




'''simple docstring'''







import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
   import torch

A					       =logging.get_logger(__name__)





def 		snake_case_				():
    return importlib.util.find_spec('''deepspeed'''	) is not None


if is_accelerate_available() and is_deepspeed_available():
   from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
   # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
   # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
   from builtins import object as DeepSpeedConfig


class      _a    (      __a					):
       def __init__(      self		:      List[Any]  ,							lowercase		:      Tuple							):



           '''simple docstring'''

           set_hf_deepspeed_config(self							)
           dep_version_check('''accelerate'''							)
           dep_version_check('''deepspeed'''							)
           super().__init__(lowercase							)






class      _a    (      __a					):
       def __init__(      self		:      Any  ,							lowercase		:      Dict							):



           '''simple docstring'''

           super().__init__(lowercase							)
           UpperCAmelCase     			=						None
           UpperCAmelCase     			=						[]
       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           if self._dtype is None:
               raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype'''							)
           return self._dtype
       def        A (      self		:      Dict  ,							lowercase		:      Optional[int]							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.get_value(lowercase							)
           if val is None:
               return False
           else:
               return val == "auto"
       def        A (      self		:      int  ,							lowercase		:      Optional[int]  ,							lowercase		:      Optional[int]  ,							lowercase		:      int=None  ,							lowercase		:      List[Any]=True							):



           '''simple docstring'''

           UpperCAmelCase			,	UpperCAmelCase     			=						self.find_config_node(lowercase							)
           if config is None:
               return

           if config.get(lowercase							) == "auto":
               UpperCAmelCase     			=						hf_val
               return

           if not must_match:
               return

           UpperCAmelCase     			=						config.get(lowercase							)
           if ds_val is not None and ds_val != hf_val:
               self.mismatches.append(f"- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}"							)

       __a							:	Optional[int]  							=   partialmethod(__a				,				must_match=__a					)
       def        A (      self		:      int  ,							lowercase		:      str							):



           '''simple docstring'''

           UpperCAmelCase     			=						args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
           self.fill_match(
               '''train_micro_batch_size_per_gpu'''  ,							args.per_device_train_batch_size  ,							'''per_device_train_batch_size'''							)
           self.fill_match('''gradient_accumulation_steps'''  ,							args.gradient_accumulation_steps  ,							'''gradient_accumulation_steps'''							)
           self.fill_match('''train_batch_size'''  ,							lowercase  ,							'''train_batch_size (calculated)'''							)
           self.fill_match('''gradient_clipping'''  ,							args.max_grad_norm  ,							'''max_grad_norm'''							)

           self.fill_match('''optimizer.params.lr'''  ,							args.learning_rate  ,							'''learning_rate'''							)
           self.fill_match('''optimizer.params.betas'''  ,							[args.adam_betaa, args.adam_betaa]  ,							'''adam_beta1+adam_beta2'''							)
           self.fill_match('''optimizer.params.eps'''  ,							args.adam_epsilon  ,							'''adam_epsilon'''							)
           self.fill_match('''optimizer.params.weight_decay'''  ,							args.weight_decay  ,							'''weight_decay'''							)

           self.fill_only('''scheduler.params.warmup_min_lr'''  ,							0							)  # not a trainer arg
           self.fill_match('''scheduler.params.warmup_max_lr'''  ,							args.learning_rate  ,							'''learning_rate'''							)
           # total_num_steps - will get set in trainer_config_finalize

           # fp16
           if args.fpaa or args.fpaa_full_eval:
               UpperCAmelCase     			=						'''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
           else:
               UpperCAmelCase     			=						None

           if args.save_on_each_node:
               # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
               UpperCAmelCase     			=						self.config.get('''checkpoint'''  ,							{}							)
               UpperCAmelCase     			=						args.save_on_each_node

           # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
           # any here unless the user did the work
           self.fill_match(
               '''fp16.enabled'''  ,							((args.fpaa or args.fpaa_full_eval) and fpaa_backend == '''amp''')  ,							'''fp16|fp16_full_eval+fp16_backend(amp)'''  ,							)

           # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
           # ZeRO features
           self.fill_match('''amp.enabled'''  ,							fpaa_backend == '''apex'''  ,							'''fp16+fp16_backend(apex)'''							)
           self.fill_match('''amp.opt_level'''  ,							args.fpaa_opt_level  ,							'''fp16_opt_level'''							)

           self.fill_match('''bf16.enabled'''  ,							(args.bfaa or args.bfaa_full_eval)  ,							'''bf16|bf16_full_eval'''							)

           # deepspeed's default mode is fp16 unless there is a config that says differently
           if self.is_true('''bf16.enabled'''							):
               UpperCAmelCase     			=						torch.bfloataa
           elif self.is_false('''fp16.enabled'''							):
               UpperCAmelCase     			=						torch.floataa
           else:
               UpperCAmelCase     			=						torch.floataa







       def        A (      self		:      List[str]  ,							lowercase		:      int  ,							lowercase		:      Optional[Any]  ,							lowercase		:      Union[str, Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						[
               '''zero_optimization.reduce_bucket_size''',
               '''zero_optimization.stage3_prefetch_bucket_size''',
               '''zero_optimization.stage3_param_persistence_threshold''',
           ]
           UpperCAmelCase     			=						[x for x in hidden_size_based_keys if self.is_auto(lowercase							)]

           if len(lowercase							) > 0:
               if hasattr(model.config  ,							'''hidden_size'''							):
                   UpperCAmelCase     			=						model.config.hidden_size
               elif hasattr(model.config  ,							'''hidden_sizes'''							):
                   # if there are many hidden sizes pick the largest one
                   UpperCAmelCase     			=						max(model.config.hidden_sizes							)
               else:
                   raise ValueError(
                       '''The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '''
                       '''therefore it\'s not possible to automatically fill out the following `auto` entries '''
                       f"in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing "
                       '''`auto` values for these keys with an integer value of your choice.'''							)

               self.fill_only('''zero_optimization.reduce_bucket_size'''  ,							hidden_size * hidden_size							)
               if self.is_zeroa():
                   # automatically assign the optimal config values based on model config
                   self.fill_only('''zero_optimization.stage3_prefetch_bucket_size'''  ,							0.9 * hidden_size * hidden_size							)
                   self.fill_only('''zero_optimization.stage3_param_persistence_threshold'''  ,							10 * hidden_size							)

        # scheduler
           self.fill_match('''scheduler.params.total_num_steps'''  ,							lowercase  ,							'''num_training_steps (calculated)'''							)
           self.fill_match('''scheduler.params.warmup_num_steps'''  ,							args.get_warmup_steps(lowercase							)  ,							'''warmup_steps'''							)

           if len(self.mismatches							) > 0:
               UpperCAmelCase     			=						'''\n'''.join(self.mismatches							)
               raise ValueError(
                   '''Please correct the following DeepSpeed config values that mismatch TrainingArguments'''
                   f" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'."							)






A					       =None





def 		snake_case_				(_a      :				Any	):
    # this is a special weakref global object to allow us to get to Deepspeed config from APIs
    # that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
    global _hf_deepspeed_config_weak_ref
    # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
    UpperCAmelCase     			=						weakref.ref(_a	)





def 		snake_case_				():
    # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
    global _hf_deepspeed_config_weak_ref
    UpperCAmelCase     			=						None





def 		snake_case_				():
    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
        return _hf_deepspeed_config_weak_ref().is_zeroa()
    else:
        return False





def 		snake_case_				():
    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
        return _hf_deepspeed_config_weak_ref().config
    else:
        return None





def 		snake_case_				(_a      :				Any       ,  _a      :				Optional[int]       ,  _a      :				List[Any]       ,  _a      :				Tuple       ,  _a      :				List[str]	):
    from accelerate.utils import DummyOptim, DummyScheduler

