


'''simple docstring'''






import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
  from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import DPTImageProcessor






class        __snake_case	:







    """simple docstring"""



    def __init__(       self   :  Optional[int]   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Optional[Any]=2   ,						lowerCamelCase   :  Tuple=32   ,						lowerCamelCase   :  Tuple=16   ,						lowerCamelCase   :  Optional[Any]=3   ,						lowerCamelCase   :  Optional[int]=True   ,						lowerCamelCase   :  List[str]=True   ,						lowerCamelCase   :  Dict=32   ,						lowerCamelCase   :  List[str]=4   ,						lowerCamelCase   :  List[str]=[0, 1, 2, 3]   ,						lowerCamelCase   :  Optional[Any]=4   ,						lowerCamelCase   :  Dict=37   ,						lowerCamelCase   :  Union[str, Any]="gelu"   ,						lowerCamelCase   :  str=0.1   ,						lowerCamelCase   :  Optional[int]=0.1   ,						lowerCamelCase   :  int=0.02   ,						lowerCamelCase   :  Dict=3   ,						lowerCamelCase   :  List[str]=[1, 3_84, 24, 24]   ,						lowerCamelCase   :  List[str]=True   ,						lowerCamelCase   :  str=None   ,						)       ->  Optional[int]:
      lowerCAmelCase_      :				Dict      =       parent
      lowerCAmelCase_      :				List[str]      =       batch_size
      lowerCAmelCase_      :				Optional[Any]      =       image_size
      lowerCAmelCase_      :				Tuple      =       patch_size
      lowerCAmelCase_      :				Optional[int]      =       num_channels
      lowerCAmelCase_      :				Optional[int]      =       is_training
      lowerCAmelCase_      :				Dict      =       use_labels
      lowerCAmelCase_      :				int      =       hidden_size
      lowerCAmelCase_      :				Tuple      =       num_hidden_layers
      lowerCAmelCase_      :				Dict      =       backbone_out_indices
      lowerCAmelCase_      :				Tuple      =       num_attention_heads
      lowerCAmelCase_      :				List[Any]      =       intermediate_size
      lowerCAmelCase_      :				Optional[Any]      =       hidden_act
      lowerCAmelCase_      :				Union[str, Any]      =       hidden_dropout_prob
      lowerCAmelCase_      :				Optional[Any]      =       attention_probs_dropout_prob
      lowerCAmelCase_      :				List[Any]      =       initializer_range
      lowerCAmelCase_      :				str      =       num_labels
      lowerCAmelCase_      :				Optional[int]      =       backbone_featmap_shape
      lowerCAmelCase_      :				Tuple      =       scope
      lowerCAmelCase_      :				int      =       is_hybrid
      # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
      lowerCAmelCase_      :				Optional[int]      =       (image_size // patch_size) ** 2
      lowerCAmelCase_      :				str      =       num_patches + 1

    def __lowercase       (       self   :  int		)       ->  Any:
      lowerCAmelCase_      :				Dict      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

      lowerCAmelCase_      :				Union[str, Any]      =       None
      if self.use_labels:
        lowerCAmelCase_      :				Union[str, Any]      =       ids_tensor([self.batch_size, self.image_size, self.image_size]   ,						self.num_labels		)

      lowerCAmelCase_      :				Any      =       self.get_config()

      return config, pixel_values, labels

    def __lowercase       (       self   :  List[str]		)       ->  str:
      lowerCAmelCase_      :				Any      =       {
          """global_padding""": """same""",
          """layer_type""": """bottleneck""",
          """depths""": [3, 4, 9],
          """out_features""": ["""stage1""", """stage2""", """stage3"""],
          """embedding_dynamic_padding""": True,
          """hidden_sizes""": [96, 1_92, 3_84, 7_68],
          """num_groups""": 2,
      }

      return DPTConfig(
          image_size=self.image_size   ,						patch_size=self.patch_size   ,						num_channels=self.num_channels   ,						hidden_size=self.hidden_size   ,						num_hidden_layers=self.num_hidden_layers   ,						backbone_out_indices=self.backbone_out_indices   ,						num_attention_heads=self.num_attention_heads   ,						intermediate_size=self.intermediate_size   ,						hidden_act=self.hidden_act   ,						hidden_dropout_prob=self.hidden_dropout_prob   ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,						is_decoder=lowerCamelCase   ,						initializer_range=self.initializer_range   ,						is_hybrid=self.is_hybrid   ,						backbone_config=lowerCamelCase   ,						backbone_featmap_shape=self.backbone_featmap_shape   ,						)

