# What does this PR do?

I have replaced the assert statements with appropriate exceptions in the directory `src/transformers/models/` with all models beginning with `a` and `b` letters.

Also, I have corrected error handling at places where except statements were handling AssertionError, even thought it was never to be raised. Here is an example:

```
try:
    if pointer.shape != array.shape:
        raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
except AssertionError as e:    # Incorrect line
    e.args += (pointer.shape, array.shape)
    raise
```

I changed the above to:

```
try:
    if pointer.shape != array.shape:
        raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
except ValueError as e:    # Corrected the line
    e.args += (pointer.shape, array.shape)
    raise
```

Fixes #12789 


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

@ArthurZucker @sgugger 

Replace assert statements with exceptions. # What does this PR do?

I have replaced the assert statements with appropriate exceptions in the directory `src/transformers/models/` with all models beginning with `a` and `b` letters.

Also, I have corrected error handling at places where except statements were handling AssertionError, even thought it was never to be raised. Here is an example:

```
try:
    if pointer.shape != array.shape:
        raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
except AssertionError as e:    # Incorrect line
    e.args += (pointer.shape, array.shape)
    raise
```

I changed the above to:

```
try:
    if pointer.shape != array.shape:
        raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
except ValueError as e:    # Corrected the line
    e.args += (pointer.shape, array.shape)
    raise
```

Fixes #12789 


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

@ArthurZucker @sgugger 

# coding=utf-8
# Copyright 2020 Google and 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.

import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


PATTERNS = [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["memory_attention", "encoder_attn"],
    ["attention", "attn"],
    ["/", "."],
    [".LayerNorm.gamma", "_layer_norm.weight"],
    [".LayerNorm.beta", "_layer_norm.bias"],
    ["r.layer_", "r.layers."],
    ["output_proj", "out_proj"],
    ["ffn.dense_1.", "fc2."],
    ["ffn.dense.", "fc1."],
    ["ffn_layer_norm", "final_layer_norm"],
    ["kernel", "weight"],
    ["encoder_layer_norm.", "encoder.layer_norm."],
    ["decoder_layer_norm.", "decoder.layer_norm."],
    ["embeddings.weights", "shared.weight"],
]


def rename_state_dict_key(k):
    for pegasus_name, hf_name in PATTERNS:
        k = k.replace(pegasus_name, hf_name)
    return k


# See appendix C of paper for all hyperparams


def convert_pegasus(tf_weights: dict, cfg_updates: dict) -> PegasusForConditionalGeneration:
    cfg_kwargs = DEFAULTS.copy()
    cfg_kwargs.update(cfg_updates)
    cfg = PegasusConfig(**cfg_kwargs)
    torch_model = PegasusForConditionalGeneration(cfg)
    sd = torch_model.model.state_dict()
    mapping = {}
    for k, v in tf_weights.items():
        new_k = rename_state_dict_key(k)
        if new_k not in sd:
            raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})")

        if "dense" in k or "proj" in new_k:
            v = v.T
        mapping[new_k] = torch.tensor(v, dtype=sd[new_k].dtype)
        assert v.shape == sd[new_k].shape, f"{new_k}, {k}, {v.shape}, {sd[new_k].shape}"
    # make sure embedding.padding_idx is respected
    mapping["shared.weight"][cfg.pad_token_id] = torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1])
    mapping["encoder.embed_tokens.weight"] = mapping["shared.weight"]
    mapping["decoder.embed_tokens.weight"] = mapping["shared.weight"]
    empty_biases = {k: torch.zeros_like(v) for k, v in sd.items() if k.endswith("bias") and k not in mapping}
    mapping.update(**empty_biases)
    missing, extra = torch_model.model.load_state_dict(mapping, strict=False)
    unexpected_missing = [
        k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
    ]
    assert unexpected_missing == [], f"no matches found for the following torch keys {unexpected_missing}"
    assert extra == [], f"no matches found for the following tf keys {extra}"
    return torch_model


def get_tf_weights_as_numpy(path="./ckpt/aeslc/model.ckpt-32000") -> Dict:
    init_vars = tf.train.list_variables(path)
    tf_weights = {}
    ignore_name = ["Adafactor", "global_step"]
    for name, shape in tqdm(init_vars, desc="converting tf checkpoint to dict"):
        skip_key = any(pat in name for pat in ignore_name)
        if skip_key:
            continue
        array = tf.train.load_variable(path, name)
        tf_weights[name] = array
    return tf_weights


def convert_pegasus_ckpt_to_pytorch(ckpt_path: str, save_dir: str):
    # save tokenizer first
    dataset = Path(ckpt_path).parent.name
    desired_max_model_length = task_specific_params[f"summarization_{dataset}"]["max_position_embeddings"]
    tok = PegasusTokenizer.from_pretrained("sshleifer/pegasus", model_max_length=desired_max_model_length)
    assert tok.model_max_length == desired_max_model_length
    tok.save_pretrained(save_dir)

