code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import dataclasses
  import enum
  import functools
  import math
  import re
  
  # The following script is adapted from the script of TaPas.
  # Original: https://github.com/google-research/tapas/master/wikisql_utils.py
  from typing import Any, List, Text
  
  
  _lowerCamelCase							:		Dict										=						'none'
  _lowerCamelCase							:		Union[str, Any]										=						'none'
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = value.split(lowercase_						)
  		return len(lowercase_						) > 1 and any((len(lowercase_						) == 3 for x in split)						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if isinstance(lowercase_		,							lowercase_						):
  				return value
  		if isinstance(lowercase_		,							lowercase_						):
  				return float(lowercase_						)
  		if not isinstance(lowercase_		,							lowercase_						):
  				raise ValueError("Argument value is not a string. Can't parse it as float"						)
  		_snake_case			 = value
  
  		try:
  				# Example: 1,000.7
  				if "." in sanitized and "," in sanitized:
  						return float(sanitized.replace(","		,							""						)						)
  				# 1,000
  				if "," in sanitized and _split_thousands(","		,							lowercase_						):
  						return float(sanitized.replace(","		,							""						)						)
  				# 5,5556
  				if "," in sanitized and sanitized.count(","						) == 1 and not _split_thousands(","		,							lowercase_						):
  						return float(sanitized.replace(","		,							"."						)						)
  				# 0.0.0.1
  				if sanitized.count("."						) > 1:
  						return float(sanitized.replace("."		,							""						)						)
  				# 0,0,0,1
  				if sanitized.count(","						) > 1:
  						return float(sanitized.replace(","		,							""						)						)
  				return float(lowercase_						)
  		except ValueError:
  				# Avoid adding the sanitized value in the error message.
  				raise ValueError("Unable to convert value to float"						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if answer is None:
  				return None
  		try:
  				_snake_case			 = convert_to_float(lowercase_						)
  				if isinstance(lowercase_		,							lowercase_						) and math.isnan(lowercase_						):
  						return None
  				return value
  		except ValueError:
  				return answer.lower()
  
  
  _lowerCamelCase							:		Tuple										=						{
      'text': lambda x: x,
      'real': convert_to_float,
  }
  
  
  
  
  
  class   lowerCamelCase_							( enum.Enum							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				0
  					lowercase__					=				1
  					lowercase__					=				2
  					lowercase__					=				3
  					lowercase__					=				4
  					lowercase__					=				5
  
  
  
  
  
  class   lowerCamelCase_							( enum.Enum							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				0
  					lowercase__					=				1
  					lowercase__					=				2
  
  
  
  
  
  @dataclasses.dataclass
  class   lowerCamelCase_							:
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				42
  					lowercase__					=				42
  					lowercase__					=				42
  
  
  
  
  _lowerCamelCase							:		int										=						re.compile(R'\w+|[^\w\s]+', re.UNICODE | re.MULTILINE | re.DOTALL)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return list(_TOKENIZER.findall(x.lower()						)						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if operator == _Operator.EQUALS:
  				return src == tgt
  		elif operator == _Operator.GREATER:
  				return src > tgt
  		elif operator == _Operator.LESSER:
  				return src < tgt
  		raise ValueError(f'''Unknown operator: {operator}'''						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = table["types"]
  		return _TYPE_CONVERTER[types[column]](lowercase_						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return isinstance(lowercase_		,							lowercase_						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		for cond in conditions:
  				_snake_case			 = row[cond.column]
  
  				_snake_case			 = _parse_value(lowercase_		,							cond.column		,							cond.cmp_value						)
  
  				if _is_string(lowercase_						) and _is_string(lowercase_						):
  						_snake_case			 = _normalize_for_match(lowercase_						)
  						_snake_case			 = _normalize_for_match(lowercase_						)
  
  				if not isinstance(lowercase_		,							type(lowercase_						)						):
  						raise ValueError("Type difference {} != {}".format(type(lowercase_						)		,							type(lowercase_						)						)						)
  
  				if not _compare(cond.operator		,							lowercase_		,							lowercase_						):
  						return False
  		return True
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if not answer_coordinates:
  				if aggregation_op == _Aggregation.COUNT:
  						return 0.0
  				else:
  						return EMPTY_ANSWER_AGG
  
