

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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE  :						Dict         =    {
    '''configuration_longformer''': [
        '''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LongformerConfig''',
        '''LongformerOnnxConfig''',
    ],
    '''tokenization_longformer''': ['''LongformerTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Tuple         =    ['''LongformerTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''LongformerForMaskedLM''',
           '''LongformerForMultipleChoice''',
           '''LongformerForQuestionAnswering''',
           '''LongformerForSequenceClassification''',
           '''LongformerForTokenClassification''',
           '''LongformerModel''',
           '''LongformerPreTrainedModel''',
           '''LongformerSelfAttention''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFLongformerForMaskedLM''',
           '''TFLongformerForMultipleChoice''',
           '''TFLongformerForQuestionAnswering''',
           '''TFLongformerForSequenceClassification''',
           '''TFLongformerForTokenClassification''',
           '''TFLongformerModel''',
           '''TFLongformerPreTrainedModel''',
           '''TFLongformerSelfAttention''',
       ]


if TYPE_CHECKING:
       from .configuration_longformer import (
           LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LongformerConfig,
           LongformerOnnxConfig,
       )
       from .tokenization_longformer import LongformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_longformer_fast import LongformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_longformer import (
                  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LongformerForMaskedLM,
                  LongformerForMultipleChoice,
                  LongformerForQuestionAnswering,
                  LongformerForSequenceClassification,
                  LongformerForTokenClassification,
                  LongformerModel,
                  LongformerPreTrainedModel,
                  LongformerSelfAttention,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_longformer import (
                  TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLongformerForMaskedLM,
                  TFLongformerForMultipleChoice,
                  TFLongformerForQuestionAnswering,
                  TFLongformerForSequenceClassification,
                  TFLongformerForTokenClassification,
                  TFLongformerModel,
                  TFLongformerPreTrainedModel,
                  TFLongformerSelfAttention,
              )

else:
       import sys

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


