



'''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(42)

__a          =		"bert-base-cased"
__a          =		"fp16"
__a          =		"bf16"
__a          =		[FPaa, BFaa]







@require_fsdp
@require_cuda
class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    def lowerCamelCase       (    self					:     Optional[int]						):
          super().setUp()

          snake_case__  :    Optional[Any]    						=      dict(
              ACCELERATE_USE_FSDP="""true""" ,     MASTER_ADDR="""localhost""" ,     MASTER_PORT="""10999""" ,     RANK="""0""" ,     LOCAL_RANK="""0""" ,     WORLD_SIZE="""1""" ,     )




    def lowerCamelCase       (    self					:     Union[str, Any]						):
          from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

          for i, strategy in enumerate(snake_case_						):
                snake_case__  :    Union[str, Any]    						=      self.dist_env.copy()
                snake_case__  :    Union[str, Any]    						=      f"{i + 1}"
                snake_case__  :    str    						=      strategy
                with mockenv_context(**snake_case_						):
                      snake_case__  :    List[str]    						=      FullyShardedDataParallelPlugin()
                      self.assertEqual(fsdp_plugin.sharding_strategy ,     ShardingStrategy(i + 1						)						)




    def lowerCamelCase       (    self					:     List[str]						):
          from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

          for i, prefetch_policy in enumerate(snake_case_						):
                snake_case__  :    Optional[int]    						=      self.dist_env.copy()
                snake_case__  :    List[str]    						=      prefetch_policy
                with mockenv_context(**snake_case_						):
                      snake_case__  :    Optional[Any]    						=      FullyShardedDataParallelPlugin()
                      if prefetch_policy == "NO_PREFETCH":
                            self.assertIsNone(fsdp_plugin.backward_prefetch						)
                      else:
                            self.assertEqual(fsdp_plugin.backward_prefetch ,     BackwardPrefetch(i + 1						)						)




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

          for i, state_dict_type in enumerate(snake_case_						):
                snake_case__  :    Dict    						=      self.dist_env.copy()
                snake_case__  :    Optional[Any]    						=      state_dict_type
                with mockenv_context(**snake_case_						):
                      snake_case__  :    Dict    						=      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 lowerCamelCase       (    self					:     Tuple						):
          snake_case__  :    Tuple    						=      AutoModel.from_pretrained(snake_case_						)
          for policy in FSDP_AUTO_WRAP_POLICY:
                snake_case__  :    Union[str, Any]    						=      self.dist_env.copy()
                snake_case__  :    Optional[Any]    						=      policy
                if policy == "TRANSFORMER_BASED_WRAP":
                      snake_case__  :    Any    						=      """BertLayer"""
                elif policy == "SIZE_BASED_WRAP":
                      snake_case__  :    Optional[int]    						=      """2000"""
                with mockenv_context(**snake_case_						):
                      snake_case__  :    str    						=      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						)

          snake_case__  :    Tuple    						=      self.dist_env.copy()
          snake_case__  :    str    						=      """TRANSFORMER_BASED_WRAP"""
          snake_case__  :    Union[str, Any]    						=      """T5Layer"""
          with mockenv_context(**snake_case_						):
                snake_case__  :    Union[str, Any]    						=      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						)						)

          snake_case__  :    List[str]    						=      self.dist_env.copy()
          snake_case__  :    List[Any]    						=      """SIZE_BASED_WRAP"""
          snake_case__  :    List[Any]    						=      """0"""
          with mockenv_context(**snake_case_						):
                snake_case__  :    Dict    						=      FullyShardedDataParallelPlugin()
                fsdp_plugin.set_auto_wrap_policy(snake_case_						)
                self.assertIsNone(fsdp_plugin.auto_wrap_policy						)




    def lowerCamelCase       (    self					:     Optional[int]						):
          from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
          from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

          for mp_dtype in dtypes:
                snake_case__  :    int    						=      self.dist_env.copy()
                snake_case__  :    List[str]    						=      mp_dtype
                with mockenv_context(**snake_case_						):
                      snake_case__  :    Optional[Any]    						=      Accelerator()
                      if mp_dtype == "fp16":
                            snake_case__  :    int    						=      torch.floataa
                      elif mp_dtype == "bf16":
                            snake_case__  :    Tuple    						=      torch.bfloataa
                      snake_case__  :    List[Any]    						=      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 lowerCamelCase       (    self					:     Tuple						):
          from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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







