






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


__a   :							Optional[int]						= [
    # 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       _SCREAMING_SNAKE_CASE    (			__lowercase :      Tuple     )    ->   int:





    """simple docstring"""

    for pegasus_name, hf_name in PATTERNS:
        __A       =						k.replace(__lowercase      ,					__lowercase     )
    return k



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      dict      ,					__lowercase :      dict     )    ->   PegasusForConditionalGeneration:





    """simple docstring"""

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





    """simple docstring"""

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



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      str     )    ->   List[str]:





    """simple docstring"""

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

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


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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a   :							Any						= {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Dict						= [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  __a   :							str						= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




