




"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__A       :						Any												=			logging.get_logger(__name__)
def     A_	(						snake_case_   :				Union[str, Any]  ,snake_case_   :				Tuple=False  ,snake_case_   :				Optional[Any]=False			):







    '''simple docstring'''
    UpperCamelCase :      Optional[Any]      				=							'backbone.' if is_semantic else ''

    UpperCamelCase :      List[Any]      				=							[]
    for i in range(config.num_hidden_layers			):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((f'{prefix}blocks.{i}.norm1.weight', f'beit.encoder.layer.{i}.layernorm_before.weight')			)
        rename_keys.append((f'{prefix}blocks.{i}.norm1.bias', f'beit.encoder.layer.{i}.layernorm_before.bias')			)
        rename_keys.append(
            (f'{prefix}blocks.{i}.attn.proj.weight', f'beit.encoder.layer.{i}.attention.output.dense.weight')			)
        rename_keys.append(
            (f'{prefix}blocks.{i}.attn.proj.bias', f'beit.encoder.layer.{i}.attention.output.dense.bias')			)
        rename_keys.append((f'{prefix}blocks.{i}.norm2.weight', f'beit.encoder.layer.{i}.layernorm_after.weight')			)
        rename_keys.append((f'{prefix}blocks.{i}.norm2.bias', f'beit.encoder.layer.{i}.layernorm_after.bias')			)
        rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.weight', f'beit.encoder.layer.{i}.intermediate.dense.weight')			)
        rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.bias', f'beit.encoder.layer.{i}.intermediate.dense.bias')			)
        rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.weight', f'beit.encoder.layer.{i}.output.dense.weight')			)
        rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.bias', f'beit.encoder.layer.{i}.output.dense.bias')			)

    # projection layer + position embeddings
    rename_keys.extend(
        [
            (f'{prefix}cls_token', """beit.embeddings.cls_token"""),
            (f'{prefix}patch_embed.proj.weight', """beit.embeddings.patch_embeddings.projection.weight"""),
            (f'{prefix}patch_embed.proj.bias', """beit.embeddings.patch_embeddings.projection.bias"""),
        ]			)

    if has_lm_head:
        # mask token + shared relative position bias + layernorm
        rename_keys.extend(
            [
                ("""mask_token""", """beit.embeddings.mask_token"""),
                (
                    """rel_pos_bias.relative_position_bias_table""",
                    """beit.encoder.relative_position_bias.relative_position_bias_table""",
                ),
                (
                    """rel_pos_bias.relative_position_index""",
                    """beit.encoder.relative_position_bias.relative_position_index""",
                ),
                ("""norm.weight""", """layernorm.weight"""),
                ("""norm.bias""", """layernorm.bias"""),
            ]			)
    elif is_semantic:
        # semantic segmentation classification heads
        rename_keys.extend(
            [
                ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
                ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
                ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
                ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
            ]			)
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
                ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
                ("""head.weight""", """classifier.weight"""),
                ("""head.bias""", """classifier.bias"""),
            ]			)

    return rename_keys
def     A_	(						snake_case_   :				Any  ,snake_case_   :				Optional[int]  ,snake_case_   :				List[str]=False  ,snake_case_   :				Tuple=False			):







    '''simple docstring'''





    for i in range(config.num_hidden_layers			):
        UpperCamelCase :      Any      				=							'backbone.' if is_semantic else ''
        # queries, keys and values
        UpperCamelCase :      Dict      				=							state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'			)
        UpperCamelCase :      List[Any]      				=							state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'			)
        UpperCamelCase :      List[Any]      				=							state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'			)

        UpperCamelCase :      Tuple      				=							in_proj_weight[
            : config.hidden_size, :
        ]
        UpperCamelCase :      Optional[int]      				=							q_bias
        UpperCamelCase :      Tuple      				=							in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        UpperCamelCase :      Union[str, Any]      				=							in_proj_weight[
            -config.hidden_size :, :
        ]
        UpperCamelCase :      int      				=							v_bias

        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        UpperCamelCase :      Dict      				=							state_dict.pop(f'{prefix}blocks.{i}.gamma_1'			)
        UpperCamelCase :      Dict      				=							state_dict.pop(f'{prefix}blocks.{i}.gamma_2'			)

