






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 : str						   =       'sshleifer/mar_enro_6_3_student'

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  int:


												"""simple docstring"""

												super().setUp()

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



					@slow
					@require_torch_gpu
					def       lowerCAmelCase				(  self				:	int							)			->  Tuple:


												"""simple docstring"""

												MarianMTModel.from_pretrained(UpperCAmelCase__							)



					@slow
					@require_torch_gpu
					def       lowerCAmelCase				(  self				:	int							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     {
												    '''$MAX_LEN''': 64,
												    '''$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
												__lowercase      :							List[Any]				=     (self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split("""finetune.py"""							)[1].strip()
												__lowercase      :							Dict				=     bash_script.replace("""\\\n"""		,     """"""							).strip().replace("""\"$@\""""		,     """"""							)
												for k, v in env_vars_to_replace.items():
																			__lowercase      :							Tuple				=     bash_script.replace(UpperCAmelCase__		,     str(UpperCAmelCase__							)							)
												__lowercase      :							Tuple				=     self.get_auto_remove_tmp_dir()

												# bash_script = bash_script.replace("--fp16 ", "")
												__lowercase      :							Union[str, Any]				=     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

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

												# Check metrics
												__lowercase      :							Tuple				=     load_json(model.metrics_save_path							)
												__lowercase      :							Optional[int]				=     metrics['''val'''][0]
												__lowercase      :							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"""]		,     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
												__lowercase      :							Dict				=     os.listdir(UpperCAmelCase__							)
												__lowercase      :							Any				=     [x for x in contents if x.endswith(""".ckpt"""							)][0]
												__lowercase      :							List[str]				=     os.path.join(args.output_dir		,     UpperCAmelCase__							)
												__lowercase      :							str				=     torch.load(UpperCAmelCase__		,     map_location="""cpu"""							)
												__lowercase      :							Optional[int]				=     '''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:
																			__lowercase      :							int				=     {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 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					@timeout_decorator.timeout(600							)
					@slow
					@require_torch_gpu
					def       lowerCAmelCase				(  self				:	str							)			->  int:


												"""simple docstring"""

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

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

																			__lowercase      :							Any				=     distill_main(UpperCAmelCase__							)

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

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







lowerCamelCase : List[str]						   =       '''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