

'''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)
lowerCAmelCase__   		=      logging.getLogger(__name__)


lowerCAmelCase__   		=      '''Hello world! cécé herlolip'''


lowerCAmelCase__   		=      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 						_A	(       A__	,							A__ ):






 """simple docstring"""



 __lowercase				    =					BertAbsConfig(
     temp_dir='''.'''	,							finetune_bert=A__	,							large=A__	,							share_emb=A__	,							use_bert_emb=A__	,							encoder='''bert'''	,							max_pos=512	,							enc_layers=6	,							enc_hidden_size=512	,							enc_heads=8	,							enc_ff_size=512	,							enc_dropout=0.2	,							dec_layers=6	,							dec_hidden_size=768	,							dec_heads=8	,							dec_ff_size=2048	,							dec_dropout=0.2	,							)
 __lowercase				    =					torch.load(A__	,							lambda A__	,							A__ : storage )
 __lowercase				    =					AbsSummarizer(A__	,							torch.device('''cpu''' )	,							A__ )
 original.eval()

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

 # prepare the model inputs
 __lowercase				    =					tokenizer.encode('''This is sample éàalj\'-.''' )
 encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(A__ )) )
 __lowercase				    =					torch.tensor(A__ ).unsqueeze(0 )
 __lowercase				    =					tokenizer.encode('''This is sample 3 éàalj\'-.''' )
 decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(A__ )) )
 __lowercase				    =					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
 __lowercase				    =					encoder_input_ids
 __lowercase				    =					decoder_input_ids
 __lowercase				    =					__lowercase				    =					None
 __lowercase				    =					None
 __lowercase				    =					__lowercase				    =					None
 __lowercase				    =					__lowercase				    =					None
 __lowercase				    =					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
 __lowercase				    =					original(A__	,							A__	,							A__	,							A__	,							A__	,							A__	,							A__ )[0]
 __lowercase				    =					original.generator(A__ )

 __lowercase				    =					new_model(
     A__	,							A__	,							A__	,							A__	,							A__ )[0]
 __lowercase				    =					new_model.generator(A__ )

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

 __lowercase				    =					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__":
 lowerCAmelCase__   		=      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.''',
 )
 lowerCAmelCase__   		=      parser.parse_args()

 convert_bertabs_checkpoints(
     args.bertabs_checkpoint_path,
     args.pytorch_dump_folder_path,
 )





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A	)  -> List[Any]:







					'''simple docstring'''







					super().__init__()
					self.register_modules(unet=A		,      scheduler=A	)







		@torch.no_grad()
		def __call__( self		,      A = 1		,      A = None		,      A = 50		,      A = "pil"		,      A = True		,      **A		,      )  -> Union[ImagePipelineOutput, Tuple]:







					'''simple docstring'''







					lowerCamelCase    		=  torch.randn(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,      generator=A		,      )
					lowerCamelCase    		=  image.to(self.device	)

					# set step values
					self.scheduler.set_timesteps(A	)

					for t in self.progress_bar(self.scheduler.timesteps	):
								# 1. predict noise model_output
								lowerCamelCase    		=  self.unet(A		,      A	).sample

								# 2. predict previous mean of image x_t-1 and add variance depending on eta
								# eta corresponds to η in paper and should be between [0, 1]
								# do x_t -> x_t-1
								lowerCamelCase    		=  self.scheduler.step(A		,      A		,      A	).prev_sample

					lowerCamelCase    		=  (image / 2 + 0.5).clamp(0		,      1	)
					lowerCamelCase    		=  image.cpu().permute(0		,      2		,      3		,      1	).numpy()
					if output_type == "pil":
								lowerCamelCase    		=  self.numpy_to_pil(A	)

					if not return_dict:
								return (image,), "This is a local test"

					return ImagePipelineOutput(images=A	), "This is a local test"

