


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


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


set_seed(42)
_A		:				Optional[Any]									=       '''sshleifer/student_marian_en_ro_6_1'''
_A		:				Optional[int]									=       '''sshleifer/tiny-mbart'''



@require_torch
class        a					(			_UpperCAmelCase   ):





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=False    ,	SCREAMING_SNAKE_CASE_					:				Dict=None    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=True    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=True    ,	SCREAMING_SNAKE_CASE_					:				int=True    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=True    ,	):
        __lowerCamelCase:				List[str]     =	self.run_trainer(
            eval_steps=1    ,	max_len=12    ,	model_name=SCREAMING_SNAKE_CASE_    ,	num_train_epochs=1    ,	distributed=SCREAMING_SNAKE_CASE_    ,	extra_args_str=SCREAMING_SNAKE_CASE_    ,	predict_with_generate=SCREAMING_SNAKE_CASE_    ,	do_train=SCREAMING_SNAKE_CASE_    ,	do_eval=SCREAMING_SNAKE_CASE_    ,	do_predict=SCREAMING_SNAKE_CASE_    ,	)
        __lowerCamelCase:				str     =	TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE_    ,	"""trainer_state.json"""							)							).log_history

        if not do_eval:
           return

        __lowerCamelCase:				Optional[int]     =	[log for log in logs if """eval_loss""" in log.keys()]

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

           __lowerCamelCase:				Any     =	eval_metrics[-1]
           assert isinstance(last_step_stats["""eval_bleu"""]    ,	SCREAMING_SNAKE_CASE_							)
           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					:				Tuple							):
        self.run_seqaseq_quick()





     @require_torch_multi_gpu
     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE_							)





     @require_torch_multi_gpu
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE_							)





     @unittest.skip("""Requires an update of the env running those tests"""							)
     @require_torch_multi_gpu
     @require_fairscale
     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE_    ,	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					:				Union[str, Any]							):
        self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE_    ,	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]							):
        self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE_    ,	extra_args_str="""--sharded_ddp zero_dp_2"""    ,	predict_with_generate=SCREAMING_SNAKE_CASE_							)





     @unittest.skip("""Requires an update of the env running those tests"""							)
     @require_torch_multi_gpu
     @require_fairscale
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        self.run_seqaseq_quick(
            distributed=SCREAMING_SNAKE_CASE_    ,	extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""    ,	predict_with_generate=SCREAMING_SNAKE_CASE_							)





     @require_apex
     @require_torch_gpu
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
        # program and it breaks other tests that run from the same pytest worker, therefore until this is
        # sorted out it must be run only in an external program, that is distributed=True in this
        # test and only under one or more gpus - if we want cpu will need to make a special test
        #
        # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
        # 2nd main() call it botches the future eval.
        #
        self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE_    ,	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=SCREAMING_SNAKE_CASE_    ,	extra_args_str="""--fp16 --fp16_backend=apex"""							)





     @parameterized.expand(["""base""", """low""", """high""", """mixed"""]							)
     @require_torch_multi_gpu
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
        __lowerCamelCase:				Dict     =	{
            # 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},
        }

        __lowerCamelCase:				int     =	experiments[experiment_id]
        __lowerCamelCase:				List[str]     =	{"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
        __lowerCamelCase:				Any     =	"""Running training"""
        with CaptureStderr() as cl:
           self.run_seqaseq_quick(**SCREAMING_SNAKE_CASE_    ,	extra_args_str=data["""extra_args_str"""]							)
        __lowerCamelCase:				Any     =	len(re.findall(SCREAMING_SNAKE_CASE_    ,	cl.err							)							)
        self.assertEqual(SCREAMING_SNAKE_CASE_    ,	data["""n_matches"""]							)





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Tuple     =	self.run_trainer(
            eval_steps=2    ,	max_len=128    ,	model_name=SCREAMING_SNAKE_CASE_    ,	learning_rate=3E-4    ,	num_train_epochs=10    ,	distributed=SCREAMING_SNAKE_CASE_    ,	)

        # Check metrics
        __lowerCamelCase:				Optional[Any]     =	TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE_    ,	"""trainer_state.json"""							)							).log_history
        __lowerCamelCase:				Optional[Any]     =	[log for log in logs if """eval_loss""" in log.keys()]
        __lowerCamelCase:				List[Any]     =	eval_metrics[0]
        __lowerCamelCase:				Optional[Any]     =	eval_metrics[-1]

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

        # test if do_predict saves generations and metrics
        __lowerCamelCase:				Optional[Any]     =	os.listdir(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	{os.path.basename(SCREAMING_SNAKE_CASE_							) for p in contents}
        assert "generated_predictions.txt" in contents
        assert "predict_results.json" in contents





     @slow
     @require_bitsandbytes
     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        from transformers.training_args import OptimizerNames

        def train_and_return_metrics(SCREAMING_SNAKE_CASE_					:				str							) -> Tuple[int, float]:
           __lowerCamelCase:				Any     =	"""--skip_memory_metrics 0"""

           __lowerCamelCase:				Optional[Any]     =	self.run_trainer(
               max_len=128    ,	model_name=SCREAMING_SNAKE_CASE_    ,	learning_rate=3E-4    ,	num_train_epochs=1    ,	optim=SCREAMING_SNAKE_CASE_    ,	distributed=SCREAMING_SNAKE_CASE_    ,	extra_args_str=SCREAMING_SNAKE_CASE_    ,	do_eval=SCREAMING_SNAKE_CASE_    ,	do_predict=SCREAMING_SNAKE_CASE_    ,	n_gpus_to_use=1    ,	)

