





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class    lowerCAmelCase							(					snake_case_			):
 UpperCAmelCase__													=		(UnCLIPScheduler,)
 def      A_	(      self  :      Optional[Any]			,     **UpperCAmelCase  :      Union[str, Any]	)			->	Any:
     lowerCamelCase__    :      Dict					 =		{
         'num_train_timesteps': 1000,
         'variance_type': 'fixed_small_log',
         'clip_sample': True,
         'clip_sample_range': 1.0,
         'prediction_type': 'epsilon',
     }

     config.update(**UpperCAmelCase	)
     return config
 def      A_	(      self  :      str	)			->	Union[str, Any]:
     for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=UpperCAmelCase	)
 def      A_	(      self  :      Optional[int]	)			->	Union[str, Any]:
     for variance in ["fixed_small_log", "learned_range"]:
         self.check_over_configs(variance_type=UpperCAmelCase	)
 def      A_	(      self  :      Union[str, Any]	)			->	str:
     for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=UpperCAmelCase	)
 def      A_	(      self  :      Optional[Any]	)			->	Optional[Any]:
     for clip_sample_range in [1, 5, 10, 20]:
         self.check_over_configs(clip_sample_range=UpperCAmelCase	)
 def      A_	(      self  :      str	)			->	Dict:
     for prediction_type in ["epsilon", "sample"]:
         self.check_over_configs(prediction_type=UpperCAmelCase	)
 def      A_	(      self  :      Optional[int]	)			->	List[Any]:
     for time_step in [0, 500, 999]:
         for prev_timestep in [None, 5, 100, 250, 500, 750]:
             if prev_timestep is not None and prev_timestep >= time_step:
                 continue

             self.check_over_forward(time_step=UpperCAmelCase			,     prev_timestep=UpperCAmelCase	)
 def      A_	(      self  :      Union[str, Any]	)			->	Any:
     lowerCamelCase__    :      Any					 =		self.scheduler_classes[0]
     lowerCamelCase__    :      Union[str, Any]					 =		self.get_scheduler_config(variance_type='fixed_small_log'	)
     lowerCamelCase__    :      Optional[Any]					 =		scheduler_class(**UpperCAmelCase	)

     assert torch.sum(torch.abs(scheduler._get_variance(0	) - 1.0000e-10	)	) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(487	) - 0.0_5_4_9_6_2_5	)	) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(999	) - 0.9_9_9_4_9_8_7	)	) < 1e-5
 def      A_	(      self  :      Optional[Any]	)			->	int:
     lowerCamelCase__    :      Dict					 =		self.scheduler_classes[0]
     lowerCamelCase__    :      Optional[int]					 =		self.get_scheduler_config(variance_type='learned_range'	)
     lowerCamelCase__    :      List[Any]					 =		scheduler_class(**UpperCAmelCase	)

     lowerCamelCase__    :      Union[str, Any]					 =		0.5

     assert scheduler._get_variance(1			,     predicted_variance=UpperCAmelCase	) - -1_0.1_7_1_2_7_9_0 < 1e-5
     assert scheduler._get_variance(487			,     predicted_variance=UpperCAmelCase	) - -5.7_9_9_8_0_5_2 < 1e-5
     assert scheduler._get_variance(999			,     predicted_variance=UpperCAmelCase	) - -0.0_0_1_0_0_1_1 < 1e-5
 def      A_	(      self  :      Optional[Any]	)			->	List[str]:
     lowerCamelCase__    :      Optional[Any]					 =		self.scheduler_classes[0]
     lowerCamelCase__    :      Any					 =		self.get_scheduler_config()
     lowerCamelCase__    :      Tuple					 =		scheduler_class(**UpperCAmelCase	)

     lowerCamelCase__    :      Union[str, Any]					 =		scheduler.timesteps

     lowerCamelCase__    :      Optional[int]					 =		self.dummy_model()
     lowerCamelCase__    :      Dict					 =		self.dummy_sample_deter
     lowerCamelCase__    :      List[str]					 =		torch.manual_seed(0	)

     for i, t in enumerate(UpperCAmelCase	):
         # 1. predict noise residual
         lowerCamelCase__    :      Union[str, Any]					 =		model(UpperCAmelCase			,     UpperCAmelCase	)

