"""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()
lowercase__   			=      logging.get_logger(__name__)







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=False						,	_SCREAMING_SNAKE_CASE=False  )      ->List[str]:
  a__:	Optional[int]     				=      'backbone.' if is_semantic else ''

  a__:	Union[str, 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  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=False						,	_SCREAMING_SNAKE_CASE=False  )      ->List[str]:
  for i in range(config.num_hidden_layers  ):
    a__:	Optional[Any]     				=      'backbone.' if is_semantic else ''
    # queries, keys and values
    a__:	Any     				=      state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight'  )
    a__:	Optional[int]     				=      state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias'  )
    a__:	List[str]     				=      state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias'  )

    a__:	Tuple     				=      in_proj_weight[
        : config.hidden_size, :
    ]
    a__:	Dict     				=      q_bias
    a__:	Optional[int]     				=      in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    a__:	int     				=      in_proj_weight[
        -config.hidden_size :, :
    ]
    a__:	List[str]     				=      v_bias

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

    a__:	List[Any]     				=      gamma_a
    a__:	str     				=      gamma_a

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

      a__:	Union[str, Any]     				=      table
      a__:	Any     				=      index







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->List[Any]:
  a__:	str     				=      dct.pop(_SCREAMING_SNAKE_CASE  )
  a__:	Tuple     				=      val







def 		__a  (      )      ->Any:
  a__:	Tuple     				=      'http://images.cocodataset.org/val2017/000000039769.jpg'
  a__:	int     				=      Image.open(requests.get(_SCREAMING_SNAKE_CASE						,	stream=_SCREAMING_SNAKE_CASE  ).raw  )
  return im







@torch.no_grad()
def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->int:
  a__:	Optional[Any]     				=      BeitConfig()
  a__:	Any     				=      False
  a__:	Optional[Any]     				=      False
  a__:	Any     				=      'huggingface/label-files'
  # set config parameters based on URL
  if checkpoint_url[-9:-4] == "pt22k":
    # masked image modeling
    a__:	int     				=      True
    a__:	Tuple     				=      True
    a__:	List[Any]     				=      True
  elif checkpoint_url[-9:-4] == "ft22k":
    # intermediate fine-tuning on ImageNet-22k
    a__:	Optional[int]     				=      True
    a__:	Any     				=      21841
    a__:	int     				=      'imagenet-22k-id2label.json'
    a__:	str     				=      json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	repo_type='dataset'  )						,	'r'  )  )
    a__:	List[str]     				=      {int(_SCREAMING_SNAKE_CASE  ): 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[9205]
    del idalabel[15027]
    a__:	List[Any]     				=      idalabel
    a__:	Tuple     				=      {v: k for k, v in idalabel.items()}
  elif checkpoint_url[-8:-4] == "to1k":
    # fine-tuning on ImageNet-1k
    a__:	Optional[Any]     				=      True
    a__:	List[str]     				=      1000
    a__:	Tuple     				=      'imagenet-1k-id2label.json'
    a__:	Any     				=      json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	repo_type='dataset'  )						,	'r'  )  )
    a__:	Optional[Any]     				=      {int(_SCREAMING_SNAKE_CASE  ): v for k, v in idalabel.items()}
    a__:	Optional[int]     				=      idalabel
    a__:	List[Any]     				=      {v: k for k, v in idalabel.items()}
    if "384" in checkpoint_url:
      a__:	List[str]     				=      384
    if "512" in checkpoint_url:
      a__:	int     				=      512
  elif "ade20k" in checkpoint_url:
    # fine-tuning
    a__:	Dict     				=      True
    a__:	Dict     				=      150
    a__:	int     				=      'ade20k-id2label.json'
    a__:	str     				=      json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	repo_type='dataset'  )						,	'r'  )  )
    a__:	Dict     				=      {int(_SCREAMING_SNAKE_CASE  ): v for k, v in idalabel.items()}
    a__:	str     				=      idalabel
    a__:	List[str]     				=      {v: k for k, v in idalabel.items()}
    a__:	Optional[Any]     				=      640
    a__:	List[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:
    a__:	Dict     				=      1024
    a__:	str     				=      4096
    a__:	Optional[Any]     				=      24
    a__:	str     				=      16
    if "ade20k" in checkpoint_url:
      a__:	Any     				=      640
      a__:	str     				=      [7, 11, 15, 23]
  else:
    raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'  )

