import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


A   :     List[Any]       =				"sshleifer/mar_enro_6_3_student"


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







  def       __A   (							self       :				Dict      )     ->		Any:
        super().setUp()

        SCREAMING_SNAKE_CASE_      =     cached_path(
            "https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"     ,   extract_compressed_file=__magic_name__     ,   )
        SCREAMING_SNAKE_CASE_      =     F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''







  @slow
  @require_torch_gpu
  def       __A   (							self       :				Any      )     ->		Any:
        MarianMTModel.from_pretrained(__magic_name__      )







  @slow
  @require_torch_gpu
  def       __A   (							self       :				Union[str, Any]      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     {
            "$MAX_LEN": 64,
            "$BS": 64,
            "$GAS": 1,
            "$ENRO_DIR": self.data_dir,
            "facebook/mbart-large-cc25": MARIAN_MODEL,
            # "val_check_interval=0.25": "val_check_interval=1.0",
            "--learning_rate=3e-5": "--learning_rate 3e-4",
            "--num_train_epochs 6": "--num_train_epochs 1",
        }

        # Clean up bash script
        SCREAMING_SNAKE_CASE_      =     (self.test_file_dir / "train_mbart_cc25_enro.sh").open().read().split("finetune.py"      )[1].strip()
        SCREAMING_SNAKE_CASE_      =     bash_script.replace("\\\n"     ,   ""      ).strip().replace("\"$@\""     ,   ""      )
        for k, v in env_vars_to_replace.items():
              SCREAMING_SNAKE_CASE_      =     bash_script.replace(__magic_name__     ,   str(__magic_name__      )      )
        SCREAMING_SNAKE_CASE_      =     self.get_auto_remove_tmp_dir()

        # bash_script = bash_script.replace("--fp16 ", "")
        SCREAMING_SNAKE_CASE_      =     F'''
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        '''.split()
        # XXX: args.gpus > 1 : handle multi_gpu in the future

        SCREAMING_SNAKE_CASE_      =     ["finetune.py"] + bash_script.split() + args
        with patch.object(__magic_name__     ,   "argv"     ,   __magic_name__      ):
              SCREAMING_SNAKE_CASE_      =     argparse.ArgumentParser()
              SCREAMING_SNAKE_CASE_      =     pl.Trainer.add_argparse_args(__magic_name__      )
              SCREAMING_SNAKE_CASE_      =     SummarizationModule.add_model_specific_args(__magic_name__     ,   os.getcwd()      )
              SCREAMING_SNAKE_CASE_      =     parser.parse_args()
              SCREAMING_SNAKE_CASE_      =     main(__magic_name__      )

        # Check metrics
        SCREAMING_SNAKE_CASE_      =     load_json(model.metrics_save_path      )
        SCREAMING_SNAKE_CASE_      =     metrics["val"][0]
        SCREAMING_SNAKE_CASE_      =     metrics["val"][-1]
        self.assertEqual(len(metrics["val"]      )     ,   (args.max_epochs / args.val_check_interval)      )
        assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}''']     ,   __magic_name__      )

        self.assertGreater(last_step_stats["val_avg_gen_time"]     ,   0.01      )
        # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
        self.assertLessEqual(last_step_stats["val_avg_gen_time"]     ,   1.0      )

        # test learning requirements:

        # 1. BLEU improves over the course of training by more than 2 pts
        self.assertGreater(last_step_stats["val_avg_bleu"] - first_step_stats["val_avg_bleu"]     ,   2      )

        # 2. BLEU finishes above 17
        self.assertGreater(last_step_stats["val_avg_bleu"]     ,   17      )

        # 3. test BLEU and val BLEU within ~1.1 pt.
        self.assertLess(abs(metrics["val"][-1]["val_avg_bleu"] - metrics["test"][-1]["test_avg_bleu"]      )     ,   1.1      )

        # check lightning ckpt can be loaded and has a reasonable statedict
        SCREAMING_SNAKE_CASE_      =     os.listdir(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     [x for x in contents if x.endswith(".ckpt"      )][0]
        SCREAMING_SNAKE_CASE_      =     os.path.join(args.output_dir     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     torch.load(__magic_name__     ,   map_location="cpu"      )
        SCREAMING_SNAKE_CASE_      =     "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
        assert expected_key in ckpt["state_dict"]
        assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

