from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers.models.swin.modeling_tf_swin import (
				    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSwinForImageClassification,
				    TFSwinForMaskedImageModeling,
				    TFSwinModel,
				)


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor



class    lowercase_    :



							def __init__(						self      ,				lowercase_      ,				lowercase_=13      ,				lowercase_=32      ,				lowercase_=2      ,				lowercase_=3      ,				lowercase_=16      ,				lowercase_=[1, 2, 1]      ,				lowercase_=[2, 2, 4]      ,				lowercase_=2      ,				lowercase_=2.0      ,				lowercase_=True      ,				lowercase_=0.0      ,				lowercase_=0.0      ,				lowercase_=0.1      ,				lowercase_="gelu"      ,				lowercase_=False      ,				lowercase_=True      ,				lowercase_=0.02      ,				lowercase_=1e-5      ,				lowercase_=True      ,				lowercase_=None      ,				lowercase_=True      ,				lowercase_=10      ,				lowercase_=8      ,				):
													_snake_case						:     Any				=    parent
													_snake_case						:     int				=    batch_size
													_snake_case						:     Any				=    image_size
													_snake_case						:     List[str]				=    patch_size
													_snake_case						:     List[str]				=    num_channels
													_snake_case						:     Union[str, Any]				=    embed_dim
													_snake_case						:     Any				=    depths
													_snake_case						:     Tuple				=    num_heads
													_snake_case						:     Any				=    window_size
													_snake_case						:     List[str]				=    mlp_ratio
													_snake_case						:     List[str]				=    qkv_bias
													_snake_case						:     Union[str, Any]				=    hidden_dropout_prob
													_snake_case						:     str				=    attention_probs_dropout_prob
													_snake_case						:     Any				=    drop_path_rate
													_snake_case						:     Any				=    hidden_act
													_snake_case						:     Tuple				=    use_absolute_embeddings
													_snake_case						:     Optional[int]				=    patch_norm
													_snake_case						:     int				=    layer_norm_eps
													_snake_case						:     Union[str, Any]				=    initializer_range
													_snake_case						:     int				=    is_training
													_snake_case						:     List[str]				=    scope
													_snake_case						:     List[Any]				=    use_labels
													_snake_case						:     Optional[Any]				=    type_sequence_label_size
													_snake_case						:     Tuple				=    encoder_stride



							def        UpperCamelCase       (						self				):
													_snake_case						:     Dict				=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

													_snake_case						:     int				=    None
													if self.use_labels:
																			_snake_case						:     Optional[int]				=    ids_tensor([self.batch_size]      ,				self.type_sequence_label_size				)

													_snake_case						:     Optional[int]				=    self.get_config()

													return config, pixel_values, labels



							def        UpperCamelCase       (						self				):
													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      ,				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													_snake_case						:     List[Any]				=    TFSwinModel(config=snake_case_				)
													_snake_case						:     List[str]				=    model(snake_case_				)

													_snake_case						:     Any				=    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
													_snake_case						:     List[str]				=    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        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													_snake_case						:     Optional[int]				=    TFSwinForMaskedImageModeling(config=snake_case_				)
													_snake_case						:     Tuple				=    model(snake_case_				)
													self.parent.assertEqual(
													    result.logits.shape      ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)				)

													# test greyscale images
													_snake_case						:     Union[str, Any]				=    1
													_snake_case						:     Any				=    TFSwinForMaskedImageModeling(snake_case_				)

													_snake_case						:     Tuple				=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
													_snake_case						:     Dict				=    model(snake_case_				)
													self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, 1, self.image_size, self.image_size)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													_snake_case						:     Tuple				=    self.type_sequence_label_size
													_snake_case						:     str				=    TFSwinForImageClassification(snake_case_				)
													_snake_case						:     Dict				=    model(snake_case_      ,				labels=snake_case_				)
													self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.type_sequence_label_size)				)

													# test greyscale images
													_snake_case						:     str				=    1
													_snake_case						:     int				=    TFSwinForImageClassification(snake_case_				)
													_snake_case						:     Optional[int]				=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
													_snake_case						:     str				=    model(snake_case_				)
													self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.type_sequence_label_size)				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     List[str]				=    self.prepare_config_and_inputs()
													_snake_case						:     Optional[int]				=    config_and_inputs
													_snake_case						:     Any				=    {'pixel_values': pixel_values}
													return config, inputs_dict




@require_tf
class    lowercase_    (     lowerCamelCase_						, lowerCamelCase_						, unittest.TestCase		):
							_lowerCamelCase						=		(
							    (
							        TFSwinModel,
							        TFSwinForImageClassification,
							        TFSwinForMaskedImageModeling,
							    )
							    if is_tf_available()
							    else ()
							)
							_lowerCamelCase						=		(
							    {'''feature-extraction''': TFSwinModel, '''image-classification''': TFSwinForImageClassification}
							    if is_tf_available()
							    else {}
							)