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

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

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

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

  a__:	Tuple     				=      image_processor(images=_SCREAMING_SNAKE_CASE						,	return_tensors='pt'  )
  a__:	int     				=      encoding['pixel_values']

  a__:	str     				=      model(_SCREAMING_SNAKE_CASE  )
  a__:	Union[str, Any]     				=      outputs.logits

  # verify logits
  a__:	Union[str, Any]     				=      torch.Size([1, 1000]  )
  if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'  ):
    a__:	Dict     				=      torch.Size([1, 196, 8192]  )
  elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'  ):
    a__:	str     				=      torch.Size([1, 196, 8192]  )
  elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'  ):
    a__:	List[Any]     				=      torch.Size([1, 21841]  )
    a__:	int     				=      torch.tensor([2.2_288, 2.4_671, 0.7_395]  )
    a__:	List[Any]     				=      2397
  elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'  ):
    a__:	int     				=      torch.Size([1, 21841]  )
    a__:	Dict     				=      torch.tensor([1.6_881, -0.2_787, 0.5_901]  )
    a__:	Tuple     				=      2396
  elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'  ):
    a__:	List[Any]     				=      torch.tensor([0.1_241, 0.0_798, -0.6_569]  )
    a__:	Tuple     				=      285
  elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'  ):
    a__:	int     				=      torch.tensor([-1.2_385, -1.0_987, -1.0_108]  )
    a__:	str     				=      281
  elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'  ):
    a__:	int     				=      torch.tensor([-1.5_303, -0.9_484, -0.3_147]  )
    a__:	Dict     				=      761
  elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'  ):
    a__:	Dict     				=      torch.tensor([0.4_610, -0.0_928, 0.2_086]  )
    a__:	Any     				=      761
  elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'  ):
    a__:	List[str]     				=      torch.tensor([-0.4_804, 0.6_257, -0.1_837]  )
    a__:	str     				=      761
  elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'  ):
    a__:	Dict     				=      torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]  )
    a__:	int     				=      761
  elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'  ):
    a__:	Optional[Any]     				=      torch.tensor([-0.3_062, 0.7_261, 0.4_852]  )
    a__:	Union[str, Any]     				=      761
  elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'  ):
    a__:	Optional[int]     				=      (1, 150, 160, 160)
    a__:	Union[str, Any]     				=      torch.tensor(
        [
            [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
            [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
            [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
        ]  )
  elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'  ):
    a__:	List[Any]     				=      (1, 150, 160, 160)
    a__:	str     				=      torch.tensor(
        [
            [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
            [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
            [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
        ]  )
  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]						,	_SCREAMING_SNAKE_CASE						,	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]						,	_SCREAMING_SNAKE_CASE						,	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(_SCREAMING_SNAKE_CASE  ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE  )
  print(F'Saving model to {pytorch_dump_folder_path}'  )
  model.save_pretrained(_SCREAMING_SNAKE_CASE  )
  print(F'Saving image processor to {pytorch_dump_folder_path}'  )
  image_processor.save_pretrained(_SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
 lowercase__   			=      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.'
 )
 lowercase__   			=      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

lowercase__   			=      logging.get_logger(__name__)







def 		__a  (      )      ->Dict:
  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   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self       ,     lowercase)      ->     int:





        '''simple docstring'''





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







class   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self       ,     lowercase)      ->     List[Any]:





        '''simple docstring'''





        super().__init__(lowercase)
        a__:	List[Any]     				=      None
        a__:	int     				=      []



      def     lowerCamelCase_  (		self)      ->     Any:





        '''simple docstring'''





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



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Optional[Any]:





        '''simple docstring'''





        a__:	Optional[int]     				=      self.get_value(lowercase)
        if val is None:
          return False
        else:
          return val == "auto"



