
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_											=						[
    # 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       ):
    """simple docstring"""






    for pegasus_name, hf_name in PATTERNS:
        snake_case_ :    Optional[Any]     			=						k.replace(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    return k

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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






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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[int]     			=						Path(__SCREAMING_SNAKE_CASE       ).parent.name
    snake_case_ :    Optional[Any]     			=						task_specific_params[f'summarization_{dataset}']["max_position_embeddings"]
    snake_case_ :    int     			=						PegasusTokenizer.from_pretrained("sshleifer/pegasus",		model_max_length=__SCREAMING_SNAKE_CASE       )
    assert tok.model_max_length == desired_max_model_length
    tok.save_pretrained(__SCREAMING_SNAKE_CASE       )

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


if __name__ == "__main__":
  a_											=						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_											=						parser.parse_args()
  if args.save_dir is None:
    a_											=						Path(args.tf_ckpt_path).parent.name
    a_											=						os.path.join("pegasus", dataset)
  convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)






'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
  from ...onnx.config import PatchingSpec
  from ...tokenization_utils_base import PreTrainedTokenizerBase


a_											=						logging.get_logger(__name__)

a_											=						{
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						"""longformer"""





 def __init__(    self ,       lowercase_ = 5_12 ,       lowercase_ = 2 ,       lowercase_ = 1 ,       lowercase_ = 0 ,       lowercase_ = 2 ,       lowercase_ = 3_05_22 ,       lowercase_ = 7_68 ,       lowercase_ = 12 ,       lowercase_ = 12 ,       lowercase_ = 30_72 ,       lowercase_ = "gelu" ,       lowercase_ = 0.1 ,       lowercase_ = 0.1 ,       lowercase_ = 5_12 ,       lowercase_ = 2 ,       lowercase_ = 0.02 ,       lowercase_ = 1E-12 ,       lowercase_ = False ,       **lowercase_ ,       ):
     super().__init__(pad_token_id=lowercase_ ,       **lowercase_)

     snake_case_ :    Dict     			=						attention_window
     snake_case_ :    Tuple     			=						sep_token_id
     snake_case_ :    Optional[Any]     			=						bos_token_id
     snake_case_ :    str     			=						eos_token_id
     snake_case_ :    Optional[int]     			=						vocab_size
     snake_case_ :    Dict     			=						hidden_size
     snake_case_ :    Optional[Any]     			=						num_hidden_layers
     snake_case_ :    int     			=						num_attention_heads
     snake_case_ :    Union[str, Any]     			=						hidden_act
     snake_case_ :    Union[str, Any]     			=						intermediate_size
     snake_case_ :    Union[str, Any]     			=						hidden_dropout_prob
     snake_case_ :    Optional[Any]     			=						attention_probs_dropout_prob
     snake_case_ :    List[str]     			=						max_position_embeddings
     snake_case_ :    str     			=						type_vocab_size
     snake_case_ :    Tuple     			=						initializer_range
     snake_case_ :    List[str]     			=						layer_norm_eps
     snake_case_ :    Tuple     			=						onnx_export






class    UpperCAmelCase_				(	snake_case__					):





 def __init__(    self ,       lowercase_ ,       lowercase_ = "default" ,       lowercase_ = None):
     super().__init__(lowercase_ ,       lowercase_ ,       lowercase_)
     snake_case_ :    Dict     			=						True





 @property
 def        snake_case__		(    self):
     if self.task == "multiple-choice":
         snake_case_ :    Tuple     			=						{0: "batch", 1: "choice", 2: "sequence"}
     else:
         snake_case_ :    int     			=						{0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
             ("global_attention_mask", dynamic_axis),
         ])





 @property
 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						super().outputs
     if self.task == "default":
         snake_case_ :    str     			=						{0: "batch"}
     return outputs





 @property
 def        snake_case__		(    self):
     return 1E-4





 @property
 def        snake_case__		(    self):
     # needs to be >= 14 to support tril operator
     return max(super().default_onnx_opset ,       14)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = -1 ,       lowercase_ = -1 ,       lowercase_ = False ,       lowercase_ = None ,       ):
     snake_case_ :    Optional[Any]     			=						super().generate_dummy_inputs(
         preprocessor=lowercase_ ,       batch_size=lowercase_ ,       seq_length=lowercase_ ,       is_pair=lowercase_ ,       framework=lowercase_)
     import torch

     # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
     # makes the export fail randomly
     snake_case_ :    Any     			=						torch.zeros_like(inputs["input_ids"])
     # make every second token global
     snake_case_ :    Tuple     			=						1

     return inputs