         # 2. predict previous mean of sample x_t-1
         lowerCamelCase__    :      Tuple					 =		scheduler.step(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     generator=UpperCAmelCase	).prev_sample

         lowerCamelCase__    :      Dict					 =		pred_prev_sample

     lowerCamelCase__    :      List[str]					 =		torch.sum(torch.abs(UpperCAmelCase	)	)
     lowerCamelCase__    :      Any					 =		torch.mean(torch.abs(UpperCAmelCase	)	)

     assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5	) < 1e-2
     assert abs(result_mean.item() - 0.3_2_8_4_7_4_3	) < 1e-3
 def      A_	(      self  :      int	)			->	Optional[int]:
     lowerCamelCase__    :      Any					 =		self.scheduler_classes[0]
     lowerCamelCase__    :      List[Any]					 =		self.get_scheduler_config()
     lowerCamelCase__    :      List[str]					 =		scheduler_class(**UpperCAmelCase	)

     scheduler.set_timesteps(25	)

     lowerCamelCase__    :      Optional[Any]					 =		scheduler.timesteps

     lowerCamelCase__    :      Optional[int]					 =		self.dummy_model()
     lowerCamelCase__    :      List[Any]					 =		self.dummy_sample_deter
     lowerCamelCase__    :      Any					 =		torch.manual_seed(0	)

     for i, t in enumerate(UpperCAmelCase	):
         # 1. predict noise residual
         lowerCamelCase__    :      Optional[Any]					 =		model(UpperCAmelCase			,     UpperCAmelCase	)

         if i + 1 == timesteps.shape[0]:
             lowerCamelCase__    :      Optional[int]					 =		None
         else:
             lowerCamelCase__    :      int					 =		timesteps[i + 1]

         # 2. predict previous mean of sample x_t-1
         lowerCamelCase__    :      Any					 =		scheduler.step(
             UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     prev_timestep=UpperCAmelCase			,     generator=UpperCAmelCase	).prev_sample

         lowerCamelCase__    :      int					 =		pred_prev_sample

     lowerCamelCase__    :      Optional[Any]					 =		torch.sum(torch.abs(UpperCAmelCase	)	)
     lowerCamelCase__    :      str					 =		torch.mean(torch.abs(UpperCAmelCase	)	)

     assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3	) < 1e-2
     assert abs(result_mean.item() - 0.3_3_6_2_0_3_8	) < 1e-3
 def      A_	(      self  :      int	)			->	List[str]:
     pass







 def      A_	(      self  :      Tuple	)			->	str:
     pass












'''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 lowerCamelCase						(     __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Union[str, Any]    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			BertAbsConfig(
        temp_dir="""."""					,       finetune_bert=__lowerCamelCase					,       large=__lowerCamelCase					,       share_emb=__lowerCamelCase					,       use_bert_emb=__lowerCamelCase					,       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					,       )
    _SCREAMING_SNAKE_CASE									=			torch.load(__lowerCamelCase					,       lambda __lowerCamelCase					,       __lowerCamelCase    : storage    )
    _SCREAMING_SNAKE_CASE									=			AbsSummarizer(__lowerCamelCase					,       torch.device("""cpu"""    )					,       __lowerCamelCase    )
    original.eval()

    _SCREAMING_SNAKE_CASE									=			BertAbsSummarizer(__lowerCamelCase					,       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"""    )
    _SCREAMING_SNAKE_CASE									=			BertTokenizer.from_pretrained("""bert-base-uncased"""    )

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

    _SCREAMING_SNAKE_CASE									=			new_model(
        __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )[0]
    _SCREAMING_SNAKE_CASE									=			new_model.generator(__lowerCamelCase    )

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

    _SCREAMING_SNAKE_CASE									=			torch.allclose(__lowerCamelCase					,       __lowerCamelCase					,       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,
      )



