import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
A__           =							logging.getLogger(__name__)


A__           =							"""Hello world! cécé herlolip"""


A__           =							namedtuple(
    """BertAbsConfig""",
    [
        """temp_dir""",
        """large""",
        """use_bert_emb""",
        """finetune_bert""",
        """encoder""",
        """share_emb""",
        """max_pos""",
        """enc_layers""",
        """enc_hidden_size""",
        """enc_heads""",
        """enc_ff_size""",
        """enc_dropout""",
        """dec_layers""",
        """dec_hidden_size""",
        """dec_heads""",
        """dec_ff_size""",
        """dec_dropout""",
    ],
)





def  _lowerCamelCase					(   a_				: str						,			a_				: str):
			lowerCamelCase						:List[Any]						  =     BertAbsConfig(
			    temp_dir='''.'''						,			finetune_bert=a_						,			large=a_						,			share_emb=a_						,			use_bert_emb=a_						,			encoder='''bert'''						,			max_pos=5_12						,			enc_layers=6						,			enc_hidden_size=5_12						,			enc_heads=8						,			enc_ff_size=5_12						,			enc_dropout=0.2						,			dec_layers=6						,			dec_hidden_size=7_68						,			dec_heads=8						,			dec_ff_size=20_48						,			dec_dropout=0.2						,			)
			lowerCamelCase						:Union[str, Any]						  =     torch.load(a_						,			lambda a_						,			a_: storage)
			lowerCamelCase						:Optional[int]						  =     AbsSummarizer(a_						,			torch.device('''cpu''')						,			a_)
			original.eval()

			lowerCamelCase						:Dict						  =     BertAbsSummarizer(a_						,			torch.device('''cpu'''))
			new_model.eval()

			# -------------------
			# Convert the weights
			# -------------------

			logging.info('''convert the model''')
			new_model.bert.load_state_dict(original.bert.state_dict())
			new_model.decoder.load_state_dict(original.decoder.state_dict())
			new_model.generator.load_state_dict(original.generator.state_dict())

			# ----------------------------------
			# Make sure the outpus are identical
			# ----------------------------------

			logging.info('''Make sure that the models\' outputs are identical''')
			lowerCamelCase						:Any						  =     BertTokenizer.from_pretrained('''bert-base-uncased''')

			# prepare the model inputs
			lowerCamelCase						:Union[str, Any]						  =     tokenizer.encode('''This is sample éàalj\'-.''')
			encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(a_)))
			lowerCamelCase						:List[Any]						  =     torch.tensor(a_).unsqueeze(0)
			lowerCamelCase						:Dict						  =     tokenizer.encode('''This is sample 3 éàalj\'-.''')
			decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(a_)))
			lowerCamelCase						:Any						  =     torch.tensor(a_).unsqueeze(0)

			# failsafe to make sure the weights reset does not affect the
			# loaded weights.
			assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight)) == 0

			# forward pass
			lowerCamelCase						:List[Any]						  =     encoder_input_ids
			lowerCamelCase						:Dict						  =     decoder_input_ids
			lowerCamelCase						:List[Any]						  =     None
			lowerCamelCase						:int						  =     None
			lowerCamelCase						:List[str]						  =     None
			lowerCamelCase						:Optional[Any]						  =     None
			lowerCamelCase						:Dict						  =     None

			# The original model does not apply the geneator layer immediatly but rather in
			# the beam search (where it combines softmax + linear layer). Since we already
			# apply the softmax in our generation process we only apply the linear layer here.
			# We make sure that the outputs of the full stack are identical
			lowerCamelCase						:List[Any]						  =     original(a_						,			a_						,			a_						,			a_						,			a_						,			a_						,			a_)[0]
			lowerCamelCase						:List[str]						  =     original.generator(a_)

			lowerCamelCase						:Union[str, Any]						  =     new_model(
			    a_						,			a_						,			a_						,			a_						,			a_)[0]
			lowerCamelCase						:Union[str, Any]						  =     new_model.generator(a_)

			lowerCamelCase						:Dict						  =     torch.max(torch.abs(output_converted_model - output_original_model)).item()
			print('''Maximum absolute difference beween weights: {:.2f}'''.format(a_))
			lowerCamelCase						:str						  =     torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
			print('''Maximum absolute difference beween weights: {:.2f}'''.format(a_))

			lowerCamelCase						:Tuple						  =     torch.allclose(a_						,			a_						,			atol=1e-3)
			if are_identical:
						logging.info('''all weights are equal up to 1e-3''')
			else:
						raise ValueError('''the weights are different. The new model is likely different from the original one.''')