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase       ,     lowercase=None       ,     lowercase=True)      ->     int:





        '''simple docstring'''





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

        if config.get(lowercase) == "auto":
          a__:	Optional[int]     				=      hf_val
          return

        if not must_match:
          return

        a__:	List[Any]     				=      config.get(lowercase)
        if ds_val is not None and ds_val != hf_val:
          self.mismatches.append(f'- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}')

      a__								=						partialmethod(__lowerCAmelCase			, must_match=__lowerCAmelCase							)



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Dict:





        '''simple docstring'''





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

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

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

        # fp16
        if args.fpaa or args.fpaa_full_eval:
          a__:	Tuple     				=      'apex' if args.fpaa_backend == 'apex' else 'amp'
        else:
          a__:	Optional[int]     				=      None

        if args.save_on_each_node:
          # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
          a__:	Union[str, Any]     				=      self.config.get('checkpoint'       ,     {})
          a__:	Tuple     				=      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'):
          a__:	Optional[int]     				=      torch.bfloataa
        elif self.is_false('fp16.enabled'):
          a__:	Tuple     				=      torch.floataa
        else:
          a__:	Tuple     				=      torch.floataa



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase       ,     lowercase)      ->     Any:





        '''simple docstring'''





        a__:	List[Any]     				=      [
            'zero_optimization.reduce_bucket_size',
            'zero_optimization.stage3_prefetch_bucket_size',
            'zero_optimization.stage3_param_persistence_threshold',
        ]
        a__:	Union[str, Any]     				=      [x for x in hidden_size_based_keys if self.is_auto(lowercase)]

        if len(lowercase) > 0:
          if hasattr(model.config       ,     'hidden_size'):
            a__:	Any     				=      model.config.hidden_size
          elif hasattr(model.config       ,     'hidden_sizes'):
            # if there are many hidden sizes pick the largest one
            a__:	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'       ,     10 * hidden_size)

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

        if len(self.mismatches) > 0:
          a__:	int     				=      '\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__   			=      None







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







def 		__a  (      )      ->List[Any]:
  # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
  global _hf_deepspeed_config_weak_ref
  a__:	Any     				=      None







def 		__a  (      )      ->Union[str, Any]:
  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 		__a  (      )      ->Tuple:
  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 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Dict:
  from accelerate.utils import DummyOptim, DummyScheduler

  a__:	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)

  a__:	Dict     				=      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.'  )
    a__:	str     				=      DummyOptim(params=_SCREAMING_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.
    a__:	Any     				=      trainer.create_optimizer()
    # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
    a__:	str     				=      True

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

  return optimizer, lr_scheduler







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=False  )      ->Dict:
  from deepspeed.utils import logger as ds_logger

  a__:	Optional[int]     				=      trainer.model
  a__:	List[Any]     				=      trainer.args

  a__:	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(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_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'  )
    a__				,       a__:	Optional[int]     				=      None, None
    a__:	Union[str, Any]     				=      None
  else:
    a__:	List[Any]     				=      None  # important for when deepspeed_init is used as re-init
    a__:	Union[str, Any]     				=      list(filter(lambda _SCREAMING_SNAKE_CASE  : p.requires_grad						,	model.parameters()  )  )
    a__				,       a__:	Optional[int]     				=      deepspeed_optim_sched(
        _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

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

  return optimizer, lr_scheduler







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

  a__:	Tuple     				=      sorted(glob.glob(F'{checkpoint_path}/global_step*'  )  )

  if len(_SCREAMING_SNAKE_CASE  ) > 0:
    logger.info(F'Attempting to resume from {checkpoint_path}'  )
    # this magically updates self.optimizer and self.lr_scheduler
    a__				,       a__:	Union[str, Any]     				=      deepspeed_engine.load_checkpoint(
        _SCREAMING_SNAKE_CASE						,	load_optimizer_states=_SCREAMING_SNAKE_CASE						,	load_lr_scheduler_states=_SCREAMING_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}'  )


