import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
      from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
      from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
      from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


A   :     Tuple       =				DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

A   :     Dict       =				"main"
# Default branch name
A   :     List[str]       =				"f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"
# One particular commit (not the top of `main`)
A   :     Tuple       =				"aaaaaaa"
# This commit does not exist, so we should 404.

A   :     int       =				"d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"
# Sha-1 of config.json on the top of `main`, for checking purposes
A   :     Tuple       =				"4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"


@contextlib.contextmanager
def    a__       (						):
      print("Welcome!" )
      yield
      print("Bye!" )


@contextlib.contextmanager
def    a__       (						):
      print("Bonjour!" )
      yield
      print("Au revoir!" )


class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				Union[str, Any]      )     ->		Any:
        # If the spec is missing, importlib would not be able to import the module dynamically.
        assert transformers.__spec__ is not None
        assert importlib.util.find_spec("transformers"      ) is not None



class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  @unittest.mock.patch("sys.stdout"     ,   new_callable=io.StringIO      )
  def       __A   (							self       :				Tuple     ,   __magic_name__       :				Union[str, Any]      )     ->		Union[str, Any]:
        with ContextManagers([]      ):
              print("Transformers are awesome!"      )
        # The print statement adds a new line at the end of the output
        self.assertEqual(mock_stdout.getvalue()     ,   "Transformers are awesome!\n"      )







  @unittest.mock.patch("sys.stdout"     ,   new_callable=io.StringIO      )
  def       __A   (							self       :				Dict     ,   __magic_name__       :				Union[str, Any]      )     ->		int:
        with ContextManagers([context_en()]      ):
              print("Transformers are awesome!"      )
        # The output should be wrapped with an English welcome and goodbye
        self.assertEqual(mock_stdout.getvalue()     ,   "Welcome!\nTransformers are awesome!\nBye!\n"      )







  @unittest.mock.patch("sys.stdout"     ,   new_callable=io.StringIO      )
  def       __A   (							self       :				Tuple     ,   __magic_name__       :				str      )     ->		Union[str, Any]:
        with ContextManagers([context_fr(), context_en()]      ):
              print("Transformers are awesome!"      )
        # The output should be wrapped with an English and French welcome and goodbye
        self.assertEqual(mock_stdout.getvalue()     ,   "Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n"      )







  @require_torch
  def       __A   (							self       :				List[str]      )     ->		Union[str, Any]:
        self.assertEqual(find_labels(__magic_name__      )     ,   ["labels"]      )
        self.assertEqual(find_labels(__magic_name__      )     ,   ["labels", "next_sentence_label"]      )
        self.assertEqual(find_labels(__magic_name__      )     ,   ["start_positions", "end_positions"]      )


        class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
          """simple docstring"""







          pass



        self.assertEqual(find_labels(__magic_name__      )     ,   ["labels"]      )







  @require_tf
  def       __A   (							self       :				List[str]      )     ->		Optional[Any]:
        self.assertEqual(find_labels(__magic_name__      )     ,   ["labels"]      )
        self.assertEqual(find_labels(__magic_name__      )     ,   ["labels", "next_sentence_label"]      )
        self.assertEqual(find_labels(__magic_name__      )     ,   ["start_positions", "end_positions"]      )


        class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
          """simple docstring"""







          pass



        self.assertEqual(find_labels(__magic_name__      )     ,   ["labels"]      )







  @require_flax
  def       __A   (							self       :				int      )     ->		Tuple:
        # Flax models don't have labels
        self.assertEqual(find_labels(__magic_name__      )     ,   []      )
        self.assertEqual(find_labels(__magic_name__      )     ,   []      )
        self.assertEqual(find_labels(__magic_name__      )     ,   []      )


        class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
          """simple docstring"""







          pass



        self.assertEqual(find_labels(__magic_name__      )     ,   []      )



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)
A   :     Optional[int]       =				logging.getLogger(__name__)


A   :     int       =				"Hello world! cécé herlolip"


A   :     List[Any]       =				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__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     BertAbsConfig(
          temp_dir="."   ,					finetune_bert=__UpperCamelCase   ,					large=__UpperCamelCase   ,					share_emb=__UpperCamelCase   ,					use_bert_emb=__UpperCamelCase   ,					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   ,					)
      SCREAMING_SNAKE_CASE_      =     torch.load(__UpperCamelCase   ,					lambda __UpperCamelCase   ,					__UpperCamelCase : storage )
      SCREAMING_SNAKE_CASE_      =     AbsSummarizer(__UpperCamelCase   ,					torch.device("cpu" )   ,					__UpperCamelCase )
      original.eval()

      SCREAMING_SNAKE_CASE_      =     BertAbsSummarizer(__UpperCamelCase   ,					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] * (5_1_2 - len(__UpperCamelCase )) )
      SCREAMING_SNAKE_CASE_      =     torch.tensor(__UpperCamelCase ).unsqueeze(0 )
      SCREAMING_SNAKE_CASE_      =     tokenizer.encode("This is sample 3 éàalj'-." )
      decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(__UpperCamelCase )) )
      SCREAMING_SNAKE_CASE_      =     torch.tensor(__UpperCamelCase ).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(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )[0]
      SCREAMING_SNAKE_CASE_      =     original.generator(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     new_model(
          __UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )[0]
      SCREAMING_SNAKE_CASE_      =     new_model.generator(__UpperCamelCase )

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

      SCREAMING_SNAKE_CASE_      =     torch.allclose(__UpperCamelCase   ,					__UpperCamelCase   ,					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__":
      A   :     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.",
      )
      A   :     int       =				parser.parse_args()

      convert_bertabs_checkpoints(
          args.bertabs_checkpoint_path,
          args.pytorch_dump_folder_path,
      )


