





'''simple docstring'''

import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def lowerCamelCase						(     __lowerCamelCase :    Dict					,       __lowerCamelCase :    Optional[Any]    )						->Union[str, Any]:
    assert isinstance(__lowerCamelCase					,       __lowerCamelCase    )
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""					,       [False, True]    )
def lowerCamelCase						(     __lowerCamelCase :    Dict					,       __lowerCamelCase :    int					,       __lowerCamelCase :    Optional[Any]    )						->Optional[int]:
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        _SCREAMING_SNAKE_CASE									=			JsonDatasetReader(__lowerCamelCase					,       cache_dir=__lowerCamelCase					,       keep_in_memory=__lowerCamelCase    ).read()
    _check_json_dataset(__lowerCamelCase					,       __lowerCamelCase    )





@pytest.mark.parametrize(
    """features"""					,       [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]					,       )
def lowerCamelCase						(     __lowerCamelCase :    Dict					,       __lowerCamelCase :    str					,       __lowerCamelCase :    str    )						->Dict:
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
    _SCREAMING_SNAKE_CASE									=			features.copy() if features else default_expected_features
    _SCREAMING_SNAKE_CASE									=			(
        Features({feature: Value(__lowerCamelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
    )
    _SCREAMING_SNAKE_CASE									=			JsonDatasetReader(__lowerCamelCase					,       features=__lowerCamelCase					,       cache_dir=__lowerCamelCase    ).read()
    _check_json_dataset(__lowerCamelCase					,       __lowerCamelCase    )





@pytest.mark.parametrize(
    """features"""					,       [
        None,
        {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""},
    ]					,       )
def lowerCamelCase						(     __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Dict    )						->Union[str, Any]:
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			{"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""}
    _SCREAMING_SNAKE_CASE									=			features.copy() if features else default_expected_features
    _SCREAMING_SNAKE_CASE									=			(
        Features({feature: Value(__lowerCamelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
    )
    _SCREAMING_SNAKE_CASE									=			JsonDatasetReader(__lowerCamelCase					,       features=__lowerCamelCase					,       cache_dir=__lowerCamelCase    ).read()
    assert isinstance(__lowerCamelCase					,       __lowerCamelCase    )
    assert dataset.num_rows == 2
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_3", "col_1", "col_2"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype





def lowerCamelCase						(     __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Tuple    )						->Tuple:
    # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
    _SCREAMING_SNAKE_CASE									=			{"""col_2""": """int64""", """col_3""": """float64""", """col_1""": """string"""}
    _SCREAMING_SNAKE_CASE									=			features.copy()
    _SCREAMING_SNAKE_CASE									=			(
        Features({feature: Value(__lowerCamelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
    )
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			JsonDatasetReader(__lowerCamelCase					,       features=__lowerCamelCase					,       cache_dir=__lowerCamelCase    ).read()
    assert isinstance(__lowerCamelCase					,       __lowerCamelCase    )
    assert dataset.num_rows == 2
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_2", "col_3", "col_1"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""split"""					,       [None, NamedSplit("""train"""    ), """train""", """test"""]    )
def lowerCamelCase						(     __lowerCamelCase :    Any					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Optional[int]    )						->Dict:
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
    _SCREAMING_SNAKE_CASE									=			JsonDatasetReader(__lowerCamelCase					,       cache_dir=__lowerCamelCase					,       split=__lowerCamelCase    ).read()
    _check_json_dataset(__lowerCamelCase					,       __lowerCamelCase    )
    assert dataset.split == split if split else "train"