			# The model has been saved with torch.save(model) and this is bound to the exact
			# directory structure. We save the state_dict instead.
			logging.info('''saving the model\'s state dictionary''')
			torch.save(
			    new_model.state_dict()						,			'''./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin''')


if __name__ == "__main__":
			A__           =							argparse.ArgumentParser()
			parser.add_argument(
			    """--bertabs_checkpoint_path""",
			    default=None,
			    type=str,
			    required=True,
			    help="""Path the official PyTorch dump.""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""",
			    default=None,
			    type=str,
			    required=True,
			    help="""Path to the output PyTorch model.""",
			)
			A__           =							parser.parse_args()

			convert_bertabs_checkpoints(
			    args.bertabs_checkpoint_path,
			    args.pytorch_dump_folder_path,
			)




import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A__           =							imread(R"""digital_image_processing/image_data/lena_small.jpg""")
A__           =							cvtColor(img, COLOR_BGR2GRAY)





def  _lowerCamelCase					(   ):
			lowerCamelCase						:int						  =     cn.convert_to_negative(a_)
			# assert negative_img array for at least one True
			assert negative_img.any()





def  _lowerCamelCase					(   ):
			with Image.open('''digital_image_processing/image_data/lena_small.jpg''') as img:
						# Work around assertion for response
						assert str(cc.change_contrast(a_						,			1_10)).startswith(
						    '''<PIL.Image.Image image mode=RGB size=100x100 at''')





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Optional[Any]						  =     canny.gen_gaussian_kernel(9						,			sigma=1.4)
			# Assert ambiguous array
			assert resp.all()





def  _lowerCamelCase					(   ):
			lowerCamelCase						:str						  =     imread('''digital_image_processing/image_data/lena_small.jpg'''						,			0)
			# assert ambiguous array for all == True
			assert canny_img.all()
			lowerCamelCase						:Optional[Any]						  =     canny.canny(a_)
			# assert canny array for at least one True
			assert canny_array.any()





def  _lowerCamelCase					(   ):
			assert gg.gaussian_filter(a_						,			5						,			sigma=0.9).all()





def  _lowerCamelCase					(   ):
			# laplace diagonals
			lowerCamelCase						:List[Any]						  =     array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]])
			lowerCamelCase						:List[Any]						  =     conv.img_convolve(a_						,			a_).astype(a_)
			assert res.any()





def  _lowerCamelCase					(   ):
			assert med.median_filter(a_						,			3).any()





def  _lowerCamelCase					(   ):
			lowerCamelCase  ,    lowerCamelCase						:Union[str, Any]						  =     sob.sobel_filter(a_)
			assert grad.any() and theta.any()





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Dict						  =     sp.make_sepia(a_						,			20)
			assert sepia.all()





def  _lowerCamelCase					(   a_				: str = "digital_image_processing/image_data/lena_small.jpg"):
			lowerCamelCase						:Any						  =     bs.Burkes(imread(a_						,			1)						,			1_20)
			burkes.process()
			assert burkes.output_img.any()





def  _lowerCamelCase					(   a_				: str = "digital_image_processing/image_data/lena_small.jpg"						,			):
			lowerCamelCase						:Tuple						  =     rs.NearestNeighbour(imread(a_						,			1)						,			4_00						,			2_00)
			nn.process()
			assert nn.output.any()





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Tuple						  =     '''digital_image_processing/image_data/lena.jpg'''

			# Reading the image and converting it to grayscale.
			lowerCamelCase						:Tuple						  =     imread(a_						,			0)

			# Test for get_neighbors_pixel function() return not None
			lowerCamelCase						:Dict						  =     0
			lowerCamelCase						:Optional[Any]						  =     0
			lowerCamelCase						:str						  =     image[x_coordinate][y_coordinate]

			lowerCamelCase						:Any						  =     lbp.get_neighbors_pixel(
			    a_						,			a_						,			a_						,			a_)

			assert neighbors_pixels is not None

			# Test for local_binary_pattern function()
			# Create a numpy array as the same height and width of read image
			lowerCamelCase						:int						  =     np.zeros((image.shape[0], image.shape[1]))

			# Iterating through the image and calculating the local binary pattern value
			# for each pixel.
			for i in range(0						,			image.shape[0]):
						for j in range(0						,			image.shape[1]):
									lowerCamelCase						:Optional[int]						  =     lbp.local_binary_value(a_						,			a_						,			a_)

			assert lbp_image.any()



