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			:							Optional[Any]     =logging.getLogger(__name__)


lowerCAmelCase			:							List[Any]     ='''Hello world! cécé herlolip'''


lowerCAmelCase			:							Tuple     =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'''
    _lowerCamelCase     :		List[Any] 						=						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=2_048   , dec_dropout=0.2   , )
    _lowerCamelCase     :		Dict 						=						torch.load(__A   , lambda __A   , __A	: storage	)
    _lowerCamelCase     :		List[Any] 						=						AbsSummarizer(__A   , torch.device("""cpu"""	)   , __A	)
    original.eval()

    _lowerCamelCase     :		List[Any] 						=						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     :		Optional[int] 						=						BertTokenizer.from_pretrained("""bert-base-uncased"""	)

    # prepare the model inputs
    _lowerCamelCase     :		Tuple 						=						tokenizer.encode("""This is sample éàalj'-."""	)
    encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__A	))	)
    _lowerCamelCase     :		Optional[int] 						=						torch.tensor(__A	).unsqueeze(0	)
    _lowerCamelCase     :		Dict 						=						tokenizer.encode("""This is sample 3 éàalj'-."""	)
    decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__A	))	)
    _lowerCamelCase     :		Dict 						=						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     :		Optional[int] 						=						encoder_input_ids
    _lowerCamelCase     :		str 						=						decoder_input_ids
    _lowerCamelCase     :		str 						=						None
    _lowerCamelCase     :		Optional[int] 						=						None
    _lowerCamelCase     :		Tuple 						=						None
    _lowerCamelCase     :		str 						=						None
    _lowerCamelCase     :		Optional[int] 						=						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     :		Dict 						=						original.generator(__A	)

    _lowerCamelCase     :		List[str] 						=						new_model(
        __A   , __A   , __A   , __A   , __A	)[0]
    _lowerCamelCase     :		List[str] 						=						new_model.generator(__A	)

    _lowerCamelCase     :		List[str] 						=						torch.max(torch.abs(output_converted_model - output_original_model	)	).item()
    print("""Maximum absolute difference beween weights: {:.2f}""".format(__A	)	)
    _lowerCamelCase     :		List[str] 						=						torch.max(torch.abs(output_converted_generator - output_original_generator	)	).item()
    print("""Maximum absolute difference beween weights: {:.2f}""".format(__A	)	)

    _lowerCamelCase     :		List[str] 						=						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			:							Union[str, Any]     =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			:							Optional[int]     =parser.parse_args()

    convert_bertabs_checkpoints(
        args.bertabs_checkpoint_path,
        args.pytorch_dump_folder_path,
    )





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       (EulerDiscreteScheduler,)
      _snake_case    		=       10



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  **_UpperCamelCase     :				Optional[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						{
              """num_train_timesteps""": 1100,
              """beta_start""": 0.0_0_0_1,
              """beta_end""": 0.0_2,
              """beta_schedule""": """linear""",
          }

          config.update(**_UpperCamelCase)
          return config



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[str]:


          """simple docstring"""


          for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Dict:


          """simple docstring"""


          for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]    ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]):
              self.check_over_configs(beta_start=_UpperCamelCase    ,  beta_end=_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Dict:


          """simple docstring"""


          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Union[str, Any]:


          """simple docstring"""


          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		str 						=						self.get_scheduler_config()
          _lowerCamelCase     :		Any 						=						scheduler_class(**_UpperCamelCase)

          scheduler.set_timesteps(self.num_inference_steps)

          _lowerCamelCase     :		str 						=						torch.manual_seed(0)

          _lowerCamelCase     :		str 						=						self.dummy_model()
          _lowerCamelCase     :		List[Any] 						=						self.dummy_sample_deter * scheduler.init_noise_sigma
          _lowerCamelCase     :		int 						=						sample.to(_UpperCamelCase)

          for i, t in enumerate(scheduler.timesteps):
              _lowerCamelCase     :		Optional[int] 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		str 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		Dict 						=						output.prev_sample

          _lowerCamelCase     :		Any 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		Any 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
          assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		Optional[Any] 						=						self.get_scheduler_config(prediction_type="""v_prediction""")
          _lowerCamelCase     :		int 						=						scheduler_class(**_UpperCamelCase)

          scheduler.set_timesteps(self.num_inference_steps)

          _lowerCamelCase     :		Any 						=						torch.manual_seed(0)

          _lowerCamelCase     :		int 						=						self.dummy_model()
          _lowerCamelCase     :		int 						=						self.dummy_sample_deter * scheduler.init_noise_sigma
          _lowerCamelCase     :		Dict 						=						sample.to(_UpperCamelCase)

          for i, t in enumerate(scheduler.timesteps):
              _lowerCamelCase     :		Optional[int] 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		str 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[Any] 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		Tuple 						=						output.prev_sample

          _lowerCamelCase     :		Union[str, Any] 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		Optional[int] 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 0.0_0_0_2) < 1E-2
          assert abs(result_mean.item() - 2.2_6_7_6E-0_6) < 1E-3



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		int 						=						self.get_scheduler_config()
          _lowerCamelCase     :		List[Any] 						=						scheduler_class(**_UpperCamelCase)

          scheduler.set_timesteps(self.num_inference_steps    ,  device=_UpperCamelCase)

          _lowerCamelCase     :		Optional[Any] 						=						torch.manual_seed(0)

          _lowerCamelCase     :		Tuple 						=						self.dummy_model()
          _lowerCamelCase     :		Optional[Any] 						=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          _lowerCamelCase     :		Tuple 						=						sample.to(_UpperCamelCase)

          for t in scheduler.timesteps:
              _lowerCamelCase     :		List[Any] 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		Any 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		List[Any] 						=						output.prev_sample

          _lowerCamelCase     :		Any 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		List[Any] 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
          assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		Optional[int] 						=						self.get_scheduler_config()
          _lowerCamelCase     :		int 						=						scheduler_class(**_UpperCamelCase    ,  use_karras_sigmas=_UpperCamelCase)

          scheduler.set_timesteps(self.num_inference_steps    ,  device=_UpperCamelCase)

          _lowerCamelCase     :		int 						=						torch.manual_seed(0)

          _lowerCamelCase     :		Tuple 						=						self.dummy_model()
          _lowerCamelCase     :		str 						=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          _lowerCamelCase     :		Optional[int] 						=						sample.to(_UpperCamelCase)

          for t in scheduler.timesteps:
              _lowerCamelCase     :		Tuple 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		Any 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[str] 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		int 						=						output.prev_sample

          _lowerCamelCase     :		Tuple 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		List[str] 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9) < 1E-2
          assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3) < 1E-3