        UpperCamelCase :      Optional[int]      				=							gamma_a
        UpperCamelCase :      Optional[Any]      				=							gamma_a

        # relative_position bias table + index
        if not has_lm_head:
            # each layer has its own relative position bias
            UpperCamelCase :      Union[str, Any]      				=							state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_bias_table'			)
            UpperCamelCase :      List[str]      				=							state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_index'			)

            UpperCamelCase :      List[str]      				=							table
            UpperCamelCase :      Tuple      				=							index
def     A_	(						snake_case_   :				List[str]  ,snake_case_   :				int  ,snake_case_   :				Union[str, Any]			):







    '''simple docstring'''
    UpperCamelCase :      Tuple      				=							dct.pop(lowerCamelCase_			)
    UpperCamelCase :      Tuple      				=							val
def     A_	(						):







    '''simple docstring'''
    UpperCamelCase :      Optional[Any]      				=							'http://images.cocodataset.org/val2017/000000039769.jpg'
    UpperCamelCase :      Optional[Any]      				=							Image.open(requests.get(lowerCamelCase_  ,stream=lowerCamelCase_			).raw			)
    return im
@torch.no_grad()
def     A_	(						snake_case_   :				Optional[int]  ,snake_case_   :				Dict			):







    '''simple docstring'''
    UpperCamelCase :      Any      				=							BeitConfig()
    UpperCamelCase :      List[str]      				=							False
    UpperCamelCase :      str      				=							False
    UpperCamelCase :      Tuple      				=							'huggingface/label-files'
    # set config parameters based on URL
    if checkpoint_url[-9:-4] == "pt22k":
        # masked image modeling
        UpperCamelCase :      Dict      				=							True
        UpperCamelCase :      Tuple      				=							True
        UpperCamelCase :      int      				=							True
    elif checkpoint_url[-9:-4] == "ft22k":
        # intermediate fine-tuning on ImageNet-22k
        UpperCamelCase :      List[str]      				=							True
        UpperCamelCase :      Optional[Any]      				=							2_1_8_4_1
        UpperCamelCase :      Tuple      				=							'imagenet-22k-id2label.json'
        UpperCamelCase :      Any      				=							json.load(open(hf_hub_download(lowerCamelCase_  ,lowerCamelCase_  ,repo_type="""dataset"""			)  ,"""r"""			)			)
        UpperCamelCase :      str      				=							{int(lowerCamelCase_			): v for k, v in idalabel.items()}
        # this dataset contains 21843 labels but the model only has 21841
        # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
        del idalabel[9_2_0_5]
        del idalabel[1_5_0_2_7]
        UpperCamelCase :      Tuple      				=							idalabel
        UpperCamelCase :      Optional[int]      				=							{v: k for k, v in idalabel.items()}
    elif checkpoint_url[-8:-4] == "to1k":
        # fine-tuning on ImageNet-1k
        UpperCamelCase :      int      				=							True
        UpperCamelCase :      Optional[Any]      				=							1_0_0_0
        UpperCamelCase :      Optional[int]      				=							'imagenet-1k-id2label.json'
        UpperCamelCase :      Optional[Any]      				=							json.load(open(hf_hub_download(lowerCamelCase_  ,lowerCamelCase_  ,repo_type="""dataset"""			)  ,"""r"""			)			)
        UpperCamelCase :      int      				=							{int(lowerCamelCase_			): v for k, v in idalabel.items()}
        UpperCamelCase :      Tuple      				=							idalabel
        UpperCamelCase :      Union[str, Any]      				=							{v: k for k, v in idalabel.items()}
        if "384" in checkpoint_url:
            UpperCamelCase :      int      				=							3_8_4
        if "512" in checkpoint_url:
            UpperCamelCase :      str      				=							5_1_2
    elif "ade20k" in checkpoint_url:
        # fine-tuning
        UpperCamelCase :      List[str]      				=							True
        UpperCamelCase :      Optional[Any]      				=							1_5_0
        UpperCamelCase :      str      				=							'ade20k-id2label.json'
        UpperCamelCase :      Dict      				=							json.load(open(hf_hub_download(lowerCamelCase_  ,lowerCamelCase_  ,repo_type="""dataset"""			)  ,"""r"""			)			)
        UpperCamelCase :      str      				=							{int(lowerCamelCase_			): v for k, v in idalabel.items()}
        UpperCamelCase :      int      				=							idalabel
        UpperCamelCase :      int      				=							{v: k for k, v in idalabel.items()}
        UpperCamelCase :      int      				=							6_4_0
        UpperCamelCase :      Union[str, Any]      				=							True
    else:
        raise ValueError("""Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\'"""			)

