
'''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)

lowerCAmelCase_      =   logging.getLogger()
lowerCAmelCase_      =   torch.cuda.is_available()
lowerCAmelCase_      =   {
    "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-08,
    "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   lowerCAmelCase(  a__    :  int      , a__    :  List[str]      ):







       '''simple docstring'''






       lowerCamelCase__				   =      '\n'.join(SCREAMING_SNAKE_CASE_      )
       Path(SCREAMING_SNAKE_CASE_      ).open("w"      ).writelines(SCREAMING_SNAKE_CASE_      )


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


lowerCAmelCase_      =   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







def   lowerCAmelCase(  a__    :  Dict      ):







       '''simple docstring'''






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







class 		snake_case_       (						A__						):



   """simple docstring"""





   @classmethod
   def     __UpperCAmelCase      ( cls):
          logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
          return cls







   @slow
   @require_torch_gpu
   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      list_models()
          lowerCamelCase__				   =      'sshleifer'
          lowerCamelCase__				   =      [x.modelId for x in model_list if x.modelId.startswith(UpperCamelCase)]
          lowerCamelCase__				   =      ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
          lowerCamelCase__				   =      []
          for m in model_ids:
                 if m in allowed_to_be_broken:
                        continue
                 try:
                        AutoConfig.from_pretrained(UpperCamelCase)
                 except Exception:
                        failures.append(UpperCamelCase)
          assert not failures, f"""The following models could not be loaded through AutoConfig: {failures}"""







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
          self._test_distiller_cli(UpperCamelCase)







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      {
              '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',
          }
          lowerCamelCase__				   =      self._test_distiller_cli(UpperCamelCase			,   check_contents=UpperCamelCase)

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

          lowerCamelCase__				   =      self.get_auto_remove_tmp_dir()
          convert_pl_to_hf(ckpts[0]			,   transformer_ckpts[0].parent			,   UpperCamelCase)
          assert os.path.exists(os.path.join(UpperCamelCase			,   "pytorch_model.bin"))







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase)
          lowerCamelCase__				   =      model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
          lowerCamelCase__				   =      torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]			,   dtype=torch.long			,   device=model.device)
          lowerCamelCase__				   =      target_ids[:, :-1].contiguous()  # Why this line?
          lowerCamelCase__				   =      target_ids[:, 1:].clone()  # why clone?
          lowerCamelCase__				   =      model(
              UpperCamelCase			,   attention_mask=UpperCamelCase			,   decoder_input_ids=UpperCamelCase			,   labels=UpperCamelCase			,   use_cache=UpperCamelCase).loss

          lowerCamelCase__				   =      model(UpperCamelCase			,   attention_mask=UpperCamelCase			,   decoder_input_ids=UpperCamelCase			,   use_cache=UpperCamelCase).logits

          lowerCamelCase__				   =      nn.functional.log_softmax(UpperCamelCase			,   dim=-1)
          lowerCamelCase__				   =      label_smoothed_nll_loss(
              UpperCamelCase			,   UpperCamelCase			,   0.1			,   ignore_index=model.config.pad_token_id)
          with self.assertRaises(UpperCamelCase):
                 # TODO: understand why this breaks
                 self.assertEqual(UpperCamelCase			,   UpperCamelCase)







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      {
              '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',
          }
          lowerCamelCase__				   =      self._test_distiller_cli(UpperCamelCase			,   check_contents=UpperCamelCase)
          assert model.model.config.model_type == "mbart"

          lowerCamelCase__				   =      list(Path(model.output_dir).glob("*.ckpt"))
          self.assertEqual(1			,   len(UpperCamelCase))
          lowerCamelCase__				   =      list(Path(model.output_dir).glob("**/*.bin"))
          lowerCamelCase__				   =      list(Path(model.output_dir).glob("best_tfmr/*"))
          assert len(UpperCamelCase) > 2
          self.assertEqual(len(UpperCamelCase)			,   2)







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      {
              '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(UpperCamelCase)







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      {
              'teacher': T5_TINY,
              'student': T5_TINIER,
              'model_name_or_path': T5_TINIER,
              'tokenizer_name': T5_TINIER,
          }
          self._test_distiller_cli(UpperCamelCase)







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase=True):
          lowerCamelCase__				   =      {
              '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(UpperCamelCase)
          lowerCamelCase__				   =      CHEAP_ARGS.copy()
          lowerCamelCase__				   =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
          lowerCamelCase__				   =      self.get_auto_remove_tmp_dir()

