


import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


__a     =			["""bart.large""", """bart.large.mnli""", """bart.large.cnn""", """bart_xsum/model.pt"""]
__a     =			{"""bart.large""": BartModel, """bart.large.mnli""": BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
 raise Exception("""requires fairseq >= 0.9.0""")


logging.set_verbosity_info()
__a     =			logging.get_logger(__name__)

__a     =			""" Hello world! cécé herlolip"""

__a     =			[
    ("""model.classification_heads.mnli.dense.weight""", """classification_head.dense.weight"""),
    ("""model.classification_heads.mnli.dense.bias""", """classification_head.dense.bias"""),
    ("""model.classification_heads.mnli.out_proj.weight""", """classification_head.out_proj.weight"""),
    ("""model.classification_heads.mnli.out_proj.bias""", """classification_head.out_proj.bias"""),
]
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Optional[Any]:
   UpperCAmelCase							=     [
       """encoder.version""",
       """decoder.version""",
       """model.encoder.version""",
       """model.decoder.version""",
       """_float_tensor""",
   ]
   for k in ignore_keys:
      state_dict.pop(_UpperCamelCase  ,						_UpperCamelCase   )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Any:
   UpperCAmelCase							=     dct.pop(_UpperCamelCase   )
   UpperCAmelCase							=     val
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Dict:
   UpperCAmelCase							=     torch.load(_UpperCamelCase  ,						map_location="""cpu"""   )
   UpperCAmelCase							=     torch.hub.load("""pytorch/fairseq"""  ,						"""bart.large.cnn"""   ).eval()
   hub_interface.model.load_state_dict(sd["""model"""]   )
   return hub_interface
def   _UpperCamelCase				(						lowerCAmelCase_   )						->str:
   UpperCAmelCase				,  UpperCAmelCase							=     emb.weight.shape
   UpperCAmelCase							=     nn.Linear(_UpperCamelCase  ,						_UpperCamelCase  ,						bias=_UpperCamelCase   )
   UpperCAmelCase							=     emb.weight.data
   return lin_layer
@torch.no_grad()
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None   )						->Tuple:






   if not os.path.exists(_UpperCamelCase   ):
      UpperCAmelCase							=     torch.hub.load("""pytorch/fairseq"""  ,						_UpperCamelCase   ).eval()
   else:
      UpperCAmelCase							=     load_xsum_checkpoint(_UpperCamelCase   )

   bart.model.upgrade_state_dict(bart.model.state_dict()   )
   if hf_checkpoint_name is None:
      UpperCAmelCase							=     checkpoint_path.replace("""."""  ,						"""-"""   )
   UpperCAmelCase							=     BartConfig.from_pretrained(_UpperCamelCase   )
   UpperCAmelCase							=     bart.encode(_UpperCamelCase   ).unsqueeze(0   )
   UpperCAmelCase							=     BartTokenizer.from_pretrained(_UpperCamelCase   ).encode(_UpperCamelCase  ,						return_tensors="""pt"""   ).unsqueeze(0   )
   if not torch.eq(_UpperCamelCase  ,						_UpperCamelCase   ).all():
      raise ValueError(
          F"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""   )

   if checkpoint_path == "bart.large.mnli":
      UpperCAmelCase							=     bart.state_dict()
      remove_ignore_keys_(_UpperCamelCase   )
      UpperCAmelCase							=     state_dict["""model.decoder.embed_tokens.weight"""]
      for src, dest in mnli_rename_keys:
         rename_key(_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase   )
      UpperCAmelCase							=     BartForSequenceClassification(_UpperCamelCase   ).eval()
      model.load_state_dict(_UpperCamelCase   )
      UpperCAmelCase							=     bart.predict("""mnli"""  ,						_UpperCamelCase  ,						return_logits=_UpperCamelCase   )
      UpperCAmelCase							=     model(_UpperCamelCase   )[0]  # logits
   else:  # no classification heads to worry about
      UpperCAmelCase							=     bart.model.state_dict()
      remove_ignore_keys_(_UpperCamelCase   )
      UpperCAmelCase							=     state_dict["""decoder.embed_tokens.weight"""]
      UpperCAmelCase							=     bart.extract_features(_UpperCamelCase   )
      if hf_checkpoint_name == "facebook/bart-large":
         UpperCAmelCase							=     BartModel(_UpperCamelCase   ).eval()
         model.load_state_dict(_UpperCamelCase   )
         UpperCAmelCase							=     model(_UpperCamelCase   ).model[0]
      else:
         UpperCAmelCase							=     BartForConditionalGeneration(_UpperCamelCase   ).eval()  # an existing summarization ckpt
         model.model.load_state_dict(_UpperCamelCase   )
         if hasattr(_UpperCamelCase  ,						"""lm_head"""   ):
            UpperCAmelCase							=     make_linear_from_emb(model.model.shared   )
         UpperCAmelCase							=     model.model(_UpperCamelCase   )[0]