    UpperCAmelCase     			=						hf_deepspeed_config.config

    # Optimizer + Scheduler
    # Currently supported combos:
    # 1. DS scheduler + DS optimizer: Yes
    # 2. HF scheduler + HF optimizer: Yes
    # 3. DS scheduler + HF optimizer: Yes
    # 4. HF scheduler + DS optimizer: No
    #
    # Unless Offload is enabled in which case it's:
    # 1. DS scheduler + DS optimizer: Yes
    # 2. HF scheduler + HF optimizer: Mostly*
    # 3. DS scheduler + HF optimizer: Mostly*
    # 4. HF scheduler + DS optimizer: No
    #
    # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

    UpperCAmelCase     			=						None
    if "optimizer" in config:
        if args.adafactor:
            raise ValueError(
                '''--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '''
                '''Only one optimizer can be configured.'''	)
        UpperCAmelCase     			=						DummyOptim(params=_a	)
    else:
        if hf_deepspeed_config.is_offload():
            logger.info(
                '''Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'''
                ''' custom optimizer has both CPU and GPU implementation (except LAMB)'''	)

        # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
        # But trainer uses AdamW by default.
        UpperCAmelCase     			=						trainer.create_optimizer()
        # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
        UpperCAmelCase     			=						True

    UpperCAmelCase     			=						None
    if "scheduler" in config:
        UpperCAmelCase     			=						DummyScheduler(_a	)
    else:
        if isinstance(_a       ,  _a	):
            raise ValueError(
                '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
                '''Please configure a scheduler in the DeepSpeed config.'''	)
        UpperCAmelCase     			=						trainer.create_scheduler(num_training_steps=_a       ,  optimizer=_a	)

    return optimizer, lr_scheduler





def 		snake_case_				(_a      :				List[Any]       ,  _a      :				List[str]       ,  _a      :				Union[str, Any]=False	):
    from deepspeed.utils import logger as ds_logger

    UpperCAmelCase     			=						trainer.model
    UpperCAmelCase     			=						trainer.args

    UpperCAmelCase     			=						trainer.accelerator.state.deepspeed_plugin.hf_ds_config

    # resume config update - some bits like `model` and `num_training_steps` only become available during train
    hf_deepspeed_config.trainer_config_finalize(_a       ,  _a       ,  _a	)

    # set the Deepspeed log level consistent with the Trainer
    ds_logger.setLevel(args.get_process_log_level()	)

    if inference:
        # only Z3 makes sense for the inference
        if not hf_deepspeed_config.is_zeroa():
            raise ValueError('''ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'''	)

        # in case the training config is re-used for inference
        hf_deepspeed_config.del_config_sub_tree('''optimizer'''	)
        hf_deepspeed_config.del_config_sub_tree('''lr_scheduler'''	)
        UpperCAmelCase			,	UpperCAmelCase     			=						None, None
        UpperCAmelCase     			=						None
    else:
        UpperCAmelCase     			=						None  # important for when deepspeed_init is used as re-init
        UpperCAmelCase     			=						list(filter(lambda _a	: p.requires_grad       ,  model.parameters()	)	)
        UpperCAmelCase			,	UpperCAmelCase     			=						deepspeed_optim_sched(
            _a       ,  _a       ,  _a       ,  _a       ,  _a	)

    # keep for quick debug:
    # from pprint import pprint; pprint(config)

    return optimizer, lr_scheduler







def 		snake_case_				(_a      :				List[str]       ,  _a      :				Optional[Any]	):
    # it's possible that the user is trying to resume from model_path, which doesn't necessarily
    # contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
    # a resume from a checkpoint and not just a local pretrained weight. So we check here if the
    # path contains what looks like a deepspeed checkpoint
    import glob

    UpperCAmelCase     			=						sorted(glob.glob(F"{checkpoint_path}/global_step*"	)	)

    if len(_a	) > 0:
        logger.info(F"Attempting to resume from {checkpoint_path}"	)
        # this magically updates self.optimizer and self.lr_scheduler
        UpperCAmelCase			,	UpperCAmelCase     			=						deepspeed_engine.load_checkpoint(
            _a       ,  load_optimizer_states=_a       ,  load_lr_scheduler_states=_a	)
        if load_path is None:
            raise ValueError(F"[deepspeed] failed to resume from checkpoint {checkpoint_path}"	)
    else:
        raise ValueError(F"Can't find a valid checkpoint at {checkpoint_path}"	)