    def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  int		)       ->  Any:
      lowerCAmelCase_      :				Optional[int]      =       DPTModel(config=lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_      :				List[str]      =       model(lowerCamelCase		)
      self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)		)

    def __lowercase       (       self   :  Dict   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  Optional[int]		)       ->  Tuple:
      lowerCAmelCase_      :				str      =       self.num_labels
      lowerCAmelCase_      :				str      =       DPTForDepthEstimation(lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_      :				int      =       model(lowerCamelCase		)
      self.parent.assertEqual(result.predicted_depth.shape   ,						(self.batch_size, self.image_size, self.image_size)		)

    def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Optional[Any]		)       ->  Optional[int]:
      lowerCAmelCase_      :				Tuple      =       self.num_labels
      lowerCAmelCase_      :				Optional[Any]      =       DPTForSemanticSegmentation(lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_      :				int      =       model(lowerCamelCase   ,						labels=lowerCamelCase		)
      self.parent.assertEqual(
          result.logits.shape   ,						(self.batch_size, self.num_labels, self.image_size, self.image_size)		)





    def __lowercase       (       self   :  Tuple		)       ->  Tuple:
      lowerCAmelCase_      :				Any      =       self.prepare_config_and_inputs()
      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				Optional[int]      =       config_and_inputs
      lowerCAmelCase_      :				Tuple      =       {"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_torch
class        __snake_case	(       _SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE     ,unittest.TestCase):







    """simple docstring"""



    lowercase       			= (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    lowercase       			= (
        {
            'depth-estimation': DPTForDepthEstimation,
            'feature-extraction': DPTModel,
            'image-segmentation': DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    lowercase       			= False
    lowercase       			= False
    lowercase       			= False

    def __lowercase       (       self   :  List[Any]		)       ->  List[Any]:
      lowerCAmelCase_      :				Union[str, Any]      =       DPTModelTester(self		)
      lowerCAmelCase_      :				Tuple      =       ConfigTester(self   ,						config_class=lowerCamelCase   ,						has_text_modality=lowerCamelCase   ,						hidden_size=37		)

    def __lowercase       (       self   :  Optional[int]		)       ->  Dict:
      self.config_tester.run_common_tests()

    @unittest.skip(reason="""DPT does not use inputs_embeds"""		)
    def __lowercase       (       self   :  Optional[int]		)       ->  str:
      pass

    def __lowercase       (       self   :  int		)       ->  str:
      lowerCAmelCase_,							lowerCAmelCase_      :				Tuple      =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCAmelCase_      :				List[str]      =       model_class(lowerCamelCase		)
        self.assertIsInstance(model.get_input_embeddings()   ,						(nn.Module)		)
        lowerCAmelCase_      :				Optional[int]      =       model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCamelCase   ,						nn.Linear		)		)

    def __lowercase       (       self   :  Optional[int]		)       ->  str:
      lowerCAmelCase_,							lowerCAmelCase_      :				Optional[int]      =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCAmelCase_      :				int      =       model_class(lowerCamelCase		)
        lowerCAmelCase_      :				Optional[int]      =       inspect.signature(model.forward		)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCAmelCase_      :				List[str]      =       [*signature.parameters.keys()]

        lowerCAmelCase_      :				Tuple      =       ["""pixel_values"""]
        self.assertListEqual(arg_names[:1]   ,						lowerCamelCase		)

    def __lowercase       (       self   :  List[str]		)       ->  List[Any]:
      lowerCAmelCase_      :				Any      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase		)

    def __lowercase       (       self   :  Optional[int]		)       ->  Optional[int]:
      lowerCAmelCase_      :				Dict      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase		)

    def __lowercase       (       self   :  List[Any]		)       ->  Dict:
      lowerCAmelCase_      :				Union[str, Any]      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase		)