    # Check results
   if fairseq_output.shape != new_model_outputs.shape:
      raise ValueError(
          F"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""   )
   if (fairseq_output != new_model_outputs).any().item():
      raise ValueError("""Some values in `fairseq_output` are different from `new_model_outputs`"""   )
   Path(_UpperCamelCase   ).mkdir(exist_ok=_UpperCamelCase   )
   model.save_pretrained(_UpperCamelCase   )


if __name__ == "__main__":
 __a     =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
 )
 parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
 parser.add_argument(
     """--hf_config""", default=None, type=str, help="""Which huggingface architecture to use: bart-large-xsum"""
 )
 __a     =			parser.parse_args()
 convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)







import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


__a     =			os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"""{bindir}/../../examples/pytorch/translation"""):
 from run_translation import main  # noqa


set_seed(42)
__a     =			"""sshleifer/student_marian_en_ro_6_1"""
__a     =			"""sshleifer/tiny-mbart"""






@require_torch
class   __lowercase				(  __snake_case  ):





      def        _lowercase				(		self						:					Dict					,       __lowerCamelCase						:					List[Any]=False					,       __lowerCamelCase						:					str=None					,       __lowerCamelCase						:					Any=True					,       __lowerCamelCase						:					Union[str, Any]=True					,       __lowerCamelCase						:					str=True					,       __lowerCamelCase						:					List[str]=True					,       )    ->   List[Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.run_trainer(
             eval_steps=1					,       max_len=1_2					,       model_name=__lowerCamelCase					,       num_train_epochs=1					,       distributed=__lowerCamelCase					,       extra_args_str=__lowerCamelCase					,       predict_with_generate=__lowerCamelCase					,       do_train=__lowerCamelCase					,       do_eval=__lowerCamelCase					,       do_predict=__lowerCamelCase					,       )
         UpperCAmelCase							=     TrainerState.load_from_json(os.path.join(__lowerCamelCase					,       """trainer_state.json"""       )       ).log_history

         if not do_eval:
            return

         UpperCAmelCase							=     [log for log in logs if """eval_loss""" in log.keys()]

         UpperCAmelCase							=     eval_metrics[0]
         if predict_with_generate:
            assert "eval_bleu" in first_step_stats

            UpperCAmelCase							=     eval_metrics[-1]
            assert isinstance(last_step_stats["""eval_bleu"""]					,       __lowerCamelCase       )
            assert not math.isnan(float(last_step_stats["""eval_loss"""]       )       ), "eval_loss must not be `nan`"





      @require_torch_non_multi_gpu
      def        _lowercase				(		self						:					Dict       )    ->   str:






         """simple docstring"""







         self.run_seqaseq_quick()





      @require_torch_multi_gpu
      def        _lowercase				(		self						:					Tuple       )    ->   Any:






         """simple docstring"""







         self.run_seqaseq_quick(distributed=__lowerCamelCase       )





      @require_torch_multi_gpu
      def        _lowercase				(		self						:					Optional[int]       )    ->   Optional[int]:






         """simple docstring"""







         self.run_seqaseq_quick(distributed=__lowerCamelCase       )





      @unittest.skip("""Requires an update of the env running those tests"""       )
      @require_torch_multi_gpu
      @require_fairscale
      def        _lowercase				(		self						:					Union[str, Any]       )    ->   Optional[int]:






         """simple docstring"""







         self.run_seqaseq_quick(distributed=__lowerCamelCase					,       extra_args_str="""--sharded_ddp simple"""       )





