


'''simple docstring'''






import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

a_       :     str 							=     logging.getLogger()
a_       :     Union[str, Any] 							=     torch.cuda.is_available()
a_       :     Dict 							=     {
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 4_2,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-0_8,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 1_2,
    """max_target_length""": 1_2,
    """val_max_target_length""": 1_2,
    """test_max_target_length""": 1_2,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}






def 						_A					(lowerCAmelCase__	:List[str]					,    lowerCAmelCase__	:Optional[int]		) ->	Optional[Any]:




     '''simple docstring'''



     _a         =				'\n'.join(UpperCamelCase__		)
     Path(UpperCamelCase__		).open('w'		).writelines(UpperCamelCase__		)


a_       :     List[Any] 							=     [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
a_       :     List[Any] 							=     ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
a_       :     Dict 							=     """patrickvonplaten/t5-tiny-random"""
a_       :     List[str] 							=     """sshleifer/t5-tinier-random"""
a_       :     Dict 							=     """sshleifer/bart-tiny-random"""
a_       :     str 							=     """sshleifer/tiny-mbart"""
a_       :     Union[str, Any] 							=     """sshleifer/tiny-marian-en-de"""
a_       :     int 							=     """stas/tiny-wmt19-en-de"""


a_       :     Union[str, Any] 							=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






def 						_A					(lowerCAmelCase__	:Dict		) ->	Optional[Any]:




     '''simple docstring'''



     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(UpperCamelCase__					,    f'{split}.source'		)					,    UpperCamelCase__		)
          _dump_articles(os.path.join(UpperCamelCase__					,    f'{split}.target'		)					,    UpperCamelCase__		)
     return tmp_dir




class 			a  (  __A							):
 @classmethod
 def 				__UpperCAmelCase		(			cls    )       ->				List[str]:
      logging.disable(logging.CRITICAL    )  # remove noisy download output from tracebacks
      return cls
 @slow
 @require_torch_gpu
 def 				__UpperCAmelCase		(			self    )       ->				Any:
      _a         =				list_models()
      _a         =				'sshleifer'
      _a         =				[x.modelId for x in model_list if x.modelId.startswith(A_    )]
      _a         =				['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
      _a         =				[]
      for m in model_ids:
           if m in allowed_to_be_broken:
                continue
           try:
                AutoConfig.from_pretrained(A_    )
           except Exception:
                failures.append(A_    )
      assert not failures, f'The following models could not be loaded through AutoConfig: {failures}'
 def 				__UpperCAmelCase		(			self    )       ->				List[Any]:
      _a         =				{'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
      self._test_distiller_cli(A_    )
 def 				__UpperCAmelCase		(			self    )       ->				int:
      _a         =				{
          'student_encoder_layers': 2,
          'student_decoder_layers': 1,
          'max_epochs': 4,
          'val_check_interval': 0.2_5,
          'alpha_hid': 2.0,
          'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
      }
      _a         =				self._test_distiller_cli(A_   ,					check_contents=A_    )

      _a         =				list(Path(model.output_dir    ).glob('*.ckpt'    )    )
      self.assertEqual(1   ,					len(A_    )    )
      _a         =				list(Path(model.output_dir    ).glob('**/*.bin'    )    )
      self.assertEqual(len(A_    )   ,					2    )
      _a         =				lmap(str.strip   ,					Path(model.hparams.data_dir    ).joinpath('test.source'    ).open().readlines()    )
      _a         =				tempfile.mktemp()  # XXX: not being cleaned up
      generate_summaries_or_translations(A_   ,					A_   ,					str(model.output_dir / 'best_tfmr'    )    )
      self.assertTrue(Path(A_    ).exists()    )

      _a         =				self.get_auto_remove_tmp_dir()
      convert_pl_to_hf(ckpts[0]   ,					transformer_ckpts[0].parent   ,					A_    )
      assert os.path.exists(os.path.join(A_   ,					'pytorch_model.bin'    )    )
 def 				__UpperCAmelCase		(			self    )       ->				Optional[int]:
      _a         =				AutoModelForSeqaSeqLM.from_pretrained(A_    )
      _a   , _a         =				model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
      _a         =				torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]   ,					dtype=torch.long   ,					device=model.device    )
      _a         =				target_ids[:, :-1].contiguous()  # Why this line?
      _a         =				target_ids[:, 1:].clone()  # why clone?
      _a         =				model(
          A_   ,					attention_mask=A_   ,					decoder_input_ids=A_   ,					labels=A_   ,					use_cache=A_    ).loss

      _a         =				model(A_   ,					attention_mask=A_   ,					decoder_input_ids=A_   ,					use_cache=A_    ).logits

