




import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


_lowerCamelCase     :		Optional[int]								=      os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'''{bindir}/../../examples/pytorch/translation'''):
  from run_translation import main  # noqa


set_seed(42)
_lowerCamelCase     :		int								=      """sshleifer/student_marian_en_ro_6_1"""
_lowerCamelCase     :		List[str]								=      """sshleifer/tiny-mbart"""


@require_torch
class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     List[Any]=False ,			UpperCAmelCase__				:     int=None ,			UpperCAmelCase__				:     Optional[Any]=True ,			UpperCAmelCase__				:     Dict=True ,			UpperCAmelCase__				:     Tuple=True ,			UpperCAmelCase__				:     Union[str, Any]=True ,			)						->List[str]:





           '''simple docstring'''

           A__							=   self.run_trainer(
               eval_steps=1 ,			max_len=12 ,			model_name=_lowercase ,			num_train_epochs=1 ,			distributed=_lowercase ,			extra_args_str=_lowercase ,			predict_with_generate=_lowercase ,			do_train=_lowercase ,			do_eval=_lowercase ,			do_predict=_lowercase ,			)
           A__							=   TrainerState.load_from_json(os.path.join(_lowercase ,			'''trainer_state.json''')).log_history

           if not do_eval:
                  return

           A__							=   [log for log in logs if '''eval_loss''' in log.keys()]

           A__							=   eval_metrics[0]
           if predict_with_generate:
                  assert "eval_bleu" in first_step_stats

                  A__							=   eval_metrics[-1]
                  assert isinstance(last_step_stats['''eval_bleu'''] ,			_lowercase)
                  assert not math.isnan(float(last_step_stats['''eval_loss'''])), "eval_loss must not be `nan`"


    @require_torch_non_multi_gpu
    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Any:





           '''simple docstring'''

           self.run_seqaseq_quick()


    @require_torch_multi_gpu
    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->Optional[int]:





           '''simple docstring'''

           self.run_seqaseq_quick(distributed=_lowercase)


    @require_torch_multi_gpu
    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Any:





           '''simple docstring'''

           self.run_seqaseq_quick(distributed=_lowercase)


    @unittest.skip('''Requires an update of the env running those tests''')
    @require_torch_multi_gpu
    @require_fairscale
    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Tuple:





           '''simple docstring'''

           self.run_seqaseq_quick(distributed=_lowercase ,			extra_args_str='''--sharded_ddp simple''')


    @unittest.skip('''Requires an update of the env running those tests''')
    @require_torch_multi_gpu
    @require_fairscale
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Tuple:





           '''simple docstring'''

           self.run_seqaseq_quick(distributed=_lowercase ,			extra_args_str='''--sharded_ddp simple --fp16''')


    @unittest.skip('''Requires an update of the env running those tests''')
    @require_torch_multi_gpu
    @require_fairscale
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->str:





           '''simple docstring'''

           self.run_seqaseq_quick(distributed=_lowercase ,			extra_args_str='''--sharded_ddp zero_dp_2''' ,			predict_with_generate=_lowercase)


    @unittest.skip('''Requires an update of the env running those tests''')
    @require_torch_multi_gpu
    @require_fairscale
    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->Optional[int]:





           '''simple docstring'''

           self.run_seqaseq_quick(
               distributed=_lowercase ,			extra_args_str='''--sharded_ddp zero_dp_2 --fp16''' ,			predict_with_generate=_lowercase)


    @require_apex
    @require_torch_gpu
    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Tuple:





           '''simple docstring'''

           self.run_seqaseq_quick(distributed=_lowercase ,			extra_args_str='''--fp16 --fp16_backend=apex''')
           # test 2nd time - was getting eval_loss': nan'
           # to reproduce the problem set distributed=False
           self.run_seqaseq_quick(distributed=_lowercase ,			extra_args_str='''--fp16 --fp16_backend=apex''')


    @parameterized.expand(['''base''', '''low''', '''high''', '''mixed'''])
    @require_torch_multi_gpu
    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     List[Any])						->Any:





           '''simple docstring'''

           A__							=   {
               # test with the default log_level - should be info and thus log info once
               '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
               # test with low log_level and log_level_replica - should be noisy on all processes
               # now the info string should appear twice on 2 processes
               '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
               # test with high log_level and low log_level_replica
               # now the info string should appear once only on the replica
               '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
               # test with high log_level and log_level_replica - should be quiet on all processes
               '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
           }

