





'''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							:     Union[str, Any]		     ="src/transformers"
__lowerCAmelCase							:     Tuple		     ="docs/source/en"
__lowerCAmelCase							:     List[Any]		     ="."





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

    A__    =      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							:     Tuple		     =re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
__lowerCAmelCase							:     Dict		     =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							:     Dict		     =re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


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





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





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : Tuple	):
    A__    =      2 if text == "✅" or text == "❌" else len(_lowerCamelCase	)
    A__    =      (width - text_length) // 2
    A__    =      width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent





def 						UpperCamelCase    (							):
    A__    =      transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    A__    =      {
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    A__    =      {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.
    A__    =      collections.defaultdict(_lowerCamelCase	)
    A__    =      collections.defaultdict(_lowerCamelCase	)
    A__    =      collections.defaultdict(_lowerCamelCase	)
    A__    =      collections.defaultdict(_lowerCamelCase	)
    A__    =      collections.defaultdict(_lowerCamelCase	)

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

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

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





def 						UpperCamelCase    (							_lowerCamelCase : List[str]=False	):
    A__,     A__,     A__,     A__    =      _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-->"						,	)
    A__    =      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							:     int		     =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'''

import unittest

import numpy as np





def 						UpperCamelCase    (							_lowerCamelCase : np.ndarray						,	_lowerCamelCase : np.ndarray						,	_lowerCamelCase : np.ndarray						,	_lowerCamelCase : np.ndarray | None = None						,	):
    A__    =      np.shape(_lowerCamelCase	)
    A__    =      np.shape(_lowerCamelCase	)
    A__    =      np.shape(_lowerCamelCase	)

    if shape_a[0] != shape_b[0]:
        A__    =      (
            "Expected the same number of rows for A and B. "
            F"Instead found A of size {shape_a} and B of size {shape_b}"
        )
        raise ValueError(_lowerCamelCase	)

    if shape_b[1] != shape_c[1]:
        A__    =      (
            "Expected the same number of columns for B and C. "
            F"Instead found B of size {shape_b} and C of size {shape_c}"
        )
        raise ValueError(_lowerCamelCase	)

    A__    =      pseudo_inv
    if a_inv is None:
        try:
            A__    =      np.linalg.inv(_lowerCamelCase	)
        except np.linalg.LinAlgError:
            raise ValueError(
                "Input matrix A is not invertible. Cannot compute Schur complement."	)

    return mat_c - mat_b.T @ a_inv @ mat_b






class        UpperCAmelCase     (	unittest.TestCase    ):
 def 					UpperCAmelCase_							(    self				:Any							)->							None:
     A__    =      np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
     A__    =      np.array([[0, 3], [3, 0], [2, 3]]							)
     A__    =      np.array([[2, 1], [6, 3]]							)

     A__    =      schur_complement(lowercase_						,	lowercase_						,	lowercase_							)

     A__    =      np.block([[a, b], [b.T, c]]							)

     A__    =      np.linalg.det(lowercase_							)
     A__    =      np.linalg.det(lowercase_							)
     A__    =      np.linalg.det(lowercase_							)

     self.assertAlmostEqual(lowercase_						,	det_a * det_s							)
 def 					UpperCAmelCase_							(    self				:Dict							)->							None:
     A__    =      np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
     A__    =      np.array([[0, 3], [3, 0], [2, 3]]							)
     A__    =      np.array([[2, 1], [6, 3]]							)

     with self.assertRaises(lowercase_							):
         schur_complement(lowercase_						,	lowercase_						,	lowercase_							)



 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							None:
     A__    =      np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
     A__    =      np.array([[0, 3], [3, 0], [2, 3]]							)
     A__    =      np.array([[2, 1, 3], [6, 3, 5]]							)

     with self.assertRaises(lowercase_							):
         schur_complement(lowercase_						,	lowercase_						,	lowercase_							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  unittest.main()



