
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


__lowerCamelCase							=       """sshleifer/mar_enro_6_3_student"""






class   UpperCAmelCase		(						A_ ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	List[str]:


          '''simple docstring'''


          super().setUp()

          snake_case       :   List[str]							=      cached_path(
              "https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"    ,						extract_compressed_file=snake_case__    ,						)
          snake_case       :   Union[str, Any]							=      f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""



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


          '''simple docstring'''


          MarianMTModel.from_pretrained(snake_case__ )



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


          '''simple docstring'''


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

          # bash_script = bash_script.replace("--fp16 ", "")
          snake_case       :   List[str]							=      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

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

          # Check metrics
          snake_case       :   List[Any]							=      load_json(model.metrics_save_path )
          snake_case       :   Union[str, Any]							=      metrics["val"][0]
          snake_case       :   Dict							=      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}"""]    ,						snake_case__ )

          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
          snake_case       :   int							=      os.listdir(snake_case__ )
          snake_case       :   Any							=      [x for x in contents if x.endswith(".ckpt" )][0]
          snake_case       :   List[Any]							=      os.path.join(args.output_dir    ,						snake_case__ )
          snake_case       :   str							=      torch.load(snake_case__    ,						map_location="cpu" )
          snake_case       :   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:
                 snake_case       :   Any							=      {os.path.basename(snake_case__ ) 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_ ):



   @timeout_decorator.timeout(6_00 )
   @slow
   @require_torch_gpu
   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Any:


          '''simple docstring'''


          snake_case       :   Any							=      f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
          snake_case       :   List[Any]							=      {
              "--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
          snake_case       :   Dict							=      (
              (self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py" )[1].strip()
          )
          snake_case       :   Union[str, Any]							=      bash_script.replace("\\\n"    ,						"" ).strip().replace("\"$@\""    ,						"" )
          snake_case       :   str							=      bash_script.replace("--fp16 "    ,						" " )

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

                 snake_case       :   Dict							=      distill_main(snake_case__ )

          # Check metrics
          snake_case       :   Tuple							=      load_json(model.metrics_save_path )
          snake_case       :   Optional[int]							=      metrics["val"][0]
          snake_case       :   int							=      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}"""]    ,						snake_case__ )

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





__magic_name__       =			"0.18.2"

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
							if not is_onnx_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_onnx_objects import *  # noqa F403
else:
							from .pipelines import OnnxRuntimeModel

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_pt_objects import *  # noqa F403
else:
							from .models import (
							    AutoencoderKL,
							    ControlNetModel,
							    ModelMixin,
							    PriorTransformer,
							    TaFilmDecoder,
							    TransformeraDModel,
							    UNetaDModel,
							    UNetaDConditionModel,
							    UNetaDModel,
							    UNetaDConditionModel,
							    VQModel,
							)
							from .optimization import (
							    get_constant_schedule,
							    get_constant_schedule_with_warmup,
							    get_cosine_schedule_with_warmup,
							    get_cosine_with_hard_restarts_schedule_with_warmup,
							    get_linear_schedule_with_warmup,
							    get_polynomial_decay_schedule_with_warmup,
							    get_scheduler,
							)
							from .pipelines import (
							    AudioPipelineOutput,
							    ConsistencyModelPipeline,
							    DanceDiffusionPipeline,
							    DDIMPipeline,
							    DDPMPipeline,
							    DiffusionPipeline,
							    DiTPipeline,
							    ImagePipelineOutput,
							    KarrasVePipeline,
							    LDMPipeline,
							    LDMSuperResolutionPipeline,
							    PNDMPipeline,
							    RePaintPipeline,
							    ScoreSdeVePipeline,
							)
							from .schedulers import (
							    CMStochasticIterativeScheduler,
							    DDIMInverseScheduler,
							    DDIMParallelScheduler,
							    DDIMScheduler,
							    DDPMParallelScheduler,
							    DDPMScheduler,
							    DEISMultistepScheduler,
							    DPMSolverMultistepInverseScheduler,
							    DPMSolverMultistepScheduler,
							    DPMSolverSinglestepScheduler,
							    EulerAncestralDiscreteScheduler,
							    EulerDiscreteScheduler,
							    HeunDiscreteScheduler,
							    IPNDMScheduler,
							    KarrasVeScheduler,
							    KDPMaAncestralDiscreteScheduler,
							    KDPMaDiscreteScheduler,
							    PNDMScheduler,
							    RePaintScheduler,
							    SchedulerMixin,
							    ScoreSdeVeScheduler,
							    UnCLIPScheduler,
							    UniPCMultistepScheduler,
							    VQDiffusionScheduler,
							)
							from .training_utils import EMAModel

try:
							if not (is_torch_available() and is_scipy_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
							from .schedulers import LMSDiscreteScheduler

try:
							if not (is_torch_available() and is_torchsde_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
							from .schedulers import DPMSolverSDEScheduler

