

'''simple docstring'''

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__     =       'sshleifer/mar_enro_6_3_student'






class     a_						(      _a    ):

  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        super().setUp()

        UpperCamelCase      = cached_path(
            """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""      ,		extract_compressed_file=__lowerCAmelCase      ,		)
        UpperCamelCase      = F"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"

  @slow
  @require_torch_gpu
  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        MarianMTModel.from_pretrained(__lowerCAmelCase				)


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



        """simple docstring"""






        UpperCamelCase      = {
            """$MAX_LEN""": 64,
            """$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
        UpperCamelCase      = (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""				)[1].strip()
        UpperCamelCase      = bash_script.replace("""\\\n"""      ,		""""""				).strip().replace("""\"$@\""""      ,		""""""				)
        for k, v in env_vars_to_replace.items():
              UpperCamelCase      = bash_script.replace(__lowerCAmelCase      ,		str(__lowerCAmelCase				)				)
        UpperCamelCase      = self.get_auto_remove_tmp_dir()

        # bash_script = bash_script.replace("--fp16 ", "")
        UpperCamelCase      = F"\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        ".split()
        # XXX: args.gpus > 1 : handle multi_gpu in the future

        UpperCamelCase      = ["""finetune.py"""] + bash_script.split() + args
        with patch.object(__lowerCAmelCase      ,		"""argv"""      ,		__lowerCAmelCase				):
              UpperCamelCase      = argparse.ArgumentParser()
              UpperCamelCase      = pl.Trainer.add_argparse_args(__lowerCAmelCase				)
              UpperCamelCase      = SummarizationModule.add_model_specific_args(__lowerCAmelCase      ,		os.getcwd()				)
              UpperCamelCase      = parser.parse_args()
              UpperCamelCase      = main(__lowerCAmelCase				)

        # Check metrics
        UpperCamelCase      = load_json(model.metrics_save_path				)
        UpperCamelCase      = metrics["""val"""][0]
        UpperCamelCase      = 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"""]      ,		0.0_1				)
        # 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
        UpperCamelCase      = os.listdir(__lowerCAmelCase				)
        UpperCamelCase      = [x for x in contents if x.endswith(""".ckpt"""				)][0]
        UpperCamelCase      = os.path.join(args.output_dir      ,		__lowerCAmelCase				)
        UpperCamelCase      = torch.load(__lowerCAmelCase      ,		map_location="""cpu"""				)
        UpperCamelCase      = """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:
              UpperCamelCase      = {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     a_						(      _a    ):

  @timeout_decorator.timeout(600				)
  @slow
  @require_torch_gpu
  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = F"{self.test_file_dir_str}/test_data/wmt_en_ro"
        UpperCamelCase      = {
            """--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
        UpperCamelCase      = (
            (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""				)[1].strip()
        )
        UpperCamelCase      = bash_script.replace("""\\\n"""      ,		""""""				).strip().replace("""\"$@\""""      ,		""""""				)
        UpperCamelCase      = bash_script.replace("""--fp16 """      ,		""" """				)

        for k, v in env_vars_to_replace.items():
              UpperCamelCase      = bash_script.replace(__lowerCAmelCase      ,		str(__lowerCAmelCase				)				)
        UpperCamelCase      = self.get_auto_remove_tmp_dir()
        UpperCamelCase      = bash_script.replace("""--fp16"""      ,		""""""				)
        UpperCamelCase      = 6
        UpperCamelCase      = (
            ["""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				):
              UpperCamelCase      = argparse.ArgumentParser()
              UpperCamelCase      = pl.Trainer.add_argparse_args(__lowerCAmelCase				)
              UpperCamelCase      = SummarizationDistiller.add_model_specific_args(__lowerCAmelCase      ,		os.getcwd()				)
              UpperCamelCase      = parser.parse_args()
              # assert args.gpus == gpus THIS BREAKS for multi_gpu

              UpperCamelCase      = distill_main(__lowerCAmelCase				)

        # Check metrics
        UpperCamelCase      = load_json(model.metrics_save_path				)
        UpperCamelCase      = metrics["""val"""][0]
        UpperCamelCase      = 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"] >= 0.0_1

        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
        UpperCamelCase      = os.listdir(__lowerCAmelCase				)
        UpperCamelCase      = [x for x in contents if x.endswith(""".ckpt"""				)][0]
        UpperCamelCase      = os.path.join(args.output_dir      ,		__lowerCAmelCase				)
        UpperCamelCase      = torch.load(__lowerCAmelCase      ,		map_location="""cpu"""				)
        UpperCamelCase      = """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:
              UpperCamelCase      = {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








'''simple docstring'''

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__     =       {
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       ['MobileBertTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MobileBertForMaskedLM',
         'MobileBertForMultipleChoice',
         'MobileBertForNextSentencePrediction',
         'MobileBertForPreTraining',
         'MobileBertForQuestionAnswering',
         'MobileBertForSequenceClassification',
         'MobileBertForTokenClassification',
         'MobileBertLayer',
         'MobileBertModel',
         'MobileBertPreTrainedModel',
         'load_tf_weights_in_mobilebert',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFMobileBertForMaskedLM',
         'TFMobileBertForMultipleChoice',
         'TFMobileBertForNextSentencePrediction',
         'TFMobileBertForPreTraining',
         'TFMobileBertForQuestionAnswering',
         'TFMobileBertForSequenceClassification',
         'TFMobileBertForTokenClassification',
         'TFMobileBertMainLayer',
         'TFMobileBertModel',
         'TFMobileBertPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mobilebert import (
         MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileBertConfig,
         MobileBertOnnxConfig,
     )
     from .tokenization_mobilebert import MobileBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mobilebert_fast import MobileBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilebert import (
              MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileBertForMaskedLM,
              MobileBertForMultipleChoice,
              MobileBertForNextSentencePrediction,
              MobileBertForPreTraining,
              MobileBertForQuestionAnswering,
              MobileBertForSequenceClassification,
              MobileBertForTokenClassification,
              MobileBertLayer,
              MobileBertModel,
              MobileBertPreTrainedModel,
              load_tf_weights_in_mobilebert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mobilebert import (
              TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMobileBertForMaskedLM,
              TFMobileBertForMultipleChoice,
              TFMobileBertForNextSentencePrediction,
              TFMobileBertForPreTraining,
              TFMobileBertForQuestionAnswering,
              TFMobileBertForSequenceClassification,
              TFMobileBertForTokenClassification,
              TFMobileBertMainLayer,
              TFMobileBertModel,
              TFMobileBertPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE__     =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
