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			:							Tuple     ="docs/source/en"
lowerCAmelCase			:							Any     ="."







def 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    with open(__A   , """r"""   , encoding="""utf-8"""   , newline="""\n"""	) as f:
        _lowerCamelCase     :		Any 						=						f.readlines()
    # Find the start prompt.
    _lowerCamelCase     :		Dict 						=						0
    while not lines[start_index].startswith(__A	):
        start_index += 1
    start_index += 1

    _lowerCamelCase     :		Union[str, Any] 						=						start_index
    while not lines[end_index].startswith(__A	):
        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[Any]     ="Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
lowerCAmelCase			:							int     =re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
lowerCAmelCase			:							List[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.
lowerCAmelCase			:							int     =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 						A__  (					__A	):







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







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						2 if text == """✅""" or text == """❌""" else len(__A	)
    _lowerCamelCase     :		List[Any] 						=						(width - text_length) // 2
    _lowerCamelCase     :		List[str] 						=						width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		Optional[int] 						=						transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    _lowerCamelCase     :		Dict 						=						{
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    _lowerCamelCase     :		Tuple 						=						{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.
    _lowerCamelCase     :		int 						=						collections.defaultdict(__A	)
    _lowerCamelCase     :		int 						=						collections.defaultdict(__A	)
    _lowerCamelCase     :		Union[str, Any] 						=						collections.defaultdict(__A	)
    _lowerCamelCase     :		Optional[Any] 						=						collections.defaultdict(__A	)
    _lowerCamelCase     :		Any 						=						collections.defaultdict(__A	)

    # Let's lookup through all transformers object (once).
    for attr_name in dir(__A	):
        _lowerCamelCase     :		Tuple 						=						None
        if attr_name.endswith("""Tokenizer"""	):
            _lowerCamelCase     :		int 						=						slow_tokenizers
            _lowerCamelCase     :		str 						=						attr_name[:-9]
        elif attr_name.endswith("""TokenizerFast"""	):
            _lowerCamelCase     :		List[Any] 						=						fast_tokenizers
            _lowerCamelCase     :		Union[str, Any] 						=						attr_name[:-13]
        elif _re_tf_models.match(__A	) is not None:
            _lowerCamelCase     :		List[str] 						=						tf_models
            _lowerCamelCase     :		int 						=						_re_tf_models.match(__A	).groups()[0]
        elif _re_flax_models.match(__A	) is not None:
            _lowerCamelCase     :		Union[str, Any] 						=						flax_models
            _lowerCamelCase     :		Optional[Any] 						=						_re_flax_models.match(__A	).groups()[0]
        elif _re_pt_models.match(__A	) is not None:
            _lowerCamelCase     :		Optional[int] 						=						pt_models
            _lowerCamelCase     :		Any 						=						_re_pt_models.match(__A	).groups()[0]

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

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

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

    _lowerCamelCase     :		Any 						=						{True: """✅""", False: """❌"""}
    for name in model_names:
        _lowerCamelCase     :		Any 						=						model_name_to_prefix[name]
        _lowerCamelCase     :		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(__A   , __A	) for l, w in zip(__A   , __A	)]	) + "|\n"
    return table







def 						A__  (					__A=False	):







    '''simple docstring'''
    _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		str 						=						_find_text_in_file(
        filename=os.path.join(__A   , """index.md"""	)   , start_prompt="""<!--This table is updated automatically from the auto modules"""   , end_prompt="""<!-- End table-->"""   , )
    _lowerCamelCase     :		Any 						=						get_model_table_from_auto_modules()

    if current_table != new_table:
        if overwrite:
            with open(os.path.join(__A   , """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			:							Optional[int]     =parser.parse_args()

    check_model_table(args.fix_and_overwrite)





import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		Optional[int] 						=						ArgumentParser(
        description=(
            """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
        )	)

    # Optional arguments for the launch helper
    parser.add_argument("""--num_cores"""   , type=__A   , default=1   , help="""Number of TPU cores to use (1 or 8)."""	)

    # positional
    parser.add_argument(
        """training_script"""   , type=__A   , help=(
            """The full path to the single TPU training """
            """program/script to be launched in parallel, """
            """followed by all the arguments for the """
            """training script"""
        )   , )

    # rest from the training program
    parser.add_argument("""training_script_args"""   , nargs=__A	)

    return parser.parse_args()







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						parse_args()

    # Import training_script as a module.
    _lowerCamelCase     :		List[Any] 						=						Path(args.training_script	)
    sys.path.append(str(script_fpath.parent.resolve()	)	)
    _lowerCamelCase     :		Optional[Any] 						=						script_fpath.stem
    _lowerCamelCase     :		Dict 						=						importlib.import_module(__A	)

    # Patch sys.argv
    _lowerCamelCase     :		Union[str, Any] 						=						[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores	)]

    xmp.spawn(mod._mp_fn   , args=()   , nprocs=args.num_cores	)


if __name__ == "__main__":
    main()




