

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


UpperCAmelCase__ :     List[str]				    =    """sshleifer/mar_enro_6_3_student"""







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  def  _lowercase      (			self    : Union[str, Any]		)					->int:
        """simple docstring"""







        super().setUp()

        SCREAMING_SNAKE_CASE :      Dict       =   cached_path(
            """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""				,				extract_compressed_file=UpperCAmelCase__				,				)
        SCREAMING_SNAKE_CASE :      Any       =   f"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"


  @slow
  @require_torch_gpu
  def  _lowercase      (			self    : str		)					->Tuple:
        """simple docstring"""







        MarianMTModel.from_pretrained(UpperCAmelCase__		)




  @slow
  @require_torch_gpu
  def  _lowercase      (			self    : Tuple		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   {
            """$MAX_LEN""": 6_4,
            """$BS""": 6_4,
            """$GAS""": 1,
            """$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 :      Any       =   (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""		)[1].strip()
        SCREAMING_SNAKE_CASE :      List[str]       =   bash_script.replace("""\\\n"""				,				""""""		).strip().replace("""\"$@\""""				,				""""""		)
        for k, v in env_vars_to_replace.items():
              SCREAMING_SNAKE_CASE :      List[str]       =   bash_script.replace(UpperCAmelCase__				,				str(UpperCAmelCase__		)		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   self.get_auto_remove_tmp_dir()

        # bash_script = bash_script.replace("--fp16 ", "")
        SCREAMING_SNAKE_CASE :      Optional[int]       =   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

        SCREAMING_SNAKE_CASE :      Any       =   ["""finetune.py"""] + bash_script.split() + args
        with patch.object(UpperCAmelCase__				,				"""argv"""				,				UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   argparse.ArgumentParser()
              SCREAMING_SNAKE_CASE :      Optional[int]       =   pl.Trainer.add_argparse_args(UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE :      List[Any]       =   SummarizationModule.add_model_specific_args(UpperCAmelCase__				,				os.getcwd()		)
              SCREAMING_SNAKE_CASE :      Optional[int]       =   parser.parse_args()
              SCREAMING_SNAKE_CASE :      int       =   main(UpperCAmelCase__		)

        # Check metrics
        SCREAMING_SNAKE_CASE :      Tuple       =   load_json(model.metrics_save_path		)
        SCREAMING_SNAKE_CASE :      List[str]       =   metrics["""val"""][0]
        SCREAMING_SNAKE_CASE :      List[str]       =   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}"]				,				UpperCAmelCase__		)

        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"""]				,				1_7		)

        # 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 :      Any       =   os.listdir(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      str       =   [x for x in contents if x.endswith(""".ckpt"""		)][0]
        SCREAMING_SNAKE_CASE :      Optional[int]       =   os.path.join(args.output_dir				,				UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[str]       =   torch.load(UpperCAmelCase__				,				map_location="""cpu"""		)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   """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 :      str       =   {os.path.basename(UpperCAmelCase__		) 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__       (   UpperCAmelCase       ):







  """simple docstring"""




  @timeout_decorator.timeout(6_0_0		)
  @slow
  @require_torch_gpu
  def  _lowercase      (			self    : Dict		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   f"{self.test_file_dir_str}/test_data/wmt_en_ro"
        SCREAMING_SNAKE_CASE :      str       =   {
            """--fp16_opt_level=O1""": """""",
            """$MAX_LEN""": 1_2_8,
            """$BS""": 1_6,
            """$GAS""": 1,
            """$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 :      str       =   (
            (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""		)[1].strip()
        )
        SCREAMING_SNAKE_CASE :      int       =   bash_script.replace("""\\\n"""				,				""""""		).strip().replace("""\"$@\""""				,				""""""		)
        SCREAMING_SNAKE_CASE :      Tuple       =   bash_script.replace("""--fp16 """				,				""" """		)

        for k, v in env_vars_to_replace.items():
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   bash_script.replace(UpperCAmelCase__				,				str(UpperCAmelCase__		)		)
        SCREAMING_SNAKE_CASE :      str       =   self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE :      Any       =   bash_script.replace("""--fp16"""				,				""""""		)
        SCREAMING_SNAKE_CASE :      List[str]       =   6
        SCREAMING_SNAKE_CASE :      Tuple       =   (
            ["""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(UpperCAmelCase__				,				"""argv"""				,				UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE :      Optional[int]       =   argparse.ArgumentParser()
              SCREAMING_SNAKE_CASE :      Any       =   pl.Trainer.add_argparse_args(UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE :      Tuple       =   SummarizationDistiller.add_model_specific_args(UpperCAmelCase__				,				os.getcwd()		)
              SCREAMING_SNAKE_CASE :      Tuple       =   parser.parse_args()
              # assert args.gpus == gpus THIS BREAKS for multi_gpu

              SCREAMING_SNAKE_CASE :      Optional[Any]       =   distill_main(UpperCAmelCase__		)

        # Check metrics
        SCREAMING_SNAKE_CASE :      Any       =   load_json(model.metrics_save_path		)
        SCREAMING_SNAKE_CASE :      str       =   metrics["""val"""][0]
        SCREAMING_SNAKE_CASE :      Any       =   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}"]				,				UpperCAmelCase__		)

        # check lightning ckpt can be loaded and has a reasonable statedict
        SCREAMING_SNAKE_CASE :      Tuple       =   os.listdir(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   [x for x in contents if x.endswith(""".ckpt"""		)][0]
        SCREAMING_SNAKE_CASE :      List[str]       =   os.path.join(args.output_dir				,				UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Any       =   torch.load(UpperCAmelCase__				,				map_location="""cpu"""		)
        SCREAMING_SNAKE_CASE :      Tuple       =   """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 :      Tuple       =   {os.path.basename(UpperCAmelCase__		) 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_torch_available,
    is_vision_available,
)


UpperCAmelCase__ :     List[Any]				    =    {"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTConfig""", """ViTOnnxConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Union[str, Any]				    =    ["""ViTFeatureExtractor"""]
  UpperCAmelCase__ :     List[Any]				    =    ["""ViTImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     List[Any]				    =    [
      """VIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ViTForImageClassification""",
      """ViTForMaskedImageModeling""",
      """ViTModel""",
      """ViTPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Tuple				    =    [
      """TFViTForImageClassification""",
      """TFViTModel""",
      """TFViTPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     str				    =    [
      """FlaxViTForImageClassification""",
      """FlaxViTModel""",
      """FlaxViTPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_vit import ViTFeatureExtractor
    from .image_processing_vit import ViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit import (
        VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTForImageClassification,
        ViTForMaskedImageModeling,
        ViTModel,
        ViTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
  import sys

  UpperCAmelCase__ :     Optional[Any]				    =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