    # convert model
    tf_weights = get_tf_weights_as_numpy(ckpt_path)
    cfg_updates = task_specific_params[f"summarization_{dataset}"]
    if dataset == "large":
        cfg_updates["task_specific_params"] = task_specific_params
    torch_model = convert_pegasus(tf_weights, cfg_updates)
    torch_model.save_pretrained(save_dir)
    sd = torch_model.state_dict()
    sd.pop("model.decoder.embed_positions.weight")
    sd.pop("model.encoder.embed_positions.weight")
    torch.save(sd, Path(save_dir) / "pytorch_model.bin")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
    parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
    args = parser.parse_args()
    if args.save_dir is None:
        dataset = Path(args.tf_ckpt_path).parent.name
        args.save_dir = os.path.join("pegasus", dataset)
    convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)

# coding=utf-8
# Copyright 2020 Google and 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.

import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


PATTERNS = [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["memory_attention", "encoder_attn"],
    ["attention", "attn"],
    ["/", "."],
    [".LayerNorm.gamma", "_layer_norm.weight"],
    [".LayerNorm.beta", "_layer_norm.bias"],
    ["r.layer_", "r.layers."],
    ["output_proj", "out_proj"],
    ["ffn.dense_1.", "fc2."],
    ["ffn.dense.", "fc1."],
    ["ffn_layer_norm", "final_layer_norm"],
    ["kernel", "weight"],
    ["encoder_layer_norm.", "encoder.layer_norm."],
    ["decoder_layer_norm.", "decoder.layer_norm."],
    ["embeddings.weights", "shared.weight"],
]


def rename_state_dict_key(k):
    for pegasus_name, hf_name in PATTERNS:
        k = k.replace(pegasus_name, hf_name)
    return k


# See appendix C of paper for all hyperparams


def convert_pegasus(tf_weights: dict, cfg_updates: dict) -> PegasusForConditionalGeneration:
    cfg_kwargs = DEFAULTS.copy()
    cfg_kwargs.update(cfg_updates)
    cfg = PegasusConfig(**cfg_kwargs)
    torch_model = PegasusForConditionalGeneration(cfg)
    sd = torch_model.model.state_dict()
    mapping = {}
    for k, v in tf_weights.items():
        new_k = rename_state_dict_key(k)
        if new_k not in sd:
            raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})")

        if "dense" in k or "proj" in new_k:
            v = v.T
        mapping[new_k] = torch.tensor(v, dtype=sd[new_k].dtype)
        assert v.shape == sd[new_k].shape, f"{new_k}, {k}, {v.shape}, {sd[new_k].shape}"
    # make sure embedding.padding_idx is respected
    mapping["shared.weight"][cfg.pad_token_id] = torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1])
    mapping["encoder.embed_tokens.weight"] = mapping["shared.weight"]
    mapping["decoder.embed_tokens.weight"] = mapping["shared.weight"]
    empty_biases = {k: torch.zeros_like(v) for k, v in sd.items() if k.endswith("bias") and k not in mapping}
    mapping.update(**empty_biases)
    missing, extra = torch_model.model.load_state_dict(mapping, strict=False)
    unexpected_missing = [
        k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
    ]
    assert unexpected_missing == [], f"no matches found for the following torch keys {unexpected_missing}"
    assert extra == [], f"no matches found for the following tf keys {extra}"
    return torch_model


def get_tf_weights_as_numpy(path="./ckpt/aeslc/model.ckpt-32000") -> Dict:
    init_vars = tf.train.list_variables(path)
    tf_weights = {}
    ignore_name = ["Adafactor", "global_step"]
    for name, shape in tqdm(init_vars, desc="converting tf checkpoint to dict"):
        skip_key = any(pat in name for pat in ignore_name)
        if skip_key:
            continue
        array = tf.train.load_variable(path, name)
        tf_weights[name] = array
    return tf_weights


def convert_pegasus_ckpt_to_pytorch(ckpt_path: str, save_dir: str):
    # save tokenizer first
    dataset = Path(ckpt_path).parent.name
    desired_max_model_length = task_specific_params[f"summarization_{dataset}"]["max_position_embeddings"]
    tok = PegasusTokenizer.from_pretrained("sshleifer/pegasus", model_max_length=desired_max_model_length)
    assert tok.model_max_length == desired_max_model_length
    tok.save_pretrained(save_dir)

    # convert model
    tf_weights = get_tf_weights_as_numpy(ckpt_path)
    cfg_updates = task_specific_params[f"summarization_{dataset}"]
    if dataset == "large":
        cfg_updates["task_specific_params"] = task_specific_params
    torch_model = convert_pegasus(tf_weights, cfg_updates)
    torch_model.save_pretrained(save_dir)
    sd = torch_model.state_dict()
    sd.pop("model.decoder.embed_positions.weight")
    sd.pop("model.encoder.embed_positions.weight")
    torch.save(sd, Path(save_dir) / "pytorch_model.bin")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
    parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
    args = parser.parse_args()
    if args.save_dir is None:
        dataset = Path(args.tf_ckpt_path).parent.name
        args.save_dir = os.path.join("pegasus", dataset)
    convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