           # Check metrics
           __lowerCamelCase:				Union[str, Any]     =	TrainerState.load_from_json(Path(SCREAMING_SNAKE_CASE_    ,	"""trainer_state.json"""							)							).log_history
           __lowerCamelCase:				Optional[Any]     =	int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20							)
           __lowerCamelCase:				Tuple     =	int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20							)

           __lowerCamelCase:				List[str]     =	logs[0]["""train_loss"""]
           return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

        __lowerCamelCase:				Dict     =	train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value							)
        __lowerCamelCase:				Tuple     =	train_and_return_metrics(OptimizerNames.ADAMW_BNB.value							)

        __lowerCamelCase:				Optional[int]     =	gpu_alloc_mem_orig - gpu_alloc_mem_bnb

        __lowerCamelCase:				Union[str, Any]     =	gpu_peak_mem_orig + gpu_alloc_mem_orig
        __lowerCamelCase:				str     =	gpu_peak_mem_bnb + gpu_alloc_mem_bnb
        __lowerCamelCase:				Optional[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
        __lowerCamelCase:				Optional[int]     =	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(
            SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	"""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(
            SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	"""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(
            SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	F'loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				float = 3E-3    ,	SCREAMING_SNAKE_CASE_					:				str = "adafactor"    ,	SCREAMING_SNAKE_CASE_					:				bool = False    ,	SCREAMING_SNAKE_CASE_					:				str = None    ,	SCREAMING_SNAKE_CASE_					:				int = 0    ,	SCREAMING_SNAKE_CASE_					:				bool = True    ,	SCREAMING_SNAKE_CASE_					:				bool = True    ,	SCREAMING_SNAKE_CASE_					:				bool = True    ,	SCREAMING_SNAKE_CASE_					:				bool = True    ,	SCREAMING_SNAKE_CASE_					:				int = None    ,	):
        __lowerCamelCase:				str     =	self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
        __lowerCamelCase:				Dict     =	self.get_auto_remove_tmp_dir()
        __lowerCamelCase:				List[Any]     =	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(SCREAMING_SNAKE_CASE_							)}\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(SCREAMING_SNAKE_CASE_							)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        '.split()

        __lowerCamelCase:				Tuple     =	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(SCREAMING_SNAKE_CASE_							)}\n        '.split()

        __lowerCamelCase:				Tuple     =	"""
            --do_predict
        """.split()

        __lowerCamelCase:				int     =	[]
        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:
              __lowerCamelCase:				Optional[Any]     =	get_gpu_count()
           __lowerCamelCase:				Any     =	get_torch_dist_unique_port()
           __lowerCamelCase:				int     =	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()
           __lowerCamelCase:				Any     =	[sys.executable] + distributed_args + args
           # keep for quick debug
           # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
           execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,	env=self.get_env()							)
        else:
           __lowerCamelCase:				str     =	["""run_translation.py"""] + args
           with patch.object(SCREAMING_SNAKE_CASE_    ,	"""argv"""    ,	SCREAMING_SNAKE_CASE_							):
              main()

        return output_dir






_A		:				Tuple									=       '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''



def     __lowerCAmelCase						(							)       ->							None:
   __lowerCamelCase:				Optional[int]     =	input("""Enter message: """ )
   __lowerCamelCase:				Dict     =	input("""Enter key [alphanumeric]: """ )
   __lowerCamelCase:				List[Any]     =	input("""Encrypt/Decrypt [e/d]: """ )

   if mode.lower().startswith("""e""" ):
      __lowerCamelCase:				Optional[int]     =	"""encrypt"""
      __lowerCamelCase:				Optional[int]     =	encrypt_message(snake_case						,      snake_case )
   elif mode.lower().startswith("""d""" ):
      __lowerCamelCase:				Union[str, Any]     =	"""decrypt"""
      __lowerCamelCase:				Optional[Any]     =	decrypt_message(snake_case						,      snake_case )

   print(f'\n{mode.title()}ed message:' )
   print(snake_case )



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str )       ->							str:
   return translate_message(snake_case						,      snake_case						,      """encrypt""" )



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str )       ->							str:
   return translate_message(snake_case						,      snake_case						,      """decrypt""" )



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str						,      snake_case	:    str )       ->							str:
   __lowerCamelCase:				Any     =	[]
   __lowerCamelCase:				Optional[int]     =	0
   __lowerCamelCase:				Any     =	key.upper()

   for symbol in message:
      __lowerCamelCase:				int     =	LETTERS.find(symbol.upper() )
      if num != -1:
         if mode == "encrypt":
            num += LETTERS.find(key[key_index] )
         elif mode == "decrypt":
            num -= LETTERS.find(key[key_index] )

         num %= len(snake_case )

         if symbol.isupper():
            translated.append(LETTERS[num] )
         elif symbol.islower():
            translated.append(LETTERS[num].lower() )

         key_index += 1
         if key_index == len(snake_case ):
            __lowerCamelCase:				Union[str, Any]     =	0
      else:
         translated.append(snake_case )
   return "".join(snake_case )


if __name__ == "__main__":
   main()


