



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    :		Any		      =    """src/transformers"""
_lowerCamelCase    :		Optional[int]		      =    """docs/source/en"""
_lowerCamelCase    :		List[str]		      =    """."""

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     str:
					with open(__lowerCAmelCase      ,	'r'      ,	encoding='utf-8'      ,	newline='\n'   ) as f:
										SCREAMING_SNAKE_CASE							:     List[Any]						=				f.readlines()
					# Find the start prompt.
					SCREAMING_SNAKE_CASE							:     str						=				0
					while not lines[start_index].startswith(__lowerCAmelCase   ):
										start_index += 1
					start_index += 1

					SCREAMING_SNAKE_CASE							:     Tuple						=				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 |
_lowerCamelCase    :		Optional[int]		      =    """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_lowerCamelCase    :		List[str]		      =    re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_lowerCamelCase    :		Optional[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[Any]		      =    re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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

def        __a			(   __lowerCAmelCase   )     ->     List[Any]:
					SCREAMING_SNAKE_CASE							:     Dict						=				re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'      ,	__lowerCAmelCase   )
					return [m.group(0   ) for m in matches]

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     int:
					SCREAMING_SNAKE_CASE							:     Tuple						=				2 if text == '✅' or text == '❌' else len(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     int						=				(width - text_length) // 2
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				width - text_length - left_indent
					return " " * left_indent + text + " " * right_indent

def        __a			(   )     ->     List[str]:
					SCREAMING_SNAKE_CASE							:     List[Any]						=				transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
					SCREAMING_SNAKE_CASE							:     int						=				{
					    name: config_maping_names[code]
					    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
					    if code in config_maping_names
					}
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				{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.
					SCREAMING_SNAKE_CASE							:     Any						=				collections.defaultdict(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     int						=				collections.defaultdict(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     str						=				collections.defaultdict(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     List[str]						=				collections.defaultdict(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				collections.defaultdict(__lowerCAmelCase   )

					# Let's lookup through all transformers object (once).
					for attr_name in dir(__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE							:     Optional[int]						=				None
										if attr_name.endswith('Tokenizer'   ):
															SCREAMING_SNAKE_CASE							:     str						=				slow_tokenizers
															SCREAMING_SNAKE_CASE							:     Tuple						=				attr_name[:-9]
										elif attr_name.endswith('TokenizerFast'   ):
															SCREAMING_SNAKE_CASE							:     List[str]						=				fast_tokenizers
															SCREAMING_SNAKE_CASE							:     List[str]						=				attr_name[:-13]
										elif _re_tf_models.match(__lowerCAmelCase   ) is not None:
															SCREAMING_SNAKE_CASE							:     List[str]						=				tf_models
															SCREAMING_SNAKE_CASE							:     List[Any]						=				_re_tf_models.match(__lowerCAmelCase   ).groups()[0]
										elif _re_flax_models.match(__lowerCAmelCase   ) is not None:
															SCREAMING_SNAKE_CASE							:     Dict						=				flax_models
															SCREAMING_SNAKE_CASE							:     Any						=				_re_flax_models.match(__lowerCAmelCase   ).groups()[0]
										elif _re_pt_models.match(__lowerCAmelCase   ) is not None:
															SCREAMING_SNAKE_CASE							:     Dict						=				pt_models
															SCREAMING_SNAKE_CASE							:     Any						=				_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():
																									SCREAMING_SNAKE_CASE							:     Dict						=				True
																									break
																				# Try again after removing the last word in the name
																				SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				''.join(camel_case_split(__lowerCAmelCase   )[:-1]   )

    # Let's build that table!
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				list(model_name_to_config.keys()   )
					model_names.sort(key=str.lower   )
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				['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).
					SCREAMING_SNAKE_CASE							:     int						=				[len(__lowerCAmelCase   ) + 2 for c in columns]
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				max([len(__lowerCAmelCase   ) for name in model_names]   ) + 2

					# Build the table per se
					SCREAMING_SNAKE_CASE							:     int						=				'|' + '|'.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"

					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				{True: '✅', False: '❌'}
					for name in model_names:
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				model_name_to_prefix[name]
										SCREAMING_SNAKE_CASE							:     Tuple						=				[
										    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        __a			(   __lowerCAmelCase=False   )     ->     Optional[int]:
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     int						=				_find_text_in_file(
					    filename=os.path.join(__lowerCAmelCase      ,	'index.md'   )      ,	start_prompt='<!--This table is updated automatically from the auto modules'      ,	end_prompt='<!-- End table-->'      ,	)
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				get_model_table_from_auto_modules()

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

			check_model_table(args.fix_and_overwrite)




import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

def        __a			(   __lowerCAmelCase   )     ->     tuple:
					return (data["data"], data["target"])

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     np.ndarray:
					SCREAMING_SNAKE_CASE							:     int						=				XGBRegressor(verbosity=0      ,	random_state=42   )
					xgb.fit(__lowerCAmelCase      ,	__lowerCAmelCase   )
					# Predict target for test data
					SCREAMING_SNAKE_CASE							:     List[Any]						=				xgb.predict(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     str						=				predictions.reshape(len(__lowerCAmelCase   )      ,	1   )
					return predictions

def        __a			(   )     ->     None:
					SCREAMING_SNAKE_CASE							:     Dict						=				fetch_california_housing()
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				data_handling(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     str						=				train_test_split(
					    __lowerCAmelCase      ,	__lowerCAmelCase      ,	test_size=0.25      ,	random_state=1   )
					SCREAMING_SNAKE_CASE							:     int						=				xgboost(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )
					# Error printing
					print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase      ,	__lowerCAmelCase   )}'''   )
					print(F'''Mean Square Error  : {mean_squared_error(__lowerCAmelCase      ,	__lowerCAmelCase   )}'''   )


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)
			main()