


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   :					Union[str, Any]	=      [
    # 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    lowercase_	(	_A    :					str		):

      """simple docstring"""




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

def    lowercase_	(	_A    :					Dict					,      _A    :					str		):

      """simple docstring"""




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

      """simple docstring"""




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

def    lowercase_	(	_A    :					Union[str, Any]					,      _A    :					str		):

      """simple docstring"""




      lowerCamelCase__	:       Tuple					=						Path(_A		).parent.name
      lowerCamelCase__	:       Optional[int]					=						task_specific_params[F"summarization_{dataset}"]["max_position_embeddings"]
      lowerCamelCase__	:       int					=						PegasusTokenizer.from_pretrained("sshleifer/pegasus"					,      model_max_length=_A		)
      assert tok.model_max_length == desired_max_model_length
      tok.save_pretrained(_A		)

      # convert model
      lowerCamelCase__	:       List[str]					=						get_tf_weights_as_numpy(_A		)
      lowerCamelCase__	:       Dict					=						task_specific_params[F"summarization_{dataset}"]
      if dataset == "large":
            lowerCamelCase__	:       str					=						task_specific_params
      lowerCamelCase__	:       Union[str, Any]					=						convert_pegasus(_A					,      _A		)
      torch_model.save_pretrained(_A		)
      lowerCamelCase__	:       List[Any]					=						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__":
    A   :					Tuple	=      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   :					Optional[int]	=      parser.parse_args()
    if args.save_dir is None:
        A   :					Dict	=      Path(args.tf_ckpt_path).parent.name
        A   :					Tuple	=      os.path.join("pegasus", dataset)
    convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)






import cva
import numpy as np







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    Union[str, Any]      ,   __lowerCamelCase							:    float      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             if k in (0.0_4, 0.0_6):
                   lowerCamelCase__	:       int					=						k
                   lowerCamelCase__	:       List[str]					=						window_size
             else:
                   raise ValueError("invalid k value" )





       def __str__(     self							:    str ):


             '''simple docstring'''



             return str(self.k )





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						cva.imread(__lowerCamelCase      ,   0 )
             lowerCamelCase__					,     lowerCamelCase__	:       Any					=						img.shape
             lowerCamelCase__	:       list[list[int]]					=						[]
             lowerCamelCase__	:       List[Any]					=						img.copy()
             lowerCamelCase__	:       int					=						cva.cvtColor(__lowerCamelCase      ,   cva.COLOR_GRAY2RGB )
             lowerCamelCase__					,     lowerCamelCase__	:       int					=						np.gradient(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						dx**2
             lowerCamelCase__	:       Optional[Any]					=						dy**2
             lowerCamelCase__	:       int					=						dx * dy
             lowerCamelCase__	:       Union[str, Any]					=						0.0_4
             lowerCamelCase__	:       Any					=						self.window_size // 2
             for y in range(__lowerCamelCase      ,   h - offset ):
                   for x in range(__lowerCamelCase      ,   w - offset ):
                         lowerCamelCase__	:       Optional[Any]					=						ixx[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()
                         lowerCamelCase__	:       Optional[Any]					=						iyy[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()
                         lowerCamelCase__	:       str					=						ixy[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()

                         lowerCamelCase__	:       Optional[Any]					=						(wxx * wyy) - (wxy**2)
                         lowerCamelCase__	:       List[str]					=						wxx + wyy
                         lowerCamelCase__	:       List[Any]					=						det - k * (trace**2)
                         # Can change the value
                         if r > 0.5:
                               corner_list.append([x, y, r] )
                               color_img.itemset((y, x, 0)      ,   0 )
                               color_img.itemset((y, x, 1)      ,   0 )
                               color_img.itemset((y, x, 2)      ,   255 )
             return color_img, corner_list


if __name__ == "__main__":
    A   :					Tuple	=      HarrisCorner(0.0_4, 3)
    A,						A   :					Optional[int]	=      edge_detect.detect("path_to_image")
    cva.imwrite("detect.png", color_img)