        # TODO: turn on args.do_predict when PL bug fixed.
        if args.do_predict:
              SCREAMING_SNAKE_CASE_      =     {os.path.basename(__magic_name__      ) for p in contents}
              assert "test_generations.txt" in contents
              assert "test_results.txt" in contents
              # assert len(metrics["val"]) ==  desired_n_evals
              assert len(metrics["test"]      ) == 1



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







  @timeout_decorator.timeout(600      )
  @slow
  @require_torch_gpu
  def       __A   (							self       :				List[str]      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
        SCREAMING_SNAKE_CASE_      =     {
            "--fp16_opt_level=O1": "",
            "$MAX_LEN": 128,
            "$BS": 16,
            "$GAS": 1,
            "$ENRO_DIR": data_dir,
            "$m": "sshleifer/student_marian_en_ro_6_1",
            "val_check_interval=0.25": "val_check_interval=1.0",
        }

        # Clean up bash script
        SCREAMING_SNAKE_CASE_      =     (
            (self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py"      )[1].strip()
        )
        SCREAMING_SNAKE_CASE_      =     bash_script.replace("\\\n"     ,   ""      ).strip().replace("\"$@\""     ,   ""      )
        SCREAMING_SNAKE_CASE_      =     bash_script.replace("--fp16 "     ,   " "      )

        for k, v in env_vars_to_replace.items():
              SCREAMING_SNAKE_CASE_      =     bash_script.replace(__magic_name__     ,   str(__magic_name__      )      )
        SCREAMING_SNAKE_CASE_      =     self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE_      =     bash_script.replace("--fp16"     ,   ""      )
        SCREAMING_SNAKE_CASE_      =     6
        SCREAMING_SNAKE_CASE_      =     (
            ["distillation.py"]
            + bash_script.split()
            + [
                F'''--output_dir={output_dir}''',
                "--gpus=1",
                "--learning_rate=1e-3",
                F'''--num_train_epochs={epochs}''',
                "--warmup_steps=10",
                "--val_check_interval=1.0",
                "--do_predict",
            ]
        )
        with patch.object(__magic_name__     ,   "argv"     ,   __magic_name__      ):
              SCREAMING_SNAKE_CASE_      =     argparse.ArgumentParser()
              SCREAMING_SNAKE_CASE_      =     pl.Trainer.add_argparse_args(__magic_name__      )
              SCREAMING_SNAKE_CASE_      =     SummarizationDistiller.add_model_specific_args(__magic_name__     ,   os.getcwd()      )
              SCREAMING_SNAKE_CASE_      =     parser.parse_args()
              # assert args.gpus == gpus THIS BREAKS for multi_gpu

              SCREAMING_SNAKE_CASE_      =     distill_main(__magic_name__      )

        # Check metrics
        SCREAMING_SNAKE_CASE_      =     load_json(model.metrics_save_path      )
        SCREAMING_SNAKE_CASE_      =     metrics["val"][0]
        SCREAMING_SNAKE_CASE_      =     metrics["val"][-1]
        assert len(metrics["val"]      ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

        assert last_step_stats["val_avg_gen_time"] >= 0.01

        assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
        assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
        assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}''']     ,   __magic_name__      )

        # check lightning ckpt can be loaded and has a reasonable statedict
        SCREAMING_SNAKE_CASE_      =     os.listdir(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     [x for x in contents if x.endswith(".ckpt"      )][0]
        SCREAMING_SNAKE_CASE_      =     os.path.join(args.output_dir     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     torch.load(__magic_name__     ,   map_location="cpu"      )
        SCREAMING_SNAKE_CASE_      =     "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
        assert expected_key in ckpt["state_dict"]
        assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

        # TODO: turn on args.do_predict when PL bug fixed.
        if args.do_predict:
              SCREAMING_SNAKE_CASE_      =     {os.path.basename(__magic_name__      ) for p in contents}
              assert "test_generations.txt" in contents
              assert "test_results.txt" in contents
              # assert len(metrics["val"]) ==  desired_n_evals
              assert len(metrics["test"]      ) == 1



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,
      )


