




import inspect
import os

import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed

import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
    AccelerateTestCase,
    TempDirTestCase,
    execute_subprocess_async,
    require_cuda,
    require_fsdp,
    require_multi_gpu,
    slow,
)
from accelerate.utils.constants import (
    FSDP_AUTO_WRAP_POLICY,
    FSDP_BACKWARD_PREFETCH,
    FSDP_SHARDING_STRATEGY,
    FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment


set_seed(42)

_lowerCamelCase	:		Optional[int]							       =   'bert-base-cased'
_lowerCamelCase	:		List[Any]							       =   'fp16'
_lowerCamelCase	:		int							       =   'bf16'
_lowerCamelCase	:		Any							       =   [FPaa, BFaa]


@require_fsdp
@require_cuda
class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Any:





           '''simple docstring'''

           super().setUp()

           A__       =    dict(
               ACCELERATE_USE_FSDP='''true'''					, MASTER_ADDR='''localhost'''					, MASTER_PORT='''10999'''					, RANK='''0'''					, LOCAL_RANK='''0'''					, WORLD_SIZE='''1'''					, )


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





           '''simple docstring'''

           from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

           for i, strategy in enumerate(snake_case_):
                  A__       =    self.dist_env.copy()
                  A__       =    f"""{i + 1}"""
                  A__       =    strategy
                  with mockenv_context(**snake_case_):
                         A__       =    FullyShardedDataParallelPlugin()
                         self.assertEqual(fsdp_plugin.sharding_strategy					, ShardingStrategy(i + 1))


    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->Tuple:





           '''simple docstring'''

           from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

           for i, prefetch_policy in enumerate(snake_case_):
                  A__       =    self.dist_env.copy()
                  A__       =    prefetch_policy
                  with mockenv_context(**snake_case_):
                         A__       =    FullyShardedDataParallelPlugin()
                         if prefetch_policy == "NO_PREFETCH":
                                self.assertIsNone(fsdp_plugin.backward_prefetch)
                         else:
                                self.assertEqual(fsdp_plugin.backward_prefetch					, BackwardPrefetch(i + 1))


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





           '''simple docstring'''

           from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

           for i, state_dict_type in enumerate(snake_case_):
                  A__       =    self.dist_env.copy()
                  A__       =    state_dict_type
                  with mockenv_context(**snake_case_):
                         A__       =    FullyShardedDataParallelPlugin()
                         self.assertEqual(fsdp_plugin.state_dict_type					, StateDictType(i + 1))
                         if state_dict_type == "FULL_STATE_DICT":
                                self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu)
                                self.assertTrue(fsdp_plugin.state_dict_config.ranka_only)


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->int:





           '''simple docstring'''

           A__       =    AutoModel.from_pretrained(snake_case_)
           for policy in FSDP_AUTO_WRAP_POLICY:
                  A__       =    self.dist_env.copy()
                  A__       =    policy
                  if policy == "TRANSFORMER_BASED_WRAP":
                         A__       =    'BertLayer'
                  elif policy == "SIZE_BASED_WRAP":
                         A__       =    '2000'
                  with mockenv_context(**snake_case_):
                         A__       =    FullyShardedDataParallelPlugin()
                         fsdp_plugin.set_auto_wrap_policy(snake_case_)
                         if policy == "NO_WRAP":
                                self.assertIsNone(fsdp_plugin.auto_wrap_policy)
                         else:
                                self.assertIsNotNone(fsdp_plugin.auto_wrap_policy)

           A__       =    self.dist_env.copy()
           A__       =    'TRANSFORMER_BASED_WRAP'
           A__       =    'T5Layer'
           with mockenv_context(**snake_case_):
                  A__       =    FullyShardedDataParallelPlugin()
                  with self.assertRaises(snake_case_) as cm:
                         fsdp_plugin.set_auto_wrap_policy(snake_case_)
                  self.assertTrue('''Could not find the transformer layer class to wrap in the model.''' in str(cm.exception))

           A__       =    self.dist_env.copy()
           A__       =    'SIZE_BASED_WRAP'
           A__       =    '0'
           with mockenv_context(**snake_case_):
                  A__       =    FullyShardedDataParallelPlugin()
                  fsdp_plugin.set_auto_wrap_policy(snake_case_)
                  self.assertIsNone(fsdp_plugin.auto_wrap_policy)


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->int:





           '''simple docstring'''

           from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
           from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

           for mp_dtype in dtypes:
                  A__       =    self.dist_env.copy()
                  A__       =    mp_dtype
                  with mockenv_context(**snake_case_):
                         A__       =    Accelerator()
                         if mp_dtype == "fp16":
                                A__       =    torch.floataa
                         elif mp_dtype == "bf16":
                                A__       =    torch.bfloataa
                         A__       =    MixedPrecision(param_dtype=snake_case_					, reduce_dtype=snake_case_					, buffer_dtype=snake_case_)
                         self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy					, snake_case_)
                         if mp_dtype == FPaa:
                                self.assertTrue(isinstance(accelerator.scaler					, snake_case_))
                         elif mp_dtype == BFaa:
                                self.assertIsNone(accelerator.scaler)
                         AcceleratorState._reset_state(snake_case_)





