




'''simple docstring'''





import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# 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
_SCREAMING_SNAKE_CASE							:       Dict						=			"src/transformers"
_SCREAMING_SNAKE_CASE							:       Dict						=			"docs/source/en"
_SCREAMING_SNAKE_CASE							:       int						=			"."


def 					UpperCamelCase_(     snake_case							:	Any							,    snake_case							:	Union[str, Any]							,    snake_case							:	Optional[Any]					):

							'''simple docstring'''






							with open(snake_case							,    "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(snake_case					):
														start_index += 1
							start_index += 1

							snake_case_      =				start_index
							while not lines[end_index].startswith(snake_case					):
														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 |
_SCREAMING_SNAKE_CASE							:       str						=			"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_SCREAMING_SNAKE_CASE							:       str						=			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.
_SCREAMING_SNAKE_CASE							:       Dict						=			re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the transformers module imported is the one in the repo.
_SCREAMING_SNAKE_CASE							:       Dict						=			direct_transformers_import(TRANSFORMERS_PATH)


def 					UpperCamelCase_(     snake_case							:	str					):

							'''simple docstring'''






							snake_case_      =				re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"							,    snake_case					)
							return [m.group(0					) for m in matches]


def 					UpperCamelCase_(     snake_case							:	Any							,    snake_case							:	Any					):

							'''simple docstring'''






							snake_case_      =				2 if text == "✅" or text == "❌" else len(snake_case					)
							snake_case_      =				(width - text_length) // 2
							snake_case_      =				width - text_length - left_indent
							return " " * left_indent + text + " " * right_indent


def 					UpperCamelCase_(     ):

							'''simple docstring'''






							snake_case_      =				transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
							snake_case_      =				{
							    name: config_maping_names[code]
							    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
							    if code in config_maping_names
							}
							snake_case_      =				{name: config.replace("Config"							,    ""					) 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(snake_case					)
							snake_case_      =				collections.defaultdict(snake_case					)
							snake_case_      =				collections.defaultdict(snake_case					)
							snake_case_      =				collections.defaultdict(snake_case					)
							snake_case_      =				collections.defaultdict(snake_case					)

							# Let's lookup through all transformers object (once).
							for attr_name in dir(snake_case					):
														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[:-1_3]
														elif _re_tf_models.match(snake_case					) is not None:
																					snake_case_      =				tf_models
																					snake_case_      =				_re_tf_models.match(snake_case					).groups()[0]
														elif _re_flax_models.match(snake_case					) is not None:
																					snake_case_      =				flax_models
																					snake_case_      =				_re_flax_models.match(snake_case					).groups()[0]
														elif _re_pt_models.match(snake_case					) is not None:
																					snake_case_      =				pt_models
																					snake_case_      =				_re_pt_models.match(snake_case					).groups()[0]

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

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


def 					UpperCamelCase_(     snake_case							:	Any=False					):

							'''simple docstring'''






							snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      =				_find_text_in_file(
							    filename=os.path.join(snake_case							,    "index.md"					)							,    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(snake_case							,    "index.md"					)							,    "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.md` has not been updated. Run `make fix-copies` to fix this."					)


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:       Optional[int]						=			argparse.ArgumentParser()
		parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
		_SCREAMING_SNAKE_CASE							:       List[str]						=			parser.parse_args()

		check_model_table(args.fix_and_overwrite)










'''simple docstring'''





from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403


def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	complex							,    snake_case							:	str = "x"							,    snake_case							:	float = 1_0**-1_0							,    snake_case							:	int = 1							,    ):

							'''simple docstring'''






							snake_case_      =				symbols(snake_case					)
							snake_case_      =				lambdify(snake_case							,    snake_case					)
							snake_case_      =				lambdify(snake_case							,    diff(snake_case							,    snake_case					)					)

							snake_case_      =				starting_point

							while True:
														if diff_function(snake_case					) != 0:
																					snake_case_      =				prev_guess - multiplicity * func(snake_case					) / diff_function(
																					    snake_case					)
														else:
																					raise ZeroDivisionError("Could not find root"					) from None

														# Precision is checked by comparing the difference of consecutive guesses
														if abs(next_guess - prev_guess					) < precision:
																					return next_guess

														snake_case_      =				next_guess


# Let's Execute
if __name__ == "__main__":
		# Find root of trigonometric function
		# Find value of pi
		print(F"The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}")

		# Find root of polynomial
		# Find fourth Root of 5
		print(F"The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}")

		# Find value of e
		print(
		    "The root of log(y) - 1 = 0 is ",
		    F"{newton_raphson('log(y) - 1', 2, variable='y')}",
		)

		# Exponential Roots
		print(
		    "The root of exp(x) - 1 = 0 is",
		    F"{newton_raphson('exp(x) - 1', 10, precision=0.0_0_5)}",
		)

		# Find root of cos(x)
		print(F"The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}")