    def __lowercase       (       self   :  int		)       ->  Optional[Any]:
      for model_class in self.all_model_classes:
        if model_class.__name__ == "DPTForDepthEstimation":
          continue

        lowerCAmelCase_,							lowerCAmelCase_      :				List[str]      =       self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase_      :				Optional[int]      =       True

        if model_class in get_values(lowerCamelCase		):
          continue

        lowerCAmelCase_      :				Tuple      =       model_class(lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.train()
        lowerCAmelCase_      :				Dict      =       self._prepare_for_class(lowerCamelCase   ,						lowerCamelCase   ,						return_labels=lowerCamelCase		)
        lowerCAmelCase_      :				Optional[int]      =       model(**lowerCamelCase		).loss
        loss.backward()

    def __lowercase       (       self   :  int		)       ->  Dict:
      for model_class in self.all_model_classes:
        if model_class.__name__ == "DPTForDepthEstimation":
          continue

        lowerCAmelCase_,							lowerCAmelCase_      :				int      =       self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase_      :				str      =       False
        lowerCAmelCase_      :				Any      =       True

        if model_class in get_values(lowerCamelCase		) or not model_class.supports_gradient_checkpointing:
          continue
        lowerCAmelCase_      :				List[str]      =       model_class(lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.gradient_checkpointing_enable()
        model.train()
        lowerCAmelCase_      :				Optional[int]      =       self._prepare_for_class(lowerCamelCase   ,						lowerCamelCase   ,						return_labels=lowerCamelCase		)
        lowerCAmelCase_      :				Optional[Any]      =       model(**lowerCamelCase		).loss
        loss.backward()

    def __lowercase       (       self   :  Tuple		)       ->  Dict:
      lowerCAmelCase_,							lowerCAmelCase_      :				Optional[int]      =       self.model_tester.prepare_config_and_inputs_for_common()

      lowerCAmelCase_      :				str      =       _config_zero_init(lowerCamelCase		)
      for model_class in self.all_model_classes:
        lowerCAmelCase_      :				Optional[int]      =       model_class(config=lowerCamelCase		)
        # Skip the check for the backbone
        lowerCAmelCase_      :				Dict      =       []
        for name, module in model.named_modules():
          if module.__class__.__name__ == "DPTViTHybridEmbeddings":
            lowerCAmelCase_      :				Optional[Any]      =       [F'{name}.{key}' for key in module.state_dict().keys()]
            break

        for name, param in model.named_parameters():
          if param.requires_grad:
            if name in backbone_params:
              continue
            self.assertIn(
                ((param.data.mean() * 1E9).round() / 1E9).item()   ,						[0.0, 1.0]   ,						msg=F'Parameter {name} of model {model_class} seems not properly initialized'   ,						)

    @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""		)
    def __lowercase       (       self   :  List[Any]		)       ->  int:
      pass

    @slow
    def __lowercase       (       self   :  Tuple		)       ->  str:
      for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
        lowerCAmelCase_      :				Dict      =       DPTModel.from_pretrained(lowerCamelCase		)
        self.assertIsNotNone(lowerCamelCase		)





    def __lowercase       (       self   :  str		)       ->  Optional[int]:
      # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
      lowerCAmelCase_,							lowerCAmelCase_      :				List[str]      =       self.model_tester.prepare_config_and_inputs_for_common()
      lowerCAmelCase_      :				Dict      =       """add"""
      with self.assertRaises(lowerCamelCase		):
        lowerCAmelCase_      :				Tuple      =       DPTForDepthEstimation(lowerCamelCase		)







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				Union[str, Any]      =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
  return image






@require_torch
@require_vision
@slow
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  int		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				Optional[Any]      =       DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""		)
      lowerCAmelCase_      :				Optional[int]      =       DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""		).to(lowerCamelCase		)

      lowerCAmelCase_      :				Tuple      =       prepare_img()
      lowerCAmelCase_      :				Dict      =       image_processor(images=lowerCamelCase   ,						return_tensors="""pt"""		).to(lowerCamelCase		)

      # forward pass
      with torch.no_grad():
        lowerCAmelCase_      :				List[str]      =       model(**lowerCamelCase		)
        lowerCAmelCase_      :				Dict      =       outputs.predicted_depth