@require_fsdp
@require_multi_gpu
@slow
class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    def lowerCamelCase       (    self					:     List[str]						):
          super().setUp()
          snake_case__  :    Dict    						=      0.82
          snake_case__  :    List[str]    						=      [
              """fsdp_shard_grad_op_transformer_based_wrap""",
              """fsdp_full_shard_transformer_based_wrap""",
          ]
          snake_case__  :    Optional[int]    						=      {
              """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
          }
          snake_case__  :    List[str]    						=      160
          snake_case__  :    Any    						=      160

          snake_case__  :    Any    						=      inspect.getfile(accelerate.test_utils						)
          snake_case__  :    Dict    						=      os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ["""scripts""", """external_deps"""]						)




    def lowerCamelCase       (    self					:     Any						):
          snake_case__  :    Tuple    						=      os.path.join(self.test_scripts_folder ,     """test_performance.py"""						)
          snake_case__  :    Any    						=      ["""accelerate""", """launch""", """--num_processes=2""", """--num_machines=1""", """--machine_rank=0""", """--use_fsdp"""]
          for config in self.performance_configs:
                snake_case__  :    Tuple    						=      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 lowerCamelCase       (    self					:     List[Any]						):
          snake_case__  :    List[Any]    						=      os.path.join(self.test_scripts_folder ,     """test_checkpointing.py"""						)
          snake_case__  :    Dict    						=      [
              """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_						):
                snake_case__  :    str    						=      cmd.copy()
                cmd_config.append(f"--fsdp_sharding_strategy={i+1}"						)
                if strategy != "FULL_SHARD":
                      continue
                snake_case__  :    Tuple    						=      len(snake_case_						)
                for state_dict_type in FSDP_STATE_DICT_TYPE:
                      snake_case__  :    Tuple    						=      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()						)

                      snake_case__  :    Dict    						=      cmd_config[:-1]
                      snake_case__  :    Any    						=      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 lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    List[Any]    						=      os.path.join(self.test_scripts_folder ,     """test_peak_memory_usage.py"""						)
          snake_case__  :    Dict    						=      [
              """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():
                snake_case__  :    Tuple    						=      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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__a          =		logging.get_logger(__name__)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> str:
      snake_case__  :    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"blocks.{i}.norm1.weight", f"deit.encoder.layer.{i}.layernorm_before.weight")		)
            rename_keys.append((f"blocks.{i}.norm1.bias", f"deit.encoder.layer.{i}.layernorm_before.bias")		)
            rename_keys.append((f"blocks.{i}.attn.proj.weight", f"deit.encoder.layer.{i}.attention.output.dense.weight")		)
            rename_keys.append((f"blocks.{i}.attn.proj.bias", f"deit.encoder.layer.{i}.attention.output.dense.bias")		)
            rename_keys.append((f"blocks.{i}.norm2.weight", f"deit.encoder.layer.{i}.layernorm_after.weight")		)
            rename_keys.append((f"blocks.{i}.norm2.bias", f"deit.encoder.layer.{i}.layernorm_after.bias")		)
            rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"deit.encoder.layer.{i}.intermediate.dense.weight")		)
            rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"deit.encoder.layer.{i}.intermediate.dense.bias")		)
            rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"deit.encoder.layer.{i}.output.dense.weight")		)
            rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"deit.encoder.layer.{i}.output.dense.bias")		)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("""cls_token""", """deit.embeddings.cls_token"""),
              ("""dist_token""", """deit.embeddings.distillation_token"""),
              ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
              ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
              ("""pos_embed""", """deit.embeddings.position_embeddings"""),
          ]		)