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

          self.assertIn("test_generations.txt"			,   UpperCamelCase)
          self.assertIn("test_results.txt"			,   UpperCamelCase)

          lowerCamelCase__				   =      load_json(model.metrics_save_path)
          lowerCamelCase__				   =      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}"""]			,   UpperCamelCase)
          lowerCamelCase__				   =      int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1)
          self.assertEqual(len(metrics["val"])			,   UpperCamelCase)
          self.assertEqual(len(metrics["test"])			,   1)
          return model







class 		snake_case_       (						A__						):



   """simple docstring"""





   @parameterized.expand(
       [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]			,   )
   def     __UpperCAmelCase      ( self			,   UpperCamelCase):
          lowerCamelCase__				   =      CHEAP_ARGS.copy()
          lowerCamelCase__				   =      'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
          lowerCamelCase__				   =      0.1 if task == 'translation' else 0

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

          lowerCamelCase__				   =      module.model.get_input_embeddings()
          assert not input_embeds.weight.requires_grad
          if model == T5_TINY:
                 lowerCamelCase__				   =      module.model.lm_head
                 assert not lm_head.weight.requires_grad
                 assert (lm_head.weight == input_embeds.weight).all().item()
          elif model == FSMT_TINY:
                 lowerCamelCase__				   =      module.model.model
                 lowerCamelCase__				   =      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:
                 lowerCamelCase__				   =      module.model.model
                 lowerCamelCase__				   =      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

          lowerCamelCase__				   =      load_json(module.output_dir / "text_batch.json")
          assert isinstance(UpperCamelCase			,   UpperCamelCase)
          assert len(UpperCamelCase) >= 4







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      CHEAP_ARGS.copy()

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

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

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

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







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      CHEAP_ARGS.copy()

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

          lowerCamelCase__				   =      BART_TINY
          lowerCamelCase__				   =      self.get_auto_remove_tmp_dir()

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

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

          # --help test
          with pytest.raises(UpperCamelCase) as excinfo:
                 with CaptureStdout() as cs:
                        lowerCamelCase__				   =      parser.parse_args(UpperCamelCase)
                 assert False, "--help is expected to sys.exit"
          assert excinfo.type == SystemExit
          lowerCamelCase__				   =      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
          lowerCamelCase__				   =      'non_existing_scheduler'
          lowerCamelCase__				   =      {f"""--lr_scheduler={unsupported_param}"""}
          with pytest.raises(UpperCamelCase) as excinfo:
                 with CaptureStderr() as cs:
                        lowerCamelCase__				   =      parser.parse_args(UpperCamelCase)
                 assert False, "invalid argument is expected to sys.exit"
          assert excinfo.type == SystemExit
          lowerCamelCase__				   =      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
          lowerCamelCase__				   =      'cosine'
          lowerCamelCase__				   =      args_d.copy()
          lowerCamelCase__				   =      supported_param
          lowerCamelCase__				   =      argparse.Namespace(**UpperCamelCase)
          lowerCamelCase__				   =      main(UpperCamelCase)
          assert (
              getattr(model.hparams			,   "lr_scheduler") == supported_param
          ), f"""lr_scheduler={supported_param} shouldn\'t fail"""



import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						List[str]:
			# Construct configuration depending on size
			lowerCAmelCase__ : Tuple   =						{'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__ : Optional[int]   =						MODELS[model_size]  # set model hyperparameters
			lowerCAmelCase__ : Optional[int]   =						ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE_			,				n_layer=SCREAMING_SNAKE_CASE_			,				n_head=SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : List[Any]   =						ImageGPTForCausalLM(SCREAMING_SNAKE_CASE_	)

			# Load weights from numpy
			load_tf_weights_in_imagegpt(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)

			# Save pytorch-model
			lowerCAmelCase__ : Optional[int]   =						pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			lowerCAmelCase__ : Optional[int]   =						pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
			torch.save(model.state_dict()			,				SCREAMING_SNAKE_CASE_	)
			print(F'''Save configuration file to {pytorch_config_dump_path}'''	)
			with open(SCREAMING_SNAKE_CASE_			,				'w'			,				encoding='utf-8'	) as f:
						f.write(config.to_json_string()	)


if __name__ == "__main__":
					lowerCamelCase__        =						argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--imagegpt_checkpoint_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to the TensorFlow checkpoint path.""",
					)
					parser.add_argument(
					    """--model_size""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Size of the model (can be either 'small', 'medium' or 'large').""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					lowerCamelCase__        =						parser.parse_args()
					convert_imagegpt_checkpoint_to_pytorch(
					    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
					)