      # Count can support non numeric answers.
  		if aggregation_op == _Aggregation.COUNT:
  				return float(len(lowercase_						)						)
  
  		# If we have just one answer, if float returns it or try a conversion.
  		_snake_case			 = [table["rows"][i][j] for (i, j) in answer_coordinates]
  		if len(lowercase_						) == 1:
  				try:
  						return convert_to_float(values[0]						)
  				except ValueError as e:
  						if aggregation_op != _Aggregation.NONE:
  								raise e
  
  		if aggregation_op == _Aggregation.NONE:
  				return None
  
  		# Other aggregation only support numeric values. Bail out if we have strings.
  		if not all((isinstance(lowercase_		,							(int, float)						) for v in values)						):
  				return None
  
  		if aggregation_op == _Aggregation.SUM:
  				return float(sum(lowercase_						)						)
  		elif aggregation_op == _Aggregation.AVERAGE:
  				return sum(lowercase_						) / len(lowercase_						)
  		else:
  				raise ValueError(f'''Unknown aggregation: {aggregation_op}'''						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = sql_query["agg"]
  		if aggregation_op_index >= 3:
  				_snake_case			 = _Aggregation(lowercase_						)
  		else:
  				_snake_case			 = _Aggregation.NONE
  
  		_snake_case			 = sql_query["sel"]
  		_snake_case			 = [
  		    _Condition(lowercase_		,							_Operator(lowercase_						)		,							lowercase_						)
  		    for column, operator, cmp_value in zip(
  		        sql_query["conds"]["column_index"]		,							sql_query["conds"]["operator_index"]		,							sql_query["conds"]["condition"]						)
  		]
  
  		_snake_case			 = []
  		for row in range(len(table["rows"]						)						):
  				if _respect_conditions(lowercase_		,							table["rows"][row]		,							lowercase_						):
  						indices.append((row, target_column)						)
  
  		if not indices:
  				return [], aggregation_op
  
  		if len(lowercase_						) == 1:
  				return indices, aggregation_op
  
  		# Parsing of MIN/MAX.
  		if aggregation_op_index in (1, 2):
  				_snake_case			 = {2: min, 1: max}
  				_snake_case			 = [(table["rows"][i][j], index) for index, (i, j) in enumerate(lowercase_						)]
  				_snake_case			 = functools.reduce(operators[sql_query["agg"]]		,							lowercase_						)
  
  				_snake_case			 = [indices[reduced[1]]]
  				return ret, _Aggregation.NONE
  
  		return indices, aggregation_op
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if float_answer is not None:
  				return [str(lowercase_						)]
  		return [str(table["real_rows"][r][c]						) for r, c in answer_coordinates]
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case ,					_snake_case			 = _get_answer_coordinates(lowercase_		,							lowercase_						)
  		_snake_case			 = _get_float_answer(lowercase_		,							lowercase_		,							lowercase_						)
  		_snake_case			 = _get_answer_text(lowercase_		,							lowercase_		,							lowercase_						)
  		# keep the original data the same with TaPas
  		if len(lowercase_						) == 0:
  				_snake_case			 = [EMPTY_ANSWER]
  		return answer_text
  
  
  
  
  
style_context: 
  
  
  
  
  '''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
  _lowerCamelCase							:		Any										=						'src/diffusers'
  _lowerCamelCase							:		Any										=						'docs/source/en'
  _lowerCamelCase							:		int										=						'.'
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		with open(lowercase_		,							"r"		,							encoding="utf-8"		,							newline="\n"						) as f:
  				_snake_case			 = f.readlines()
  		# Find the start prompt.
  		_snake_case			 = 0
  		while not lines[start_index].startswith(lowercase_						):
  				start_index += 1
  		start_index += 1
  
