
'''simple docstring'''





import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
 from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor


class 			UpperCamelCase__	:






 def __init__(      self	:List[str]						,					_A	:str						,					_A	:Optional[Any]=13						,					_A	:Tuple=32						,					_A	:Dict=2						,					_A	:str=3						,					_A	:Union[str, Any]=16						,					_A	:Optional[Any]=[1, 2, 1]						,					_A	:Tuple=[2, 2, 4]						,					_A	:Dict=2						,					_A	:Optional[int]=2.0						,					_A	:int=True						,					_A	:List[Any]=0.0						,					_A	:Dict=0.0						,					_A	:int=0.1						,					_A	:Optional[Any]="gelu"						,					_A	:Union[str, Any]=False						,					_A	:Optional[Any]=True						,					_A	:Union[str, Any]=0.02						,					_A	:Optional[Any]=1E-5						,					_A	:Union[str, Any]=True						,					_A	:Optional[int]=None						,					_A	:int=True						,					_A	:Optional[Any]=10						,					_A	:List[Any]=8						,					_A	:Optional[Any]=["stage1", "stage2"]						,					_A	:Any=[1, 2]						,					)					->      List[Any]:







       '''simple docstring'''



       __A													=      parent
       __A													=      batch_size
       __A													=      image_size
       __A													=      patch_size
       __A													=      num_channels
       __A													=      embed_dim
       __A													=      depths
       __A													=      num_heads
       __A													=      window_size
       __A													=      mlp_ratio
       __A													=      qkv_bias
       __A													=      hidden_dropout_prob
       __A													=      attention_probs_dropout_prob
       __A													=      drop_path_rate
       __A													=      hidden_act
       __A													=      use_absolute_embeddings
       __A													=      patch_norm
       __A													=      layer_norm_eps
       __A													=      initializer_range
       __A													=      is_training
       __A													=      scope
       __A													=      use_labels
       __A													=      type_sequence_label_size
       __A													=      encoder_stride
       __A													=      out_features
       __A													=      out_indices






 def 						lowercase_    (      self	:Tuple      )					->      List[Any]:







       '''simple docstring'''



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

       __A													=      None
       if self.use_labels:
             __A													=      ids_tensor([self.batch_size]						,					self.type_sequence_label_size      )

       __A													=      self.get_config()

       return config, pixel_values, labels






 def 						lowercase_    (      self	:str      )					->      List[str]:







       '''simple docstring'''



       return SwinConfig(
           image_size=self.image_size						,					patch_size=self.patch_size						,					num_channels=self.num_channels						,					embed_dim=self.embed_dim						,					depths=self.depths						,					num_heads=self.num_heads						,					window_size=self.window_size						,					mlp_ratio=self.mlp_ratio						,					qkv_bias=self.qkv_bias						,					hidden_dropout_prob=self.hidden_dropout_prob						,					attention_probs_dropout_prob=self.attention_probs_dropout_prob						,					drop_path_rate=self.drop_path_rate						,					hidden_act=self.hidden_act						,					use_absolute_embeddings=self.use_absolute_embeddings						,					path_norm=self.patch_norm						,					layer_norm_eps=self.layer_norm_eps						,					initializer_range=self.initializer_range						,					encoder_stride=self.encoder_stride						,					out_features=self.out_features						,					out_indices=self.out_indices						,					)






 def 						lowercase_    (      self	:Tuple						,					_A	:Optional[int]						,					_A	:Dict						,					_A	:List[Any]      )					->      str:







       '''simple docstring'''



       __A													=      SwinModel(config=_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(_A      )

       __A													=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
       __A													=      int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

       self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, expected_seq_len, expected_dim)      )






 def 						lowercase_    (      self	:Optional[int]						,					_A	:Optional[Any]						,					_A	:List[str]						,					_A	:List[Any]      )					->      Dict:







       '''simple docstring'''



       __A													=      SwinBackbone(config=_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(_A      )

       # verify hidden states
       self.parent.assertEqual(len(result.feature_maps      )						,					len(config.out_features      )      )
       self.parent.assertListEqual(list(result.feature_maps[0].shape      )						,					[self.batch_size, model.channels[0], 16, 16]      )

       # verify channels
       self.parent.assertEqual(len(model.channels      )						,					len(config.out_features      )      )

       # verify backbone works with out_features=None
       __A													=      None
       __A													=      SwinBackbone(config=_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(_A      )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps      )						,					1      )
       self.parent.assertListEqual(list(result.feature_maps[0].shape      )						,					[self.batch_size, model.channels[-1], 4, 4]      )