      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 "deit" from all keys that start with "deit"
            snake_case__  :    List[Any]    						=      [(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""		) else pair for pair in rename_keys]
      else:
            # layernorm + classification heads
            rename_keys.extend(
                [
                    ("""norm.weight""", """deit.layernorm.weight"""),
                    ("""norm.bias""", """deit.layernorm.bias"""),
                    ("""head.weight""", """cls_classifier.weight"""),
                    ("""head.bias""", """cls_classifier.bias"""),
                    ("""head_dist.weight""", """distillation_classifier.weight"""),
                    ("""head_dist.bias""", """distillation_classifier.bias"""),
                ]		)

      return rename_keys





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





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      snake_case__  :    str    						=      dct.pop(_lowerCAmelCase		)
      snake_case__  :    Tuple    						=      val





def 						__snake_case(				)		-> Tuple:
      snake_case__  :    Union[str, Any]    						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
      snake_case__  :    Optional[int]    						=      Image.open(requests.get(_lowerCAmelCase     ,   stream=_lowerCAmelCase		).raw		)
      return im





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> str:
      snake_case__  :    Optional[int]    						=      DeiTConfig()
      # all deit models have fine-tuned heads
      snake_case__  :    Union[str, Any]    						=      False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      snake_case__  :    int    						=      1_000
      snake_case__  :    Any    						=      """huggingface/label-files"""
      snake_case__  :    Optional[Any]    						=      """imagenet-1k-id2label.json"""
      snake_case__  :    Tuple    						=      json.load(open(hf_hub_download(_lowerCAmelCase     ,   _lowerCAmelCase     ,   repo_type="""dataset"""		)     ,   """r"""		)		)
      snake_case__  :    List[Any]    						=      {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
      snake_case__  :    List[Any]    						=      idalabel
      snake_case__  :    List[str]    						=      {v: k for k, v in idalabel.items()}
      snake_case__  :    Tuple    						=      int(deit_name[-6:-4]		)
      snake_case__  :    Optional[Any]    						=      int(deit_name[-3:]		)
      # size of the architecture
      if deit_name[9:].startswith("""tiny"""		):
            snake_case__  :    Tuple    						=      192
            snake_case__  :    Union[str, Any]    						=      768
            snake_case__  :    Tuple    						=      12
            snake_case__  :    Union[str, Any]    						=      3
      elif deit_name[9:].startswith("""small"""		):
            snake_case__  :    str    						=      384
            snake_case__  :    Any    						=      1_536
            snake_case__  :    str    						=      12
            snake_case__  :    int    						=      6
      if deit_name[9:].startswith("""base"""		):
            pass
      elif deit_name[4:].startswith("""large"""		):
            snake_case__  :    Union[str, Any]    						=      1_024
            snake_case__  :    Any    						=      4_096
            snake_case__  :    List[Any]    						=      24
            snake_case__  :    Tuple    						=      16

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

      # load state_dict of original model, remove and rename some keys
      snake_case__  :    Optional[Any]    						=      timm_model.state_dict()
      snake_case__  :    Optional[int]    						=      create_rename_keys(_lowerCAmelCase     ,   _lowerCAmelCase		)
      for src, dest in rename_keys:
            rename_key(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
      read_in_q_k_v(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)

      # load HuggingFace model
      snake_case__  :    Optional[Any]    						=      DeiTForImageClassificationWithTeacher(_lowerCAmelCase		).eval()
      model.load_state_dict(_lowerCAmelCase		)

      # Check outputs on an image, prepared by DeiTImageProcessor
      snake_case__  :    List[Any]    						=      int(
          (256 / 224) * config.image_size		)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      snake_case__  :    Optional[Any]    						=      DeiTImageProcessor(size=_lowerCAmelCase     ,   crop_size=config.image_size		)
      snake_case__  :    str    						=      image_processor(images=prepare_img()     ,   return_tensors="""pt"""		)
      snake_case__  :    Optional[Any]    						=      encoding["""pixel_values"""]
      snake_case__  :    Tuple    						=      model(_lowerCAmelCase		)

      snake_case__  :    Optional[int]    						=      timm_model(_lowerCAmelCase		)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(_lowerCAmelCase     ,   outputs.logits     ,   atol=1e-3		)

      Path(_lowerCAmelCase		).mkdir(exist_ok=_lowerCAmelCase		)
      print(f"Saving model {deit_name} 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          =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--deit_name",
           default="vit_deit_base_distilled_patch16_224",
           type=str,
           help="Name of the DeiT 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."
       )

       __a          =		parser.parse_args()
       convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)