@pytest.mark.parametrize("""path_type"""					,       [str, list]    )
def lowerCamelCase						(     __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    str    )						->Any:
    if issubclass(__lowerCamelCase					,       __lowerCamelCase    ):
        _SCREAMING_SNAKE_CASE									=			jsonl_path
    elif issubclass(__lowerCamelCase					,       __lowerCamelCase    ):
        _SCREAMING_SNAKE_CASE									=			[jsonl_path]
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
    _SCREAMING_SNAKE_CASE									=			JsonDatasetReader(__lowerCamelCase					,       cache_dir=__lowerCamelCase    ).read()
    _check_json_dataset(__lowerCamelCase					,       __lowerCamelCase    )





def lowerCamelCase						(     __lowerCamelCase :    Tuple					,       __lowerCamelCase :    List[str]					,       __lowerCamelCase :    int=("train",)    )						->Optional[int]:
    assert isinstance(__lowerCamelCase					,       __lowerCamelCase    )
    for split in splits:
        _SCREAMING_SNAKE_CASE									=			dataset_dict[split]
        assert dataset.num_rows == 4
        assert dataset.num_columns == 3
        assert dataset.column_names == ["col_1", "col_2", "col_3"]
        for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""					,       [False, True]    )
def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Any					,       __lowerCamelCase :    int    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        _SCREAMING_SNAKE_CASE									=			JsonDatasetReader({"""train""": jsonl_path}					,       cache_dir=__lowerCamelCase					,       keep_in_memory=__lowerCamelCase    ).read()
    _check_json_datasetdict(__lowerCamelCase					,       __lowerCamelCase    )





@pytest.mark.parametrize(
    """features"""					,       [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]					,       )
def lowerCamelCase						(     __lowerCamelCase :    Tuple					,       __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    Any    )						->str:
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
    _SCREAMING_SNAKE_CASE									=			features.copy() if features else default_expected_features
    _SCREAMING_SNAKE_CASE									=			(
        Features({feature: Value(__lowerCamelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
    )
    _SCREAMING_SNAKE_CASE									=			JsonDatasetReader({"""train""": jsonl_path}					,       features=__lowerCamelCase					,       cache_dir=__lowerCamelCase    ).read()
    _check_json_datasetdict(__lowerCamelCase					,       __lowerCamelCase    )





@pytest.mark.parametrize("""split"""					,       [None, NamedSplit("""train"""    ), """train""", """test"""]    )
def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    str					,       __lowerCamelCase :    int    )						->int:
    if split:
        _SCREAMING_SNAKE_CASE									=			{split: jsonl_path}
    else:
        _SCREAMING_SNAKE_CASE									=			"""train"""
        _SCREAMING_SNAKE_CASE									=			{"""train""": jsonl_path, """test""": jsonl_path}
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
    _SCREAMING_SNAKE_CASE									=			JsonDatasetReader(__lowerCamelCase					,       cache_dir=__lowerCamelCase    ).read()
    _check_json_datasetdict(__lowerCamelCase					,       __lowerCamelCase					,       splits=list(path.keys()    )    )
    assert all(dataset[split].split == split for split in path.keys()    )





def lowerCamelCase						(     __lowerCamelCase :    List[str]    )						->Optional[Any]:
    return json.load(__lowerCamelCase    )





def lowerCamelCase						(     __lowerCamelCase :    int    )						->str:
    return [json.loads(__lowerCamelCase    ) for line in buffer]






class 					a_     :

    '''simple docstring'''


    @pytest.mark.parametrize("""lines, load_json_function""" ,		[(True, load_json_lines), (False, load_json)]       )
    def 			snake_case_(		self ,		A ,		A ,		A       )		->   Dict:
        with io.BytesIO() as buffer:
            JsonDatasetWriter(A ,		A ,		lines=A       ).write()
            buffer.seek(0       )
            _SCREAMING_SNAKE_CASE									=			load_json_function(A       )
        assert isinstance(A ,		A       )
        assert isinstance(exported_content[0] ,		A       )
        assert len(A       ) == 10


    @pytest.mark.parametrize(
        """orient, container, keys, len_at""" ,		[
            ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
            ("""split""", dict, {"""columns""", """data"""}, """data"""),
            ("""index""", dict, set("""0123456789"""       ), None),
            ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
            ("""values""", list, None, None),
            ("""table""", dict, {"""schema""", """data"""}, """data"""),
        ] ,		)
    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A       )		->   Union[str, Any]:
        with io.BytesIO() as buffer:
            JsonDatasetWriter(A ,		A ,		lines=A ,		orient=A       ).write()
            buffer.seek(0       )
            _SCREAMING_SNAKE_CASE									=			load_json(A       )
        assert isinstance(A ,		A       )
        if keys:
            if container is dict:
                assert exported_content.keys() == keys
            else:
                assert exported_content[0].keys() == keys
        else:
            assert not hasattr(A ,		"""keys"""       ) and not hasattr(exported_content[0] ,		"""keys"""       )
        if len_at:
            assert len(exported_content[len_at]       ) == 10
        else:
            assert len(A       ) == 10


