'''simple docstring'''

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)
lowercase_     		= logging.getLogger(__name__)


lowercase_     		= '''Hello world! cécé herlolip'''


lowercase_     		= 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 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=BertAbsConfig(
						    temp_dir='.'			,      finetune_bert=a__			,      large=a__			,      share_emb=a__			,      use_bert_emb=a__			,      encoder='bert'			,      max_pos=5_1_2			,      enc_layers=6			,      enc_hidden_size=5_1_2			,      enc_heads=8			,      enc_ff_size=5_1_2			,      enc_dropout=0.2			,      dec_layers=6			,      dec_hidden_size=7_6_8			,      dec_heads=8			,      dec_ff_size=2_0_4_8			,      dec_dropout=0.2			,      )
						_lowerCAmelCase										=torch.load(a__			,      lambda a__			,      a__      : storage      )
						_lowerCAmelCase										=AbsSummarizer(a__			,      torch.device('cpu'      )			,      a__      )
						original.eval()

						_lowerCAmelCase										=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										=BertTokenizer.from_pretrained('bert-base-uncased'      )

						# prepare the model inputs
						_lowerCAmelCase										=tokenizer.encode('This is sample éàalj\'-.'      )
						encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(a__      ))      )
						_lowerCAmelCase										=torch.tensor(a__      ).unsqueeze(0      )
						_lowerCAmelCase										=tokenizer.encode('This is sample 3 éàalj\'-.'      )
						decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(a__      ))      )
						_lowerCAmelCase										=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										=encoder_input_ids
						_lowerCAmelCase										=decoder_input_ids
						_lowerCAmelCase										=_lowerCAmelCase										=None
						_lowerCAmelCase										=None
						_lowerCAmelCase										=_lowerCAmelCase										=None
						_lowerCAmelCase										=_lowerCAmelCase										=None
						_lowerCAmelCase										=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										=original(a__			,      a__			,      a__			,      a__			,      a__			,      a__			,      a__      )[0]
						_lowerCAmelCase										=original.generator(a__      )

						_lowerCAmelCase										=new_model(
						    a__			,      a__			,      a__			,      a__			,      a__      )[0]
						_lowerCAmelCase										=new_model.generator(a__      )

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

						_lowerCAmelCase										=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__":
							lowercase_     		= 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.''',
							)
							lowercase_     		= parser.parse_args()

							convert_bertabs_checkpoints(
							    args.bertabs_checkpoint_path,
							    args.pytorch_dump_folder_path,
							)






'''simple docstring'''

from PIL import Image



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						def brightness(a__      ) -> float:
												return 1_2_8 + level + (c - 1_2_8)

						if not -255.0 <= level <= 255.0:
												raise ValueError('level must be between -255.0 (black) and 255.0 (white)'      )
						return img.point(a__      )


if __name__ == "__main__":
							# Load image
							with Image.open('''image_data/lena.jpg''') as img:
														# Change brightness to 100
														lowercase_     		= change_brightness(img, 100)
														brigt_img.save('''image_data/lena_brightness.png''', format='''png''')





