

'''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__       (    _SCREAMING_SNAKE_CASE   :       Optional[Any]	,				_SCREAMING_SNAKE_CASE   :       int				)     ->					Tuple:


	"""simple docstring"""





	UpperCAmelCase_     :      Dict						=  BertAbsConfig(
	    temp_dir="."	,				finetune_bert=_SCREAMING_SNAKE_CASE	,				large=_SCREAMING_SNAKE_CASE	,				share_emb=_SCREAMING_SNAKE_CASE	,				use_bert_emb=_SCREAMING_SNAKE_CASE	,				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	,				)
	UpperCAmelCase_     :      Tuple						=  torch.load(_SCREAMING_SNAKE_CASE	,				lambda _SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				: storage				)
	UpperCAmelCase_     :      int						=  AbsSummarizer(_SCREAMING_SNAKE_CASE	,				torch.device("cpu"				)	,				_SCREAMING_SNAKE_CASE				)
	original.eval()

	UpperCAmelCase_     :      Union[str, Any]						=  BertAbsSummarizer(_SCREAMING_SNAKE_CASE	,				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"				)
	UpperCAmelCase_     :      Tuple						=  BertTokenizer.from_pretrained("bert-base-uncased"				)

	# prepare the model inputs
	UpperCAmelCase_     :      Union[str, Any]						=  tokenizer.encode("This is sample éàalj'-."				)
	encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(_SCREAMING_SNAKE_CASE				))				)
	UpperCAmelCase_     :      Any						=  torch.tensor(_SCREAMING_SNAKE_CASE				).unsqueeze(0				)
	UpperCAmelCase_     :      Optional[Any]						=  tokenizer.encode("This is sample 3 éàalj'-."				)
	decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(_SCREAMING_SNAKE_CASE				))				)
	UpperCAmelCase_     :      str						=  torch.tensor(_SCREAMING_SNAKE_CASE				).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
	UpperCAmelCase_     :      Tuple						=  encoder_input_ids
	UpperCAmelCase_     :      Optional[int]						=  decoder_input_ids
	UpperCAmelCase_     :      Union[str, Any]						=  None
	UpperCAmelCase_     :      Union[str, Any]						=  None
	UpperCAmelCase_     :      Optional[Any]						=  None
	UpperCAmelCase_     :      List[str]						=  None
	UpperCAmelCase_     :      Tuple						=  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
	UpperCAmelCase_     :      List[str]						=  original(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)[0]
	UpperCAmelCase_     :      Any						=  original.generator(_SCREAMING_SNAKE_CASE				)

	UpperCAmelCase_     :      List[Any]						=  new_model(
	    _SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)[0]
	UpperCAmelCase_     :      List[str]						=  new_model.generator(_SCREAMING_SNAKE_CASE				)

	UpperCAmelCase_     :      Tuple						=  torch.max(torch.abs(output_converted_model - output_original_model				)				).item()
	print("Maximum absolute difference beween weights: {:.2f}".format(_SCREAMING_SNAKE_CASE				)				)
	UpperCAmelCase_     :      str						=  torch.max(torch.abs(output_converted_generator - output_original_generator				)				).item()
	print("Maximum absolute difference beween weights: {:.2f}".format(_SCREAMING_SNAKE_CASE				)				)

	UpperCAmelCase_     :      int						=  torch.allclose(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				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,
					)







"""simple docstring"""





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			42
					lowercase__												=			42







					def __init__(			self     :							int  ,lowercase_     :							UNetaDModel  ,lowercase_     :							ScoreSdeVeScheduler						):
						super().__init__()
						self.register_modules(unet=lowercase_  ,scheduler=lowercase_						)







					@torch.no_grad()
					def __call__(			self     :							List[str]  ,lowercase_     :							int = 1  ,lowercase_     :							int = 2_0_0_0  ,lowercase_     :							Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,lowercase_     :							Optional[str] = "pil"  ,lowercase_     :							bool = True  ,**lowercase_     :							Dict  ,):
						lowerCAmelCase__      :		str    							=      self.unet.config.sample_size
						lowerCAmelCase__      :		int    							=      (batch_size, 3, img_size, img_size)

						lowerCAmelCase__      :		List[Any]    							=      self.unet

						lowerCAmelCase__      :		Tuple    							=      randn_tensor(lowercase_  ,generator=lowercase_						) * self.scheduler.init_noise_sigma
						lowerCAmelCase__      :		List[Any]    							=      sample.to(self.device						)

						self.scheduler.set_timesteps(lowercase_						)
						self.scheduler.set_sigmas(lowercase_						)

						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
							lowerCAmelCase__      :		Tuple    							=      self.scheduler.sigmas[i] * torch.ones(shape[0]  ,device=self.device						)

							# correction step
							for _ in range(self.scheduler.config.correct_steps						):
								lowerCAmelCase__      :		str    							=      self.unet(lowercase_  ,lowercase_						).sample
								lowerCAmelCase__      :		List[str]    							=      self.scheduler.step_correct(lowercase_  ,lowercase_  ,generator=lowercase_						).prev_sample

							# prediction step
							lowerCAmelCase__      :		Dict    							=      model(lowercase_  ,lowercase_						).sample
							lowerCAmelCase__      :		Optional[int]    							=      self.scheduler.step_pred(lowercase_  ,lowercase_  ,lowercase_  ,generator=lowercase_						)

							lowerCAmelCase__							,lowerCAmelCase__      :		Optional[Any]    							=      output.prev_sample, output.prev_sample_mean

						lowerCAmelCase__      :		List[str]    							=      sample_mean.clamp(0  ,1						)
						lowerCAmelCase__      :		Union[str, Any]    							=      sample.cpu().permute(0  ,2  ,3  ,1						).numpy()
						if output_type == "pil":
							lowerCAmelCase__      :		Union[str, Any]    							=      self.numpy_to_pil(lowercase_						)

						if not return_dict:
							return (sample,)

						return ImagePipelineOutput(images=lowercase_						)




