
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


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


set_seed(4_2)
lowercase__							:							Optional[Any]   		=			"sshleifer/student_marian_en_ro_6_1"
lowercase__							:							List[Any]   		=			"sshleifer/tiny-mbart"






@require_torch
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			self.run_trainer(
													    eval_steps=1     ,	max_len=12     ,	model_name=SCREAMING_SNAKE_CASE_     ,	num_train_epochs=1     ,	distributed=SCREAMING_SNAKE_CASE_     ,	extra_args_str=SCREAMING_SNAKE_CASE_     ,	predict_with_generate=SCREAMING_SNAKE_CASE_     ,	do_train=SCREAMING_SNAKE_CASE_     ,	do_eval=SCREAMING_SNAKE_CASE_     ,	do_predict=SCREAMING_SNAKE_CASE_     ,	)
													__UpperCamelCase       =			TrainerState.load_from_json(os.path.join(SCREAMING_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''']     ,	SCREAMING_SNAKE_CASE_			)
																			assert not math.isnan(float(last_step_stats['''eval_loss''']			)			), "eval_loss must not be `nan`"



							@require_torch_non_multi_gpu
							def     A__		(  self			)->   Any:





													'''simple docstring'''






													self.run_seqaseq_quick()



							@require_torch_multi_gpu
							def     A__		(  self			)->   int:





													'''simple docstring'''






													self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE_			)



							@require_torch_multi_gpu
							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE_			)



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





													'''simple docstring'''






													self.run_seqaseq_quick(distributed=SCREAMING_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     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													self.run_seqaseq_quick(distributed=SCREAMING_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     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE_     ,	extra_args_str='''--sharded_ddp zero_dp_2'''     ,	predict_with_generate=SCREAMING_SNAKE_CASE_			)



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





													'''simple docstring'''






													self.run_seqaseq_quick(
													    distributed=SCREAMING_SNAKE_CASE_     ,	extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''     ,	predict_with_generate=SCREAMING_SNAKE_CASE_			)



							@require_apex
							@require_torch_gpu
							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													self.run_seqaseq_quick(distributed=SCREAMING_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=SCREAMING_SNAKE_CASE_     ,	extra_args_str='''--fp16 --fp16_backend=apex'''			)



							@parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']			)
							@require_torch_multi_gpu
							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   int:





													'''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(**SCREAMING_SNAKE_CASE_     ,	extra_args_str=data['''extra_args_str''']			)
													__UpperCamelCase       =			len(re.findall(SCREAMING_SNAKE_CASE_     ,	cl.err			)			)
													self.assertEqual(SCREAMING_SNAKE_CASE_     ,	data['''n_matches''']			)



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





													'''simple docstring'''






													__UpperCamelCase       =			self.run_trainer(
													    eval_steps=2     ,	max_len=128     ,	model_name=SCREAMING_SNAKE_CASE_     ,	learning_rate=3E-4     ,	num_train_epochs=10     ,	distributed=SCREAMING_SNAKE_CASE_     ,	)

													# Check metrics
													__UpperCamelCase       =			TrainerState.load_from_json(os.path.join(SCREAMING_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''']     ,	SCREAMING_SNAKE_CASE_			)

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



							@slow
							@require_bitsandbytes
							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													from transformers.training_args import OptimizerNames

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

																			__UpperCamelCase       =			self.run_trainer(
																			    max_len=128     ,	model_name=SCREAMING_SNAKE_CASE_     ,	learning_rate=3E-4     ,	num_train_epochs=1     ,	optim=SCREAMING_SNAKE_CASE_     ,	distributed=SCREAMING_SNAKE_CASE_     ,	extra_args_str=SCREAMING_SNAKE_CASE_     ,	do_eval=SCREAMING_SNAKE_CASE_     ,	do_predict=SCREAMING_SNAKE_CASE_     ,	n_gpus_to_use=1     ,	)

																			# Check metrics
																			__UpperCamelCase       =			TrainerState.load_from_json(Path(SCREAMING_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(
													    SCREAMING_SNAKE_CASE_     ,	SCREAMING_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(
													    SCREAMING_SNAKE_CASE_     ,	SCREAMING_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(
													    SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = 3E-3     ,	SCREAMING_SNAKE_CASE_ = "adafactor"     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = 0     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = None     ,	)->   Dict:





													'''simple docstring'''






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

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

													return output_dir






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase__							:							Tuple									=			logging.get_logger(__name__)

lowercase__							:							int									=			{
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  'table-transformer'
							_snake_case     		=  ['past_key_values']
							_snake_case     		=  {
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=100     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=2048     ,	SCREAMING_SNAKE_CASE_=8     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=2048     ,	SCREAMING_SNAKE_CASE_=8     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_="relu"     ,	SCREAMING_SNAKE_CASE_=256     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=1.0     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_="sine"     ,	SCREAMING_SNAKE_CASE_="resnet50"     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=0.1     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Tuple:





													'''simple docstring'''






													if backbone_config is not None and use_timm_backbone:
																			raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''			)

													if not use_timm_backbone:
																			if backbone_config is None:
																									logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''			)
																									__UpperCamelCase       =			CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']			)
																			elif isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			):
																									__UpperCamelCase       =			backbone_config.get('''model_type'''			)
																									__UpperCamelCase       =			CONFIG_MAPPING[backbone_model_type]
																									__UpperCamelCase       =			config_class.from_dict(SCREAMING_SNAKE_CASE_			)
																			# set timm attributes to None
																			__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			None, None, None

													__UpperCamelCase       =			use_timm_backbone
													__UpperCamelCase       =			backbone_config
													__UpperCamelCase       =			num_channels
													__UpperCamelCase       =			num_queries
													__UpperCamelCase       =			d_model
													__UpperCamelCase       =			encoder_ffn_dim
													__UpperCamelCase       =			encoder_layers
													__UpperCamelCase       =			encoder_attention_heads
													__UpperCamelCase       =			decoder_ffn_dim
													__UpperCamelCase       =			decoder_layers
													__UpperCamelCase       =			decoder_attention_heads
													__UpperCamelCase       =			dropout
													__UpperCamelCase       =			attention_dropout
													__UpperCamelCase       =			activation_dropout
													__UpperCamelCase       =			activation_function
													__UpperCamelCase       =			init_std
													__UpperCamelCase       =			init_xavier_std
													__UpperCamelCase       =			encoder_layerdrop
													__UpperCamelCase       =			decoder_layerdrop
													__UpperCamelCase       =			encoder_layers
													__UpperCamelCase       =			auxiliary_loss
													__UpperCamelCase       =			position_embedding_type
													__UpperCamelCase       =			backbone
													__UpperCamelCase       =			use_pretrained_backbone
													__UpperCamelCase       =			dilation
													# Hungarian matcher
													__UpperCamelCase       =			class_cost
													__UpperCamelCase       =			bbox_cost
													__UpperCamelCase       =			giou_cost
													# Loss coefficients
													__UpperCamelCase       =			mask_loss_coefficient
													__UpperCamelCase       =			dice_loss_coefficient
													__UpperCamelCase       =			bbox_loss_coefficient
													__UpperCamelCase       =			giou_loss_coefficient
													__UpperCamelCase       =			eos_coefficient
													super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return self.encoder_attention_heads



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return self.d_model






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  version.parse('1.11'    )



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





													'''simple docstring'''






													return OrderedDict(
													    [
													        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
													        ('''pixel_mask''', {0: '''batch'''}),
													    ]			)



							@property
							def     A__		(  self			)->   float:





													'''simple docstring'''






													return 1E-5



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return 12




