





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


_SCREAMING_SNAKE_CASE 		=						[
    # 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 							SCREAMING_SNAKE_CASE__  (      __a				):
	for pegasus_name, hf_name in PATTERNS:
		snake_case_						:     str     =		k.replace(a__				,	a__				)
	return k


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


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	# save tokenizer first
	snake_case_						:     int     =		Path(a__				).parent.name
	snake_case_						:     str     =		task_specific_params[f"""summarization_{dataset}"""]['max_position_embeddings']
	snake_case_						:     Optional[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
	snake_case_						:     Dict     =		get_tf_weights_as_numpy(a__				)
	snake_case_						:     int     =		task_specific_params[f"""summarization_{dataset}"""]
	if dataset == "large":
		snake_case_						:     Optional[int]     =		task_specific_params
	snake_case_						:     Any     =		convert_pegasus(a__				,	a__				)
	torch_model.save_pretrained(a__				)
	snake_case_						:     Union[str, 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__":
					_SCREAMING_SNAKE_CASE 		=						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.""")
					_SCREAMING_SNAKE_CASE 		=						parser.parse_args()
					if args.save_dir is None:
										_SCREAMING_SNAKE_CASE 		=						Path(args.tf_ckpt_path).parent.name
										_SCREAMING_SNAKE_CASE 		=						os.path.join("""pegasus""", dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)








from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	snake_case_						:     List[Any]     =		k_size // 2
	snake_case_  ,snake_case_						:     Any     =		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
	snake_case_						:     Optional[Any]     =		1 / (2 * pi * sigma) * exp(-(square(__a				) + square(__a				)) / (2 * square(__a				))				)
	return g


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_  ,snake_case_						:     Union[str, Any]     =		image.shape[0], image.shape[1]
	# dst image height and width
	snake_case_						:     int     =		height - k_size + 1
	snake_case_						:     Optional[int]     =		width - k_size + 1

	# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
	snake_case_						:     Optional[Any]     =		zeros((dst_height * dst_width, k_size * k_size)				)
	snake_case_						:     Tuple     =		0
	for i, j in product(range(__a				)				,	range(__a				)				):
		snake_case_						:     Optional[int]     =		ravel(image[i : i + k_size, j : j + k_size]				)
		snake_case_						:     str     =		window
		row += 1

	#  turn the kernel into shape(k*k, 1)
	snake_case_						:     List[Any]     =		gen_gaussian_kernel(__a				,	__a				)
	snake_case_						:     str     =		ravel(__a				)

	# reshape and get the dst image
	snake_case_						:     Optional[int]     =		dot(__a				,	__a				).reshape(__a				,	__a				).astype(__a				)

	return dst


if __name__ == "__main__":
						# read original image
						_SCREAMING_SNAKE_CASE 		=						imread(R"""../image_data/lena.jpg""")
						# turn image in gray scale value
						_SCREAMING_SNAKE_CASE 		=						cvtColor(img, COLOR_BGR2GRAY)

						# get values with two different mask size
						_SCREAMING_SNAKE_CASE 		=						gaussian_filter(gray, 3, sigma=1)
						_SCREAMING_SNAKE_CASE 		=						gaussian_filter(gray, 5, sigma=0.8)

						# show result images
						imshow("""gaussian filter with 3x3 mask""", gaussianaxa)
						imshow("""gaussian filter with 5x5 mask""", gaussianaxa)
						waitKey()