'''simple docstring'''







import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
   from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import PoolFormerImageProcessor


class      _a    (      __a					):
       def        A (      self		:      Tuple							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.config_class(**self.inputs_dict							)
           self.parent.assertTrue(hasattr(lowercase  ,							'''hidden_sizes'''							)							)
           self.parent.assertTrue(hasattr(lowercase  ,							'''num_encoder_blocks'''							)							)






class      _a    :
       def __init__(      self		:      Any  ,							lowercase		:      Any  ,							lowercase		:      Optional[Any]=13  ,							lowercase		:      List[str]=64  ,							lowercase		:      Optional[Any]=3  ,							lowercase		:      List[str]=4  ,							lowercase		:      Optional[int]=[2, 2, 2, 2]  ,							lowercase		:      Tuple=[8, 4, 2, 1]  ,							lowercase		:      Optional[int]=[16, 32, 64, 128]  ,							lowercase		:      Tuple=[1, 4, 8, 16]  ,							lowercase		:      List[Any]=False  ,							lowercase		:      List[Any]=True  ,							lowercase		:      List[str]="gelu"  ,							lowercase		:      Any=0.1  ,							lowercase		:      Dict=0.02  ,							lowercase		:      str=3  ,							lowercase		:      Optional[Any]=None  ,							):



           '''simple docstring'''

           UpperCAmelCase     			=						parent
           UpperCAmelCase     			=						batch_size
           UpperCAmelCase     			=						image_size
           UpperCAmelCase     			=						num_channels
           UpperCAmelCase     			=						num_encoder_blocks
           UpperCAmelCase     			=						sr_ratios
           UpperCAmelCase     			=						depths
           UpperCAmelCase     			=						hidden_sizes
           UpperCAmelCase     			=						downsampling_rates
           UpperCAmelCase     			=						is_training
           UpperCAmelCase     			=						use_labels
           UpperCAmelCase     			=						hidden_act
           UpperCAmelCase     			=						hidden_dropout_prob
           UpperCAmelCase     			=						initializer_range
           UpperCAmelCase     			=						num_labels
           UpperCAmelCase     			=						scope
       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

           UpperCAmelCase     			=						None
           if self.use_labels:
               UpperCAmelCase     			=						ids_tensor([self.batch_size, self.image_size, self.image_size]  ,							self.num_labels							)

           UpperCAmelCase     			=						PoolFormerConfig(
               image_size=self.image_size  ,							num_channels=self.num_channels  ,							num_encoder_blocks=self.num_encoder_blocks  ,							depths=self.depths  ,							hidden_sizes=self.hidden_sizes  ,							hidden_act=self.hidden_act  ,							hidden_dropout_prob=self.hidden_dropout_prob  ,							initializer_range=self.initializer_range  ,							)

           return config, pixel_values, labels
       def        A (      self		:      Optional[Any]  ,							lowercase		:      List[Any]  ,							lowercase		:      Dict  ,							lowercase		:      Union[str, Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						PoolFormerModel(config=lowercase							)
           model.to(lowercase							)
           model.eval()
           UpperCAmelCase     			=						model(lowercase							)
           UpperCAmelCase     			=						UpperCAmelCase     			=						self.image_size // 32.0
           self.parent.assertEqual(
               result.last_hidden_state.shape  ,							(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)							)







       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.prepare_config_and_inputs()
           UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase     			=						config_and_inputs
           UpperCAmelCase     			=						{'''pixel_values''': pixel_values}
           return config, inputs_dict






@require_torch
class      _a    (      __a				,				__a				,				unittest.TestCase					):
       __a							:	Optional[int]  							=   (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
       __a							:	Dict  							=   (
           {"""feature-extraction""": PoolFormerModel, """image-classification""": PoolFormerForImageClassification}
           if is_torch_available()
           else {}
       )

       __a							:	str  							=   False
       __a							:	Optional[int]  							=   False
       __a							:	Tuple  							=   False
       __a							:	Union[str, Any]  							=   False
       __a							:	Dict  							=   False
       def        A (      self		:      Any							):