    @pytest.mark.parametrize("""lines, load_json_function""" ,		[(True, load_json_lines), (False, load_json)]       )
    def 			snake_case_(		self ,		A ,		A ,		A       )		->   Tuple:
        with io.BytesIO() as buffer:
            JsonDatasetWriter(A ,		A ,		lines=A ,		num_proc=2       ).write()
            buffer.seek(0       )
            _SCREAMING_SNAKE_CASE									=			load_json_function(A       )
        assert isinstance(A ,		A       )
        assert isinstance(exported_content[0] ,		A       )
        assert len(A       ) == 10


    @pytest.mark.parametrize(
        """orient, container, keys, len_at""" ,		[
            ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
            ("""split""", dict, {"""columns""", """data"""}, """data"""),
            ("""index""", dict, set("""0123456789"""       ), None),
            ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
            ("""values""", list, None, None),
            ("""table""", dict, {"""schema""", """data"""}, """data"""),
        ] ,		)
    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A       )		->   List[Any]:
        with io.BytesIO() as buffer:
            JsonDatasetWriter(A ,		A ,		lines=A ,		orient=A ,		num_proc=2       ).write()
            buffer.seek(0       )
            _SCREAMING_SNAKE_CASE									=			load_json(A       )
        assert isinstance(A ,		A       )
        if keys:
            if container is dict:
                assert exported_content.keys() == keys
            else:
                assert exported_content[0].keys() == keys
        else:
            assert not hasattr(A ,		"""keys"""       ) and not hasattr(exported_content[0] ,		"""keys"""       )
        if len_at:
            assert len(exported_content[len_at]       ) == 10
        else:
            assert len(A       ) == 10


    def 			snake_case_(		self ,		A       )		->   int:
        with pytest.raises(A       ):
            with io.BytesIO() as buffer:
                JsonDatasetWriter(A ,		A ,		num_proc=0       )





    @pytest.mark.parametrize("""compression, extension""" ,		[("""gzip""", """gz"""), ("""bz2""", """bz2"""), ("""xz""", """xz""")]       )
    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			tmp_path_factory.mktemp("""data"""       ) / f'test.json.{extension}'
        _SCREAMING_SNAKE_CASE									=			str(shared_datadir / f'test_file.json.{extension}'       )
        JsonDatasetWriter(A ,		A ,		compression=A       ).write()

        with fsspec.open(A ,		"""rb""" ,		compression="""infer"""       ) as f:
            _SCREAMING_SNAKE_CASE									=			f.read()
        with fsspec.open(A ,		"""rb""" ,		compression="""infer"""       ) as f:
            _SCREAMING_SNAKE_CASE									=			f.read()
        assert exported_content == original_content




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   :     str       =				logging.get_logger(__name__)


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase=False ):
      SCREAMING_SNAKE_CASE_      =     []
      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"
            SCREAMING_SNAKE_CASE_      =     [(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    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase=False ):
      for i in range(config.num_hidden_layers ):
            if base_model:
                  SCREAMING_SNAKE_CASE_      =     ""
            else:
                  SCREAMING_SNAKE_CASE_      =     "deit."
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            SCREAMING_SNAKE_CASE_      =     state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
            SCREAMING_SNAKE_CASE_      =     state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
            # next, add query, keys and values (in that order) to the state dict
            SCREAMING_SNAKE_CASE_      =     in_proj_weight[
                : config.hidden_size, :
            ]
            SCREAMING_SNAKE_CASE_      =     in_proj_bias[: config.hidden_size]
            SCREAMING_SNAKE_CASE_      =     in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE_      =     in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            SCREAMING_SNAKE_CASE_      =     in_proj_weight[
                -config.hidden_size :, :
            ]
            SCREAMING_SNAKE_CASE_      =     in_proj_bias[-config.hidden_size :]


