"""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


__lowerCAmelCase       :			Dict   	=[
    # 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__			(							lowerCAmelCase__	:Union[str, Any]						)					->       str:
      '''simple docstring'''


      for pegasus_name, hf_name in PATTERNS:
            lowercase								=      k.replace(lowerCAmelCase__   ,						lowerCAmelCase__						)
      return k


def      UpperCAmelCase__			(							lowerCAmelCase__	:dict   ,						lowerCAmelCase__	:dict						)					->       PegasusForConditionalGeneration:
      '''simple docstring'''


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


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


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:str						)					->       Any:
      '''simple docstring'''


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

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


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




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase       :			Optional[int]   	={
    """configuration_chinese_clip""": [
        """CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ChineseCLIPConfig""",
        """ChineseCLIPOnnxConfig""",
        """ChineseCLIPTextConfig""",
        """ChineseCLIPVisionConfig""",
    ],
    """processing_chinese_clip""": ["""ChineseCLIPProcessor"""],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase       :			List[Any]   	=["""ChineseCLIPFeatureExtractor"""]
       __lowerCAmelCase       :			List[Any]   	=["""ChineseCLIPImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase       :			Tuple   	=[
           """CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """ChineseCLIPModel""",
           """ChineseCLIPPreTrainedModel""",
           """ChineseCLIPTextModel""",
           """ChineseCLIPVisionModel""",
       ]

if TYPE_CHECKING:
       from .configuration_chinese_clip import (
           CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           ChineseCLIPConfig,
           ChineseCLIPOnnxConfig,
           ChineseCLIPTextConfig,
           ChineseCLIPVisionConfig,
       )
       from .processing_chinese_clip import ChineseCLIPProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_chinese_clip import (
                  CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ChineseCLIPModel,
                  ChineseCLIPPreTrainedModel,
                  ChineseCLIPTextModel,
                  ChineseCLIPVisionModel,
              )

else:
       import sys

       __lowerCAmelCase       :			Optional[Any]   	=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



