# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Utility that checks the big table in the file docs/source/en/index.md and potentially updates it.

Use from the root of the repo with:

```bash
python utils/check_inits.py
```

for a check that will error in case of inconsistencies (used by `make repo-consistency`).

To auto-fix issues run:

```bash
python utils/check_inits.py --fix_and_overwrite
```

which is used by `make fix-copies`.
"""

import argparse
import collections
import os
import re
from typing import List

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
TRANSFORMERS_PATH = "src/transformers"
PATH_TO_DOCS = "docs/source/en"
REPO_PATH = "."


def _find_text_in_file(filename: str, start_prompt: str, end_prompt: str) -> str:
    """
    Find the text in filename between two prompts.

    Args:
        filename (`str`): The file to search into.
        start_prompt (`str`): A string to look for at the start of the content searched.
        end_prompt (`str`): A string that will mark the end of the content to look for.

    Returns:
        `str`: The content between the prompts.
    """
    with open(filename, "r", encoding="utf-8", newline="\n") as f:
        lines = f.readlines()

    # Find the start prompt.
    start_index = 0
    while not lines[start_index].startswith(start_prompt):
        start_index += 1
    start_index += 1

    # Now go until the end prompt.
    end_index = start_index
    while not lines[end_index].startswith(end_prompt):
        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


# Regexes that match TF/Flax/PT model names. Add here suffixes that are used to identify models, separated by |
_re_tf_models = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_re_flax_models = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch after the two previous regexes.
_re_pt_models = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


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


def camel_case_split(identifier: str) -> List[str]:
    """
    Split a camel-cased name into words.

    Args:
        identifier (`str`): The camel-cased name to parse.

    Returns:
        `List[str]`: The list of words in the identifier (as seprated by capital letters).

    Example:

    ```py
    >>> camel_case_split("CamelCasedClass")
    ["Camel", "Cased", "Class"]
    ```
    """
    # Regex thanks to https://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python
    matches = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)", identifier)
    return [m.group(0) for m in matches]


def _center_text(text: str, width: int) -> str:
    """
    Utility that will add spaces on the left and right of a text to make it centered for a given width.

    Args:
        text (`str`): The text to center.
        width (`int`): The desired length of the result.

    Returns:
        `str`: A text of length `width` with the original `text` in the middle.
    """
    text_length = 2 if text == "✅" or text == "❌" else len(text)
    left_indent = (width - text_length) // 2
    right_indent = width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent


SPECIAL_MODEL_NAME_LINK_MAPPING = {
    "Data2VecAudio": "[Data2VecAudio](model_doc/data2vec)",
    "Data2VecText": "[Data2VecText](model_doc/data2vec)",
    "Data2VecVision": "[Data2VecVision](model_doc/data2vec)",
    "DonutSwin": "[DonutSwin](model_doc/donut)",
}

MODEL_NAMES_WITH_SAME_CONFIG = {
    "BARThez": "BART",
    "BARTpho": "BART",
    "BertJapanese": "BERT",
    "BERTweet": "BERT",
    "BORT": "BERT",
    "ByT5": "T5",
    "CPM": "OpenAI GPT-2",
    "DePlot": "Pix2Struct",
    "DialoGPT": "OpenAI GPT-2",
    "DiT": "BEiT",
    "FLAN-T5": "T5",
    "FLAN-UL2": "T5",
    "HerBERT": "BERT",
    "LayoutXLM": "LayoutLMv2",
    "Llama2": "LLaMA",
    "Llama3": "LLaMA",
    "MADLAD-400": "T5",
    "MatCha": "Pix2Struct",
    "mBART-50": "mBART",
    "Megatron-GPT2": "OpenAI GPT-2",
    "mLUKE": "LUKE",
    "MMS": "Wav2Vec2",
    "NLLB": "M2M100",
    "PhoBERT": "BERT",
    "T5v1.1": "T5",
    "TAPEX": "BART",
    "UL2": "T5",
    "Wav2Vec2Phoneme": "Wav2Vec2",
    "XLM-V": "XLM-RoBERTa",
    "XLS-R": "Wav2Vec2",
    "XLSR-Wav2Vec2": "Wav2Vec2",
}
MODEL_NAMES_TO_IGNORE = ["CLIPVisionModel", "SiglipVisionModel", "ChineseCLIPVisionModel"]


