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


UpperCAmelCase_           =				'''sshleifer/mar_enro_6_3_student'''




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    super().setUp()

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



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



    """simple docstring"""



    MarianMTModel.from_pretrained(_lowercase				)



   @slow
   @require_torch_gpu
   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



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

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

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

    # Check metrics
    _UpperCamelCase:  List[Any]    						=			load_json(model.metrics_save_path				)
    _UpperCamelCase:  Tuple    						=			metrics['''val'''][0]
    _UpperCamelCase:  Optional[int]    						=			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}"""]		,	_lowercase				)

    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
    _UpperCamelCase:  int    						=			os.listdir(_lowercase				)
    _UpperCamelCase:  List[Any]    						=			[x for x in contents if x.endswith('''.ckpt'''				)][0]
    _UpperCamelCase:  List[Any]    						=			os.path.join(args.output_dir		,	_lowercase				)
    _UpperCamelCase:  Any    						=			torch.load(_lowercase		,	map_location='''cpu'''				)
    _UpperCamelCase:  str    						=			'''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:
     _UpperCamelCase:  List[Any]    						=			{os.path.basename(_lowercase				) 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 			__magic_name__	(		__a     ):





   """simple docstring"""




   @timeout_decorator.timeout(600				)
   @slow
   @require_torch_gpu
   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



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

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

     _UpperCamelCase:  Tuple    						=			distill_main(_lowercase				)

    # Check metrics
    _UpperCamelCase:  Union[str, Any]    						=			load_json(model.metrics_save_path				)
    _UpperCamelCase:  Any    						=			metrics['''val'''][0]
    _UpperCamelCase:  Optional[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}"""]		,	_lowercase				)

    # check lightning ckpt can be loaded and has a reasonable statedict
    _UpperCamelCase:  Tuple    						=			os.listdir(_lowercase				)
    _UpperCamelCase:  Tuple    						=			[x for x in contents if x.endswith('''.ckpt'''				)][0]
    _UpperCamelCase:  Tuple    						=			os.path.join(args.output_dir		,	_lowercase				)
    _UpperCamelCase:  int    						=			torch.load(_lowercase		,	map_location='''cpu'''				)
    _UpperCamelCase:  Tuple    						=			'''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:
     _UpperCamelCase:  Optional[int]    						=			{os.path.basename(_lowercase				) 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
def    lowerCAmelCase_  (		lowercase:				Dict		)     ->					Any:







 '''simple docstring'''
 _UpperCamelCase:  int    						=			[]
 _UpperCamelCase:  Dict    						=			set({'''(''', '''[''', '''{'''}		)
 _UpperCamelCase:  int    						=			set({''')''', ''']''', '''}'''}		)
 _UpperCamelCase:  Dict    						=			{'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}

 for i in range(len(lowercase		)		):
  if s[i] in open_brackets:
   stack.append(s[i]		)

  elif s[i] in closed_brackets and (
      len(lowercase		) == 0 or (len(lowercase		) > 0 and open_to_closed[stack.pop()] != s[i])
  ):
   return False

 return len(lowercase		) == 0




def    lowerCAmelCase_  (		)     ->					int:







 '''simple docstring'''
 _UpperCamelCase:  List[str]    						=			input('''Enter sequence of brackets: '''		)
 if is_balanced(lowercase		):
  print(lowercase	,       '''is balanced'''		)
 else:
  print(lowercase	,       '''is not balanced'''		)


if __name__ == "__main__":
  main()