try:
							if not (is_torch_available() and is_transformers_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
							from .pipelines import (
							    AltDiffusionImgaImgPipeline,
							    AltDiffusionPipeline,
							    AudioLDMPipeline,
							    CycleDiffusionPipeline,
							    IFImgaImgPipeline,
							    IFImgaImgSuperResolutionPipeline,
							    IFInpaintingPipeline,
							    IFInpaintingSuperResolutionPipeline,
							    IFPipeline,
							    IFSuperResolutionPipeline,
							    ImageTextPipelineOutput,
							    KandinskyImgaImgPipeline,
							    KandinskyInpaintPipeline,
							    KandinskyPipeline,
							    KandinskyPriorPipeline,
							    KandinskyVaaControlnetImgaImgPipeline,
							    KandinskyVaaControlnetPipeline,
							    KandinskyVaaImgaImgPipeline,
							    KandinskyVaaInpaintPipeline,
							    KandinskyVaaPipeline,
							    KandinskyVaaPriorEmbaEmbPipeline,
							    KandinskyVaaPriorPipeline,
							    LDMTextToImagePipeline,
							    PaintByExamplePipeline,
							    SemanticStableDiffusionPipeline,
							    ShapEImgaImgPipeline,
							    ShapEPipeline,
							    StableDiffusionAttendAndExcitePipeline,
							    StableDiffusionControlNetImgaImgPipeline,
							    StableDiffusionControlNetInpaintPipeline,
							    StableDiffusionControlNetPipeline,
							    StableDiffusionDepthaImgPipeline,
							    StableDiffusionDiffEditPipeline,
							    StableDiffusionImageVariationPipeline,
							    StableDiffusionImgaImgPipeline,
							    StableDiffusionInpaintPipeline,
							    StableDiffusionInpaintPipelineLegacy,
							    StableDiffusionInstructPixaPixPipeline,
							    StableDiffusionLatentUpscalePipeline,
							    StableDiffusionLDMaDPipeline,
							    StableDiffusionModelEditingPipeline,
							    StableDiffusionPanoramaPipeline,
							    StableDiffusionParadigmsPipeline,
							    StableDiffusionPipeline,
							    StableDiffusionPipelineSafe,
							    StableDiffusionPixaPixZeroPipeline,
							    StableDiffusionSAGPipeline,
							    StableDiffusionUpscalePipeline,
							    StableUnCLIPImgaImgPipeline,
							    StableUnCLIPPipeline,
							    TextToVideoSDPipeline,
							    TextToVideoZeroPipeline,
							    UnCLIPImageVariationPipeline,
							    UnCLIPPipeline,
							    UniDiffuserModel,
							    UniDiffuserPipeline,
							    UniDiffuserTextDecoder,
							    VersatileDiffusionDualGuidedPipeline,
							    VersatileDiffusionImageVariationPipeline,
							    VersatileDiffusionPipeline,
							    VersatileDiffusionTextToImagePipeline,
							    VideoToVideoSDPipeline,
							    VQDiffusionPipeline,
							)

try:
							if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
							from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
							if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
							from .pipelines import StableDiffusionKDiffusionPipeline

try:
							if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
							from .pipelines import (
							    OnnxStableDiffusionImgaImgPipeline,
							    OnnxStableDiffusionInpaintPipeline,
							    OnnxStableDiffusionInpaintPipelineLegacy,
							    OnnxStableDiffusionPipeline,
							    OnnxStableDiffusionUpscalePipeline,
							    StableDiffusionOnnxPipeline,
							)

try:
							if not (is_torch_available() and is_librosa_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
							from .pipelines import AudioDiffusionPipeline, Mel

try:
							if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
							from .pipelines import SpectrogramDiffusionPipeline

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_flax_objects import *  # noqa F403
else:
							from .models.controlnet_flax import FlaxControlNetModel
							from .models.modeling_flax_utils import FlaxModelMixin
							from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
							from .models.vae_flax import FlaxAutoencoderKL
							from .pipelines import FlaxDiffusionPipeline
							from .schedulers import (
							    FlaxDDIMScheduler,
							    FlaxDDPMScheduler,
							    FlaxDPMSolverMultistepScheduler,
							    FlaxKarrasVeScheduler,
							    FlaxLMSDiscreteScheduler,
							    FlaxPNDMScheduler,
							    FlaxSchedulerMixin,
							    FlaxScoreSdeVeScheduler,
							)


try:
							if not (is_flax_available() and is_transformers_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
							from .pipelines import (
							    FlaxStableDiffusionControlNetPipeline,
							    FlaxStableDiffusionImgaImgPipeline,
							    FlaxStableDiffusionInpaintPipeline,
							    FlaxStableDiffusionPipeline,
							)

try:
							if not (is_note_seq_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_note_seq_objects import *  # noqa F403
else:
							from .pipelines import MidiProcessor




