





'''simple docstring'''


import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							[
    ("bert.bert", "visual_bert"),
    ("bert.cls", "cls"),
    ("bert.classifier", "cls"),
    ("token_type_embeddings_visual", "visual_token_type_embeddings"),
    ("position_embeddings_visual", "visual_position_embeddings"),
    ("projection", "visual_projection"),
]

_SCREAMING_SNAKE_CASE	 =							[
    "nlvr2_coco_pre_trained.th",
    "nlvr2_fine_tuned.th",
    "nlvr2_pre_trained.th",
    "vcr_coco_pre_train.th",
    "vcr_fine_tune.th",
    "vcr_pre_train.th",
    "vqa_coco_pre_trained.th",
    "vqa_fine_tuned.th",
    "vqa_pre_trained.th",
]


def 	__a(SCREAMING_SNAKE_CASE_  :					Union[str, Any]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      torch.load(SCREAMING_SNAKE_CASE_		,       map_location="cpu"    )
    return sd


def 	__a(SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					Any		,       SCREAMING_SNAKE_CASE_  :					str=rename_keys_prefix    ):



    '''simple docstring'''





    _lowerCAmelCase				=      OrderedDict()
    _lowerCAmelCase				=      torch.arange(config.max_position_embeddings    ).expand((1, -1)    )
    # detector_d = OrderedDict()
    for key in d:
        if "detector" in key:
            # detector_d[key.replace('detector.','')] = d[key]
            continue
        _lowerCAmelCase				=      key
        for name_pair in rename_keys_prefix:
            _lowerCAmelCase				=      new_key.replace(name_pair[0]		,       name_pair[1]    )
        _lowerCAmelCase				=      d[key]
        if key == "bert.cls.predictions.decoder.weight":
            # Old bert code didn't have `decoder.bias`, but was added separately
            _lowerCAmelCase				=      new_d["cls.predictions.bias"]
    return new_d


@torch.no_grad()
def 	__a(SCREAMING_SNAKE_CASE_  :					Tuple		,       SCREAMING_SNAKE_CASE_  :					Tuple    ):



    '''simple docstring'''





    assert (
        checkpoint_path.split("/"    )[-1] in ACCEPTABLE_CHECKPOINTS
    ), F'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''

    # Get Config
    if "pre" in checkpoint_path:
        _lowerCAmelCase				=      "pretraining"
        if "vcr" in checkpoint_path:
            _lowerCAmelCase				=      {"visual_embedding_dim": 512}
        elif "vqa_advanced" in checkpoint_path:
            _lowerCAmelCase				=      {"visual_embedding_dim": 2048}
        elif "vqa" in checkpoint_path:
            _lowerCAmelCase				=      {"visual_embedding_dim": 2048}
        elif "nlvr" in checkpoint_path:
            _lowerCAmelCase				=      {"visual_embedding_dim": 1024}
        else:
            raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.'''    )
    else:
        if "vcr" in checkpoint_path:
            _lowerCAmelCase				=      {"visual_embedding_dim": 512}
            _lowerCAmelCase				=      "multichoice"
        elif "vqa_advanced" in checkpoint_path:
            _lowerCAmelCase				=      {"visual_embedding_dim": 2048}
            _lowerCAmelCase				=      "vqa_advanced"
        elif "vqa" in checkpoint_path:
            _lowerCAmelCase				=      {"visual_embedding_dim": 2048, "num_labels": 3129}
            _lowerCAmelCase				=      "vqa"
        elif "nlvr" in checkpoint_path:
            _lowerCAmelCase				=      {
                "visual_embedding_dim": 1024,
                "num_labels": 2,
            }
            _lowerCAmelCase				=      "nlvr"

    _lowerCAmelCase				=      VisualBertConfig(**SCREAMING_SNAKE_CASE_    )