      # verify the predicted depth
      lowerCAmelCase_      :				Tuple      =       torch.Size((1, 3_84, 3_84)		)
      self.assertEqual(predicted_depth.shape   ,						lowerCamelCase		)

      lowerCAmelCase_      :				Any      =       torch.tensor(
          [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]		).to(lowerCamelCase		)

      self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00   ,						lowerCamelCase   ,						atol=1E-4		)		)







'''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


__A :  Tuple    							=				"sshleifer/mar_enro_6_3_student"






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __lowercase       (       self   :  List[Any]		)       ->  Optional[Any]:
      super().setUp()

      lowerCAmelCase_      :				Any      =       cached_path(
          """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""   ,						extract_compressed_file=lowerCamelCase   ,						)
      lowerCAmelCase_      :				Optional[Any]      =       F'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'

    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  str		)       ->  str:
      MarianMTModel.from_pretrained(lowerCamelCase		)





    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  List[Any]		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				str      =       {
          """$MAX_LEN""": 64,
          """$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
      lowerCAmelCase_      :				Dict      =       (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""		)[1].strip()
      lowerCAmelCase_      :				Optional[int]      =       bash_script.replace("""\\\n"""   ,						""""""		).strip().replace("""\"$@\""""   ,						""""""		)
      for k, v in env_vars_to_replace.items():
        lowerCAmelCase_      :				Optional[int]      =       bash_script.replace(lowerCamelCase   ,						str(lowerCamelCase		)		)
      lowerCAmelCase_      :				Optional[Any]      =       self.get_auto_remove_tmp_dir()

      # bash_script = bash_script.replace("--fp16 ", "")
      lowerCAmelCase_      :				Tuple      =       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

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

      # Check metrics
      lowerCAmelCase_      :				int      =       load_json(model.metrics_save_path		)
      lowerCAmelCase_      :				Optional[Any]      =       metrics["""val"""][0]
      lowerCAmelCase_      :				Tuple      =       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
      lowerCAmelCase_      :				Union[str, Any]      =       os.listdir(lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       [x for x in contents if x.endswith(""".ckpt"""		)][0]
      lowerCAmelCase_      :				Union[str, Any]      =       os.path.join(args.output_dir   ,						lowerCamelCase		)
      lowerCAmelCase_      :				int      =       torch.load(lowerCamelCase   ,						map_location="""cpu"""		)
      lowerCAmelCase_      :				List[str]      =       """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:
        lowerCAmelCase_      :				List[str]      =       {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        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @timeout_decorator.timeout(6_00		)
    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  Optional[Any]		)       ->  Dict:
      lowerCAmelCase_      :				List[str]      =       F'{self.test_file_dir_str}/test_data/wmt_en_ro'
      lowerCAmelCase_      :				Dict      =       {
          """--fp16_opt_level=O1""": """""",
          """$MAX_LEN""": 1_28,
          """$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
      lowerCAmelCase_      :				int      =       (
          (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""		)[1].strip()
      )
      lowerCAmelCase_      :				str      =       bash_script.replace("""\\\n"""   ,						""""""		).strip().replace("""\"$@\""""   ,						""""""		)
      lowerCAmelCase_      :				Tuple      =       bash_script.replace("""--fp16 """   ,						""" """		)

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

        lowerCAmelCase_      :				str      =       distill_main(lowerCamelCase		)

      # Check metrics
      lowerCAmelCase_      :				Union[str, Any]      =       load_json(model.metrics_save_path		)
      lowerCAmelCase_      :				Union[str, Any]      =       metrics["""val"""][0]
      lowerCAmelCase_      :				Union[str, 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}']   ,						lowerCamelCase		)

      # check lightning ckpt can be loaded and has a reasonable statedict
      lowerCAmelCase_      :				Union[str, Any]      =       os.listdir(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       [x for x in contents if x.endswith(""".ckpt"""		)][0]
      lowerCAmelCase_      :				Optional[int]      =       os.path.join(args.output_dir   ,						lowerCamelCase		)
      lowerCAmelCase_      :				int      =       torch.load(lowerCamelCase   ,						map_location="""cpu"""		)
      lowerCAmelCase_      :				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:
        lowerCAmelCase_      :				Union[str, Any]      =       {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