       # verify channels
       self.parent.assertEqual(len(model.channels      )						,					1      )






 def 						lowercase_    (      self	:int						,					_A	:Optional[int]						,					_A	:int						,					_A	:Union[str, Any]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      SwinForMaskedImageModeling(config=_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(_A      )
       self.parent.assertEqual(
           result.logits.shape						,					(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

       # test greyscale images
       __A													=      1
       __A													=      SwinForMaskedImageModeling(_A      )
       model.to(_A      )
       model.eval()

       __A													=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       __A													=      model(_A      )
       self.parent.assertEqual(result.logits.shape						,					(self.batch_size, 1, self.image_size, self.image_size)      )






 def 						lowercase_    (      self	:List[Any]						,					_A	:Union[str, Any]						,					_A	:List[Any]						,					_A	:Tuple      )					->      Any:







       '''simple docstring'''



       __A													=      self.type_sequence_label_size
       __A													=      SwinForImageClassification(_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(_A						,					labels=_A      )
       self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       __A													=      1
       __A													=      SwinForImageClassification(_A      )
       model.to(_A      )
       model.eval()

       __A													=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       __A													=      model(_A      )
       self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.type_sequence_label_size)      )






 def 						lowercase_    (      self	:Dict      )					->      Dict:







       '''simple docstring'''



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


@require_torch
class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     str    					=       (
     (
         SwinModel,
         SwinBackbone,
         SwinForImageClassification,
         SwinForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 UpperCAmelCase__     :     Optional[Any]    					=       (
     {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
     if is_torch_available()
     else {}
 )
 UpperCAmelCase__     :     Dict    					=       True

 UpperCAmelCase__     :     List[Any]    					=       False
 UpperCAmelCase__     :     Tuple    					=       False
 UpperCAmelCase__     :     str    					=       False






 def 						lowercase_    (      self	:Optional[Any]      )					->      List[Any]:







       '''simple docstring'''



       __A													=      SwinModelTester(self      )
       __A													=      ConfigTester(self						,					config_class=_A						,					embed_dim=37      )






 def 						lowercase_    (      self	:List[Any]      )					->      str:







       '''simple docstring'''



       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()






 def 						lowercase_    (      self	:Union[str, Any]      )					->      Optional[Any]:







       '''simple docstring'''



       return






 def 						lowercase_    (      self	:Union[str, Any]      )					->      List[Any]:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_A      )






 @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'      )
 def 						lowercase_    (      self	:int      )					->      Any:







       '''simple docstring'''



       pass






 def 						lowercase_    (      self	:Dict      )					->      Dict:







       '''simple docstring'''



       super().test_training_gradient_checkpointing()






 def 						lowercase_    (      self	:List[str]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*_A      )






 def 						lowercase_    (      self	:Optional[int]      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_A      )






 def 						lowercase_    (      self	:Optional[Any]      )					->      Dict:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_A      )






 @unittest.skip(reason='Swin does not use inputs_embeds'      )
 def 						lowercase_    (      self	:Union[str, Any]      )					->      Dict:







       '''simple docstring'''



       pass






 @unittest.skip(reason='Swin Transformer does not use feedforward chunking'      )
 def 						lowercase_    (      self	:str      )					->      Any:







       '''simple docstring'''



       pass






 def 						lowercase_    (      self	:List[str]      )					->      Dict:







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __A													=      model_class(_A      )
             self.assertIsInstance(model.get_input_embeddings()						,					(nn.Module)      )
             __A													=      model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_A						,					nn.Linear      )      )






 def 						lowercase_    (      self	:List[str]      )					->      List[str]:







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

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

             __A													=      ['pixel_values']
             self.assertListEqual(arg_names[:1]						,					_A      )






 def 						lowercase_    (      self	:Dict      )					->      Union[str, Any]:







       '''simple docstring'''



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

       for model_class in self.all_model_classes:
             __A													=      True
             __A													=      False
             __A													=      True
             __A													=      model_class(_A      )
             model.to(_A      )
             model.eval()
             with torch.no_grad():
                   __A													=      model(**self._prepare_for_class(_A						,					_A      )      )
             __A													=      outputs.attentions
             __A													=      len(self.model_tester.depths      )
             self.assertEqual(len(_A      )						,					_A      )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             __A													=      True
             __A													=      config.window_size**2
             __A													=      model_class(_A      )
             model.to(_A      )
             model.eval()
             with torch.no_grad():
                   __A													=      model(**self._prepare_for_class(_A						,					_A      )      )
             __A													=      outputs.attentions
             self.assertEqual(len(_A      )						,					_A      )

             self.assertListEqual(
                 list(attentions[0].shape[-3:]      )						,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,					)
             __A													=      len(_A      )