      @unittest.skip("""Requires an update of the env running those tests"""       )
      @require_torch_multi_gpu
      @require_fairscale
      def        _lowercase				(		self						:					List[Any]       )    ->   Union[str, Any]:






         """simple docstring"""







         self.run_seqaseq_quick(distributed=__lowerCamelCase					,       extra_args_str="""--sharded_ddp simple --fp16"""       )





      @unittest.skip("""Requires an update of the env running those tests"""       )
      @require_torch_multi_gpu
      @require_fairscale
      def        _lowercase				(		self						:					Dict       )    ->   Tuple:






         """simple docstring"""







         self.run_seqaseq_quick(distributed=__lowerCamelCase					,       extra_args_str="""--sharded_ddp zero_dp_2"""					,       predict_with_generate=__lowerCamelCase       )





      @unittest.skip("""Requires an update of the env running those tests"""       )
      @require_torch_multi_gpu
      @require_fairscale
      def        _lowercase				(		self						:					Optional[int]       )    ->   Dict:






         """simple docstring"""







         self.run_seqaseq_quick(
             distributed=__lowerCamelCase					,       extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""					,       predict_with_generate=__lowerCamelCase       )





      @require_apex
      @require_torch_gpu
      def        _lowercase				(		self						:					str       )    ->   Optional[Any]:






         """simple docstring"""







         self.run_seqaseq_quick(distributed=__lowerCamelCase					,       extra_args_str="""--fp16 --fp16_backend=apex"""       )
         # test 2nd time - was getting eval_loss': nan'
         # to reproduce the problem set distributed=False
         self.run_seqaseq_quick(distributed=__lowerCamelCase					,       extra_args_str="""--fp16 --fp16_backend=apex"""       )





      @parameterized.expand(["""base""", """low""", """high""", """mixed"""]       )
      @require_torch_multi_gpu
      def        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					Optional[Any]       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     {
             # test with the default log_level - should be info and thus log info once
             """base""": {"""extra_args_str""": """""", """n_matches""": 1},
             # test with low log_level and log_level_replica - should be noisy on all processes
             # now the info string should appear twice on 2 processes
             """low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
             # test with high log_level and low log_level_replica
             # now the info string should appear once only on the replica
             """high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
             # test with high log_level and log_level_replica - should be quiet on all processes
             """mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
         }

         UpperCAmelCase							=     experiments[experiment_id]
         UpperCAmelCase							=     {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
         UpperCAmelCase							=     """Running training"""
         with CaptureStderr() as cl:
            self.run_seqaseq_quick(**__lowerCamelCase					,       extra_args_str=data["""extra_args_str"""]       )
         UpperCAmelCase							=     len(re.findall(__lowerCamelCase					,       cl.err       )       )
         self.assertEqual(__lowerCamelCase					,       data["""n_matches"""]       )





      @slow
      def        _lowercase				(		self						:					Union[str, Any]       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     self.run_trainer(
             eval_steps=2					,       max_len=1_2_8					,       model_name=__lowerCamelCase					,       learning_rate=3e-4					,       num_train_epochs=1_0					,       distributed=__lowerCamelCase					,       )

         # Check metrics
         UpperCAmelCase							=     TrainerState.load_from_json(os.path.join(__lowerCamelCase					,       """trainer_state.json"""       )       ).log_history
         UpperCAmelCase							=     [log for log in logs if """eval_loss""" in log.keys()]
         UpperCAmelCase							=     eval_metrics[0]
         UpperCAmelCase							=     eval_metrics[-1]

         assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
         assert isinstance(last_step_stats["""eval_bleu"""]					,       __lowerCamelCase       )

         # test if do_predict saves generations and metrics
         UpperCAmelCase							=     os.listdir(__lowerCamelCase       )
         UpperCAmelCase							=     {os.path.basename(__lowerCamelCase       ) for p in contents}
         assert "generated_predictions.txt" in contents
         assert "predict_results.json" in contents





      @slow
      @require_bitsandbytes
      def        _lowercase				(		self						:					str       )    ->   int:






         """simple docstring"""







         from transformers.training_args import OptimizerNames

         def train_and_return_metrics(__lowerCamelCase						:					str       ) -> Tuple[int, float]:
            UpperCAmelCase							=     """--skip_memory_metrics 0"""

