





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


set_seed(4_2)
A    :					Union[str, Any] =      '''sshleifer/student_marian_en_ro_6_1'''
A    :					Any =      '''sshleifer/tiny-mbart'''


@require_torch
class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			def    a_       (			self   :    Any							,	__lowerCAmelCase   :    List[Any]=False							,	__lowerCAmelCase   :    int=None							,	__lowerCAmelCase   :    Dict=True							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    List[str]=True							,	__lowerCAmelCase   :    Tuple=True							,	)	->	Union[str, Any]:







					"""simple docstring"""






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

					if not do_eval:
							return

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

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

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






			@require_torch_non_multi_gpu
			def    a_       (			self   :    Tuple   )	->	Dict:







					"""simple docstring"""






					self.run_seqaseq_quick()






			@require_torch_multi_gpu
			def    a_       (			self   :    List[str]   )	->	Any:







					"""simple docstring"""






					self.run_seqaseq_quick(distributed=__lowerCAmelCase   )






			@require_torch_multi_gpu
			def    a_       (			self   :    int   )	->	Tuple:







					"""simple docstring"""






					self.run_seqaseq_quick(distributed=__lowerCAmelCase   )






			@unittest.skip("""Requires an update of the env running those tests"""   )
			@require_torch_multi_gpu
			@require_fairscale
			def    a_       (			self   :    Dict   )	->	Union[str, Any]:







					"""simple docstring"""






					self.run_seqaseq_quick(distributed=__lowerCAmelCase							,	extra_args_str="""--sharded_ddp simple"""   )






			@unittest.skip("""Requires an update of the env running those tests"""   )
			@require_torch_multi_gpu
			@require_fairscale
			def    a_       (			self   :    List[Any]   )	->	List[str]:







					"""simple docstring"""






					self.run_seqaseq_quick(distributed=__lowerCAmelCase							,	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    a_       (			self   :    Tuple   )	->	Tuple:







					"""simple docstring"""






					self.run_seqaseq_quick(distributed=__lowerCAmelCase							,	extra_args_str="""--sharded_ddp zero_dp_2"""							,	predict_with_generate=__lowerCAmelCase   )






			@unittest.skip("""Requires an update of the env running those tests"""   )
			@require_torch_multi_gpu
			@require_fairscale
			def    a_       (			self   :    int   )	->	Dict:







					"""simple docstring"""






					self.run_seqaseq_quick(
					    distributed=__lowerCAmelCase							,	extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""							,	predict_with_generate=__lowerCAmelCase   )






			@require_apex
			@require_torch_gpu
			def    a_       (			self   :    Dict   )	->	str:







					"""simple docstring"""






					self.run_seqaseq_quick(distributed=__lowerCAmelCase							,	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=__lowerCAmelCase							,	extra_args_str="""--fp16 --fp16_backend=apex"""   )






			@parameterized.expand(["""base""", """low""", """high""", """mixed"""]   )
			@require_torch_multi_gpu
			def    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    Dict   )	->	Tuple:







					"""simple docstring"""






					A__        =   {
					    # 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},
					}

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






			@slow
			def    a_       (			self   :    List[str]   )	->	int:







					"""simple docstring"""






					A__        =   self.run_trainer(
					    eval_steps=2							,	max_len=1_28							,	model_name=__lowerCAmelCase							,	learning_rate=3e-4							,	num_train_epochs=10							,	distributed=__lowerCAmelCase							,	)

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

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

					# test if do_predict saves generations and metrics
					A__        =   os.listdir(__lowerCAmelCase   )
					A__        =   {os.path.basename(__lowerCAmelCase   ) for p in contents}
					assert "generated_predictions.txt" in contents
					assert "predict_results.json" in contents






			@slow
			@require_bitsandbytes
			def    a_       (			self   :    List[Any]   )	->	Any:







					"""simple docstring"""






					from transformers.training_args import OptimizerNames

					def train_and_return_metrics(__lowerCAmelCase   :    str   ) -> Tuple[int, float]:
							A__        =   """--skip_memory_metrics 0"""

							A__        =   self.run_trainer(
							    max_len=1_28							,	model_name=__lowerCAmelCase							,	learning_rate=3e-4							,	num_train_epochs=1							,	optim=__lowerCAmelCase							,	distributed=__lowerCAmelCase							,	extra_args_str=__lowerCAmelCase							,	do_eval=__lowerCAmelCase							,	do_predict=__lowerCAmelCase							,	n_gpus_to_use=1							,	)

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

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

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

					A__        =   gpu_alloc_mem_orig - gpu_alloc_mem_bnb

					A__        =   gpu_peak_mem_orig + gpu_alloc_mem_orig
					A__        =   gpu_peak_mem_bnb + gpu_alloc_mem_bnb
					A__        =   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
					A__        =   1_20

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







			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    float = 3e-3							,	__lowerCAmelCase   :    str = "adafactor"							,	__lowerCAmelCase   :    bool = False							,	__lowerCAmelCase   :    str = None							,	__lowerCAmelCase   :    int = 0							,	__lowerCAmelCase   :    bool = True							,	__lowerCAmelCase   :    bool = True							,	__lowerCAmelCase   :    bool = True							,	__lowerCAmelCase   :    bool = True							,	__lowerCAmelCase   :    int = None							,	)	->	Dict:







					"""simple docstring"""






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

					A__        =   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(__lowerCAmelCase   )}\n        '.split()