             # Check attention is always last and order is fine
             __A													=      True
             __A													=      True
             __A													=      model_class(_A      )
             model.to(_A      )
             model.eval()
             with torch.no_grad():
                   __A													=      model(**self._prepare_for_class(_A						,					_A      )      )

             # also another +1 for reshaped_hidden_states
             __A													=      1 if model_class.__name__ == 'SwinBackbone' else 2
             self.assertEqual(out_len + added_hidden_states						,					len(_A      )      )

             __A													=      outputs.attentions

             self.assertEqual(len(_A      )						,					_A      )

             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]      )						,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,					)






 def 						lowercase_    (      self	:int						,					_A	:int						,					_A	:Dict						,					_A	:Optional[Any]						,					_A	:Dict      )					->      Dict:







       '''simple docstring'''



       __A													=      model_class(_A      )
       model.to(_A      )
       model.eval()

       with torch.no_grad():
             __A													=      model(**self._prepare_for_class(_A						,					_A      )      )

       __A													=      outputs.hidden_states

       __A													=      getattr(
           self.model_tester						,					'expected_num_hidden_layers'						,					len(self.model_tester.depths      ) + 1      )
       self.assertEqual(len(_A      )						,					_A      )

       # Swin has a different seq_length
       __A													=      (
           config.patch_size
           if isinstance(config.patch_size						,					collections.abc.Iterable      )
           else (config.patch_size, config.patch_size)
       )

       __A													=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]      )						,					[num_patches, self.model_tester.embed_dim]						,					)

       if not model_class.__name__ == "SwinBackbone":
             __A													=      outputs.reshaped_hidden_states
             self.assertEqual(len(_A      )						,					_A      )

             __A						,   __A						,   __A						,   __A													=      reshaped_hidden_states[0].shape
             __A													=      (
                 reshaped_hidden_states[0].view(_A						,					_A						,					height * width      ).permute(0						,					2						,					1      )
             )
             self.assertListEqual(
                 list(reshaped_hidden_states.shape[-2:]      )						,					[num_patches, self.model_tester.embed_dim]						,					)






 def 						lowercase_    (      self	:int      )					->      Optional[Any]:







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

       __A													=      (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size						,					collections.abc.Iterable      )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )

       for model_class in self.all_model_classes:
             __A													=      True
             self.check_hidden_states_output(_A						,					_A						,					_A						,					_A      )

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

             self.check_hidden_states_output(_A						,					_A						,					_A						,					_A      )






 def 						lowercase_    (      self	:int      )					->      Union[str, Any]:







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()
       __A													=      3

       __A													=      (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size						,					collections.abc.Iterable      )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )
       __A													=      (
           config.patch_size
           if isinstance(config.patch_size						,					collections.abc.Iterable      )
           else (config.patch_size, config.patch_size)
       )

       __A													=      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       __A													=      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
             __A													=      True
             self.check_hidden_states_output(_A						,					_A						,					_A						,					(padded_height, padded_width)      )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             __A													=      True
             self.check_hidden_states_output(_A						,					_A						,					_A						,					(padded_height, padded_width)      )






 @slow
 def 						lowercase_    (      self	:Optional[Any]      )					->      Dict:







       '''simple docstring'''



       for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __A													=      SwinModel.from_pretrained(_A      )
             self.assertIsNotNone(_A      )






 def 						lowercase_    (      self	:str      )					->      List[str]:







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

       __A													=      _config_zero_init(_A      )
       for model_class in self.all_model_classes:
             __A													=      model_class(config=_A      )
             for name, param in model.named_parameters():
                   if "embeddings" not in name and param.requires_grad:
                         self.assertIn(
                             ((param.data.mean() * 1E9).round() / 1E9).item()						,					[0.0, 1.0]						,					msg=F'Parameter {name} of model {model_class} seems not properly initialized'						,					)


@require_vision
@require_torch
class 			UpperCamelCase__	(     unittest.TestCase):






 @cached_property
 def 						lowercase_    (      self	:Any      )					->      Any:







       '''simple docstring'''



       return (
           AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'      )
           if is_vision_available()
           else None
       )






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







       '''simple docstring'''



       __A													=      SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'      ).to(_A      )
       __A													=      self.default_image_processor

       __A													=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
       __A													=      image_processor(images=_A						,					return_tensors='pt'      ).to(_A      )