							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False



							def        UpperCamelCase       (						self				):
													_snake_case						:     Optional[Any]				=    TFSwinModelTester(self				)
													_snake_case						:     Union[str, Any]				=    ConfigTester(self      ,				config_class=snake_case_      ,				embed_dim=37				)



							def        UpperCamelCase       (						self				):
													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        UpperCamelCase       (						self				):
													return



							def        UpperCamelCase       (						self				):
													_snake_case						:     List[str]				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*snake_case_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Any				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     List[Any]				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*snake_case_				)



							@unittest.skip(reason="Swin does not use inputs_embeds"				)
							def        UpperCamelCase       (						self				):
													pass



							def        UpperCamelCase       (						self				):
													_snake_case						:     Any				=    self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			_snake_case						:     List[Any]				=    model_class(snake_case_				)
																			self.assertIsInstance(model.get_input_embeddings()      ,				tf.keras.layers.Layer				)
																			_snake_case						:     Tuple				=    model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(snake_case_      ,				tf.keras.layers.Dense				)				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     str				=    self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			_snake_case						:     Union[str, Any]				=    model_class(snake_case_				)
																			_snake_case						:     str				=    inspect.signature(model.call				)
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			_snake_case						:     Optional[Any]				=    [*signature.parameters.keys()]

																			_snake_case						:     List[Any]				=    ['pixel_values']
																			self.assertListEqual(arg_names[:1]      ,				snake_case_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    self.model_tester.prepare_config_and_inputs_for_common()
													_snake_case						:     List[str]				=    True

													for model_class in self.all_model_classes:
																			_snake_case						:     Union[str, Any]				=    True
																			_snake_case						:     Dict				=    False
																			_snake_case						:     int				=    True
																			_snake_case						:     str				=    model_class(snake_case_				)
																			_snake_case						:     str				=    model(**self._prepare_for_class(snake_case_      ,				snake_case_				)				)
																			_snake_case						:     int				=    outputs.attentions
																			_snake_case						:     List[str]				=    len(self.model_tester.depths				)
																			self.assertEqual(len(snake_case_				)      ,				snake_case_				)

																			# check that output_attentions also work using config
																			del inputs_dict["output_attentions"]
																			_snake_case						:     Optional[Any]				=    True
																			_snake_case						:     List[Any]				=    config.window_size**2
																			_snake_case						:     List[Any]				=    model_class(snake_case_				)
																			_snake_case						:     Optional[Any]				=    model(**self._prepare_for_class(snake_case_      ,				snake_case_				)				)
																			_snake_case						:     Tuple				=    outputs.attentions
																			self.assertEqual(len(snake_case_				)      ,				snake_case_				)

																			self.assertListEqual(
																			    list(attentions[0].shape[-3:]				)      ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,				)
																			_snake_case						:     List[Any]				=    len(snake_case_				)

																			# Check attention is always last and order is fine
																			_snake_case						:     int				=    True
																			_snake_case						:     Union[str, Any]				=    True
																			_snake_case						:     int				=    model_class(snake_case_				)
																			_snake_case						:     List[Any]				=    model(**self._prepare_for_class(snake_case_      ,				snake_case_				)				)

																			if hasattr(self.model_tester      ,				"num_hidden_states_types"				):
																									_snake_case						:     Union[str, Any]				=    self.model_tester.num_hidden_states_types
																			else:
																									# also another +1 for reshaped_hidden_states
																									_snake_case						:     Optional[Any]				=    2
																			self.assertEqual(out_len + added_hidden_states      ,				len(snake_case_				)				)

																			_snake_case						:     Optional[int]				=    outputs.attentions

																			self.assertEqual(len(snake_case_				)      ,				snake_case_				)

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



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													_snake_case						:     Optional[int]				=    model_class(snake_case_				)
													_snake_case						:     List[str]				=    model(**self._prepare_for_class(snake_case_      ,				snake_case_				)				)
													_snake_case						:     Dict				=    outputs.hidden_states

													_snake_case						:     str				=    getattr(
													    self.model_tester      ,				"expected_num_hidden_layers"      ,				len(self.model_tester.depths				) + 1				)
													self.assertEqual(len(snake_case_				)      ,				snake_case_				)

													# Swin has a different seq_length
													_snake_case						:     List[Any]				=    to_atuple(config.patch_size				)

													_snake_case						:     str				=    (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]      ,				)

