


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

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






def    a__       ( __lowercase   ,	__lowercase      )		->		int:
				_A         =	2 if text == "✅" or text == "❌" else len(__lowercase      )
				_A         =	(width - text_length) // 2
				_A         =	width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent






def    a__       ( )		->		List[str]:
				_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(__lowercase      )
				_A         =	collections.defaultdict(__lowercase      )
				_A         =	collections.defaultdict(__lowercase      )
				_A         =	collections.defaultdict(__lowercase      )
				_A         =	collections.defaultdict(__lowercase      )

				# Let's lookup through all diffusers object (once).
				for attr_name in dir(__lowercase      ):
								_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(__lowercase      ) is not None:
												_A         =	tf_models
												_A         =	_re_tf_models.match(__lowercase      ).groups()[0]
								elif _re_flax_models.match(__lowercase      ) is not None:
												_A         =	flax_models
												_A         =	_re_flax_models.match(__lowercase      ).groups()[0]
								elif _re_pt_models.match(__lowercase      ) is not None:
												_A         =	pt_models
												_A         =	_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():
																				_A         =	True
																				break
																# Try again after removing the last word in the name
																_A         =	"".join(camel_case_split(__lowercase      )[:-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(__lowercase      ) + 2 for c in columns]
				_A         =	max([len(__lowercase      ) for name in model_names]      ) + 2

				# Build the table per se
				_A         =	"|" + "|".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"

				_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(__lowercase   ,	__lowercase      ) for l, w in zip(__lowercase   ,	__lowercase      )]      ) + "|\n"
				return table






def    a__       ( __lowercase=False      )		->		Tuple:
				_A     ,						_A     ,						_A     ,						_A         =	_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-->"   ,	)
				_A         =	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__":
		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"""




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


__A     :    int    =		'none'
__A     :    List[Any]    =		'none'
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					value.split(lowercase__      )
					return len(lowercase__      ) > 1 and any((len(lowercase__      ) == 3 for x in split)      )
def    __SCREAMING_SNAKE_CASE						(						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"      )
					A			=					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    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					if answer is None:
										return None
					try:
										A			=					convert_to_float(lowercase__      )
										if isinstance(lowercase__			,							lowercase__      ) and math.isnan(lowercase__      ):
															return None
										return value
					except ValueError:
										return answer.lower()


__A     :    List[str]    =		{
    'text': lambda x: x,
    'real': convert_to_float,
}





class 			__UpperCamelCase (   enum.Enum						):
		SCREAMING_SNAKE_CASE								=    0
		SCREAMING_SNAKE_CASE								=    1
		SCREAMING_SNAKE_CASE								=    2
		SCREAMING_SNAKE_CASE								=    3
		SCREAMING_SNAKE_CASE								=    4
		SCREAMING_SNAKE_CASE								=    5





class 			__UpperCamelCase (   enum.Enum						):
		SCREAMING_SNAKE_CASE								=    0
		SCREAMING_SNAKE_CASE								=    1
		SCREAMING_SNAKE_CASE								=    2





@dataclasses.dataclass
class 			__UpperCamelCase :
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    42

__A     :    int    =		re.compile(R'\w+|[^\w\s]+', re.UNICODE | re.MULTILINE | re.DOTALL)
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					return list(_TOKENIZER.findall(x.lower()      )      )
def    __SCREAMING_SNAKE_CASE						(						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    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					table["types"]
					return _TYPE_CONVERTER[types[column]](lowercase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					return isinstance(lowercase__			,							lowercase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					for cond in conditions:
										A			=					row[cond.column]

										A			=					_parse_value(lowercase__			,							cond.column			,							cond.cmp_value      )

										if _is_string(lowercase__      ) and _is_string(lowercase__      ):
															A			=					_normalize_for_match(lowercase__      )
															A			=					_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    __SCREAMING_SNAKE_CASE						(						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.
					A			=					[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    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					sql_query["agg"]
					if aggregation_op_index >= 3:
										A			=					_Aggregation(lowercase__      )
					else:
										A			=					_Aggregation.NONE

					A			=					sql_query["sel"]
					A			=					[
					    _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"]      )
					]

					A			=					[]
					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):
										A			=					{2: min, 1: max}
										A			=					[(table["rows"][i][j], index) for index, (i, j) in enumerate(lowercase__      )]
										A			=					functools.reduce(operators[sql_query["agg"]]			,							lowercase__      )

										A			=					[indices[reduced[1]]]
										return ret, _Aggregation.NONE

					return indices, aggregation_op
def    __SCREAMING_SNAKE_CASE						(						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    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			,    A			=					_get_answer_coordinates(lowercase__			,							lowercase__      )
					A			=					_get_float_answer(lowercase__			,							lowercase__			,							lowercase__      )
					A			=					_get_answer_text(lowercase__			,							lowercase__			,							lowercase__      )
					# keep the original data the same with TaPas
					if len(lowercase__      ) == 0:
										A			=					[EMPTY_ANSWER]
					return answer_text