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





           '''simple docstring'''

           from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

           for flag in [True, False]:
                  A__       =    self.dist_env.copy()
                  A__       =    str(snake_case_).lower()
                  with mockenv_context(**snake_case_):
                         A__       =    FullyShardedDataParallelPlugin()
                         self.assertEqual(fsdp_plugin.cpu_offload					, CPUOffload(offload_params=snake_case_))


@require_fsdp
@require_multi_gpu
@slow
class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->List[str]:





           '''simple docstring'''

           super().setUp()
           A__       =    0.82
           A__       =    [
               'fsdp_shard_grad_op_transformer_based_wrap',
               'fsdp_full_shard_transformer_based_wrap',
           ]
           A__       =    {
               'multi_gpu_fp16': 3_200,
               'fsdp_shard_grad_op_transformer_based_wrap_fp16': 2_000,
               'fsdp_full_shard_transformer_based_wrap_fp16': 1_900,
               # Disabling below test as it overwhelms the RAM memory usage
               # on CI self-hosted runner leading to tests getting killed.
               # "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500,  # fp16 was leading to indefinite hang
           }
           A__       =    160
           A__       =    160

           A__       =    inspect.getfile(accelerate.test_utils)
           A__       =    os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['''scripts''', '''external_deps'''])


    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Any:





           '''simple docstring'''

           A__       =    os.path.join(self.test_scripts_folder					, '''test_performance.py''')
           A__       =    ['accelerate', 'launch', '--num_processes=2', '--num_machines=1', '--machine_rank=0', '--use_fsdp']
           for config in self.performance_configs:
                  A__       =    cmd.copy()
                  for i, strategy in enumerate(snake_case_):
                         if strategy.lower() in config:
                                cmd_config.append(f"""--fsdp_sharding_strategy={i+1}""")
                                break

                  if "fp32" in config:
                         cmd_config.append('''--mixed_precision=no''')
                  else:
                         cmd_config.append('''--mixed_precision=fp16''')

                  if "cpu_offload" in config:
                         cmd_config.append('''--fsdp_offload_params=True''')

                  for policy in FSDP_AUTO_WRAP_POLICY:
                         if policy.lower() in config:
                                cmd_config.append(f"""--fsdp_auto_wrap_policy={policy}""")
                                break

                  if policy == "TRANSFORMER_BASED_WRAP":
                         cmd_config.append('''--fsdp_transformer_layer_cls_to_wrap=BertLayer''')
                  elif policy == "SIZE_BASED_WRAP":
                         cmd_config.append('''--fsdp_min_num_params=2000''')

                  cmd_config.extend(
                      [
                          self.test_file_path,
                          f"""--output_dir={self.tmpdir}""",
                          f"""--performance_lower_bound={self.performance_lower_bound}""",
                      ])
                  with patch_environment(omp_num_threads=1):
                         execute_subprocess_async(snake_case_					, env=os.environ.copy())


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->Any:





           '''simple docstring'''

           A__       =    os.path.join(self.test_scripts_folder					, '''test_checkpointing.py''')
           A__       =    [
               'accelerate',
               'launch',
               '--num_processes=2',
               '--num_machines=1',
               '--machine_rank=0',
               '--use_fsdp',
               '--mixed_precision=fp16',
               '--fsdp_transformer_layer_cls_to_wrap=BertLayer',
           ]

           for i, strategy in enumerate(snake_case_):
                  A__       =    cmd.copy()
                  cmd_config.append(f"""--fsdp_sharding_strategy={i+1}""")
                  if strategy != "FULL_SHARD":
                         continue
                  A__       =    len(snake_case_)
                  for state_dict_type in FSDP_STATE_DICT_TYPE:
                         A__       =    cmd_config[:state_dict_config_index]
                         cmd_config.append(f"""--fsdp_state_dict_type={state_dict_type}""")
                         cmd_config.extend(
                             [
                                 self.test_file_path,
                                 f"""--output_dir={self.tmpdir}""",
                                 '''--partial_train_epoch=1''',
                             ])
                         with patch_environment(omp_num_threads=1):
                                execute_subprocess_async(snake_case_					, env=os.environ.copy())

                         A__       =    cmd_config[:-1]
                         A__       =    os.path.join(self.tmpdir					, '''epoch_0''')
                         cmd_config.extend(
                             [
                                 f"""--resume_from_checkpoint={resume_from_checkpoint}""",
                             ])
                         with patch_environment(omp_num_threads=1):
                                execute_subprocess_async(snake_case_					, env=os.environ.copy())