           '''simple docstring'''

           UpperCAmelCase     			=						PoolFormerModelTester(self							)
           UpperCAmelCase     			=						PoolFormerConfigTester(self  ,							config_class=lowercase							)
       def        A (      self		:      Any							):



           '''simple docstring'''

           self.config_tester.run_common_tests()
       def        A (      self		:      Optional[int]							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase							)
       @unittest.skip('''PoolFormer does not use inputs_embeds'''							)
       def        A (      self		:      Dict							):



           '''simple docstring'''

           pass
       @unittest.skip('''PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'''							)
       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           pass
       def        A (      self		:      Dict							):



           '''simple docstring'''

           def check_hidden_states_output(lowercase		:      int  ,							lowercase		:      Any  ,							lowercase		:      Union[str, Any]							):
               UpperCAmelCase     			=						model_class(lowercase							)
               model.to(lowercase							)
               model.eval()

               with torch.no_grad():
                   UpperCAmelCase     			=						model(**self._prepare_for_class(lowercase  ,							lowercase							)							)

               UpperCAmelCase     			=						outputs.hidden_states

               UpperCAmelCase     			=						self.model_tester.num_encoder_blocks
               self.assertEqual(len(lowercase							)  ,							lowercase							)

               # verify the first hidden states (first block)
               self.assertListEqual(
                   list(hidden_states[0].shape[-3:]							)  ,							[
                       self.model_tester.hidden_sizes[0],
                       self.model_tester.image_size // 4,
                       self.model_tester.image_size // 4,
                   ]  ,							)

           UpperCAmelCase			,	UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               UpperCAmelCase     			=						True
               check_hidden_states_output(lowercase  ,							lowercase  ,							lowercase							)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               UpperCAmelCase     			=						True

               check_hidden_states_output(lowercase  ,							lowercase  ,							lowercase							)
       def        A (      self		:      Optional[Any]							):



           '''simple docstring'''

           if not self.model_tester.is_training:
               return

           UpperCAmelCase			,	UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs_for_common()
           UpperCAmelCase     			=						True

           for model_class in self.all_model_classes:
               if model_class in get_values(lowercase							):
                   continue
               UpperCAmelCase     			=						model_class(lowercase							)
               model.to(lowercase							)
               model.train()
               UpperCAmelCase     			=						self._prepare_for_class(lowercase  ,							lowercase  ,							return_labels=lowercase							)
               UpperCAmelCase     			=						model(**lowercase							).loss
               loss.backward()
       def        A (      self		:      str							):



           '''simple docstring'''

           UpperCAmelCase			,	UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               UpperCAmelCase     			=						model_class(lowercase							)
               UpperCAmelCase     			=						inspect.signature(model.forward							)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               UpperCAmelCase     			=						[*signature.parameters.keys()]

               UpperCAmelCase     			=						['''pixel_values''']
               self.assertListEqual(arg_names[:1]  ,							lowercase							)







       @slow
       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase     			=						PoolFormerModel.from_pretrained(lowercase							)
               self.assertIsNotNone(lowercase							)






def 		snake_case_				():
    UpperCAmelCase     			=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
    return image







@require_torch
class      _a    (      unittest.TestCase					):
       @slow
       def        A (      self		:      str							):



           '''simple docstring'''

           UpperCAmelCase     			=						PoolFormerImageProcessor()
           UpperCAmelCase     			=						PoolFormerForImageClassification.from_pretrained('''sail/poolformer_s12'''							).to(lowercase							)

           UpperCAmelCase     			=						image_processor(images=prepare_img()  ,							return_tensors='''pt'''							).to(lowercase							)

           # forward pass
           with torch.no_grad():
               UpperCAmelCase     			=						model(**lowercase							)

           # verify the logits
           UpperCAmelCase     			=						torch.Size((1, 1_000)							)
           self.assertEqual(outputs.logits.shape  ,							lowercase							)

           UpperCAmelCase     			=						torch.tensor([-0.6113, 0.1685, -0.0492]							).to(lowercase							)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,							lowercase  ,							atol=1E-4							)							)




