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


__magic_name__								=[
    # 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    (						A							):
     for pegasus_name, hf_name in PATTERNS:
          UpperCamelCase__										=      k.replace(A ,							A							)
     return k







def  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      DEFAULTS.copy()
     cfg_kwargs.update(A							)
     UpperCamelCase__										=      PegasusConfig(**A							)
     UpperCamelCase__										=      PegasusForConditionalGeneration(A							)
     UpperCamelCase__										=      torch_model.model.state_dict()
     UpperCamelCase__										=      {}
     for k, v in tf_weights.items():
          UpperCamelCase__										=      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:
               UpperCamelCase__										=      v.T
          UpperCamelCase__										=      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
     UpperCamelCase__										=      torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1]							)
     UpperCamelCase__										=      mapping['''shared.weight''']
     UpperCamelCase__										=      mapping['''shared.weight''']
     UpperCamelCase__										=      {k: torch.zeros_like(A							) for k, v in sd.items() if k.endswith('''bias'''							) and k not in mapping}
     mapping.update(**A							)
     UpperCamelCase__						,       UpperCamelCase__										=      torch_model.model.load_state_dict(A ,							strict=A							)
     UpperCamelCase__										=      [
         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    (						A="./ckpt/aeslc/model.ckpt-32000"							):
     UpperCamelCase__										=      tf.train.list_variables(A							)
     UpperCamelCase__										=      {}
     UpperCamelCase__										=      ['''Adafactor''', '''global_step''']
     for name, shape in tqdm(A ,							desc='''converting tf checkpoint to dict'''							):
          UpperCamelCase__										=      any(pat in name for pat in ignore_name							)
          if skip_key:
               continue
          UpperCamelCase__										=      tf.train.load_variable(A ,							A							)
          UpperCamelCase__										=      array
     return tf_weights







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

     # convert model
     UpperCamelCase__										=      get_tf_weights_as_numpy(A							)
     UpperCamelCase__										=      task_specific_params[f"summarization_{dataset}"]
     if dataset == "large":
          UpperCamelCase__										=      task_specific_params
     UpperCamelCase__										=      convert_pegasus(A ,							A							)
     torch_model.save_pretrained(A							)
     UpperCamelCase__										=      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__":
     __magic_name__								=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.''')
     __magic_name__								=parser.parse_args()
     if args.save_dir is None:
          __magic_name__								=Path(args.tf_ckpt_path).parent.name
          __magic_name__								=os.path.join('''pegasus''', dataset)
     convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)




__magic_name__								={
    '''Pillow''': '''Pillow<10.0.0''',
    '''accelerate''': '''accelerate>=0.20.3''',
    '''av''': '''av==9.2.0''',
    '''beautifulsoup4''': '''beautifulsoup4''',
    '''black''': '''black~=23.1''',
    '''codecarbon''': '''codecarbon==1.2.0''',
    '''cookiecutter''': '''cookiecutter==1.7.3''',
    '''dataclasses''': '''dataclasses''',
    '''datasets''': '''datasets!=2.5.0''',
    '''decord''': '''decord==0.6.0''',
    '''deepspeed''': '''deepspeed>=0.9.3''',
    '''diffusers''': '''diffusers''',
    '''dill''': '''dill<0.3.5''',
    '''evaluate''': '''evaluate>=0.2.0''',
    '''fairscale''': '''fairscale>0.3''',
    '''faiss-cpu''': '''faiss-cpu''',
    '''fastapi''': '''fastapi''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1,<=0.7.0''',
    '''ftfy''': '''ftfy''',
    '''fugashi''': '''fugashi>=1.0''',
    '''GitPython''': '''GitPython<3.1.19''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.14.1,<1.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''ipadic''': '''ipadic>=1.0.0,<2.0''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2,<=0.4.13''',
    '''jaxlib''': '''jaxlib>=0.1.65,<=0.4.13''',
    '''jieba''': '''jieba''',
    '''kenlm''': '''kenlm''',
    '''keras-nlp''': '''keras-nlp>=0.3.1''',
    '''librosa''': '''librosa''',
    '''nltk''': '''nltk''',
    '''natten''': '''natten>=0.14.6''',
    '''numpy''': '''numpy>=1.17''',
    '''onnxconverter-common''': '''onnxconverter-common''',
    '''onnxruntime-tools''': '''onnxruntime-tools>=1.4.2''',
    '''onnxruntime''': '''onnxruntime>=1.4.0''',
    '''opencv-python''': '''opencv-python''',
    '''optuna''': '''optuna''',
    '''optax''': '''optax>=0.0.8,<=0.1.4''',
    '''packaging''': '''packaging>=20.0''',
    '''parameterized''': '''parameterized''',
    '''phonemizer''': '''phonemizer''',
    '''protobuf''': '''protobuf''',
    '''psutil''': '''psutil''',
    '''pyyaml''': '''pyyaml>=5.1''',
    '''pydantic''': '''pydantic<2''',
    '''pytest''': '''pytest>=7.2.0''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''python''': '''python>=3.8.0''',
    '''ray[tune]''': '''ray[tune]''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''rhoknp''': '''rhoknp>=1.1.0,<1.3.1''',
    '''rjieba''': '''rjieba''',
    '''rouge-score''': '''rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1''',
    '''ruff''': '''ruff>=0.0.241,<=0.0.259''',
    '''sacrebleu''': '''sacrebleu>=1.4.12,<2.0.0''',
    '''sacremoses''': '''sacremoses''',
    '''safetensors''': '''safetensors>=0.3.1''',
    '''sagemaker''': '''sagemaker>=2.31.0''',
    '''scikit-learn''': '''scikit-learn''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''sigopt''': '''sigopt''',
    '''starlette''': '''starlette''',
    '''sudachipy''': '''sudachipy>=0.6.6''',
    '''sudachidict_core''': '''sudachidict_core>=20220729''',
    '''tensorflow-cpu''': '''tensorflow-cpu>=2.6,<2.14''',
    '''tensorflow''': '''tensorflow>=2.6,<2.14''',
    '''tensorflow-text''': '''tensorflow-text<2.14''',
    '''tf2onnx''': '''tf2onnx''',
    '''timeout-decorator''': '''timeout-decorator''',
    '''timm''': '''timm''',
    '''tokenizers''': '''tokenizers>=0.11.1,!=0.11.3,<0.14''',
    '''torch''': '''torch>=1.9,!=1.12.0''',
    '''torchaudio''': '''torchaudio''',
    '''torchvision''': '''torchvision''',
    '''pyctcdecode''': '''pyctcdecode>=0.4.0''',
    '''tqdm''': '''tqdm>=4.27''',
    '''unidic''': '''unidic>=1.0.2''',
    '''unidic_lite''': '''unidic_lite>=1.0.7''',
    '''urllib3''': '''urllib3<2.0.0''',
    '''uvicorn''': '''uvicorn''',
}



