




"""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


__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     _A					( __lowercase    ):





       """simple docstring"""



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




def     _A					( __lowercase							,     __lowercase    ):





       """simple docstring"""



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





       """simple docstring"""



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




def     _A					( __lowercase							,     __lowercase    ):





       """simple docstring"""



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

       # convert model
       lowerCamelCase__          =						get_tf_weights_as_numpy(__lowercase    )
       lowerCamelCase__          =						task_specific_params[f"""summarization_{dataset}"""]
       if dataset == "large":
              lowerCamelCase__          =						task_specific_params
       lowerCamelCase__          =						convert_pegasus(__lowercase							,     __lowercase    )
       torch_model.save_pretrained(__lowercase    )
       lowerCamelCase__          =						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__":
  __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)









'''simple docstring'''





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey





class 		__SCREAMING_SNAKE_CASE							:







				def __init__(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Union[str, Any]		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										if dst_width < 0 or dst_height < 0:
																raise ValueError('''Destination width/height should be > 0'''   )

										lowercase :							Union[str, Any]    		=img
										lowercase :							Union[str, Any]    		=img.shape[1]
										lowercase :							str    		=img.shape[0]
										lowercase :							Union[str, Any]    		=dst_width
										lowercase :							str    		=dst_height

										lowercase :							str    		=self.src_w / self.dst_w
										lowercase :							Optional[Any]    		=self.src_h / self.dst_h

										lowercase :							int    		=(
										    np.ones((self.dst_h, self.dst_w, 3)		,					np.uinta   ) * 255
										)







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										for i in range(self.dst_h   ):
																for j in range(self.dst_w   ):
																						lowercase :							List[Any]    		=self.img[self.get_y(UpperCAmelCase__   )][self.get_x(UpperCAmelCase__   )]







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										return int(self.ratio_x * x   )







				def  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										return int(self.ratio_y * y   )


if __name__ == "__main__":
							UpperCamelCase_						,   UpperCamelCase_    						=       800, 600
							UpperCamelCase_    						=       imread("""image_data/lena.jpg""", 1)
							UpperCamelCase_    						=       NearestNeighbour(im, dst_w, dst_h)
							n.process()

							imshow(
							    f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
							)
							waitKey(0)
							destroyAllWindows()