    # Load State Dict
    _lowerCAmelCase				=      load_state_dict(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      get_new_dict(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )

    if model_type == "pretraining":
        _lowerCAmelCase				=      VisualBertForPreTraining(SCREAMING_SNAKE_CASE_    )
    elif model_type == "vqa":
        _lowerCAmelCase				=      VisualBertForQuestionAnswering(SCREAMING_SNAKE_CASE_    )
    elif model_type == "nlvr":
        _lowerCAmelCase				=      VisualBertForVisualReasoning(SCREAMING_SNAKE_CASE_    )
    elif model_type == "multichoice":
        _lowerCAmelCase				=      VisualBertForMultipleChoice(SCREAMING_SNAKE_CASE_    )

    model.load_state_dict(SCREAMING_SNAKE_CASE_    )
    # Save Checkpoints
    Path(SCREAMING_SNAKE_CASE_    ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_    )
    model.save_pretrained(SCREAMING_SNAKE_CASE_    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE	 =							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
       parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
       _SCREAMING_SNAKE_CASE	 =							parser.parse_args()
       convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)









'''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	 =							"src/transformers"
_SCREAMING_SNAKE_CASE	 =							"docs/source/en"
_SCREAMING_SNAKE_CASE	 =							"."


def 	__a(SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					Tuple		,       SCREAMING_SNAKE_CASE_  :					Union[str, Any]    ):



    '''simple docstring'''





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

    _lowerCAmelCase				=      start_index
    while not lines[end_index].startswith(SCREAMING_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	 =							"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_SCREAMING_SNAKE_CASE	 =							re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_SCREAMING_SNAKE_CASE	 =							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	 =							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	 =							direct_transformers_import(TRANSFORMERS_PATH)


def 	__a(SCREAMING_SNAKE_CASE_  :					Union[str, Any]    ):



    '''simple docstring'''





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


def 	__a(SCREAMING_SNAKE_CASE_  :					Dict		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





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


def 	__a():



    '''simple docstring'''





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

    # Let's lookup through all transformers object (once).
    for attr_name in dir(SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      None
        if attr_name.endswith("Tokenizer"    ):
            _lowerCAmelCase				=      slow_tokenizers
            _lowerCAmelCase				=      attr_name[:-9]
        elif attr_name.endswith("TokenizerFast"    ):
            _lowerCAmelCase				=      fast_tokenizers
            _lowerCAmelCase				=      attr_name[:-13]
        elif _re_tf_models.match(SCREAMING_SNAKE_CASE_    ) is not None:
            _lowerCAmelCase				=      tf_models
            _lowerCAmelCase				=      _re_tf_models.match(SCREAMING_SNAKE_CASE_    ).groups()[0]
        elif _re_flax_models.match(SCREAMING_SNAKE_CASE_    ) is not None:
            _lowerCAmelCase				=      flax_models
            _lowerCAmelCase				=      _re_flax_models.match(SCREAMING_SNAKE_CASE_    ).groups()[0]
        elif _re_pt_models.match(SCREAMING_SNAKE_CASE_    ) is not None:
            _lowerCAmelCase				=      pt_models
            _lowerCAmelCase				=      _re_pt_models.match(SCREAMING_SNAKE_CASE_    ).groups()[0]

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

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

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

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


def 	__a(SCREAMING_SNAKE_CASE_  :					Union[str, Any]=False    ):



    '''simple docstring'''





    _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      _find_text_in_file(
        filename=os.path.join(SCREAMING_SNAKE_CASE_		,       "index.md"    )		,       start_prompt="<!--This table is updated automatically from the auto modules"		,       end_prompt="<!-- End table-->"		,       )
    _lowerCAmelCase				=      get_model_table_from_auto_modules()

    if current_table != new_table:
        if overwrite:
            with open(os.path.join(SCREAMING_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	 =							argparse.ArgumentParser()
       parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
       _SCREAMING_SNAKE_CASE	 =							parser.parse_args()

       check_model_table(args.fix_and_overwrite)