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     dct.pop(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     val


def    a__       (						):
      SCREAMING_SNAKE_CASE_      =     "http://images.cocodataset.org/val2017/000000039769.jpg"
      SCREAMING_SNAKE_CASE_      =     Image.open(requests.get(__UpperCamelCase   ,					stream=__UpperCamelCase ).raw )
      return im


@torch.no_grad()
def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     DeiTConfig()
      # all deit models have fine-tuned heads
      SCREAMING_SNAKE_CASE_      =     False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      SCREAMING_SNAKE_CASE_      =     1_0_0_0
      SCREAMING_SNAKE_CASE_      =     "huggingface/label-files"
      SCREAMING_SNAKE_CASE_      =     "imagenet-1k-id2label.json"
      SCREAMING_SNAKE_CASE_      =     json.load(open(hf_hub_download(__UpperCamelCase   ,					__UpperCamelCase   ,					repo_type="dataset" )   ,					"r" ) )
      SCREAMING_SNAKE_CASE_      =     {int(__UpperCamelCase ): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_      =     idalabel
      SCREAMING_SNAKE_CASE_      =     {v: k for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_      =     int(deit_name[-6:-4] )
      SCREAMING_SNAKE_CASE_      =     int(deit_name[-3:] )
      # size of the architecture
      if deit_name[9:].startswith("tiny" ):
            SCREAMING_SNAKE_CASE_      =     1_9_2
            SCREAMING_SNAKE_CASE_      =     7_6_8
            SCREAMING_SNAKE_CASE_      =     1_2
            SCREAMING_SNAKE_CASE_      =     3
      elif deit_name[9:].startswith("small" ):
            SCREAMING_SNAKE_CASE_      =     3_8_4
            SCREAMING_SNAKE_CASE_      =     1_5_3_6
            SCREAMING_SNAKE_CASE_      =     1_2
            SCREAMING_SNAKE_CASE_      =     6
      if deit_name[9:].startswith("base" ):
            pass
      elif deit_name[4:].startswith("large" ):
            SCREAMING_SNAKE_CASE_      =     1_0_2_4
            SCREAMING_SNAKE_CASE_      =     4_0_9_6
            SCREAMING_SNAKE_CASE_      =     2_4
            SCREAMING_SNAKE_CASE_      =     1_6

      # load original model from timm
      SCREAMING_SNAKE_CASE_      =     timm.create_model(__UpperCamelCase   ,					pretrained=__UpperCamelCase )
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      SCREAMING_SNAKE_CASE_      =     timm_model.state_dict()
      SCREAMING_SNAKE_CASE_      =     create_rename_keys(__UpperCamelCase   ,					__UpperCamelCase )
      for src, dest in rename_keys:
            rename_key(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )
      read_in_q_k_v(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )

      # load HuggingFace model
      SCREAMING_SNAKE_CASE_      =     DeiTForImageClassificationWithTeacher(__UpperCamelCase ).eval()
      model.load_state_dict(__UpperCamelCase )

      # Check outputs on an image, prepared by DeiTImageProcessor
      SCREAMING_SNAKE_CASE_      =     int(
          (2_5_6 / 2_2_4) * config.image_size )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      SCREAMING_SNAKE_CASE_      =     DeiTImageProcessor(size=__UpperCamelCase   ,					crop_size=config.image_size )
      SCREAMING_SNAKE_CASE_      =     image_processor(images=prepare_img()   ,					return_tensors="pt" )
      SCREAMING_SNAKE_CASE_      =     encoding["pixel_values"]
      SCREAMING_SNAKE_CASE_      =     model(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     timm_model(__UpperCamelCase )
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(__UpperCamelCase   ,					outputs.logits   ,					atol=1E-3 )

      Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
      print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}''' )
      model.save_pretrained(__UpperCamelCase )
      print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
      image_processor.save_pretrained(__UpperCamelCase )


if __name__ == "__main__":
      A   :     Tuple       =				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   :     Dict       =				parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)


