"""simple docstring"""







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


__SCREAMING_SNAKE_CASE								=os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"{bindir}/../../examples/pytorch/translation"):
       from run_translation import main  # noqa


set_seed(42)
__SCREAMING_SNAKE_CASE								="sshleifer/student_marian_en_ro_6_1"
__SCREAMING_SNAKE_CASE								="sshleifer/tiny-mbart"







@require_torch
class  UpperCamelCase	(    lowercase_						):
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase=False  ,__UpperCamelCase=None  ,__UpperCamelCase=True  ,__UpperCamelCase=True  ,__UpperCamelCase=True  ,__UpperCamelCase=True  ,)			->   Optional[Any]:


        '''simple docstring'''


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

        if not do_eval:
             return

        lowercase_			:       Any									=			[log for log in logs if 'eval_loss' in log.keys()]

        lowercase_			:       Optional[Any]									=			eval_metrics[0]
        if predict_with_generate:
             assert "eval_bleu" in first_step_stats

             lowercase_			:       Dict									=			eval_metrics[-1]
             assert isinstance(last_step_stats['eval_bleu']  ,__UpperCamelCase    )
             assert not math.isnan(float(last_step_stats['eval_loss']    )    ), "eval_loss must not be `nan`"
   @require_torch_non_multi_gpu
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        self.run_seqaseq_quick()
   @require_torch_multi_gpu
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        self.run_seqaseq_quick(distributed=__UpperCamelCase    )
   @require_torch_multi_gpu
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        self.run_seqaseq_quick(distributed=__UpperCamelCase    )
   @unittest.skip('Requires an update of the env running those tests'    )
   @require_torch_multi_gpu
   @require_fairscale
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        self.run_seqaseq_quick(distributed=__UpperCamelCase  ,extra_args_str='--sharded_ddp simple'    )
   @unittest.skip('Requires an update of the env running those tests'    )
   @require_torch_multi_gpu
   @require_fairscale
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        self.run_seqaseq_quick(distributed=__UpperCamelCase  ,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     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        self.run_seqaseq_quick(distributed=__UpperCamelCase  ,extra_args_str='--sharded_ddp zero_dp_2'  ,predict_with_generate=__UpperCamelCase    )
   @unittest.skip('Requires an update of the env running those tests'    )
   @require_torch_multi_gpu
   @require_fairscale
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        self.run_seqaseq_quick(
            distributed=__UpperCamelCase  ,extra_args_str='--sharded_ddp zero_dp_2 --fp16'  ,predict_with_generate=__UpperCamelCase    )
   @require_apex
   @require_torch_gpu
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        self.run_seqaseq_quick(distributed=__UpperCamelCase  ,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=__UpperCamelCase  ,extra_args_str='--fp16 --fp16_backend=apex'    )
   @parameterized.expand(['base', 'low', 'high', 'mixed']    )
   @require_torch_multi_gpu
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			{
            # 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},
        }

        lowercase_			:       Dict									=			experiments[experiment_id]
        lowercase_			:       Optional[Any]									=			{'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
        lowercase_			:       Optional[int]									=			'Running training'
        with CaptureStderr() as cl:
             self.run_seqaseq_quick(**__UpperCamelCase  ,extra_args_str=data['extra_args_str']    )
        lowercase_			:       Union[str, Any]									=			len(re.findall(__UpperCamelCase  ,cl.err    )    )
        self.assertEqual(__UpperCamelCase  ,data['n_matches']    )
   @slow
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			self.run_trainer(
            eval_steps=2  ,max_len=128  ,model_name=__UpperCamelCase  ,learning_rate=3e-4  ,num_train_epochs=10  ,distributed=__UpperCamelCase  ,)

        # Check metrics
        lowercase_			:       Dict									=			TrainerState.load_from_json(os.path.join(__UpperCamelCase  ,'trainer_state.json'    )    ).log_history
        lowercase_			:       Union[str, Any]									=			[log for log in logs if 'eval_loss' in log.keys()]
        lowercase_			:       Union[str, Any]									=			eval_metrics[0]
        lowercase_			:       str									=			eval_metrics[-1]

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

        # test if do_predict saves generations and metrics
        lowercase_			:       Dict									=			os.listdir(__UpperCamelCase    )
        lowercase_			:       Any									=			{os.path.basename(__UpperCamelCase    ) for p in contents}
        assert "generated_predictions.txt" in contents
        assert "predict_results.json" in contents
   @slow
   @require_bitsandbytes
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        from transformers.training_args import OptimizerNames

        def train_and_return_metrics(__UpperCamelCase    ) -> Tuple[int, float]:
             lowercase_			:       Dict									=			'--skip_memory_metrics 0'

             lowercase_			:       int									=			self.run_trainer(
                 max_len=128  ,model_name=__UpperCamelCase  ,learning_rate=3e-4  ,num_train_epochs=1  ,optim=__UpperCamelCase  ,distributed=__UpperCamelCase  ,extra_args_str=__UpperCamelCase  ,do_eval=__UpperCamelCase  ,do_predict=__UpperCamelCase  ,n_gpus_to_use=1  ,)