													_snake_case						:     str				=    outputs.reshaped_hidden_states
													self.assertEqual(len(snake_case_				)      ,				snake_case_				)

													_snake_case						:     Optional[Any]				=    reshaped_hidden_states[0].shape

													_snake_case						:     Tuple				=    tf.reshape(reshaped_hidden_states[0]      ,				(batch_size, num_channels, height * width)				)
													_snake_case						:     Tuple				=    tf.transpose(snake_case_      ,				(0, 2, 1)				)

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



							def        UpperCamelCase       (						self				):
													_snake_case						:     Dict				=    self.model_tester.prepare_config_and_inputs_for_common()

													_snake_case						:     Optional[Any]				=    to_atuple(self.model_tester.image_size				)

													for model_class in self.all_model_classes:
																			_snake_case						:     Optional[int]				=    True
																			self.check_hidden_states_output(snake_case_      ,				snake_case_      ,				snake_case_      ,				snake_case_				)

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			_snake_case						:     Any				=    True

																			self.check_hidden_states_output(snake_case_      ,				snake_case_      ,				snake_case_      ,				snake_case_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    self.model_tester.prepare_config_and_inputs_for_common()
													_snake_case						:     Tuple				=    3

													_snake_case						:     Optional[int]				=    to_atuple(self.model_tester.image_size				)
													_snake_case						:     List[str]				=    to_atuple(config.patch_size				)

													_snake_case						:     Any				=    image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
													_snake_case						:     Dict				=    image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

													for model_class in self.all_model_classes:
																			_snake_case						:     Optional[Any]				=    True
																			self.check_hidden_states_output(snake_case_      ,				snake_case_      ,				snake_case_      ,				(padded_height, padded_width)				)

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			_snake_case						:     str				=    True
																			self.check_hidden_states_output(snake_case_      ,				snake_case_      ,				snake_case_      ,				(padded_height, padded_width)				)



							@slow
							def        UpperCamelCase       (						self				):

													for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			_snake_case						:     Tuple				=    TFSwinModel.from_pretrained(snake_case_				)
																			self.assertIsNotNone(snake_case_				)




@require_vision
@require_tf
class    lowercase_    (     unittest.TestCase		):



							@cached_property
							def        UpperCamelCase       (						self				):
													return (
													    AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"				)
													    if is_vision_available()
													    else None
													)



							@slow
							def        UpperCamelCase       (						self				):
													_snake_case						:     List[Any]				=    TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"				)
													_snake_case						:     List[str]				=    self.default_image_processor

													_snake_case						:     Tuple				=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
													_snake_case						:     Optional[Any]				=    image_processor(images=snake_case_      ,				return_tensors="tf"				)

													# forward pass
													_snake_case						:     Optional[int]				=    model(snake_case_				)

													# verify the logits
													_snake_case						:     Tuple				=    tf.TensorShape((1, 1_000)				)
													self.assertEqual(outputs.logits.shape      ,				snake_case_				)
													_snake_case						:     Union[str, Any]				=    tf.constant([-0.0_948, -0.6_454, -0.0_921]				)
													self.assertTrue(np.allclose(outputs.logits[0, :3]      ,				snake_case_      ,				atol=1e-4				)				)
'''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

snake_case__     :			Dict      			=  logging.get_logger(__name__)


def 					_lowerCamelCase    (      ):



     """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      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_       ):







  '''simple docstring'''






  def __init__( self      ,		snake_case_						):




       '''simple docstring'''

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







class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_       ):







  '''simple docstring'''






  def __init__( self      ,		snake_case_						):




       '''simple docstring'''

       super().__init__(snake_case_						)
       UpperCAmelCase_							:   Tuple   =      None
       UpperCAmelCase_							:   Any   =      []


  def _UpperCamelCase     ( self						):




       '''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      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   Tuple   =      self.get_value(snake_case_						)
       if val is None:
            return False
       else:
            return val == "auto"


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_=None      ,		snake_case_=True						):




       '''simple docstring'''

       UpperCAmelCase_   ,			UpperCAmelCase_							:   int   =      self.find_config_node(snake_case_						)
       if config is None:
            return

       if config.get(snake_case_						) == "auto":
            UpperCAmelCase_							:   int   =      hf_val
            return

       if not must_match:
            return

       UpperCAmelCase_							:   Optional[Any]   =      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}'''						)

  lowerCamelCase_		:Tuple						    =		partialmethod(lowerCamelCase_						,	must_match=lowerCamelCase_       )


  def _UpperCamelCase     ( self      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   Union[str, Any]   =      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_							:   Any   =      'apex' if args.fpaa_backend == 'apex' else 'amp'
       else:
            UpperCAmelCase_							:   Union[str, Any]   =      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_							:   List[str]   =      self.config.get('checkpoint'      ,		{}						)
            UpperCAmelCase_							:   str   =      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_							:   Dict   =      torch.bfloataa
       elif self.is_false('fp16.enabled'						):
            UpperCAmelCase_							:   List[Any]   =      torch.floataa
       else:
            UpperCAmelCase_							:   Optional[int]   =      torch.floataa






