
import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 	a_		(	__lowercase       :     Dict			)			->  int:
			_snake_case          =      [
			    'encoder.version',
			    'decoder.version',
			    'model.encoder.version',
			    'model.decoder.version',
			    '_float_tensor',
			    'decoder.output_projection.weight',
			]
			for k in ignore_keys:
						state_dict.pop(__lowercase	, __lowercase			)






def 	a_		(	__lowercase       :     List[str]			)			->  str:
			_snake_case     , _snake_case          =      emb.weight.shape
			_snake_case          =      nn.Linear(__lowercase	, __lowercase	, bias=__lowercase			)
			_snake_case          =      emb.weight.data
			return lin_layer






def 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     Optional[Any]="facebook/mbart-large-en-ro"	, __lowercase       :     Union[str, Any]=False	, __lowercase       :     Optional[Any]=False			)			->  int:
			_snake_case          =      torch.load(__lowercase	, map_location='cpu'			)['model']
			remove_ignore_keys_(__lowercase			)
			_snake_case          =      state_dict['encoder.embed_tokens.weight'].shape[0]

			_snake_case          =      MBartConfig.from_pretrained(__lowercase	, vocab_size=__lowercase			)
			if mbart_aa and finetuned:
						_snake_case          =      'relu'

			_snake_case          =      state_dict['decoder.embed_tokens.weight']
			_snake_case          =      MBartForConditionalGeneration(__lowercase			)
			model.model.load_state_dict(__lowercase			)

			if finetuned:
						_snake_case          =      make_linear_from_emb(model.model.shared			)

			return model


if __name__ == "__main__":
			_lowerCamelCase	:    List[str]          =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
			)
			parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument(
			    '''--hf_config''',
			    default='''facebook/mbart-large-cc25''',
			    type=str,
			    help='''Which huggingface architecture to use: mbart-large''',
			)
			parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
			parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
			_lowerCamelCase	:    List[str]          =      parser.parse_args()
			_lowerCamelCase	:    Tuple          =      convert_fairseq_mbart_checkpoint_from_disk(
			    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
			)
			model.save_pretrained(args.pytorch_dump_folder_path)

from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def 	a_		(	__lowercase       :     np.ndarray	, __lowercase       :     np.ndarray	, __lowercase       :     np.ndarray	, __lowercase       :     int	, __lowercase       :     int			)			->  np.ndarray:
			_snake_case          =      cva.getAffineTransform(__lowercase	, __lowercase			)
			return cva.warpAffine(__lowercase	, __lowercase	, (rows, cols)			)


if __name__ == "__main__":
			# read original image
			_lowerCamelCase	:    Optional[Any]          =      cva.imread(
			    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
			)
			# turn image in gray scale value
			_lowerCamelCase	:    List[str]          =      cva.cvtColor(image, cva.COLOR_BGR2GRAY)
			# get image shape
			_lowerCamelCase   ,					_lowerCamelCase	:    List[Any]          =      gray_img.shape

			# set different points to rotate image
			_lowerCamelCase	:    str          =      np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
			_lowerCamelCase	:    Optional[Any]          =      np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
			_lowerCamelCase	:    List[str]          =      np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
			_lowerCamelCase	:    Dict          =      np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

			# add all rotated images in a list
			_lowerCamelCase	:    int          =      [
			    gray_img,
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			]

			# plot different image rotations
			_lowerCamelCase	:    Any          =      plt.figure(1)
			_lowerCamelCase	:    List[Any]          =      ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
			for i, image in enumerate(images):
						plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
						plt.title(titles[i])
						plt.axis('''off''')
						plt.subplots_adjust(left=0.0, bottom=0.0_5, right=1.0, top=0.9_5)
			plt.show()