



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


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


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


@require_torch
class       _lowercase   (a_      ):
				'''simple docstring'''







				def       _lowerCamelCase					( self     , snake_case__=False     , snake_case__=None     , snake_case__=True     , snake_case__=True     , snake_case__=True     , snake_case__=True     , ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.run_trainer(
									    eval_steps=1     , max_len=12     , 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__     , )
									UpperCamelCase_		  =   TrainerState.load_from_json(os.path.join(snake_case__     , "trainer_state.json"    )    ).log_history

									if not do_eval:
														return

									UpperCamelCase_		  =   [log for log in logs if "eval_loss" in log.keys()]

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

														UpperCamelCase_		  =   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       _lowerCamelCase					( self    ):

									'''simple docstring'''


									self.run_seqaseq_quick()

				@require_torch_multi_gpu
				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									self.run_seqaseq_quick(distributed=snake_case__    )

				@require_torch_multi_gpu
				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									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       _lowerCamelCase					( self    ):

									'''simple docstring'''


									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       _lowerCamelCase					( self    ):

									'''simple docstring'''


									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       _lowerCamelCase					( self    ):

									'''simple docstring'''


									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       _lowerCamelCase					( self    ):

									'''simple docstring'''


									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       _lowerCamelCase					( self    ):

									'''simple docstring'''


									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       _lowerCamelCase					( self     , snake_case__    ):

									'''simple docstring'''


									UpperCamelCase_		  =   {
									    # 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},
									}

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

				@slow
				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


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

									# Check metrics
									UpperCamelCase_		  =   TrainerState.load_from_json(os.path.join(snake_case__     , "trainer_state.json"    )    ).log_history
									UpperCamelCase_		  =   [log for log in logs if "eval_loss" in log.keys()]
									UpperCamelCase_		  =   eval_metrics[0]
									UpperCamelCase_		  =   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
									UpperCamelCase_		  =   os.listdir(snake_case__    )
									UpperCamelCase_		  =   {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       _lowerCamelCase					( self    ):

									'''simple docstring'''


									from transformers.training_args import OptimizerNames

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

														UpperCamelCase_		  =   self.run_trainer(
														    max_len=128     , 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
														UpperCamelCase_		  =   TrainerState.load_from_json(Path(snake_case__     , "trainer_state.json"    )    ).log_history
														UpperCamelCase_		  =   int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20    )
														UpperCamelCase_		  =   int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20    )

														UpperCamelCase_		  =   logs[0]["train_loss"]
														return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

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

									UpperCamelCase_		  =   gpu_alloc_mem_orig - gpu_alloc_mem_bnb

									UpperCamelCase_		  =   gpu_peak_mem_orig + gpu_alloc_mem_orig
									UpperCamelCase_		  =   gpu_peak_mem_bnb + gpu_alloc_mem_bnb
									UpperCamelCase_		  =   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
									UpperCamelCase_		  =   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(
									    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       _lowerCamelCase					( self     , snake_case__     , snake_case__     , snake_case__     , snake_case__ = 3e-3     , snake_case__ = "adafactor"     , snake_case__ = False     , snake_case__ = None     , snake_case__ = 0     , snake_case__ = True     , snake_case__ = True     , snake_case__ = True     , snake_case__ = True     , snake_case__ = None     , ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
									UpperCamelCase_		  =   self.get_auto_remove_tmp_dir()
									UpperCamelCase_		  =   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(snake_case__    )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(snake_case__    )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

									UpperCamelCase_		  =   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(snake_case__    )}
        """.split()

									UpperCamelCase_		  =   "\n            --do_predict\n        ".split()

									UpperCamelCase_		  =   []
									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:
																			UpperCamelCase_		  =   get_gpu_count()
														UpperCamelCase_		  =   get_torch_dist_unique_port()
														UpperCamelCase_		  =   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()
														UpperCamelCase_		  =   [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:
														UpperCamelCase_		  =   ["run_translation.py"] + args
														with patch.object(snake_case__     , "argv"     , snake_case__    ):
																			main()

									return output_dir





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase   :							Union[str, Any]							 =logging.get_logger(__name__)

UpperCAmelCase   :							Dict							 ={"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}


class       _lowercase   (a_      ):
				'''simple docstring'''







				lowercase__   						=						"""ctrl"""
				lowercase__   						=						["""past_key_values"""]
				lowercase__   						=						{
				    """max_position_embeddings""": """n_positions""",
				    """hidden_size""": """n_embd""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}

				def __init__( self     , snake_case__=24_6534     , snake_case__=256     , snake_case__=1280     , snake_case__=8192     , snake_case__=48     , snake_case__=16     , snake_case__=0.1     , snake_case__=0.1     , snake_case__=1e-6     , snake_case__=0.02     , snake_case__=True     , **snake_case__     , ):

									'''simple docstring'''


									UpperCamelCase_		  =   vocab_size
									UpperCamelCase_		  =   n_positions
									UpperCamelCase_		  =   n_embd
									UpperCamelCase_		  =   n_layer
									UpperCamelCase_		  =   n_head
									UpperCamelCase_		  =   dff
									UpperCamelCase_		  =   resid_pdrop
									UpperCamelCase_		  =   embd_pdrop
									UpperCamelCase_		  =   layer_norm_epsilon
									UpperCamelCase_		  =   initializer_range

									UpperCamelCase_		  =   use_cache

									super().__init__(**snake_case__    )