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->int:





           '''simple docstring'''

           A__       =    os.path.join(self.test_scripts_folder					, '''test_peak_memory_usage.py''')
           A__       =    [
               'accelerate',
               'launch',
               '--num_processes=2',
               '--num_machines=1',
               '--machine_rank=0',
           ]
           for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
                  A__       =    cmd.copy()
                  if "fp16" in spec:
                         cmd_config.extend(['''--mixed_precision=fp16'''])
                  else:
                         cmd_config.extend(['''--mixed_precision=no'''])

                  if "multi_gpu" in spec:
                         continue
                  else:
                         cmd_config.extend(['''--use_fsdp'''])
                         for i, strategy in enumerate(snake_case_):
                                if strategy.lower() in spec:
                                       cmd_config.append(f"""--fsdp_sharding_strategy={i+1}""")
                                       break

                         if "cpu_offload" in spec:
                                cmd_config.append('''--fsdp_offload_params=True''')

                         for policy in FSDP_AUTO_WRAP_POLICY:
                                if policy.lower() in spec:
                                       cmd_config.append(f"""--fsdp_auto_wrap_policy={policy}""")
                                       break

                         if policy == "TRANSFORMER_BASED_WRAP":
                                cmd_config.append('''--fsdp_transformer_layer_cls_to_wrap=BertLayer''')
                         elif policy == "SIZE_BASED_WRAP":
                                cmd_config.append('''--fsdp_min_num_params=2000''')

                  cmd_config.extend(
                      [
                          self.test_file_path,
                          f"""--output_dir={self.tmpdir}""",
                          f"""--peak_memory_upper_bound={peak_mem_upper_bound}""",
                          f"""--n_train={self.n_train}""",
                          f"""--n_val={self.n_val}""",
                      ])
                  with patch_environment(omp_num_threads=1):
                         execute_subprocess_async(snake_case_					, env=os.environ.copy())









"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_     :						Optional[Any]						=					logging.get_logger(__name__)





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=False					):






  """simple docstring"""





  A_  :       Optional[Any]										=  []

  # fmt: off
  # stem:
  rename_keys.append(('cls_token', 'vit.embeddings.cls_token')					)
  rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')					)

  rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')					)
  rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')					)

  # backbone
  rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')					)
  rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')					)
  rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')					)

  for stage_idx in range(len(config.backbone_config.depths					)					):
    for layer_idx in range(config.backbone_config.depths[stage_idx]					):
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")					)
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")					)
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")					)
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")					)
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")					)
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")					)
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")					)
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")					)
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")					)

    rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")					)
    rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")					)
    rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")					)

  # transformer encoder
  for i in range(config.num_hidden_layers					):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")					)
    rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")					)
    rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")					)
    rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")					)
    rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")					)
    rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")					)
    rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")					)
    rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")					)
    rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")					)
    rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")					)

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ('norm.weight', 'layernorm.weight'),
            ('norm.bias', 'layernorm.bias'),
            ('pre_logits.fc.weight', 'pooler.dense.weight'),
            ('pre_logits.fc.bias', 'pooler.dense.bias'),
        ]					)

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    A_  :       List[str]										=  [(pair[0], pair[1][4:]) if pair[1].startswith('vit'					) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ('norm.weight', 'vit.layernorm.weight'),
            ('norm.bias', 'vit.layernorm.bias'),
            ('head.weight', 'classifier.weight'),
            ('head.bias', 'classifier.bias'),
        ]					)
  # fmt: on

  return rename_keys





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=False					):






  """simple docstring"""





  for i in range(config.num_hidden_layers					):
    if base_model:
      A_  :       List[str]										=  ''
    else:
      A_  :       Dict										=  'vit.'
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    A_  :       str										=  state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""					)
    A_  :       List[Any]										=  state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""					)
    # next, add query, keys and values (in that order) to the state dict
    A_  :       List[Any]										=  in_proj_weight[
        : config.hidden_size, :
    ]
    A_  :       Tuple										=  in_proj_bias[: config.hidden_size]
    A_  :       Union[str, Any]										=  in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    A_  :       Dict										=  in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    A_  :       Optional[Any]										=  in_proj_weight[
        -config.hidden_size :, :
    ]
    A_  :       Tuple										=  in_proj_bias[-config.hidden_size :]





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[str]										=  ['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(_UpperCAmelCase ,					_UpperCAmelCase					)





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Any										=  dct.pop(_UpperCAmelCase					)
  A_  :       Optional[int]										=  val





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       Optional[int]										=  'http://images.cocodataset.org/val2017/000000039769.jpg'
  A_  :       int										=  Image.open(requests.get(_UpperCAmelCase ,					stream=_UpperCAmelCase					).raw					)
  return im