       # forward pass
       with torch.no_grad():
             __A													=      model(**_A      )

       # verify the logits
       __A													=      torch.Size((1, 1_000)      )
       self.assertEqual(outputs.logits.shape						,					_A      )
       __A													=      torch.tensor([-0.0_948, -0.6_454, -0.0_921]      ).to(_A      )
       self.assertTrue(torch.allclose(outputs.logits[0, :3]						,					_A						,					atol=1E-4      )      )


@require_torch
class 			UpperCamelCase__	(     unittest.TestCase   ,  SCREAMING_SNAKE_CASE):
 UpperCAmelCase__     :     int    					=       (SwinBackbone,) if is_torch_available() else ()
 UpperCAmelCase__     :     Optional[int]    					=       SwinConfig






 def 						lowercase_    (      self	:str      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      SwinModelTester(self      )












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

lowercase_      :							int    							=					logging.get_logger(__name__)


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						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        __lowerCAmelCase					(			UpperCAmelCase__					):







			def __init__(			self       :     Dict       ,       snake_case__       :     Dict ):




									"""simple docstring"""


									set_hf_deepspeed_config(self )
									dep_version_check("accelerate" )
									dep_version_check("deepspeed" )
									super().__init__(snake_case__ )






class        __lowerCAmelCase					(			UpperCAmelCase__					):







			def __init__(			self       :     str       ,       snake_case__       :     Optional[Any] ):




									"""simple docstring"""


									super().__init__(snake_case__ )
									_UpperCAmelCase						=	None
									_UpperCAmelCase						=	[]







			def UpperCamelCase     (			self       :     List[Any] ):




									"""simple docstring"""


									if self._dtype is None:
															raise ValueError("trainer_config_process() wasn't called yet to tell dtype" )
									return self._dtype







			def UpperCamelCase     (			self       :     str       ,       snake_case__       :     Dict ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.get_value(snake_case__ )
									if val is None:
															return False
									else:
															return val == "auto"







			def UpperCamelCase     (			self       :     Union[str, Any]       ,       snake_case__       :     Optional[int]       ,       snake_case__       :     Optional[Any]       ,       snake_case__       :     Dict=None       ,       snake_case__       :     int=True ):




									"""simple docstring"""


									_UpperCAmelCase				,					_UpperCAmelCase						=	self.find_config_node(snake_case__ )
									if config is None:
															return

									if config.get(snake_case__ ) == "auto":
															_UpperCAmelCase						=	hf_val
															return

									if not must_match:
															return

									_UpperCAmelCase						=	config.get(snake_case__ )
									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}""" )

			snake_case_ :					Optional[Any]						=	partialmethod(UpperCAmelCase__   ,						must_match=UpperCAmelCase__					)







			def UpperCamelCase     (			self       :     Any       ,       snake_case__       :     Optional[int] ):




									"""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"       ,       snake_case__       ,       "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 UpperCamelCase     (			self       :     Tuple       ,       snake_case__       :     Union[str, Any]       ,       snake_case__       :     Dict       ,       snake_case__       :     List[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(snake_case__ )]

									if len(snake_case__ ) > 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"       ,       snake_case__       ,       "num_training_steps (calculated)" )
									self.fill_match("scheduler.params.warmup_num_steps"       ,       args.get_warmup_steps(snake_case__ )       ,       "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'.""" )




lowercase_      :							Union[str, Any]    							=					None


def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						global _hf_deepspeed_config_weak_ref
						# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
						_UpperCAmelCase						=	weakref.ref(snake_case_		)


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						global _hf_deepspeed_config_weak_ref
						_UpperCAmelCase						=	None


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						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 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						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 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						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=snake_case_		)
						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(snake_case_		)
						else:
												if isinstance(snake_case_      ,       snake_case_		):
																		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=snake_case_      ,       optimizer=snake_case_		)

						return optimizer, lr_scheduler


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_=False		):

						'''simple docstring'''


						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(snake_case_      ,       snake_case_      ,       snake_case_		)

						# 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 snake_case_		: p.requires_grad      ,       model.parameters()		)		)
												_UpperCAmelCase				,					_UpperCAmelCase						=	deepspeed_optim_sched(
												    snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		)

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

						return optimizer, lr_scheduler





def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						import glob

						_UpperCAmelCase						=	sorted(glob.glob(f"""{checkpoint_path}/global_step*"""		)		)

						if len(snake_case_		) > 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(
												    snake_case_      ,       load_optimizer_states=snake_case_      ,       load_lr_scheduler_states=snake_case_		)
												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}"""		)




