





"""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			      =	'''sshleifer/mar_enro_6_3_student'''




class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					def 			_lowerCamelCase		(    self  ):
									super().setUp()

									__a			:    Dict      							=	cached_path(
									    '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''						,      extract_compressed_file=_UpperCAmelCase						,      )
									__a			:    int      							=	f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""







					@slow
					@require_torch_gpu
					def 			_lowerCamelCase		(    self  ):
									MarianMTModel.from_pretrained(_UpperCAmelCase  )







					@slow
					@require_torch_gpu
					def 			_lowerCamelCase		(    self  ):
									__a			:    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
									__a			:    List[Any]      							=	(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''  )[1].strip()
									__a			:    Dict      							=	bash_script.replace('''\\\n'''						,      ''''''  ).strip().replace('''"$@"'''						,      ''''''  )
									for k, v in env_vars_to_replace.items():
													__a			:    Dict      							=	bash_script.replace(_UpperCAmelCase						,      str(_UpperCAmelCase  )  )
									__a			:    Dict      							=	self.get_auto_remove_tmp_dir()

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

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

									# Check metrics
									__a			:    int      							=	load_json(model.metrics_save_path  )
									__a			:    Tuple      							=	metrics['''val'''][0]
									__a			:    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''']						,      0.0_1  )
									# 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
									__a			:    Optional[int]      							=	os.listdir(_UpperCAmelCase  )
									__a			:    Optional[int]      							=	[x for x in contents if x.endswith('''.ckpt'''  )][0]
									__a			:    Tuple      							=	os.path.join(args.output_dir						,      _UpperCAmelCase  )
									__a			:    int      							=	torch.load(_UpperCAmelCase						,      map_location='''cpu'''  )
									__a			:    Optional[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:
													__a			:    Optional[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 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




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

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

													__a			:    List[Any]      							=	distill_main(_UpperCAmelCase  )

									# Check metrics
									__a			:    Union[str, Any]      							=	load_json(model.metrics_save_path  )
									__a			:    Union[str, Any]      							=	metrics['''val'''][0]
									__a			:    Tuple      							=	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"] >= 0.0_1

									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
									__a			:    Optional[Any]      							=	os.listdir(_UpperCAmelCase  )
									__a			:    Dict      							=	[x for x in contents if x.endswith('''.ckpt'''  )][0]
									__a			:    str      							=	os.path.join(args.output_dir						,      _UpperCAmelCase  )
									__a			:    Optional[int]      							=	torch.load(_UpperCAmelCase						,      map_location='''cpu'''  )
									__a			:    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:
													__a			:    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

"""simple docstring"""






from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url





class 				a__					(						A__       ):
     UpperCAmelCase__         =      ''''''
     UpperCAmelCase__         =      '''hf-legacy'''  # "hf://"" is reserved for hffs



     def __init__(							self :Dict      ,  _lowerCamelCase :Optional[DatasetInfo] = None      ,  _lowerCamelCase :Optional[str] = None      ,  **_lowerCamelCase :Tuple      ,  ):







      '''simple docstring'''



      super().__init__(self      ,  **_lowerCamelCase   )
      UpperCamelCase_     :					List[str]   	=repo_info
      UpperCamelCase_     :					Any   	=token
      UpperCamelCase_     :					Tuple   	=None



     def  lowerCamelCase_						(							self :Dict   ):







      '''simple docstring'''



      if self.dir_cache is None:
       UpperCamelCase_     :					Any   	={}
       for hf_file in self.repo_info.siblings:
        # TODO(QL): add sizes
        UpperCamelCase_     :					Optional[Any]   	={
            'name': hf_file.rfilename,
            'size': None,
            'type': 'file',
        }
        self.dir_cache.update(
            {
                str(_lowerCamelCase   ): {'name': str(_lowerCamelCase   ), 'size': None, 'type': 'directory'}
                for d in list(PurePosixPath(hf_file.rfilename   ).parents   )[:-1]
            }   )



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :str      ,  _lowerCamelCase :str = "rb"      ,  **_lowerCamelCase :str      ,  ):







      '''simple docstring'''



      if not isinstance(self.repo_info      ,  _lowerCamelCase   ):
       raise NotImplementedError(f'''Open is only implemented for dataset repositories, but got {self.repo_info}'''   )
      UpperCamelCase_     :					List[Any]   	=hf_hub_url(self.repo_info.id      ,  _lowerCamelCase      ,  revision=self.repo_info.sha   )
      return fsspec.open(
          _lowerCamelCase      ,  mode=_lowerCamelCase      ,  headers=get_authentication_headers_for_url(_lowerCamelCase      ,  use_auth_token=self.token   )      ,  client_kwargs={'trust_env': True}      ,  ).open()



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :Tuple      ,  **_lowerCamelCase :Any   ):







      '''simple docstring'''



      self._get_dirs()
      UpperCamelCase_     :					Tuple   	=self._strip_protocol(_lowerCamelCase   )
      if path in self.dir_cache:
       return self.dir_cache[path]
      else:
       raise FileNotFoundError(_lowerCamelCase   )




     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :List[str]      ,  _lowerCamelCase :List[Any]=False      ,  **_lowerCamelCase :Any   ):







      '''simple docstring'''



      self._get_dirs()
      UpperCamelCase_     :					str   	=PurePosixPath(path.strip('/'   )   )
      UpperCamelCase_     :					List[str]   	={}
      for p, f in self.dir_cache.items():
       UpperCamelCase_     :					List[Any]   	=PurePosixPath(p.strip('/'   )   )
       UpperCamelCase_     :					Tuple   	=p.parent
       if root == path:
        UpperCamelCase_     :					int   	=f
      UpperCamelCase_     :					Optional[int]   	=list(paths.values()   )
      if detail:
       return out
      else:
       return sorted(f['name'] for f in out   )