    # size of the architecture
    if "base" in checkpoint_url:
        pass
    elif "large" in checkpoint_url:
        UpperCamelCase :      Union[str, Any]      				=							1_0_2_4
        UpperCamelCase :      Union[str, Any]      				=							4_0_9_6
        UpperCamelCase :      str      				=							2_4
        UpperCamelCase :      Tuple      				=							1_6
        if "ade20k" in checkpoint_url:
            UpperCamelCase :      str      				=							6_4_0
            UpperCamelCase :      List[Any]      				=							[7, 1_1, 1_5, 2_3]
    else:
        raise ValueError("""Should either find \'base\' or \'large\' in checkpoint URL"""			)

    # load state_dict of original model, remove and rename some keys
    UpperCamelCase :      int      				=							torch.hub.load_state_dict_from_url(lowerCamelCase_  ,map_location="""cpu"""  ,check_hash=lowerCamelCase_			)
    UpperCamelCase :      Optional[Any]      				=							state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

    UpperCamelCase :      Union[str, Any]      				=							create_rename_keys(lowerCamelCase_  ,has_lm_head=lowerCamelCase_  ,is_semantic=lowerCamelCase_			)
    for src, dest in rename_keys:
        rename_key(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_			)
    read_in_q_k_v(lowerCamelCase_  ,lowerCamelCase_  ,has_lm_head=lowerCamelCase_  ,is_semantic=lowerCamelCase_			)
    if is_semantic:
        # add prefix to decoder keys
        for key, val in state_dict.copy().items():
            UpperCamelCase :      Optional[Any]      				=							state_dict.pop(lowerCamelCase_			)
            if key.startswith("""backbone.fpn"""			):
                UpperCamelCase :      Dict      				=							key.replace("""backbone.fpn"""  ,"""fpn"""			)
            UpperCamelCase :      List[Any]      				=							val

    # load HuggingFace model
    if checkpoint_url[-9:-4] == "pt22k":
        UpperCamelCase :      Optional[int]      				=							BeitForMaskedImageModeling(lowerCamelCase_			)
    elif "ade20k" in checkpoint_url:
        UpperCamelCase :      Optional[Any]      				=							BeitForSemanticSegmentation(lowerCamelCase_			)
    else:
        UpperCamelCase :      Tuple      				=							BeitForImageClassification(lowerCamelCase_			)
    model.eval()
    model.load_state_dict(lowerCamelCase_			)

    # Check outputs on an image
    if is_semantic:
        UpperCamelCase :      str      				=							BeitImageProcessor(size=config.image_size  ,do_center_crop=lowerCamelCase_			)
        UpperCamelCase :      Union[str, Any]      				=							load_dataset("""hf-internal-testing/fixtures_ade20k"""  ,split="""test"""			)
        UpperCamelCase :      Dict      				=							Image.open(ds[0]["""file"""]			)
    else:
        UpperCamelCase :      str      				=							BeitImageProcessor(
            size=config.image_size  ,resample=PILImageResampling.BILINEAR  ,do_center_crop=lowerCamelCase_			)
        UpperCamelCase :      Optional[int]      				=							prepare_img()

    UpperCamelCase :      List[str]      				=							image_processor(images=lowerCamelCase_  ,return_tensors="""pt"""			)
    UpperCamelCase :      Dict      				=							encoding['pixel_values']

    UpperCamelCase :      List[Any]      				=							model(lowerCamelCase_			)
    UpperCamelCase :      List[Any]      				=							outputs.logits