      _a         =				nn.functional.log_softmax(A_   ,					dim=-1    )
      _a   , _a         =				label_smoothed_nll_loss(
          A_   ,					A_   ,					0.1   ,					ignore_index=model.config.pad_token_id    )
      with self.assertRaises(A_    ):
           # TODO: understand why this breaks
           self.assertEqual(A_   ,					A_    )
 def 				__UpperCAmelCase		(			self    )       ->				List[str]:
      _a         =				{
          'student_encoder_layers': 2,
          'student_decoder_layers': 1,
          'num_train_epochs': 4,
          'val_check_interval': 0.2_5,
          'alpha_hid': 2.0,
          'task': 'translation',
          'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
          'tokenizer_name': MBART_TINY,
          'teacher': MBART_TINY,
          'src_lang': 'en_XX',
          'tgt_lang': 'ro_RO',
      }
      _a         =				self._test_distiller_cli(A_   ,					check_contents=A_    )
      assert model.model.config.model_type == "mbart"

      _a         =				list(Path(model.output_dir    ).glob('*.ckpt'    )    )
      self.assertEqual(1   ,					len(A_    )    )
      _a         =				list(Path(model.output_dir    ).glob('**/*.bin'    )    )
      _a         =				list(Path(model.output_dir    ).glob('best_tfmr/*'    )    )
      assert len(A_    ) > 2
      self.assertEqual(len(A_    )   ,					2    )
 def 				__UpperCAmelCase		(			self    )       ->				Any:
      _a         =				{
          'student_encoder_layers': 1,
          'student_decoder_layers': 1,
          'alpha_hid': 2.0,
          'teacher': T5_TINY,
          'model_name_or_path': T5_TINY,
          'tokenizer_name': T5_TINY,
      }
      self._test_distiller_cli(A_    )
 def 				__UpperCAmelCase		(			self    )       ->				Optional[int]:
      _a         =				{
          'teacher': T5_TINY,
          'student': T5_TINIER,
          'model_name_or_path': T5_TINIER,
          'tokenizer_name': T5_TINIER,
      }
      self._test_distiller_cli(A_    )




 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__=True    )       ->				Tuple:
      _a         =				{
          'label_smoothing': 0.0,
          'early_stopping_patience': -1,
          'train_batch_size': 1,
          'eval_batch_size': 2,
          'max_epochs': 2,
          'alpha_mlm': 0.2,
          'alpha_ce': 0.8,
          'do_predict': True,
          'model_name_or_path': 'sshleifer/tinier_bart',
          'teacher': CHEAP_ARGS['model_name_or_path'],
          'val_check_interval': 0.5,
      }
      default_updates.update(A_    )
      _a         =				CHEAP_ARGS.copy()
      _a         =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
      _a         =				self.get_auto_remove_tmp_dir()

      args_d.update(data_dir=A_   ,					output_dir=A_   ,					**A_    )
      _a         =				distill_main(argparse.Namespace(**A_    )    )
      if not check_contents:
           return model
      _a         =				os.listdir(A_    )
      _a         =				{os.path.basename(A_    ) for p in contents}
      _a         =				[p for p in contents if p.endswith('ckpt'    )]
      assert len(A_    ) > 0

      self.assertIn('test_generations.txt'   ,					A_    )
      self.assertIn('test_results.txt'   ,					A_    )

      _a         =				load_json(model.metrics_save_path    )
      _a         =				metrics['val'][-1]
      self.assertGreaterEqual(last_step_stats['val_avg_gen_time']   ,					0.0_1    )
      self.assertGreaterEqual(1.0   ,					last_step_stats['val_avg_gen_time']    )
      self.assertIsInstance(last_step_stats[f'val_avg_{model.val_metric}']   ,					A_    )
      _a         =				int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1    )
      self.assertEqual(len(metrics['val']    )   ,					A_    )
      self.assertEqual(len(metrics['test']    )   ,					1    )
      return model





class 			a  (  __A							):
 @parameterized.expand(
     [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]   ,					)
 def 				__UpperCAmelCase		(			self   ,					__magic_name__    )       ->				str:
      _a         =				CHEAP_ARGS.copy()
      _a         =				'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
      _a         =				0.1 if task == 'translation' else 0

      _a         =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
      _a         =				self.get_auto_remove_tmp_dir()
      args_d.update(
          data_dir=A_   ,					model_name_or_path=A_   ,					tokenizer_name=A_   ,					train_batch_size=2   ,					eval_batch_size=2   ,					output_dir=A_   ,					do_predict=A_   ,					task=A_   ,					src_lang='en_XX'   ,					tgt_lang='ro_RO'   ,					freeze_encoder=A_   ,					freeze_embeds=A_   ,					)
      assert "n_train" in args_d
      _a         =				argparse.Namespace(**A_    )
      _a         =				main(A_    )

      _a         =				module.model.get_input_embeddings()
      assert not input_embeds.weight.requires_grad
      if model == T5_TINY:
           _a         =				module.model.lm_head
           assert not lm_head.weight.requires_grad
           assert (lm_head.weight == input_embeds.weight).all().item()
      elif model == FSMT_TINY:
           _a         =				module.model.model
           _a         =				fsmt.decoder.embed_positions
           assert not embed_pos.weight.requires_grad
           assert not fsmt.decoder.embed_tokens.weight.requires_grad
           # check that embeds are not the same
           assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
      else:
           _a         =				module.model.model
           _a         =				bart.decoder.embed_positions
           assert not embed_pos.weight.requires_grad
           assert not bart.shared.weight.requires_grad
           # check that embeds are the same
           assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
           assert bart.decoder.embed_tokens == bart.shared