            UpperCAmelCase							=     self.run_trainer(
                max_len=1_2_8					,       model_name=__lowerCamelCase					,       learning_rate=3e-4					,       num_train_epochs=1					,       optim=__lowerCamelCase					,       distributed=__lowerCamelCase					,       extra_args_str=__lowerCamelCase					,       do_eval=__lowerCamelCase					,       do_predict=__lowerCamelCase					,       n_gpus_to_use=1					,       )

            # Check metrics
            UpperCAmelCase							=     TrainerState.load_from_json(Path(__lowerCamelCase					,       """trainer_state.json"""       )       ).log_history
            UpperCAmelCase							=     int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**2_0       )
            UpperCAmelCase							=     int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**2_0       )

            UpperCAmelCase							=     logs[0]["""train_loss"""]
            return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

         UpperCAmelCase				,  UpperCAmelCase				,  UpperCAmelCase							=     train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value       )
         UpperCAmelCase				,  UpperCAmelCase				,  UpperCAmelCase							=     train_and_return_metrics(OptimizerNames.ADAMW_BNB.value       )

         UpperCAmelCase							=     gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         UpperCAmelCase							=     gpu_peak_mem_orig + gpu_alloc_mem_orig
         UpperCAmelCase							=     gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         UpperCAmelCase							=     gpu_total_mem_orig - gpu_total_mem_bnb

         # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
         # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
         # in 2 bytes and the diff in optim memory usage is derived as so:
         #
         # - normal 25*8=~200MB (8 bytes per param)
         # - bnb    25*2= ~50MB (2 bytes per param)
         #
         # Thus we should expect ~150MB total memory saved.
         #
         # Peak memory should be the same - the total should be different by about that same margin
         #
         # After leaving a small margin to accommodate for differences between gpus let's check
         # that we have at least 120MB in savings
         UpperCAmelCase							=     1_2_0

         # uncomment the following if this test starts failing - requires py38 for a new print feature
         # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
         # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
         # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
         # print(f"{gpu_alloc_mem_diff=}MB")
         # print(f"{gpu_peak_mem_diff=}MB")
         # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
         # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

         self.assertGreater(
             __lowerCamelCase					,       __lowerCamelCase					,       """should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
             F""" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"""
             F""" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB"""					,       )

         self.assertGreater(
             __lowerCamelCase					,       __lowerCamelCase					,       """should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
             F""" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"""
             F""" gpu_total_mem_bnb={gpu_total_mem_bnb}MB"""					,       )

         self.assertEqual(
             __lowerCamelCase					,       __lowerCamelCase					,       F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""       )





      def        _lowercase				(		self						:					Any					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					float = 3e-3					,       __lowerCamelCase						:					str = "adafactor"					,       __lowerCamelCase						:					bool = False					,       __lowerCamelCase						:					str = None					,       __lowerCamelCase						:					int = 0					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					int = None					,       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
         UpperCAmelCase							=     self.get_auto_remove_tmp_dir()
         UpperCAmelCase							=     F"""
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(__lowerCamelCase       )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(__lowerCamelCase       )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

         UpperCAmelCase							=     F"""
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(__lowerCamelCase       )}
        """.split()

         UpperCAmelCase							=     """
            --do_predict
        """.split()

         UpperCAmelCase							=     []
         if do_train:
            args += args_train

         if do_eval:
            args += args_eval

         if do_predict:
            args += args_predict

         if predict_with_generate:
            args += "--predict_with_generate".split()

         if do_train:
            if optim == "adafactor":
               args += "--adafactor".split()
            else:
               args += F"""--optim {optim}""".split()

         if extra_args_str is not None:
            args += extra_args_str.split()

         if distributed:
            if n_gpus_to_use is None:
               UpperCAmelCase							=     get_gpu_count()
            UpperCAmelCase							=     get_torch_dist_unique_port()
            UpperCAmelCase							=     F"""
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            """.split()
            UpperCAmelCase							=     [sys.executable] + distributed_args + args
            # keep for quick debug
            # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
            execute_subprocess_async(__lowerCamelCase					,       env=self.get_env()       )
         else:
            UpperCAmelCase							=     ["""run_translation.py"""] + args
            with patch.object(__lowerCamelCase					,       """argv"""					,       __lowerCamelCase       ):
               main()

         return output_dir



