






"""simple docstring"""



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(4_2)

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

@require_fsdp
@require_cuda
class        a     (   a_	):







    def 			UpperCamelCase_      (			self       ):
      super().setUp()

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







    def 			UpperCamelCase_      (			self       ):
      from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

      for i, strategy in enumerate(_lowerCamelCase       ):
        lowercase							    =       self.dist_env.copy()
        lowercase							    =       F'{i + 1}'
        lowercase							    =       strategy
        with mockenv_context(**_lowerCamelCase       ):
          lowercase							    =       FullyShardedDataParallelPlugin()
          self.assertEqual(fsdp_plugin.sharding_strategy							,		ShardingStrategy(i + 1       )       )







    def 			UpperCamelCase_      (			self       ):
      from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

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







    def 			UpperCamelCase_      (			self       ):
      from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

      for i, state_dict_type in enumerate(_lowerCamelCase       ):
        lowercase							    =       self.dist_env.copy()
        lowercase							    =       state_dict_type
        with mockenv_context(**_lowerCamelCase       ):
          lowercase							    =       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 			UpperCamelCase_      (			self       ):
      lowercase							    =       AutoModel.from_pretrained(_lowerCamelCase       )
      for policy in FSDP_AUTO_WRAP_POLICY:
        lowercase							    =       self.dist_env.copy()
        lowercase							    =       policy
        if policy == "TRANSFORMER_BASED_WRAP":
          lowercase							    =       'BertLayer'
        elif policy == "SIZE_BASED_WRAP":
          lowercase							    =       '2000'
        with mockenv_context(**_lowerCamelCase       ):
          lowercase							    =       FullyShardedDataParallelPlugin()
          fsdp_plugin.set_auto_wrap_policy(_lowerCamelCase       )
          if policy == "NO_WRAP":
            self.assertIsNone(fsdp_plugin.auto_wrap_policy       )
          else:
            self.assertIsNotNone(fsdp_plugin.auto_wrap_policy       )

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

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







    def 			UpperCamelCase_      (			self       ):
      from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
      from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

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







    def 			UpperCamelCase_      (			self       ):
      from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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


@require_fsdp
@require_multi_gpu
@slow
class        a     (   a_	):







    def 			UpperCamelCase_      (			self       ):
      super().setUp()
      lowercase							    =       0.8_2
      lowercase							    =       [
          'fsdp_shard_grad_op_transformer_based_wrap',
          'fsdp_full_shard_transformer_based_wrap',
      ]
      lowercase							    =       {
          'multi_gpu_fp16': 3_2_0_0,
          'fsdp_shard_grad_op_transformer_based_wrap_fp16': 2_0_0_0,
          'fsdp_full_shard_transformer_based_wrap_fp16': 1_9_0_0,
          # 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
      }
      lowercase							    =       1_6_0
      lowercase							    =       1_6_0

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







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       os.path.join(self.test_scripts_folder							,		'test_performance.py'       )
      lowercase							    =       ['accelerate', 'launch', '--num_processes=2', '--num_machines=1', '--machine_rank=0', '--use_fsdp']
      for config in self.performance_configs:
        lowercase							    =       cmd.copy()
        for i, strategy in enumerate(_lowerCamelCase       ):
          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(_lowerCamelCase							,		env=os.environ.copy()       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       os.path.join(self.test_scripts_folder							,		'test_checkpointing.py'       )
      lowercase							    =       [
          '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(_lowerCamelCase       ):
        lowercase							    =       cmd.copy()
        cmd_config.append(F'--fsdp_sharding_strategy={i+1}'       )
        if strategy != "FULL_SHARD":
          continue
        lowercase							    =       len(_lowerCamelCase       )
        for state_dict_type in FSDP_STATE_DICT_TYPE:
          lowercase							    =       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(_lowerCamelCase							,		env=os.environ.copy()       )

          lowercase							    =       cmd_config[:-1]
          lowercase							    =       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(_lowerCamelCase							,		env=os.environ.copy()       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       os.path.join(self.test_scripts_folder							,		'test_peak_memory_usage.py'       )
      lowercase							    =       [
          '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():
        lowercase							    =       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(_lowerCamelCase       ):
            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(_lowerCamelCase							,		env=os.environ.copy()       )