           A__							=   experiments[experiment_id]
           A__							=   {'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
           A__							=   '''Running training'''
           with CaptureStderr() as cl:
                  self.run_seqaseq_quick(**_lowercase ,			extra_args_str=data['''extra_args_str'''])
           A__							=   len(re.findall(_lowercase ,			cl.err))
           self.assertEqual(_lowercase ,			data['''n_matches'''])


    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Tuple:





           '''simple docstring'''

           A__							=   self.run_trainer(
               eval_steps=2 ,			max_len=128 ,			model_name=_lowercase ,			learning_rate=3e-4 ,			num_train_epochs=10 ,			distributed=_lowercase ,			)

           # Check metrics
           A__							=   TrainerState.load_from_json(os.path.join(_lowercase ,			'''trainer_state.json''')).log_history
           A__							=   [log for log in logs if '''eval_loss''' in log.keys()]
           A__							=   eval_metrics[0]
           A__							=   eval_metrics[-1]

           assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
           assert isinstance(last_step_stats['''eval_bleu'''] ,			_lowercase)

           # test if do_predict saves generations and metrics
           A__							=   os.listdir(_lowercase)
           A__							=   {os.path.basename(_lowercase) for p in contents}
           assert "generated_predictions.txt" in contents
           assert "predict_results.json" in contents


    @slow
    @require_bitsandbytes
    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Dict:





           '''simple docstring'''

           from transformers.training_args import OptimizerNames

           def train_and_return_metrics(UpperCAmelCase__				:     List[Any]) -> Tuple[int, float]:
                  A__							=   '''--skip_memory_metrics 0'''

                  A__							=   self.run_trainer(
                      max_len=128 ,			model_name=_lowercase ,			learning_rate=3e-4 ,			num_train_epochs=1 ,			optim=_lowercase ,			distributed=_lowercase ,			extra_args_str=_lowercase ,			do_eval=_lowercase ,			do_predict=_lowercase ,			n_gpus_to_use=1 ,			)

                  # Check metrics
                  A__							=   TrainerState.load_from_json(Path(_lowercase ,			'''trainer_state.json''')).log_history
                  A__							=   int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20)
                  A__							=   int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20)

                  A__							=   logs[0]['''train_loss''']
                  return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

           A__			,							A__			,							A__							=   train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value)
           A__			,							A__			,							A__							=   train_and_return_metrics(OptimizerNames.ADAMW_BNB.value)

           A__							=   gpu_alloc_mem_orig - gpu_alloc_mem_bnb

           A__							=   gpu_peak_mem_orig + gpu_alloc_mem_orig
           A__							=   gpu_peak_mem_bnb + gpu_alloc_mem_bnb
           A__							=   gpu_total_mem_orig - gpu_total_mem_bnb

           # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
           # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
           # in 2 bytes and the diff in optim memory usage is derived as so:
           #
           # - normal 25*8=~200MB (8 bytes per param)
           # - bnb    25*2= ~50MB (2 bytes per param)
           #
           # Thus we should expect ~150MB total memory saved.
           #
           # Peak memory should be the same - the total should be different by about that same margin
           #
           # After leaving a small margin to accommodate for differences between gpus let's check
           # that we have at least 120MB in savings
           A__							=   120

           # uncomment the following if this test starts failing - requires py38 for a new print feature
           # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
           # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
           # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
           # print(f"{gpu_alloc_mem_diff=}MB")
           # print(f"{gpu_peak_mem_diff=}MB")
           # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
           # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

           self.assertGreater(
               _lowercase ,			_lowercase ,			'''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
               f""" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"""
               f""" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB""" ,			)

           self.assertGreater(
               _lowercase ,			_lowercase ,			'''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
               f""" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"""
               f""" gpu_total_mem_bnb={gpu_total_mem_bnb}MB""" ,			)

           self.assertEqual(
               _lowercase ,			_lowercase ,			f"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}""")





    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     List[Any] = 3e-3 ,			UpperCAmelCase__				:     List[str] = "adafactor" ,			UpperCAmelCase__				:     List[Any] = False ,			UpperCAmelCase__				:     Optional[int] = None ,			UpperCAmelCase__				:     Dict = 0 ,			UpperCAmelCase__				:     Optional[int] = True ,			UpperCAmelCase__				:     Optional[Any] = True ,			UpperCAmelCase__				:     List[str] = True ,			UpperCAmelCase__				:     str = True ,			UpperCAmelCase__				:     Optional[Any] = None ,			)						->Optional[Any]:





           '''simple docstring'''

           A__							=   self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
           A__							=   self.get_auto_remove_tmp_dir()
           A__							=   f"""\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(_lowercase)}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(_lowercase)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        """.split()

           A__							=   f"""\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(_lowercase)}\n        """.split()

           A__							=   '''\n            --do_predict\n        '''.split()

           A__							=   []
           if do_train:
                  args += args_train

           if do_eval:
                  args += args_eval

           if do_predict:
                  args += args_predict

           if predict_with_generate:
                  args += "--predict_with_generate".split()

           if do_train:
                  if optim == "adafactor":
                         args += "--adafactor".split()
                  else:
                         args += f"""--optim {optim}""".split()

           if extra_args_str is not None:
                  args += extra_args_str.split()

           if distributed:
                  if n_gpus_to_use is None:
                         A__							=   get_gpu_count()
                  A__							=   get_torch_dist_unique_port()
                  A__							=   f"""\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            """.split()
                  A__							=   [sys.executable] + distributed_args + args
                  # keep for quick debug
                  # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
                  execute_subprocess_async(_lowercase ,			env=self.get_env())
           else:
                  A__							=   ['''run_translation.py'''] + args
                  with patch.object(_lowercase ,			'''argv''' ,			_lowercase):
                         main()

           return output_dir








def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	str:



       """simple docstring"""

       A__							=   []
       A__							=   []
       A__							=   {
           '''^''': 3,
           '''*''': 2,
           '''/''': 2,
           '''%''': 2,
           '''+''': 1,
           '''-''': 1,
       }  # Priority of each operator
       A__							=   len(lowercase_						) if (len(lowercase_						) > 7) else 7

       # Print table header for output
       print(
           '''Symbol'''.center(8						)			,    '''Stack'''.center(lowercase_						)			,    '''Postfix'''.center(lowercase_						)			,    sep=''' | '''			,    )
       print('''-''' * (print_width * 3 + 7)						)

       for x in infix:
              if x.isalpha() or x.isdigit():
                     post_fix.append(lowercase_						)  # if x is Alphabet / Digit, add it to Postfix
              elif x == "(":
                     stack.append(lowercase_						)  # if x is "(" push to Stack
              elif x == ")":  # if x is ")" pop stack until "(" is encountered
                     while stack[-1] != "(":
                            post_fix.append(stack.pop()						)  # Pop stack & add the content to Postfix
                     stack.pop()
              else:
                     if len(lowercase_						) == 0:
                            stack.append(lowercase_						)  # If stack is empty, push x to stack
                     else:  # while priority of x is not > priority of element in the stack
                            while len(lowercase_						) > 0 and priority[x] <= priority[stack[-1]]:
                                   post_fix.append(stack.pop()						)  # pop stack & add to Postfix
                            stack.append(lowercase_						)  # push x to stack

              print(
                  x.center(8						)			,    (''''''.join(lowercase_						)).ljust(lowercase_						)			,    (''''''.join(lowercase_						)).ljust(lowercase_						)			,    sep=''' | '''			,    )  # Output in tabular format

       while len(lowercase_						) > 0:  # while stack is not empty
              post_fix.append(stack.pop()						)  # pop stack & add to Postfix
              print(
                  ''' '''.center(8						)			,    (''''''.join(lowercase_						)).ljust(lowercase_						)			,    (''''''.join(lowercase_						)).ljust(lowercase_						)			,    sep=''' | '''			,    )  # Output in tabular format

       return "".join(lowercase_						)  # return Postfix as str







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Optional[Any]:



       """simple docstring"""

       A__							=   list(infix[::-1]						)  # reverse the infix equation

       for i in range(len(lowercase_						)						):
              if infix[i] == "(":
                     A__							=   ''')'''  # change "(" to ")"
              elif infix[i] == ")":
                     A__							=   '''('''  # change ")" to "("

       return (infix_2_postfix(''''''.join(lowercase_						)						))[
           ::-1
       ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
  _lowerCamelCase     :		List[str]								=       input("""\nEnter an Infix Equation = """)  # Input an Infix equation
  _lowerCamelCase     :		Dict								=       """""".join(Infix.split())  # Remove spaces from the input
  print("""\n\t""", Infix, """(Infix) -> """, infix_2_prefix(Infix), """(Prefix)""")


