

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


_SCREAMING_SNAKE_CASE  :						str         =    '''sshleifer/mar_enro_6_3_student'''






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  def    lowercase_	(							self		:	Dict      )	->							List[str]:
       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=__lowerCamelCase			,     )
       SCREAMING_SNAKE_CASE__					=					f'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''






  @slow
  @require_torch_gpu
  def    lowercase_	(							self		:	int      )	->							Dict:
       MarianMTModel.from_pretrained(__lowerCamelCase      )







  @slow
  @require_torch_gpu
  def    lowercase_	(							self		:	Optional[Any]      )	->							Optional[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(__lowerCamelCase			,     str(__lowerCamelCase      )      )
       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(__lowerCamelCase			,     '''argv'''			,     __lowerCamelCase      ):
            SCREAMING_SNAKE_CASE__					=					argparse.ArgumentParser()
            SCREAMING_SNAKE_CASE__					=					pl.Trainer.add_argparse_args(__lowerCamelCase      )
            SCREAMING_SNAKE_CASE__					=					SummarizationModule.add_model_specific_args(__lowerCamelCase			,     os.getcwd()      )
            SCREAMING_SNAKE_CASE__					=					parser.parse_args()
            SCREAMING_SNAKE_CASE__					=					main(__lowerCamelCase      )

       # 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}''']			,     __lowerCamelCase      )

       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(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					[x for x in contents if x.endswith('''.ckpt'''      )][0]
       SCREAMING_SNAKE_CASE__					=					os.path.join(args.output_dir			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					torch.load(__lowerCamelCase			,     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(__lowerCamelCase      ) 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  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  @timeout_decorator.timeout(600      )
  @slow
  @require_torch_gpu
  def    lowercase_	(							self		:	str      )	->							str:
       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(__lowerCamelCase			,     str(__lowerCamelCase      )      )
       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(__lowerCamelCase			,     '''argv'''			,     __lowerCamelCase      ):
            SCREAMING_SNAKE_CASE__					=					argparse.ArgumentParser()
            SCREAMING_SNAKE_CASE__					=					pl.Trainer.add_argparse_args(__lowerCamelCase      )
            SCREAMING_SNAKE_CASE__					=					SummarizationDistiller.add_model_specific_args(__lowerCamelCase			,     os.getcwd()      )
            SCREAMING_SNAKE_CASE__					=					parser.parse_args()
            # assert args.gpus == gpus THIS BREAKS for multi_gpu

            SCREAMING_SNAKE_CASE__					=					distill_main(__lowerCamelCase      )

       # 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}''']			,     __lowerCamelCase      )

       # check lightning ckpt can be loaded and has a reasonable statedict
       SCREAMING_SNAKE_CASE__					=					os.listdir(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					[x for x in contents if x.endswith('''.ckpt'''      )][0]
       SCREAMING_SNAKE_CASE__					=					os.path.join(args.output_dir			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					torch.load(__lowerCamelCase			,     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(__lowerCamelCase      ) 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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE  :						List[Any]         =    {
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Optional[int]         =    ['''RobertaTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Tuple         =    [
           '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''RobertaForCausalLM''',
           '''RobertaForMaskedLM''',
           '''RobertaForMultipleChoice''',
           '''RobertaForQuestionAnswering''',
           '''RobertaForSequenceClassification''',
           '''RobertaForTokenClassification''',
           '''RobertaModel''',
           '''RobertaPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFRobertaForCausalLM''',
           '''TFRobertaForMaskedLM''',
           '''TFRobertaForMultipleChoice''',
           '''TFRobertaForQuestionAnswering''',
           '''TFRobertaForSequenceClassification''',
           '''TFRobertaForTokenClassification''',
           '''TFRobertaMainLayer''',
           '''TFRobertaModel''',
           '''TFRobertaPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    [
           '''FlaxRobertaForCausalLM''',
           '''FlaxRobertaForMaskedLM''',
           '''FlaxRobertaForMultipleChoice''',
           '''FlaxRobertaForQuestionAnswering''',
           '''FlaxRobertaForSequenceClassification''',
           '''FlaxRobertaForTokenClassification''',
           '''FlaxRobertaModel''',
           '''FlaxRobertaPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
       from .tokenization_roberta import RobertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roberta_fast import RobertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta import (
                  ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaForCausalLM,
                  RobertaForMaskedLM,
                  RobertaForMultipleChoice,
                  RobertaForQuestionAnswering,
                  RobertaForSequenceClassification,
                  RobertaForTokenClassification,
                  RobertaModel,
                  RobertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta import (
                  TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaForCausalLM,
                  TFRobertaForMaskedLM,
                  TFRobertaForMultipleChoice,
                  TFRobertaForQuestionAnswering,
                  TFRobertaForSequenceClassification,
                  TFRobertaForTokenClassification,
                  TFRobertaMainLayer,
                  TFRobertaModel,
                  TFRobertaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta import (
                  FlaxRobertaForCausalLM,
                  FlaxRobertaForMaskedLM,
                  FlaxRobertaForMultipleChoice,
                  FlaxRobertaForQuestionAnswering,
                  FlaxRobertaForSequenceClassification,
                  FlaxRobertaForTokenClassification,
                  FlaxRobertaModel,
                  FlaxRobertaPreTrainedModel,
              )

else:
       import sys

       _SCREAMING_SNAKE_CASE  :						str         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