def get_model_table_from_auto_modules() -> str:
    """
    Generates an up-to-date model table from the content of the auto modules.
    """
    # Dictionary model names to config.
    config_maping_names = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    model_name_to_config = {
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    model_name_to_prefix = {name: config.replace("Config", "") for name, config in model_name_to_config.items()}

    # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
    pt_models = collections.defaultdict(bool)
    tf_models = collections.defaultdict(bool)
    flax_models = collections.defaultdict(bool)

    # Let's lookup through all transformers object (once).
    for attr_name in dir(transformers_module):
        lookup_dict = None
        if _re_tf_models.match(attr_name) is not None:
            lookup_dict = tf_models
            attr_name = _re_tf_models.match(attr_name).groups()[0]
        elif _re_flax_models.match(attr_name) is not None:
            lookup_dict = flax_models
            attr_name = _re_flax_models.match(attr_name).groups()[0]
        elif _re_pt_models.match(attr_name) is not None:
            lookup_dict = pt_models
            attr_name = _re_pt_models.match(attr_name).groups()[0]

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

    # Let's build that table!
    model_names = list(model_name_to_config.keys()) + list(MODEL_NAMES_WITH_SAME_CONFIG.keys())

    # model name to doc link mapping
    model_names_mapping = transformers_module.models.auto.configuration_auto.MODEL_NAMES_MAPPING
    model_name_to_link_mapping = {value: f"[{value}](model_doc/{key})" for key, value in model_names_mapping.items()}
    # update mapping with special model names
    model_name_to_link_mapping = {
        k: SPECIAL_MODEL_NAME_LINK_MAPPING[k] if k in SPECIAL_MODEL_NAME_LINK_MAPPING else v
        for k, v in model_name_to_link_mapping.items()
    }

    # MaskFormerSwin and TimmBackbone are backbones and so not meant to be loaded and used on their own. Instead, they define architectures which can be loaded using the AutoBackbone API.
    names_to_exclude = ["MaskFormerSwin", "TimmBackbone", "Speech2Text2"]
    model_names = [name for name in model_names if name not in names_to_exclude]
    model_names.sort(key=str.lower)

    columns = ["Model", "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).

    widths = [len(c) + 2 for c in columns]
    widths[0] = max([len(doc_link) for doc_link in model_name_to_link_mapping.values()]) + 2

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

    check = {True: "✅", False: "❌"}

    for name in model_names:
        if name in MODEL_NAMES_TO_IGNORE:
            continue
        if name in MODEL_NAMES_WITH_SAME_CONFIG.keys():
            prefix = model_name_to_prefix[MODEL_NAMES_WITH_SAME_CONFIG[name]]
        else:
            prefix = model_name_to_prefix[name]
        line = [
            model_name_to_link_mapping[name],
            check[pt_models[prefix]],
            check[tf_models[prefix]],
            check[flax_models[prefix]],
        ]
        table += "|" + "|".join([_center_text(l, w) for l, w in zip(line, widths)]) + "|\n"
    return table


def check_model_table(overwrite=False):
    """
    Check the model table in the index.md is consistent with the state of the lib and potentially fix it.

    Args:
        overwrite (`bool`, *optional*, defaults to `False`):
            Whether or not to overwrite the table when it's not up to date.
    """
    current_table, start_index, end_index, lines = _find_text_in_file(
        filename=os.path.join(PATH_TO_DOCS, "index.md"),
        start_prompt="<!--This table is updated automatically from the auto modules",
        end_prompt="<!-- End table-->",
    )
    new_table = get_model_table_from_auto_modules()

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

    check_model_table(args.fix_and_overwrite)
