

'''simple docstring'''





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


UpperCamelCase__  :      Optional[Any]			=    [
    # 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 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[Any]       ):





    """simple docstring"""


    for pegasus_name, hf_name in PATTERNS:
        SCREAMING_SNAKE_CASE_									=						k.replace(a__ ,   a__       )
    return k


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						DEFAULTS.copy()
    cfg_kwargs.update(a__       )
    SCREAMING_SNAKE_CASE_									=						PegasusConfig(**a__       )
    SCREAMING_SNAKE_CASE_									=						PegasusForConditionalGeneration(a__       )
    SCREAMING_SNAKE_CASE_									=						torch_model.model.state_dict()
    SCREAMING_SNAKE_CASE_									=						{}
    for k, v in tf_weights.items():
        SCREAMING_SNAKE_CASE_									=						rename_state_dict_key(a__       )
        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:
            SCREAMING_SNAKE_CASE_									=						v.T
        SCREAMING_SNAKE_CASE_									=						torch.tensor(a__ ,   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
    SCREAMING_SNAKE_CASE_									=						torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]       )
    SCREAMING_SNAKE_CASE_									=						mapping['shared.weight']
    SCREAMING_SNAKE_CASE_									=						mapping['shared.weight']
    SCREAMING_SNAKE_CASE_									=						{k: torch.zeros_like(a__       ) for k, v in sd.items() if k.endswith('bias'       ) and k not in mapping}
    mapping.update(**a__       )
    SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						torch_model.model.load_state_dict(a__ ,   strict=a__       )
    SCREAMING_SNAKE_CASE_									=						[
        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 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple="./ckpt/aeslc/model.ckpt-32000"       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						tf.train.list_variables(a__       )
    SCREAMING_SNAKE_CASE_									=						{}
    SCREAMING_SNAKE_CASE_									=						['Adafactor', 'global_step']
    for name, shape in tqdm(a__ ,   desc='converting tf checkpoint to dict'       ):
        SCREAMING_SNAKE_CASE_									=						any(pat in name for pat in ignore_name       )
        if skip_key:
            continue
        SCREAMING_SNAKE_CASE_									=						tf.train.load_variable(a__ ,   a__       )
        SCREAMING_SNAKE_CASE_									=						array
    return tf_weights


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		List[Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						Path(a__       ).parent.name
    SCREAMING_SNAKE_CASE_									=						task_specific_params[f"""summarization_{dataset}"""]['max_position_embeddings']
    SCREAMING_SNAKE_CASE_									=						PegasusTokenizer.from_pretrained('sshleifer/pegasus' ,   model_max_length=a__       )
    assert tok.model_max_length == desired_max_model_length
    tok.save_pretrained(a__       )

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


if __name__ == "__main__":
       UpperCamelCase__  :      List[str]			=    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.")
       UpperCamelCase__  :      Dict			=    parser.parse_args()
       if args.save_dir is None:
              UpperCamelCase__  :      Tuple			=    Path(args.tf_ckpt_path).parent.name
              UpperCamelCase__  :      Tuple			=    os.path.join("pegasus", dataset)
       convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)





def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    for n in range(1 ,   1_000_000       ):
        yield n * (n + 1) // 2


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						1
    SCREAMING_SNAKE_CASE_									=						2
    while i * i <= n:
        SCREAMING_SNAKE_CASE_									=						0
        while n % i == 0:
            n //= i
            multiplicity += 1
        divisors_count *= multiplicity + 1
        i += 1
    if n > 1:
        divisors_count *= 2
    return divisors_count


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    return next(i for i in triangle_number_generator() if count_divisors(_SCREAMING_SNAKE_CASE       ) > 500       )


if __name__ == "__main__":
       print(solution())