					A__        =   """
            --do_predict
        """.split()

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

					return output_dir










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

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


A    :					str =      logging.get_logger(__name__)

A    :					Union[str, Any] =      {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Optional[Any]							=    '''layoutlmv3'''






			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    Optional[int]=5_02_65							,	__lowerCAmelCase   :    Tuple=7_68							,	__lowerCAmelCase   :    Union[str, Any]=12							,	__lowerCAmelCase   :    Any=12							,	__lowerCAmelCase   :    List[Any]=30_72							,	__lowerCAmelCase   :    Optional[int]="gelu"							,	__lowerCAmelCase   :    Dict=0.1							,	__lowerCAmelCase   :    List[Any]=0.1							,	__lowerCAmelCase   :    Dict=5_12							,	__lowerCAmelCase   :    Any=2							,	__lowerCAmelCase   :    Dict=0.0_2							,	__lowerCAmelCase   :    List[str]=1e-5							,	__lowerCAmelCase   :    List[str]=1							,	__lowerCAmelCase   :    int=0							,	__lowerCAmelCase   :    Dict=2							,	__lowerCAmelCase   :    Tuple=10_24							,	__lowerCAmelCase   :    List[str]=1_28							,	__lowerCAmelCase   :    Optional[int]=1_28							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Optional[Any]=32							,	__lowerCAmelCase   :    Any=1_28							,	__lowerCAmelCase   :    str=64							,	__lowerCAmelCase   :    Optional[int]=2_56							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    List[str]=True							,	__lowerCAmelCase   :    int=2_24							,	__lowerCAmelCase   :    Dict=3							,	__lowerCAmelCase   :    List[Any]=16							,	__lowerCAmelCase   :    Dict=None							,	**__lowerCAmelCase   :    Optional[Any]							,	)	->	Dict:







					"""simple docstring"""






					super().__init__(
					    vocab_size=__lowerCAmelCase							,	hidden_size=__lowerCAmelCase							,	num_hidden_layers=__lowerCAmelCase							,	num_attention_heads=__lowerCAmelCase							,	intermediate_size=__lowerCAmelCase							,	hidden_act=__lowerCAmelCase							,	hidden_dropout_prob=__lowerCAmelCase							,	attention_probs_dropout_prob=__lowerCAmelCase							,	max_position_embeddings=__lowerCAmelCase							,	type_vocab_size=__lowerCAmelCase							,	initializer_range=__lowerCAmelCase							,	layer_norm_eps=__lowerCAmelCase							,	pad_token_id=__lowerCAmelCase							,	bos_token_id=__lowerCAmelCase							,	eos_token_id=__lowerCAmelCase							,	**__lowerCAmelCase							,	)
					A__        =   max_ad_position_embeddings
					A__        =   coordinate_size
					A__        =   shape_size
					A__        =   has_relative_attention_bias
					A__        =   rel_pos_bins
					A__        =   max_rel_pos
					A__        =   has_spatial_attention_bias
					A__        =   rel_ad_pos_bins
					A__        =   max_rel_ad_pos
					A__        =   text_embed
					A__        =   visual_embed
					A__        =   input_size
					A__        =   num_channels
					A__        =   patch_size
					A__        =   classifier_dropout


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    version.parse('''1.12'''    )






			@property
			def    a_       (			self   :    int   )	->	Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					if self.task in ["question-answering", "sequence-classification"]:
							return OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
							        ("""bbox""", {0: """batch""", 1: """sequence"""}),
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							    ]   )
					else:
							return OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
							        ("""bbox""", {0: """batch""", 1: """sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
							        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
							    ]   )






			@property
			def    a_       (			self   :    Optional[int]   )	->	float:







					"""simple docstring"""






					return 1e-5






			@property
			def    a_       (			self   :    Tuple   )	->	int:







					"""simple docstring"""






					return 12







			def    a_       (			self   :    str							,	__lowerCAmelCase   :    "ProcessorMixin"							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    bool = False							,	__lowerCAmelCase   :    Optional["TensorType"] = None							,	__lowerCAmelCase   :    int = 3							,	__lowerCAmelCase   :    int = 40							,	__lowerCAmelCase   :    int = 40							,	)	->	Mapping[str, Any]:







					"""simple docstring"""






					setattr(processor.image_processor							,	"""apply_ocr"""							,	__lowerCAmelCase   )

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					A__        =   compute_effective_axis_dimension(
					    __lowerCAmelCase							,	fixed_dimension=OnnxConfig.default_fixed_batch							,	num_token_to_add=0   )
					# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
					A__        =   processor.tokenizer.num_special_tokens_to_add(__lowerCAmelCase   )
					A__        =   compute_effective_axis_dimension(
					    __lowerCAmelCase							,	fixed_dimension=OnnxConfig.default_fixed_sequence							,	num_token_to_add=__lowerCAmelCase   )
					# Generate dummy inputs according to compute batch and sequence
					A__        =   [[""" """.join([processor.tokenizer.unk_token]   ) * seq_length]] * batch_size

					# Generate dummy bounding boxes
					A__        =   [[[48, 84, 73, 1_28]]] * batch_size

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
					A__        =   self._generate_dummy_images(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )

					A__        =   dict(
					    processor(
					        __lowerCAmelCase							,	text=__lowerCAmelCase							,	boxes=__lowerCAmelCase							,	return_tensors=__lowerCAmelCase							,	)   )

					return inputs



