

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 				a  (	UpperCAmelCase				):


						def        _UpperCAmelCase				(       self						,					A_=False						,					A_=None						,					A_=True						,					A_=True						,					A_=True						,					A_=True						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    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_						,					)
										_UpperCAmelCase       :		Tuple 		=    TrainerState.load_from_json(os.path.join(A_						,					"trainer_state.json"	)	).log_history

										if not do_eval:
														return

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

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

														_UpperCAmelCase       :		Any 		=    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        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										self.run_seqaseq_quick()


						@require_torch_multi_gpu
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										self.run_seqaseq_quick(distributed=A_	)


						@require_torch_multi_gpu
						def        _UpperCAmelCase				(       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        _UpperCAmelCase				(       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        _UpperCAmelCase				(       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        _UpperCAmelCase				(       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        _UpperCAmelCase				(       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        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										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        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


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


						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.run_trainer(
										    eval_steps=2						,					max_len=128						,					model_name=A_						,					learning_rate=3e-4						,					num_train_epochs=10						,					distributed=A_						,					)

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

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


						@slow
						@require_bitsandbytes
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										from transformers.training_args import OptimizerNames

										def train_and_return_metrics(A_	) -> Tuple[int, float]:
														_UpperCAmelCase       :		Optional[int] 		=    "--skip_memory_metrics 0"

														_UpperCAmelCase       :		List[str] 		=    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
														_UpperCAmelCase       :		Union[str, Any] 		=    TrainerState.load_from_json(Path(A_						,					"trainer_state.json"	)	).log_history
														_UpperCAmelCase       :		Optional[int] 		=    int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20	)
														_UpperCAmelCase       :		Union[str, Any] 		=    int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20	)

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

										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		Tuple 		=    train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value	)
										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    train_and_return_metrics(OptimizerNames.ADAMW_BNB.value	)

										_UpperCAmelCase       :		int 		=    gpu_alloc_mem_orig - gpu_alloc_mem_bnb

										_UpperCAmelCase       :		Union[str, Any] 		=    gpu_peak_mem_orig + gpu_alloc_mem_orig
										_UpperCAmelCase       :		Union[str, Any] 		=    gpu_peak_mem_bnb + gpu_alloc_mem_bnb
										_UpperCAmelCase       :		List[str] 		=    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       :		List[Any] 		=    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        _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						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
										_UpperCAmelCase       :		str 		=    self.get_auto_remove_tmp_dir()
										_UpperCAmelCase       :		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(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()

										_UpperCAmelCase       :		int 		=    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()

										_UpperCAmelCase       :		Optional[Any] 		=    "\n            --do_predict\n        ".split()

										_UpperCAmelCase       :		Tuple 		=    []
										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       :		Optional[int] 		=    get_gpu_count()
														_UpperCAmelCase       :		List[str] 		=    get_torch_dist_unique_port()
														_UpperCAmelCase       :		Tuple 		=    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()
														_UpperCAmelCase       :		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(A_						,					env=self.get_env()	)
										else:
														_UpperCAmelCase       :		Union[str, Any] 		=    ["run_translation.py"] + args
														with patch.object(A_						,					"argv"						,					A_	):
																		main()

										return output_dir





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
					from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"bloom"
						_lowercase    						=			["past_key_values"]
						_lowercase    						=			{
						    "num_hidden_layers": "n_layer",
						    "num_attention_heads": "n_head",
						}