  		_snake_case			 = 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 |
  _lowerCamelCase							:		List[Any]										=						'Model|Encoder|Decoder|ForConditionalGeneration'
  # Regexes that match TF/Flax/PT model names.
  _lowerCamelCase							:		List[Any]										=						re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
  _lowerCamelCase							:		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.
  _lowerCamelCase							:		Optional[int]										=						re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
  
  
  # This is to make sure the diffusers module imported is the one in the repo.
  _lowerCamelCase							:		Union[str, Any]										=						importlib.util.spec_from_file_location(
      'diffusers',
      os.path.join(TRANSFORMERS_PATH, '__init__.py'),
      submodule_search_locations=[TRANSFORMERS_PATH],
  )
  _lowerCamelCase							:		Optional[int]										=						spec.loader.load_module()
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"		,							lowercase_						)
  		return [m.group(0						) for m in matches]
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = 2 if text == "✅" or text == "❌" else len(lowercase_						)
  		_snake_case			 = (width - text_length) // 2
  		_snake_case			 = width - text_length - left_indent
  		return " " * left_indent + text + " " * right_indent
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  		_snake_case			 = {
  		    name: config_mapping_names[code]
  		    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
  		    if code in config_mapping_names
  		}
  		_snake_case			 = {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			 = collections.defaultdict(lowercase_						)
  		_snake_case			 = collections.defaultdict(lowercase_						)
  		_snake_case			 = collections.defaultdict(lowercase_						)
  		_snake_case			 = collections.defaultdict(lowercase_						)
  		_snake_case			 = collections.defaultdict(lowercase_						)
  
  		# Let's lookup through all diffusers object (once).
  		for attr_name in dir(lowercase_						):
  				_snake_case			 = None
  				if attr_name.endswith("Tokenizer"						):
  						_snake_case			 = slow_tokenizers
  						_snake_case			 = attr_name[:-9]
  				elif attr_name.endswith("TokenizerFast"						):
  						_snake_case			 = fast_tokenizers
  						_snake_case			 = attr_name[:-13]
  				elif _re_tf_models.match(lowercase_						) is not None:
  						_snake_case			 = tf_models
  						_snake_case			 = _re_tf_models.match(lowercase_						).groups()[0]
  				elif _re_flax_models.match(lowercase_						) is not None:
  						_snake_case			 = flax_models
  						_snake_case			 = _re_flax_models.match(lowercase_						).groups()[0]
  				elif _re_pt_models.match(lowercase_						) is not None:
  						_snake_case			 = pt_models
  						_snake_case			 = _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			 = True
  										break
  								# Try again after removing the last word in the name
  								_snake_case			 = "".join(camel_case_split(lowercase_						)[:-1]						)
  
      # Let's build that table!
  		_snake_case			 = list(model_name_to_config.keys()						)
  		model_names.sort(key=str.lower						)
  		_snake_case			 = ["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			 = [len(lowercase_						) + 2 for c in columns]
  		_snake_case			 = max([len(lowercase_						) for name in model_names]						) + 2
  
  		# Build the table per se
  		_snake_case			 = "|" + "|".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			 = {True: "✅", False: "❌"}
  		for name in model_names:
  				_snake_case			 = model_name_to_prefix[name]
  				_snake_case			 = [
  				    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 	_lowercase	(			lowercase_=False						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case ,					_snake_case ,					_snake_case ,					_snake_case			 = _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			 = 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__":
  						_lowerCamelCase							:		Dict										=						argparse.ArgumentParser()
  						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
  						_lowerCamelCase							:		Dict										=						parser.parse_args()
  
  						check_model_table(args.fix_and_overwrite)
  
  
  
  
  
  