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_							:						Union[str, Any]					=			os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'''{bindir}/../../examples/pytorch/translation'''):
	from run_translation import main  # noqa


set_seed(42)
lowerCamelCase_							:						int					=			"""sshleifer/student_marian_en_ro_6_1"""
lowerCamelCase_							:						Union[str, Any]					=			"""sshleifer/tiny-mbart"""


@require_torch
class 		a__				(  _UpperCamelCase						):



			def __SCREAMING_SNAKE_CASE				(		self ,		UpperCAmelCase=False ,		UpperCAmelCase=None ,		UpperCAmelCase=True ,		UpperCAmelCase=True ,		UpperCAmelCase=True ,		UpperCAmelCase=True ,		)					->       List[Any]:
								__a						   =  self.run_trainer(
								    eval_steps=1 ,		max_len=1_2 ,		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 ,		)
								__a						   =  TrainerState.load_from_json(os.path.join(__a ,		'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'] ,		__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       )					->       Dict:
								self.run_seqaseq_quick()



			@require_torch_multi_gpu
			def __SCREAMING_SNAKE_CASE				(		self       )					->       Optional[int]:
								self.run_seqaseq_quick(distributed=__a       )



			@require_torch_multi_gpu
			def __SCREAMING_SNAKE_CASE				(		self       )					->       Optional[Any]:
								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       )					->       int:
								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       )					->       str:
								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       )					->       Any:
								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       )					->       List[Any]:
								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       )					->       int:
								# 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 ,		UpperCAmelCase       )					->       Union[str, Any]:
								# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
								__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(**__a ,		extra_args_str=data['extra_args_str']       )
								__a						   =  len(re.findall(__a ,		cl.err       )       )
								self.assertEqual(__a ,		data['n_matches']       )



			@slow
			def __SCREAMING_SNAKE_CASE				(		self       )					->       Dict:
								__a						   =  self.run_trainer(
								    eval_steps=2 ,		max_len=1_2_8 ,		model_name=__a ,		learning_rate=3e-4 ,		num_train_epochs=1_0 ,		distributed=__a ,		)

								# Check metrics
								__a						   =  TrainerState.load_from_json(os.path.join(__a ,		'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'] ,		__a       )

								# test if do_predict saves generations and metrics
								__a						   =  os.listdir(__a       )
								__a						   =  {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       )					->       Any:
								from transformers.training_args import OptimizerNames

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

													__a						   =  self.run_trainer(
													    max_len=1_2_8 ,		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
													__a						   =  TrainerState.load_from_json(Path(__a ,		'trainer_state.json'       )       ).log_history
													__a						   =  int(logs[0]['train_mem_gpu_peaked_delta'] / 2**2_0       )
													__a						   =  int(logs[0]['train_mem_gpu_alloc_delta'] / 2**2_0       )

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

								__a						   =  train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value       )
								__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_2_0

								# 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 ,		UpperCAmelCase ,		UpperCAmelCase ,		UpperCAmelCase ,		UpperCAmelCase = 3e-3 ,		UpperCAmelCase = "adafactor" ,		UpperCAmelCase = False ,		UpperCAmelCase = None ,		UpperCAmelCase = 0 ,		UpperCAmelCase = True ,		UpperCAmelCase = True ,		UpperCAmelCase = True ,		UpperCAmelCase = True ,		UpperCAmelCase = None ,		)					->       List[str]:
								__a						   =  self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
								__a						   =  self.get_auto_remove_tmp_dir()
								__a						   =  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(__a       )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(__a       )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

								__a						   =  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(__a       )}
        '''.split()

								__a						   =  "\n            --do_predict\n        ".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'''
                -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()
													__a						   =  [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:
													__a						   =  ["run_translation.py"] + args
													with patch.object(__a ,		'argv' ,		__a       ):
																		main()

								return output_dir











"""simple docstring"""


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


lowerCamelCase__						= logging.get_logger(__name__)

lowerCamelCase__						= {
    "google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json",
    "google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:int						  =       "mobilenet_v1"

  def __init__(		self :							Optional[int]       , __a :							List[str]=3       , __a :							Union[str, Any]=224       , __a :							Tuple=1.0       , __a :							List[Any]=8       , __a :							Union[str, Any]="relu6"       , __a :							Dict=True       , __a :							Tuple=0.9_99       , __a :							Dict=0.02       , __a :							Any=0.0_01       , **__a :							Any       , )		->					int:
        super().__init__(**__a						)

        if depth_multiplier <= 0:
              raise ValueError("depth_multiplier must be greater than zero."						)

        _UpperCamelCase   :						Any    =							num_channels
        _UpperCamelCase   :						Optional[Any]    =							image_size
        _UpperCamelCase   :						Optional[Any]    =							depth_multiplier
        _UpperCamelCase   :						int    =							min_depth
        _UpperCamelCase   :						List[str]    =							hidden_act
        _UpperCamelCase   :						Union[str, Any]    =							tf_padding
        _UpperCamelCase   :						int    =							classifier_dropout_prob
        _UpperCamelCase   :						Optional[int]    =							initializer_range
        _UpperCamelCase   :						Union[str, Any]    =							layer_norm_eps





class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:List[Any]						  =       version.parse("1.11" )

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					Mapping[str, Mapping[int, str]]:
        return OrderedDict([("pixel_values", {0: "batch"})]						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					Mapping[str, Mapping[int, str]]:
        if self.task == "image-classification":
              return OrderedDict([("logits", {0: "batch"})]						)
        else:
              return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]						)


  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple						)		->					float:
        return 1e-4