    # verify logits
    UpperCamelCase :      List[str]      				=							torch.Size([1, 1_0_0_0]			)
    if checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k"""			):
        UpperCamelCase :      Union[str, Any]      				=							torch.Size([1, 1_9_6, 8_1_9_2]			)
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k"""			):
        UpperCamelCase :      Tuple      				=							torch.Size([1, 1_9_6, 8_1_9_2]			)
    elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22k"""			):
        UpperCamelCase :      Optional[Any]      				=							torch.Size([1, 2_1_8_4_1]			)
        UpperCamelCase :      Any      				=							torch.tensor([2.2288, 2.4671, 0.7395]			)
        UpperCamelCase :      Union[str, Any]      				=							2_3_9_7
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k"""			):
        UpperCamelCase :      str      				=							torch.Size([1, 2_1_8_4_1]			)
        UpperCamelCase :      int      				=							torch.tensor([1.6881, -0.2787, 0.5901]			)
        UpperCamelCase :      List[str]      				=							2_3_9_6
    elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k"""			):
        UpperCamelCase :      Tuple      				=							torch.tensor([0.1241, 0.0798, -0.6569]			)
        UpperCamelCase :      Union[str, Any]      				=							2_8_5
    elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k"""			):
        UpperCamelCase :      Any      				=							torch.tensor([-1.2385, -1.0987, -1.0108]			)
        UpperCamelCase :      Tuple      				=							2_8_1
    elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k"""			):
        UpperCamelCase :      int      				=							torch.tensor([-1.5303, -0.9484, -0.3147]			)
        UpperCamelCase :      str      				=							7_6_1
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k"""			):
        UpperCamelCase :      Union[str, Any]      				=							torch.tensor([0.4610, -0.0928, 0.2086]			)
        UpperCamelCase :      str      				=							7_6_1
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k"""			):
        UpperCamelCase :      str      				=							torch.tensor([-0.4804, 0.6257, -0.1837]			)
        UpperCamelCase :      Optional[int]      				=							7_6_1
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k"""			):
        UpperCamelCase :      List[str]      				=							torch.tensor([[-0.5122, 0.5117, -0.2113]]			)
        UpperCamelCase :      Dict      				=							7_6_1
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k"""			):
        UpperCamelCase :      Union[str, Any]      				=							torch.tensor([-0.3062, 0.7261, 0.4852]			)
        UpperCamelCase :      List[str]      				=							7_6_1
    elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k"""			):
        UpperCamelCase :      Dict      				=							(1, 1_5_0, 1_6_0, 1_6_0)
        UpperCamelCase :      Optional[Any]      				=							torch.tensor(
            [
                [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
                [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
                [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
            ]			)
    elif checkpoint_url[:-4].endswith("""beit_large_patch16_640_pt22k_ft22ktoade20k"""			):
        UpperCamelCase :      Dict      				=							(1, 1_5_0, 1_6_0, 1_6_0)
        UpperCamelCase :      int      				=							torch.tensor(
            [
                [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
                [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
                [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
            ]			)
    else:
        raise ValueError("""Can\'t verify logits as model is not supported"""			)

    assert logits.shape == expected_shape, "Shape of logits not as expected"
    if not has_lm_head:
        if is_semantic:
            assert torch.allclose(
                logits[0, :3, :3, :3]  ,lowerCamelCase_  ,atol=1e-3			), "First elements of logits not as expected"
        else:
            print("""Predicted class idx:"""  ,logits.argmax(-1			).item()			)
            assert torch.allclose(
                logits[0, :3]  ,lowerCamelCase_  ,atol=1e-3			), "First elements of logits not as expected"
            assert logits.argmax(-1			).item() == expected_class_idx, "Predicted class index not as expected"

    Path(lowerCamelCase_			).mkdir(exist_ok=lowerCamelCase_			)
    print(f'Saving model to {pytorch_dump_folder_path}'			)
    model.save_pretrained(lowerCamelCase_			)
    print(f'Saving image processor to {pytorch_dump_folder_path}'			)
    image_processor.save_pretrained(lowerCamelCase_			)


if __name__ == "__main__":
  __A       :						List[str]												=			argparse.ArgumentParser()

  parser.add_argument(
      '''--checkpoint_url''',
      default='''https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth''',
      type=str,
      help='''URL to the original PyTorch checkpoint (.pth file).''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
  )
  __A       :						Optional[Any]												=			parser.parse_args()
  convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





'''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}'''   )






