




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


set_seed(4_2)
lowerCamelCase			:			Dict								=       '''sshleifer/student_marian_en_ro_6_1'''
lowerCamelCase			:			str								=       '''sshleifer/tiny-mbart'''




@require_torch
class 		__lowercase						(lowerCAmelCase__				):




      """simple docstring"""





      def       UpperCAmelCase ( self							, A=False							, A=None							, A=True							, A=True							, A=True							, A=True							, )						->							Optional[int]:
         snake_case :							Dict           =			self.run_trainer(
             eval_steps=1							, max_len=1_2							, model_name=snake_case_							, num_train_epochs=1							, distributed=snake_case_							, extra_args_str=snake_case_							, predict_with_generate=snake_case_							, do_train=snake_case_							, do_eval=snake_case_							, do_predict=snake_case_							, )
         snake_case :							Tuple           =			TrainerState.load_from_json(os.path.join(snake_case_							, """trainer_state.json"""      )      ).log_history

         if not do_eval:
            return

         snake_case :							Dict           =			[log for log in logs if """eval_loss""" in log.keys()]

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

            snake_case :							int           =			eval_metrics[-1]
            assert isinstance(last_step_stats["""eval_bleu"""]							, snake_case_      )
            assert not math.isnan(float(last_step_stats["""eval_loss"""]      )      ), "eval_loss must not be `nan`"

      @require_torch_non_multi_gpu
      def       UpperCAmelCase ( self      )						->							List[str]:
         self.run_seqaseq_quick()

      @require_torch_multi_gpu
      def       UpperCAmelCase ( self      )						->							Dict:
         self.run_seqaseq_quick(distributed=snake_case_      )

      @require_torch_multi_gpu
      def       UpperCAmelCase ( self      )						->							int:
         self.run_seqaseq_quick(distributed=snake_case_      )

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

      @unittest.skip("""Requires an update of the env running those tests"""      )
      @require_torch_multi_gpu
      @require_fairscale
      def       UpperCAmelCase ( self      )						->							Dict:
         self.run_seqaseq_quick(
             distributed=snake_case_							, extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""							, predict_with_generate=snake_case_      )

      @require_apex
      @require_torch_gpu
      def       UpperCAmelCase ( self      )						->							Any:
         self.run_seqaseq_quick(distributed=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=snake_case_							, extra_args_str="""--fp16 --fp16_backend=apex"""      )

      @parameterized.expand(["""base""", """low""", """high""", """mixed"""]      )
      @require_torch_multi_gpu
      def       UpperCAmelCase ( self							, A      )						->							List[str]:
         snake_case :							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},
         }

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

      @slow
      def       UpperCAmelCase ( self      )						->							Optional[Any]:
         snake_case :							Dict           =			self.run_trainer(
             eval_steps=2							, max_len=1_2_8							, model_name=snake_case_							, learning_rate=3e-4							, num_train_epochs=1_0							, distributed=snake_case_							, )

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

         # test if do_predict saves generations and metrics
         snake_case :							Optional[int]           =			os.listdir(snake_case_      )
         snake_case :							List[str]           =			{os.path.basename(snake_case_      ) for p in contents}
         assert "generated_predictions.txt" in contents
         assert "predict_results.json" in contents

      @slow
      @require_bitsandbytes
      def       UpperCAmelCase ( self      )						->							Optional[int]:
         from transformers.training_args import OptimizerNames

         def train_and_return_metrics(A      ) -> Tuple[int, float]:
            snake_case :							Tuple           =			"""--skip_memory_metrics 0"""

            snake_case :							Tuple           =			self.run_trainer(
                max_len=1_2_8							, model_name=snake_case_							, learning_rate=3e-4							, num_train_epochs=1							, optim=snake_case_							, distributed=snake_case_							, extra_args_str=snake_case_							, do_eval=snake_case_							, do_predict=snake_case_							, n_gpus_to_use=1							, )

            # Check metrics
            snake_case :							Optional[int]           =			TrainerState.load_from_json(Path(snake_case_							, """trainer_state.json"""      )      ).log_history
            snake_case :							List[Any]           =			int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**2_0      )
            snake_case :							List[Any]           =			int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**2_0      )

            snake_case :							Optional[int]           =			logs[0]["""train_loss"""]
            return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

         snake_case      ,   snake_case      ,   snake_case :							Optional[Any]           =			train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value      )
         snake_case      ,   snake_case      ,   snake_case :							int           =			train_and_return_metrics(OptimizerNames.ADAMW_BNB.value      )

         snake_case :							Any           =			gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         snake_case :							Dict           =			gpu_peak_mem_orig + gpu_alloc_mem_orig
         snake_case :							str           =			gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         snake_case :							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
         snake_case :							Optional[int]           =			1_2_0

         # 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(
             snake_case_							, 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(
             snake_case_							, 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(
             snake_case_							, snake_case_							, f"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""      )

      def       UpperCAmelCase ( self							, A							, A							, A							, A = 3e-3							, A = "adafactor"							, A = False							, A = None							, A = 0							, A = True							, A = True							, A = True							, A = True							, A = None							, )						->							Tuple:
         snake_case :							List[str]           =			self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
         snake_case :							Optional[Any]           =			self.get_auto_remove_tmp_dir()
         snake_case :							Union[str, 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(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(snake_case_      )}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        """.split()

         snake_case :							Any           =			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(snake_case_      )}\n        """.split()

         snake_case :							str           =			"""
            --do_predict
        """.split()

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

         return output_dir









a			:			Any												=					[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = 0
			while number:
						# Increased Speed Slightly by checking every 5 digits together.
						sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00]
						number //= 10_00_00

			return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
a			:			list[bool | None]												=					[None] * 10000000
a			:			int												=					True
a			:			Any												=					False


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if CHAINS[number - 1] is not None:
						return CHAINS[number - 1]  # type: ignore

			__lowercase			       = chain(next_number(_UpperCamelCase	)	)
			__lowercase			       = number_chain

			while number < 10_00_00_00:
						__lowercase			       = number_chain
						number *= 10

			return number_chain


def 		lowercase_	(  _UpperCamelCase = 10_00_00_00	):


			'''simple docstring'''







			for i in range(1							,      _UpperCamelCase	):
						if CHAINS[i] is None:
									chain(i + 1	)

			return CHAINS[:number].count(_UpperCamelCase	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(f'''{solution() = }''')


