



"""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     =			[
    # 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      __magic_name__				(							lowercase ):
   for pegasus_name, hf_name in PATTERNS:
      SCREAMING_SNAKE_CASE_: Optional[int]     			=k.replace(lowercase							,			lowercase )
   return k





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





def      __magic_name__				(							lowercase							,			lowercase ):
   # save tokenizer first
   SCREAMING_SNAKE_CASE_: List[Any]     			=Path(lowercase ).parent.name
   SCREAMING_SNAKE_CASE_: Optional[int]     			=task_specific_params[f'''summarization_{dataset}''']["""max_position_embeddings"""]
   SCREAMING_SNAKE_CASE_: List[str]     			=PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""							,			model_max_length=lowercase )
   assert tok.model_max_length == desired_max_model_length
   tok.save_pretrained(lowercase )

   # convert model
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=get_tf_weights_as_numpy(lowercase )
   SCREAMING_SNAKE_CASE_: List[str]     			=task_specific_params[f'''summarization_{dataset}''']
   if dataset == "large":
      SCREAMING_SNAKE_CASE_: Any     			=task_specific_params
   SCREAMING_SNAKE_CASE_: List[str]     			=convert_pegasus(lowercase							,			lowercase )
   torch_model.save_pretrained(lowercase )
   SCREAMING_SNAKE_CASE_: int     			=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__":
   _UpperCAmelCase     =			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     =			parser.parse_args()
   if args.save_dir is None:
      _UpperCAmelCase     =			Path(args.tf_ckpt_path).parent.name
      _UpperCAmelCase     =			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 ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


_UpperCAmelCase     =			{"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			["""DPTFeatureExtractor"""]
   _UpperCAmelCase     =			["""DPTImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """DPTForDepthEstimation""",
       """DPTForSemanticSegmentation""",
       """DPTModel""",
       """DPTPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_dpt import DPTFeatureExtractor
      from .image_processing_dpt import DPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dpt import (
          DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPTForDepthEstimation,
          DPTForSemanticSegmentation,
          DPTModel,
          DPTPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