						def __init__(       self						,					A_=250880						,					A_=64						,					A_=2						,					A_=8						,					A_=1e-5						,					A_=0.02						,					A_=True						,					A_=1						,					A_=2						,					A_=False						,					A_=0.0						,					A_=0.0						,					A_=1						,					A_=False						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    vocab_size
										# Backward compatibility with n_embed kwarg
										_UpperCAmelCase       :		str 		=    kwargs.pop("n_embed"						,					A_	)
										_UpperCAmelCase       :		List[Any] 		=    hidden_size if n_embed is None else n_embed
										_UpperCAmelCase       :		Dict 		=    n_layer
										_UpperCAmelCase       :		Optional[Any] 		=    n_head
										_UpperCAmelCase       :		Optional[Any] 		=    layer_norm_epsilon
										_UpperCAmelCase       :		List[Any] 		=    initializer_range
										_UpperCAmelCase       :		Union[str, Any] 		=    use_cache
										_UpperCAmelCase       :		List[str] 		=    pretraining_tp
										_UpperCAmelCase       :		List[Any] 		=    apply_residual_connection_post_layernorm
										_UpperCAmelCase       :		int 		=    hidden_dropout
										_UpperCAmelCase       :		List[str] 		=    attention_dropout

										_UpperCAmelCase       :		int 		=    bos_token_id
										_UpperCAmelCase       :		str 		=    eos_token_id
										_UpperCAmelCase       :		Optional[Any] 		=    slow_but_exact

										super().__init__(bos_token_id=A_						,					eos_token_id=A_						,					**A_	)







class 				a  (	UpperCAmelCase				):
						_lowercase    						=			version.parse("1.12"				)


						def __init__(       self						,					A_						,					A_ = "default"						,					A_ = None						,					A_ = False						,					):







										'''simple docstring'''


										super().__init__(A_						,					task=A_						,					patching_specs=A_						,					use_past=A_	)
										if not getattr(self._config						,					"pad_token_id"						,					A_	):
														# TODO: how to do that better?
														_UpperCAmelCase       :		str 		=    0


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}	)
										if self.use_past:
														# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
														self.fill_with_past_key_values_(A_						,					direction="inputs"						,					inverted_values_shape=A_	)
														_UpperCAmelCase       :		Optional[int] 		=    {0: "batch", 1: "past_sequence + sequence"}
										else:
														_UpperCAmelCase       :		Union[str, Any] 		=    {0: "batch", 1: "sequence"}

										return common_inputs


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._config.n_layer


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._config.n_head


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 1e-3


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = -1						,					A_ = -1						,					A_ = False						,					A_ = None						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    super(A_						,					self	).generate_dummy_inputs(
										    A_						,					batch_size=A_						,					seq_length=A_						,					is_pair=A_						,					framework=A_	)

										# We need to order the input in the way they appears in the forward()
										_UpperCAmelCase       :		Tuple 		=    OrderedDict({"input_ids": common_inputs["input_ids"]}	)

										# Need to add the past_keys
										if self.use_past:
														if not is_torch_available():
																		raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
														else:
																		import torch

																		_UpperCAmelCase		,							_UpperCAmelCase       :		Union[str, Any] 		=    common_inputs["input_ids"].shape
																		# Not using the same length for past_key_values
																		_UpperCAmelCase       :		List[Any] 		=    seqlen + 2
																		_UpperCAmelCase       :		Dict 		=    self._config.hidden_size // self.num_attention_heads
																		_UpperCAmelCase       :		List[Any] 		=    (
																		    batch * self.num_attention_heads,
																		    head_dim,
																		    past_key_values_length,
																		)
																		_UpperCAmelCase       :		Optional[Any] 		=    (
																		    batch * self.num_attention_heads,
																		    past_key_values_length,
																		    head_dim,
																		)
																		_UpperCAmelCase       :		Tuple 		=    [
																		    (torch.zeros(A_	), torch.zeros(A_	)) for _ in range(self.num_layers	)
																		]

										_UpperCAmelCase       :		int 		=    common_inputs["attention_mask"]
										if self.use_past:
														_UpperCAmelCase       :		str 		=    ordered_inputs["attention_mask"].dtype
														_UpperCAmelCase       :		Optional[int] 		=    torch.cat(
														    [ordered_inputs["attention_mask"], torch.ones(A_						,					A_						,					dtype=A_	)]						,					dim=1	)

										return ordered_inputs





						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 13


