pt_models
   _lowercase				 =_re_pt_models.match(_lowerCamelCase		).groups()[0]

  if lookup_dict is not None:
   while len(_lowerCamelCase		) > 0:
    if attr_name in model_name_to_prefix.values():
     _lowercase				 =True
     break
    # Try again after removing the last word in the name
    _lowercase				 ="".join(camel_case_split(_lowerCamelCase		)[:-1]		)

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

 # Build the table per se
 _lowercase				 ="|" + "|".join([_center_text(_lowerCamelCase	,		_lowerCamelCase		) for c, w in zip(_lowerCamelCase	,		_lowerCamelCase		)]		) + "|\n"
 # Use ":-----:" format to center-aligned table cell texts
 table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]		) + "|\n"

 _lowercase				 ={True: "✅", False: "❌"}
 for name in model_names:
  _lowercase				 =model_name_to_prefix[name]
  _lowercase				 =[
      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(_lowerCamelCase	,		_lowerCamelCase		) for l, w in zip(_lowerCamelCase	,		_lowerCamelCase		)]		) + "|\n"
 return table
def 							UpperCAmelCase_   (				__snake_case=False		)	->     Any:







 """simple docstring"""


 _lowercase				 =_find_text_in_file(
     filename=os.path.join(_lowerCamelCase	,		'''index.mdx'''		)	,		start_prompt='''<!--This table is updated automatically from the auto modules'''	,		end_prompt='''<!-- End table-->'''	,		)
 _lowercase				 =get_model_table_from_auto_modules()

 if current_table != new_table:
  if overwrite:
   with open(os.path.join(_lowerCamelCase	,		'''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)






import math
from decimal import Decimal, getcontext




def 						UpperCAmelCase (    _lowerCamelCase = 400_0000     ):

			try:
						A				:       Any														=    int(_lowerCamelCase     )
			except (TypeError, ValueError):
						raise TypeError("Parameter n must be int or castable to int."     )
			if n <= 0:
						raise ValueError("Parameter n must be greater than or equal to one."     )
			A				:       int														=    100
			A				:       Tuple														=    (Decimal(5     ) ** Decimal(0.5     ) + 1) / Decimal(2     )

			A				:       str														=    (math.floor(math.log(n * (phi + 2)			,     _lowerCamelCase     ) - 1     ) // 3) * 3 + 2
			A				:       Optional[int]														=    Decimal(round(phi ** Decimal(index + 1     )     )     ) / (phi + 2)
			A				:       str														=    num // 2
			return int(_lowerCamelCase     )


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