


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	:						Dict         =							"sshleifer/mar_enro_6_3_student"

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							def        __snake_case   (				self			:    Dict							)      ->       Any:
														super().setUp()

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

							@slow
							@require_torch_gpu
							def        __snake_case   (				self			:    str							)      ->       Optional[Any]:
														MarianMTModel.from_pretrained(lowerCamelCase							)






							@slow
							@require_torch_gpu
							def        __snake_case   (				self			:    Union[str, Any]							)      ->       List[str]:
														__snake_case						:     List[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
														__snake_case						:     Optional[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						:     List[str]									=  bash_script.replace(lowerCamelCase       ,      str(lowerCamelCase							)							)
														__snake_case						:     Any									=  self.get_auto_remove_tmp_dir()

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

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

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




							"""simple docstring"""


							@timeout_decorator.timeout(600							)
							@slow
							@require_torch_gpu
							def        __snake_case   (				self			:    Tuple							)      ->       int:
														__snake_case						:     int									=  F'{self.test_file_dir_str}/test_data/wmt_en_ro'
														__snake_case						:     Optional[Any]									=  {
														    "--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						:     List[Any]									=  (
														    (self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py"							)[1].strip()
														)
														__snake_case						:     int									=  bash_script.replace("\\\n"       ,      ""							).strip().replace("\"$@\""       ,      ""							)
														__snake_case						:     List[str]									=  bash_script.replace("--fp16 "       ,      " "							)

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

																					__snake_case						:     Union[str, Any]									=  distill_main(lowerCamelCase							)

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


_lowerCamelCase      :					int					    =      {
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Tuple					    =      [
       '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ASTForAudioClassification''',
       '''ASTModel''',
       '''ASTPreTrainedModel''',
   ]

try:
   if not is_speech_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Optional[Any]					    =      ['''ASTFeatureExtractor''']

if TYPE_CHECKING:
   from .configuration_audio_spectrogram_transformer import (
       AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       ASTConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_audio_spectrogram_transformer import (
          AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ASTForAudioClassification,
          ASTModel,
          ASTPreTrainedModel,
      )

   try:
      if not is_speech_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
   import sys

   _lowerCamelCase      :					Optional[int]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