             # Check metrics
             lowercase_			:       int									=			TrainerState.load_from_json(Path(__UpperCamelCase  ,'trainer_state.json'    )    ).log_history
             lowercase_			:       List[str]									=			int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20    )
             lowercase_			:       Dict									=			int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20    )

             lowercase_			:       List[str]									=			logs[0]['train_loss']
             return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

        lowercase_							,						lowercase_							,						lowercase_			:       Dict									=			train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value    )
        lowercase_							,						lowercase_							,						lowercase_			:       Dict									=			train_and_return_metrics(OptimizerNames.ADAMW_BNB.value    )

        lowercase_			:       Dict									=			gpu_alloc_mem_orig - gpu_alloc_mem_bnb

        lowercase_			:       Dict									=			gpu_peak_mem_orig + gpu_alloc_mem_orig
        lowercase_			:       str									=			gpu_peak_mem_bnb + gpu_alloc_mem_bnb
        lowercase_			:       Any									=			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
        lowercase_			:       List[str]									=			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(
            __UpperCamelCase  ,__UpperCamelCase  ,'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(
            __UpperCamelCase  ,__UpperCamelCase  ,'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(
            __UpperCamelCase  ,__UpperCamelCase  ,f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''    )







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase = 3e-3  ,__UpperCamelCase = "adafactor"  ,__UpperCamelCase = False  ,__UpperCamelCase = None  ,__UpperCamelCase = 0  ,__UpperCamelCase = True  ,__UpperCamelCase = True  ,__UpperCamelCase = True  ,__UpperCamelCase = True  ,__UpperCamelCase = None  ,)			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
        lowercase_			:       List[Any]									=			self.get_auto_remove_tmp_dir()
        lowercase_			:       Tuple									=			f'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(__UpperCamelCase    )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(__UpperCamelCase    )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

        lowercase_			:       int									=			f'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(__UpperCamelCase    )}
        '''.split()

        lowercase_			:       Optional[Any]									=			'\n            --do_predict\n        '.split()

        lowercase_			:       Any									=			[]
        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:
                  lowercase_			:       Optional[int]									=			get_gpu_count()
             lowercase_			:       Optional[int]									=			get_torch_dist_unique_port()
             lowercase_			:       List[Any]									=			f'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
             lowercase_			:       Union[str, Any]									=			[sys.executable] + distributed_args + args
             # keep for quick debug
             # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
             execute_subprocess_async(__UpperCamelCase  ,env=self.get_env()    )
        else:
             lowercase_			:       Dict									=			['run_translation.py'] + args
             with patch.object(__UpperCamelCase  ,'argv'  ,__UpperCamelCase    ):
                  main()

        return output_dir


"""simple docstring"""







__SCREAMING_SNAKE_CASE								="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"



def     lowercase__( __SCREAMING_SNAKE_CASE							:  bytes   ):
     # Make sure the supplied data is a bytes-like object
     if not isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Dict									=			F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
          raise TypeError(__SCREAMING_SNAKE_CASE   )

     lowercase_			:       List[Any]									=			''.join(bin(__SCREAMING_SNAKE_CASE   )[2:].zfill(8   ) for byte in data   )

     lowercase_			:       List[Any]									=			len(__SCREAMING_SNAKE_CASE   ) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          lowercase_			:       Optional[int]									=			b'=' * ((6 - len(__SCREAMING_SNAKE_CASE   ) % 6) // 2)

          # Append binary_stream with arbitrary binary digits (0's by default) to make its
          # length a multiple of 6.
          binary_stream += "0" * (6 - len(__SCREAMING_SNAKE_CASE   ) % 6)
     else:
          lowercase_			:       List[Any]									=			b''

     # Encode every 6 binary digits to their corresponding Base64 character
     return (
         "".join(
             B64_CHARSET[int(binary_stream[index : index + 6]  ,    2   )]
             for index in range(0  ,    len(__SCREAMING_SNAKE_CASE   )  ,    6   )   ).encode()
         + padding
     )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str   ):
     # Make sure encoded_data is either a string or a bytes-like object
     if not isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ) and not isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Tuple									=			(
              'argument should be a bytes-like object or ASCII string, '
              F'''not \'{encoded_data.__class__.__name__}\''''
          )
          raise TypeError(__SCREAMING_SNAKE_CASE   )

     # In case encoded_data is a bytes-like object, make sure it contains only
     # ASCII characters so we convert it to a string object
     if isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          try:
               lowercase_			:       List[str]									=			encoded_data.decode('utf-8'   )
          except UnicodeDecodeError:
               raise ValueError('base64 encoded data should only contain ASCII characters'   )

     lowercase_			:       List[str]									=			encoded_data.count('='   )

     # Check if the encoded string contains non base64 characters
     if padding:
          assert all(
              char in B64_CHARSET for char in encoded_data[:-padding]   ), "Invalid base64 character(s) found."
     else:
          assert all(
              char in B64_CHARSET for char in encoded_data   ), "Invalid base64 character(s) found."

     # Check the padding
     assert len(__SCREAMING_SNAKE_CASE   ) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          lowercase_			:       Union[str, Any]									=			encoded_data[:-padding]

          lowercase_			:       List[Any]									=			''.join(
              bin(B64_CHARSET.index(__SCREAMING_SNAKE_CASE   )   )[2:].zfill(6   ) for char in encoded_data   )[: -padding * 2]
     else:
          lowercase_			:       int									=			''.join(
              bin(B64_CHARSET.index(__SCREAMING_SNAKE_CASE   )   )[2:].zfill(6   ) for char in encoded_data   )

     lowercase_			:       str									=			[
         int(binary_stream[index : index + 8]  ,    2   )
         for index in range(0  ,    len(__SCREAMING_SNAKE_CASE   )  ,    8   )
     ]

     return bytes(__SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