  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[str]   =      [
           'zero_optimization.reduce_bucket_size',
           'zero_optimization.stage3_prefetch_bucket_size',
           'zero_optimization.stage3_param_persistence_threshold',
       ]
       UpperCAmelCase_							:   str   =      [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_							:   List[Any]   =      model.config.hidden_size
            elif hasattr(model.config      ,		'hidden_sizes'						):
                 # if there are many hidden sizes pick the largest one
                 UpperCAmelCase_							:   List[str]   =      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'      ,		1_0 * 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_							:   List[Any]   =      '\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\'.'''						)







snake_case__     :			List[str]      			=  None


def 					_lowerCamelCase    (      lowerCamelCase_					:							Optional[int]   ):



     """simple docstring"""





     global _hf_deepspeed_config_weak_ref
     # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
     UpperCAmelCase_							:   List[str]   =      weakref.ref(lowerCamelCase_   )


def 					_lowerCamelCase    (      ):



     """simple docstring"""





     global _hf_deepspeed_config_weak_ref
     UpperCAmelCase_							:   int   =      None


def 					_lowerCamelCase    (      ):



     """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 					_lowerCamelCase    (      ):



     """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 					_lowerCamelCase    (      lowerCamelCase_					:							Optional[int]	,     lowerCamelCase_					:							Optional[Any]	,     lowerCamelCase_					:							int	,     lowerCamelCase_					:							Union[str, Any]	,     lowerCamelCase_					:							Union[str, Any]   ):



     """simple docstring"""





     from accelerate.utils import DummyOptim, DummyScheduler

     UpperCAmelCase_							:   Optional[Any]   =      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_							:   int   =      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_							:   int   =      DummyOptim(params=lowerCamelCase_   )
     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_							:   Tuple   =      trainer.create_optimizer()
          # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
          UpperCAmelCase_							:   Any   =      True

     UpperCAmelCase_							:   int   =      None
     if "scheduler" in config:
          UpperCAmelCase_							:   Union[str, Any]   =      DummyScheduler(lowerCamelCase_   )
     else:
          if isinstance(lowerCamelCase_	,     lowerCamelCase_   ):
               raise ValueError(
                   'Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '
                   'Please configure a scheduler in the DeepSpeed config.'   )
          UpperCAmelCase_							:   List[Any]   =      trainer.create_scheduler(num_training_steps=lowerCamelCase_	,     optimizer=lowerCamelCase_   )

     return optimizer, lr_scheduler


def 					_lowerCamelCase    (      lowerCamelCase_					:							List[str]	,     lowerCamelCase_					:							str	,     lowerCamelCase_					:							str=False   ):



     """simple docstring"""





     from deepspeed.utils import logger as ds_logger

     UpperCAmelCase_							:   List[str]   =      trainer.model
     UpperCAmelCase_							:   List[Any]   =      trainer.args

     UpperCAmelCase_							:   Optional[int]   =      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(lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_   )

     # 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_							:   List[Any]   =      None, None
          UpperCAmelCase_							:   str   =      None
     else:
          UpperCAmelCase_							:   Union[str, Any]   =      None  # important for when deepspeed_init is used as re-init
          UpperCAmelCase_							:   Tuple   =      list(filter(lambda lowerCamelCase_   : p.requires_grad	,     model.parameters()   )   )
          UpperCAmelCase_   ,			UpperCAmelCase_							:   List[str]   =      deepspeed_optim_sched(
              lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_   )

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

     return optimizer, lr_scheduler





def 					_lowerCamelCase    (      lowerCamelCase_					:							Tuple	,     lowerCamelCase_					:							int   ):



     """simple docstring"""





     import glob

     UpperCAmelCase_							:   Dict   =      sorted(glob.glob(F'''{checkpoint_path}/global_step*'''   )   )

     if len(lowerCamelCase_   ) > 0:
          logger.info(F'''Attempting to resume from {checkpoint_path}'''   )
          # this magically updates self.optimizer and self.lr_scheduler
          UpperCAmelCase_   ,			UpperCAmelCase_							:   str   =      deepspeed_engine.load_checkpoint(
              lowerCamelCase_	,     load_optimizer_states=lowerCamelCase_	,     load_lr_scheduler_states=lowerCamelCase_   )
          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}'''   )