@torch.no_grad()
def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=False					):






  """simple docstring"""





  A_  :       List[Any]										=  BitConfig(
      global_padding='same' ,					layer_type='bottleneck' ,					depths=(3, 4, 9) ,					out_features=['stage3'] ,					embedding_dynamic_padding=_UpperCAmelCase ,					)
  A_  :       Optional[int]										=  ViTHybridConfig(backbone_config=_UpperCAmelCase ,					image_size=384 ,					num_labels=1000					)
  A_  :       Union[str, Any]										=  False

  # load original model from timm
  A_  :       List[Any]										=  timm.create_model(_UpperCAmelCase ,					pretrained=_UpperCAmelCase					)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  A_  :       Tuple										=  timm_model.state_dict()
  if base_model:
    remove_classification_head_(_UpperCAmelCase					)
  A_  :       Any										=  create_rename_keys(_UpperCAmelCase ,					_UpperCAmelCase					)
  for src, dest in rename_keys:
    rename_key(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)
  read_in_q_k_v(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)

  A_  :       Union[str, Any]										=  'huggingface/label-files'
  A_  :       Dict										=  'imagenet-1k-id2label.json'
  A_  :       List[str]										=  json.load(open(hf_hub_download(_UpperCAmelCase ,					_UpperCAmelCase ,					repo_type='dataset'					) ,					'r'					)					)
  A_  :       str										=  {int(_UpperCAmelCase					): v for k, v in idalabel.items()}
  A_  :       Any										=  idalabel
  A_  :       Optional[int]										=  {v: k for k, v in idalabel.items()}

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    A_  :       List[Any]										=  ViTHybridModel(_UpperCAmelCase					).eval()
  else:
    A_  :       str										=  ViTHybridForImageClassification(_UpperCAmelCase					).eval()
  model.load_state_dict(_UpperCAmelCase					)

  # create image processor
  A_  :       Dict										=  create_transform(**resolve_data_config({} ,					model=_UpperCAmelCase					)					)
  A_  :       List[str]										=  transform.transforms

  A_  :       List[str]										=  {
      'bilinear': PILImageResampling.BILINEAR,
      'bicubic': PILImageResampling.BICUBIC,
      'nearest': PILImageResampling.NEAREST,
  }

  A_  :       Tuple										=  ViTHybridImageProcessor(
      do_resize=_UpperCAmelCase ,					size={'shortest_edge': timm_transforms[0].size} ,					resample=pillow_resamplings[timm_transforms[0].interpolation.value] ,					do_center_crop=_UpperCAmelCase ,					crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]} ,					do_normalize=_UpperCAmelCase ,					image_mean=timm_transforms[-1].mean.tolist() ,					image_std=timm_transforms[-1].std.tolist() ,					)

  A_  :       Optional[Any]										=  prepare_img()
  A_  :       Any										=  transform(_UpperCAmelCase					).unsqueeze(0					)
  A_  :       Dict										=  processor(_UpperCAmelCase ,					return_tensors='pt'					).pixel_values

  # verify pixel values
  assert torch.allclose(_UpperCAmelCase ,					_UpperCAmelCase					)

  # verify logits
  with torch.no_grad():
    A_  :       List[Any]										=  model(_UpperCAmelCase					)
    A_  :       List[str]										=  outputs.logits

  print('Predicted class:' ,					logits.argmax(-1					).item()					)
  if base_model:
    A_  :       Union[str, Any]										=  timm_model.forward_features(_UpperCAmelCase					)
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(_UpperCAmelCase ,					outputs.pooler_output ,					atol=1E-3					)
  else:
    A_  :       Tuple										=  timm_model(_UpperCAmelCase					)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(_UpperCAmelCase ,					outputs.logits ,					atol=1E-3					)
  print('Looks ok!'					)

  if pytorch_dump_folder_path is not None:
    Path(_UpperCAmelCase					).mkdir(exist_ok=_UpperCAmelCase					)
    print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""					)
    model.save_pretrained(_UpperCAmelCase					)
    print(f"""Saving processor to {pytorch_dump_folder_path}"""					)
    processor.save_pretrained(_UpperCAmelCase					)

  if push_to_hub:
    print(f"""Pushing model and processor to the hub {vit_name}"""					)
    model.push_to_hub(f"""ybelkada/{vit_name}"""					)
    processor.push_to_hub(f"""ybelkada/{vit_name}"""					)


if __name__ == "__main__":
   lowerCamelCase_     :						Optional[Any]						=					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--vit_name',
       default='vit_base_r50_s16_384',
       type=str,
       help='Name of the hybrid ViT timm model you\'d like to convert.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )
   parser.add_argument(
       '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
   )

   lowerCamelCase_     :						List[str]						=					parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)