



"""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
__lowerCAmelCase       :					List[Any]      					=					'''src/transformers'''
__lowerCAmelCase       :					Dict      					=					'''docs/source/en'''
__lowerCAmelCase       :					Optional[Any]      					=					'''.'''
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any					, __UpperCamelCase      :			List[str]					, __UpperCamelCase      :			Optional[int]    ):


						'''simple docstring'''






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

						snake_case_   : Union[str, Any]						=			start_index
						while not lines[end_index].startswith(__UpperCamelCase    ):
												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       :					int      					=					'''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
__lowerCAmelCase       :					int      					=					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       :					List[str]      					=					re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the transformers module imported is the one in the repo.
__lowerCAmelCase       :					int      					=					direct_transformers_import(TRANSFORMERS_PATH)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






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


						'''simple docstring'''






						snake_case_   : Dict						=			2 if text == """✅""" or text == """❌""" else len(__UpperCamelCase    )
						snake_case_   : Tuple						=			(width - text_length) // 2
						snake_case_   : Any						=			width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : List[Any]						=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						snake_case_   : Optional[Any]						=			{
						    name: config_maping_names[code]
						    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
						    if code in config_maping_names
						}
						snake_case_   : List[str]						=			{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_   : Dict						=			collections.defaultdict(__UpperCamelCase    )
						snake_case_   : List[Any]						=			collections.defaultdict(__UpperCamelCase    )
						snake_case_   : str						=			collections.defaultdict(__UpperCamelCase    )
						snake_case_   : str						=			collections.defaultdict(__UpperCamelCase    )
						snake_case_   : Dict						=			collections.defaultdict(__UpperCamelCase    )

						# Let's lookup through all transformers object (once).
						for attr_name in dir(__UpperCamelCase    ):
												snake_case_   : Union[str, Any]						=			None
												if attr_name.endswith("""Tokenizer"""    ):
																		snake_case_   : Dict						=			slow_tokenizers
																		snake_case_   : Any						=			attr_name[:-9]
												elif attr_name.endswith("""TokenizerFast"""    ):
																		snake_case_   : Optional[Any]						=			fast_tokenizers
																		snake_case_   : str						=			attr_name[:-1_3]
												elif _re_tf_models.match(__UpperCamelCase    ) is not None:
																		snake_case_   : List[Any]						=			tf_models
																		snake_case_   : Tuple						=			_re_tf_models.match(__UpperCamelCase    ).groups()[0]
												elif _re_flax_models.match(__UpperCamelCase    ) is not None:
																		snake_case_   : Dict						=			flax_models
																		snake_case_   : Tuple						=			_re_flax_models.match(__UpperCamelCase    ).groups()[0]
												elif _re_pt_models.match(__UpperCamelCase    ) is not None:
																		snake_case_   : List[Any]						=			pt_models
																		snake_case_   : Any						=			_re_pt_models.match(__UpperCamelCase    ).groups()[0]

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

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

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

						snake_case_   : List[Any]						=			{True: """✅""", False: """❌"""}
						for name in model_names:
												snake_case_   : Union[str, Any]						=			model_name_to_prefix[name]
												snake_case_   : Optional[int]						=			[
												    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(__UpperCamelCase					, __UpperCamelCase    ) for l, w in zip(__UpperCamelCase					, __UpperCamelCase    )]    ) + "|\n"
						return table
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any=False    ):


						'''simple docstring'''






						snake_case_			,		snake_case_			,		snake_case_			,		snake_case_   : Optional[int]						=			_find_text_in_file(
						    filename=os.path.join(__UpperCamelCase					, """index.md"""    )					, start_prompt="""<!--This table is updated automatically from the auto modules"""					, end_prompt="""<!-- End table-->"""					, )
						snake_case_   : int						=			get_model_table_from_auto_modules()

						if current_table != new_table:
												if overwrite:
																		with open(os.path.join(__UpperCamelCase					, """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__":
					__lowerCAmelCase       :					List[Any]      					=					argparse.ArgumentParser()
					parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
					__lowerCAmelCase       :					Any      					=					parser.parse_args()

					check_model_table(args.fix_and_overwrite)






"""simple docstring"""





from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						def is_in_circle(__UpperCamelCase      :			float					, __UpperCamelCase      :			float    ) -> bool:
												snake_case_   : Dict						=			sqrt((x**2) + (y**2)    )
												# Our circle has a radius of 1, so a distance
												# greater than 1 would land outside the circle.
												return distance_from_centre <= 1

						# The proportion of guesses that landed in the circle
						snake_case_   : Tuple						=			mean(
						    int(is_in_circle(uniform(-1.0					, 1.0    )					, uniform(-1.0					, 1.0    )    )    )
						    for _ in range(__UpperCamelCase    )    )
						# The ratio of the area for circle to square is pi/4.
						snake_case_   : Union[str, Any]						=			proportion * 4
						print(F'The estimated value of pi is {pi_estimate}'    )
						print(F'The numpy value of pi is {pi}'    )
						print(F'The total error is {abs(pi - pi_estimate    )}'    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			Callable[[float], float]					, __UpperCamelCase      :			float = 0.0					, __UpperCamelCase      :			float = 1.0					, ):


						'''simple docstring'''






						return mean(
						    function_to_integrate(uniform(__UpperCamelCase					, __UpperCamelCase    )    ) for _ in range(__UpperCamelCase    )    ) * (max_value - min_value)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			float = 0.0					, __UpperCamelCase      :			float = 1.0    ):


						'''simple docstring'''






						def identity_function(__UpperCamelCase      :			float    ) -> float:
												return x

						snake_case_   : int						=			area_under_curve_estimator(
						    __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
						snake_case_   : str						=			(max_value * max_value - min_value * min_value) / 2

						print("""******************"""    )
						print(F'Estimating area under y=x where x varies from {min_value} to {max_value}'    )
						print(F'Estimated value is {estimated_value}'    )
						print(F'Expected value is {expected_value}'    )
						print(F'Total error is {abs(estimated_value - expected_value    )}'    )
						print("""******************"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						def function_to_integrate(__UpperCamelCase      :			float    ) -> float:
												return sqrt(4.0 - x * x    )

						snake_case_   : List[Any]						=			area_under_curve_estimator(
						    __UpperCamelCase					, __UpperCamelCase					, 0.0					, 2.0    )

						print("""******************"""    )
						print("""Estimating pi using area_under_curve_estimator"""    )
						print(F'Estimated value is {estimated_value}'    )
						print(F'Expected value is {pi}'    )
						print(F'Total error is {abs(estimated_value - pi    )}'    )
						print("""******************"""    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()

