


"""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


__snake_case             =   """sshleifer/mar_enro_6_3_student"""







class _lowerCAmelCase    (				snake_case_     ):







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       super().setUp()

       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__						,     )
       snake_case   :				List[Any]				 =   F'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'







   @slow
   @require_torch_gpu
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       MarianMTModel.from_pretrained(UpperCamelCase__						)







   @slow
   @require_torch_gpu
   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




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

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

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

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







   @timeout_decorator.timeout(600						)
   @slow
   @require_torch_gpu
   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




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

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

           snake_case   :				Dict				 =   distill_main(UpperCamelCase__						)

       # Check metrics
       snake_case   :				str				 =   load_json(model.metrics_save_path						)
       snake_case   :				Tuple				 =   metrics["val"][0]
       snake_case   :				List[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
       snake_case   :				Dict				 =   os.listdir(UpperCamelCase__						)
       snake_case   :				List[str]				 =   [x for x in contents if x.endswith(".ckpt"						)][0]
       snake_case   :				str				 =   os.path.join(args.output_dir						,     UpperCamelCase__						)
       snake_case   :				Union[str, Any]				 =   torch.load(UpperCamelCase__						,     map_location="cpu"						)
       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:
           snake_case   :				Union[str, 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









"""simple docstring"""



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)







@dataclass
class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Union[List[PIL.Image.Image], np.ndarray]
   __UpperCAmelCase			:	Optional[List[bool]]


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
  from .pipeline_cycle_diffusion import CycleDiffusionPipeline
  from .pipeline_stable_diffusion import StableDiffusionPipeline
  from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
  from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
  from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
  from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
  from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
  from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
  from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
  from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
  from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
  from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
  from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
  from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
  from .pipeline_stable_unclip import StableUnCLIPPipeline
  from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
  from .safety_checker import StableDiffusionSafetyChecker
  from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
  from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.26.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import (
      StableDiffusionDepthaImgPipeline,
      StableDiffusionDiffEditPipeline,
      StableDiffusionPixaPixZeroPipeline,
  )
else:
  from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
  from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
  from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
  if not (
      is_torch_available()
      and is_transformers_available()
      and is_k_diffusion_available()
      and is_k_diffusion_version(""">=""", """0.0.12""")
  ):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
  from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
  if not (is_transformers_available() and is_onnx_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_onnx_objects import *  # noqa F403
else:
  from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
  from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
  from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
  from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
  from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
  import flax







  @flax.struct.dataclass
  class _lowerCAmelCase    (				snake_case_     ):
     __UpperCAmelCase			:	np.ndarray
     __UpperCAmelCase			:	List[bool]



  from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
  from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
  from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
  from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
  from .safety_checker_flax import FlaxStableDiffusionSafetyChecker