      _a         =				load_json(module.output_dir / 'text_batch.json'    )
      assert isinstance(A_   ,					A_    )
      assert len(A_    ) >= 4
 def 				__UpperCAmelCase		(			self    )       ->				List[Any]:
      _a         =				CHEAP_ARGS.copy()

      _a         =				'summarization'
      _a         =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )

      args_d.update(
          data_dir=A_   ,					tokenizer_name=A_   ,					train_batch_size=2   ,					eval_batch_size=2   ,					do_predict=A_   ,					task=A_   ,					src_lang='en_XX'   ,					tgt_lang='ro_RO'   ,					freeze_encoder=A_   ,					freeze_embeds=A_   ,					)

      # test models whose config includes the extra_model_args
      _a         =				BART_TINY
      _a         =				self.get_auto_remove_tmp_dir()
      _a         =				args_d.copy()
      args_da.update(
          model_name_or_path=A_   ,					output_dir=A_   ,					)
      _a         =				('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
      for p in extra_model_params:
           _a         =				0.5
      _a         =				argparse.Namespace(**A_    )
      _a         =				main(A_    )
      for p in extra_model_params:
           assert getattr(model.config   ,					A_    ) == 0.5, f'failed to override the model config for param {p}'

      # test models whose config doesn't include the extra_model_args
      _a         =				T5_TINY
      _a         =				self.get_auto_remove_tmp_dir()
      _a         =				args_d.copy()
      args_da.update(
          model_name_or_path=A_   ,					output_dir=A_   ,					)
      _a         =				'encoder_layerdrop'
      _a         =				0.5
      _a         =				argparse.Namespace(**A_    )
      with pytest.raises(A_    ) as excinfo:
           _a         =				main(A_    )
      assert str(excinfo.value    ) == f'model config doesn\'t have a `{unsupported_param}` attribute'




 def 				__UpperCAmelCase		(			self    )       ->				Optional[Any]:
      _a         =				CHEAP_ARGS.copy()

      _a         =				'summarization'
      _a         =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )

      _a         =				BART_TINY
      _a         =				self.get_auto_remove_tmp_dir()

      args_d.update(
          data_dir=A_   ,					model_name_or_path=A_   ,					output_dir=A_   ,					tokenizer_name=A_   ,					train_batch_size=2   ,					eval_batch_size=2   ,					do_predict=A_   ,					task=A_   ,					src_lang='en_XX'   ,					tgt_lang='ro_RO'   ,					freeze_encoder=A_   ,					freeze_embeds=A_   ,					)

      # emulate finetune.py
      _a         =				argparse.ArgumentParser()
      _a         =				pl.Trainer.add_argparse_args(A_    )
      _a         =				SummarizationModule.add_model_specific_args(A_   ,					os.getcwd()    )
      _a         =				{'--help': True}

      # --help test
      with pytest.raises(A_    ) as excinfo:
           with CaptureStdout() as cs:
                _a         =				parser.parse_args(A_    )
           assert False, "--help is expected to sys.exit"
      assert excinfo.type == SystemExit
      _a         =				lightning_base.arg_to_scheduler_metavar
      assert expected in cs.out, "--help is expected to list the supported schedulers"

      # --lr_scheduler=non_existing_scheduler test
      _a         =				'non_existing_scheduler'
      _a         =				{f'--lr_scheduler={unsupported_param}'}
      with pytest.raises(A_    ) as excinfo:
           with CaptureStderr() as cs:
                _a         =				parser.parse_args(A_    )
           assert False, "invalid argument is expected to sys.exit"
      assert excinfo.type == SystemExit
      _a         =				f'invalid choice: \'{unsupported_param}\''
      assert expected in cs.err, f'should have bailed on invalid choice of scheduler {unsupported_param}'

      # --lr_scheduler=existing_scheduler test
      _a         =				'cosine'
      _a         =				args_d.copy()
      _a         =				supported_param
      _a         =				argparse.Namespace(**A_    )
      _a         =				main(A_    )
      assert (
          getattr(model.hparams   ,					'lr_scheduler'    ) == supported_param
      ), f'lr_scheduler={supported_param} shouldn\'t fail'






"""simple docstring"""






import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			CanineConfig()
  _SCREAMING_SNAKE_CASE        =			CanineModel(UpperCamelCase__    )
  model.eval()

  print(F'''Building PyTorch model from configuration: {config}'''    )

  # Load weights from tf checkpoint
  load_tf_weights_in_canine(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    )

  # Save pytorch-model (weights and configuration)
  print(F'''Save PyTorch model to {pytorch_dump_path}'''    )
  model.save_pretrained(UpperCamelCase__    )

  # Save tokenizer files
  _SCREAMING_SNAKE_CASE        =			CanineTokenizer()
  print(F'''Save tokenizer files to {pytorch_dump_path}'''    )
  tokenizer.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
    lowerCamelCase				:     Dict								=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--tf_checkpoint_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
    )
    parser.add_argument(
        """--pytorch_dump_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to a folder where the PyTorch model will be placed.""",
    )
    lowerCamelCase				:     str								=  parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
