



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	:   Tuple    =   "sshleifer/student_marian_en_ro_6_1"
_lowerCAmelCase	:   Optional[Any]    =   "sshleifer/tiny-mbart"


@require_torch
class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_=False    ,a_=None    ,a_=True    ,a_=True    ,a_=True    ,a_=True    ,):







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

						if not do_eval:
										return

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

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

										lowerCAmelCase__		  =  eval_metrics[-1]
										assert isinstance(last_step_stats['eval_bleu']    ,a_						)
										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						):







						"""simple docstring"""
						self.run_seqaseq_quick()
		@require_torch_multi_gpu
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self.run_seqaseq_quick(distributed=a_						)
		@require_torch_multi_gpu
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self.run_seqaseq_quick(distributed=a_						)
		@unittest.skip('Requires an update of the env running those tests'						)
		@require_torch_multi_gpu
		@require_fairscale
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self.run_seqaseq_quick(distributed=a_    ,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						):







						"""simple docstring"""
						self.run_seqaseq_quick(distributed=a_    ,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						):







						"""simple docstring"""
						self.run_seqaseq_quick(distributed=a_    ,extra_args_str='--sharded_ddp zero_dp_2'    ,predict_with_generate=a_						)
		@unittest.skip('Requires an update of the env running those tests'						)
		@require_torch_multi_gpu
		@require_fairscale
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self.run_seqaseq_quick(
						    distributed=a_    ,extra_args_str='--sharded_ddp zero_dp_2 --fp16'    ,predict_with_generate=a_						)
		@require_apex
		@require_torch_gpu
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# 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=a_    ,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=a_    ,extra_args_str='--fp16 --fp16_backend=apex'						)
		@parameterized.expand(['base', 'low', 'high', 'mixed']						)
		@require_torch_multi_gpu
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
						lowerCAmelCase__		  =  {
						    # 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__		  =  experiments[experiment_id]
						lowerCAmelCase__		  =  {'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
						lowerCAmelCase__		  =  'Running training'
						with CaptureStderr() as cl:
										self.run_seqaseq_quick(**a_    ,extra_args_str=data['extra_args_str']						)
						lowerCAmelCase__		  =  len(re.findall(a_    ,cl.err						)						)
						self.assertEqual(a_    ,data['n_matches']						)
		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.run_trainer(
						    eval_steps=2    ,max_len=128    ,model_name=a_    ,learning_rate=3e-4    ,num_train_epochs=10    ,distributed=a_    ,)

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

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

						# test if do_predict saves generations and metrics
						lowerCAmelCase__		  =  os.listdir(a_						)
						lowerCAmelCase__		  =  {os.path.basename(a_						) for p in contents}
						assert "generated_predictions.txt" in contents
						assert "predict_results.json" in contents
		@slow
		@require_bitsandbytes
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						from transformers.training_args import OptimizerNames

						def train_and_return_metrics(a_						) -> Tuple[int, float]:
										lowerCAmelCase__		  =  '--skip_memory_metrics 0'

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

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

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

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

						lowerCAmelCase__		  =  gpu_alloc_mem_orig - gpu_alloc_mem_bnb

						lowerCAmelCase__		  =  gpu_peak_mem_orig + gpu_alloc_mem_orig
						lowerCAmelCase__		  =  gpu_peak_mem_bnb + gpu_alloc_mem_bnb
						lowerCAmelCase__		  =  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__		  =  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(
						    a_    ,a_    ,'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(
						    a_    ,a_    ,'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(
						    a_    ,a_    ,f'loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'						)



		def 						SCREAMING_SNAKE_CASE_					(							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    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
						lowerCAmelCase__		  =  self.get_auto_remove_tmp_dir()
						lowerCAmelCase__		  =  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(a_						)}\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(a_						)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        '.split()

						lowerCAmelCase__		  =  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(a_						)}\n        '.split()

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

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

						return output_dir





from typing import Dict

from .base import GenericTensor, Pipeline


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_=None    ,a_=None    ,a_=None    ,**a_						):







						"""simple docstring"""
						if tokenize_kwargs is None:
										lowerCAmelCase__		  =  {}

						if truncation is not None:
										if "truncation" in tokenize_kwargs:
														raise ValueError(
														    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'						)
										lowerCAmelCase__		  =  truncation

						lowerCAmelCase__		  =  tokenize_kwargs

						lowerCAmelCase__		  =  {}
						if return_tensors is not None:
										lowerCAmelCase__		  =  return_tensors

						return preprocess_params, {}, postprocess_params
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,**a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.framework
						lowerCAmelCase__		  =  self.tokenizer(a_    ,return_tensors=a_    ,**a_						)
						return model_inputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model(**a_						)
						return model_outputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=False						):







						"""simple docstring"""
						# [0] is the first available tensor, logits or last_hidden_state.
						if return_tensors:
										return model_outputs[0]
						if self.framework == "pt":
										return model_outputs[0].tolist()
						elif self.framework == "tf":
										return model_outputs[0].numpy().tolist()



		def __call__(							self    ,*a_    ,**a_						):







						"""simple docstring"""
						return super().__call__(*a_    ,**a_						)