"""simple docstring"""



import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]    ):





  '''simple docstring'''





  lowercase							    =       []
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
          f'stage{idx}.patch_embed.proj.weight',
      )    )
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
          f'stage{idx}.patch_embed.proj.bias',
      )    )
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
          f'stage{idx}.patch_embed.norm.weight',
      )    )
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
          f'stage{idx}.patch_embed.norm.bias',
      )    )
  return embed
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]		,					__snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       []
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj.bias',
      )    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc1.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc1.bias')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc2.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc2.bias')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', f'stage{idx}.blocks.{cnt}.norm1.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', f'stage{idx}.blocks.{cnt}.norm1.bias')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', f'stage{idx}.blocks.{cnt}.norm2.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', f'stage{idx}.blocks.{cnt}.norm2.bias')    )
  return attention_weights
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       []
  token.append((f'cvt.encoder.stages.{idx}.cls_token', 'stage2.cls_token')    )
  return token
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       []
  head.append(('layernorm.weight', 'norm.weight')    )
  head.append(('layernorm.bias', 'norm.bias')    )
  head.append(('classifier.weight', 'head.weight')    )
  head.append(('classifier.bias', 'head.bias')    )
  return head
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]		,					__snake_case			:  Dict		,					__snake_case			:  List[str]		,					__snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  lowercase							    =       'imagenet-1k-id2label.json'
  lowercase							    =       10_00

  lowercase							    =       'huggingface/label-files'
  lowercase							    =       num_labels
  lowercase							    =       json.load(open(cached_download(hf_hub_url(__snake_case		,					__snake_case		,					repo_type='dataset'    )    )		,					'r'    )    )
  lowercase							    =       {int(__snake_case    ): v for k, v in idalabel.items()}

  lowercase							    =       idalabel
  lowercase							    =       {v: k for k, v in idalabel.items()}

  lowercase							    =       lowercase							    =       CvtConfig(num_labels=__snake_case		,					idalabel=__snake_case		,					labelaid=__snake_case    )

  # For depth size 13 (13 = 1+2+10)
  if cvt_model.rsplit('/'		,					1    )[-1][4:6] == "13":
    lowercase							    =       [1, 2, 10]

  # For depth size 21 (21 = 1+4+16)
  elif cvt_model.rsplit('/'		,					1    )[-1][4:6] == "21":
    lowercase							    =       [1, 4, 16]

  # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
  else:
    lowercase							    =       [2, 2, 20]
    lowercase							    =       [3, 12, 16]
    lowercase							    =       [1_92, 7_68, 10_24]

  lowercase							    =       CvtForImageClassification(__snake_case    )
  lowercase							    =       AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'    )
  lowercase							    =       image_size
  lowercase							    =       torch.load(__snake_case		,					map_location=torch.device('cpu'    )    )

  lowercase							    =       OrderedDict()
  lowercase							    =       []

  for idx in range(len(config.depth    )    ):
    if config.cls_token[idx]:
      lowercase							    =       list_of_state_dict + cls_token(__snake_case    )
    lowercase							    =       list_of_state_dict + embeddings(__snake_case    )
    for cnt in range(config.depth[idx]    ):
      lowercase							    =       list_of_state_dict + attention(__snake_case		,					__snake_case    )

  lowercase							    =       list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(__snake_case    )
  for i in range(len(__snake_case    )    ):
    lowercase							    =       original_weights[list_of_state_dict[i][1]]

  model.load_state_dict(__snake_case    )
  model.save_pretrained(__snake_case    )
  image_processor.save_pretrained(__snake_case    )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
    _UpperCamelCase		:  Optional[Any]						=		argparse.ArgumentParser()
    parser.add_argument(
        '--cvt_model',
        default='cvt-w24',
        type=str,
        help='Name of the cvt model you\'d like to convert.',
    )
    parser.add_argument(
        '--image_size',
        default=3_8_4,
        type=int,
        help='Input Image Size',
    )
    parser.add_argument(
        '--cvt_file_name',
        default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
        type=str,
        help='Input Image Size',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )

    _UpperCamelCase		:  Tuple						=		parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)


