"""simple docstring"""


import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
 from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class 					a					:



  def __init__(		self   :    str						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    List[Any]=3						,  lowerCamelCase_   :    Dict=32						,  lowerCamelCase_   :    Dict=3						,  lowerCamelCase_   :    Optional[Any]=10						,  lowerCamelCase_   :    Any=[10, 20, 30, 40]						,  lowerCamelCase_   :    Tuple=[1, 1, 2, 1]						,  lowerCamelCase_   :    Dict=True						,  lowerCamelCase_   :    Optional[int]=True						,  lowerCamelCase_   :    Tuple="relu"						,  lowerCamelCase_   :    Any=3						,  lowerCamelCase_   :    List[Any]=None						,  lowerCamelCase_   :    Tuple=["stage2", "stage3", "stage4"]						,  lowerCamelCase_   :    List[str]=[2, 3, 4]						,  )      -> Dict:
      __a			      =						parent
      __a			      =						batch_size
      __a			      =						image_size
      __a			      =						num_channels
      __a			      =						embeddings_size
      __a			      =						hidden_sizes
      __a			      =						depths
      __a			      =						is_training
      __a			      =						use_labels
      __a			      =						hidden_act
      __a			      =						num_labels
      __a			      =						scope
      __a			      =						len(lowerCamelCase_     )
      __a			      =						out_features
      __a			      =						out_indices



  def        lowerCAmelCase_				(		self   :    Dict     )      -> int:
      __a			      =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

      __a			      =						None
      if self.use_labels:
          __a			      =						ids_tensor([self.batch_size]						,  self.num_labels     )

      __a			      =						self.get_config()

      return config, pixel_values, labels



  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> str:
      return ResNetConfig(
          num_channels=self.num_channels						,  embeddings_size=self.embeddings_size						,  hidden_sizes=self.hidden_sizes						,  depths=self.depths						,  hidden_act=self.hidden_act						,  num_labels=self.num_labels						,  out_features=self.out_features						,  out_indices=self.out_indices						,  )



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    int     )      -> List[Any]:
      __a			      =						ResNetModel(config=lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						model(lowerCamelCase_     )
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape						,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,  )



  def        lowerCAmelCase_				(		self   :    Optional[int]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    List[Any]     )      -> Optional[int]:
      __a			      =						self.num_labels
      __a			      =						ResNetForImageClassification(lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						model(lowerCamelCase_						,  labels=lowerCamelCase_     )
      self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_labels)     )



  def        lowerCAmelCase_				(		self   :    List[Any]						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Optional[Any]     )      -> List[str]:
      __a			      =						ResNetBackbone(config=lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						model(lowerCamelCase_     )

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps     )						,  len(config.out_features     )     )
      self.parent.assertListEqual(list(result.feature_maps[0].shape     )						,  [self.batch_size, self.hidden_sizes[1], 4, 4]     )

      # verify channels
      self.parent.assertEqual(len(model.channels     )						,  len(config.out_features     )     )
      self.parent.assertListEqual(model.channels						,  config.hidden_sizes[1:]     )

      # verify backbone works with out_features=None
      __a			      =						None
      __a			      =						ResNetBackbone(config=lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						model(lowerCamelCase_     )

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps     )						,  1     )
      self.parent.assertListEqual(list(result.feature_maps[0].shape     )						,  [self.batch_size, self.hidden_sizes[-1], 1, 1]     )

      # verify channels
      self.parent.assertEqual(len(model.channels     )						,  1     )
      self.parent.assertListEqual(model.channels						,  [config.hidden_sizes[-1]]     )



  def        lowerCAmelCase_				(		self   :    List[Any]     )      -> Any:
      __a			      =						self.prepare_config_and_inputs()
      __a      ,  __a      ,  __a			      =						config_and_inputs
      __a			      =						{"""pixel_values""": pixel_values}
      return config, inputs_dict






@require_torch
class 					a					(				A_   ,		A_   ,		unittest.TestCase							):
  A_ :				List[Any] 			=							(
      (
          ResNetModel,
          ResNetForImageClassification,
          ResNetBackbone,
      )
      if is_torch_available()
      else ()
  )
  A_ :				List[str] 			=							(
      {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
      if is_torch_available()
      else {}
  )

  A_ :				Union[str, Any] 			=							True
  A_ :				Optional[Any] 			=							False
  A_ :				Dict 			=							False
  A_ :				str 			=							False
  A_ :				str 			=							False



  def        lowerCAmelCase_				(		self   :    Optional[Any]     )      -> Any:
      __a			      =						ResNetModelTester(self     )
      __a			      =						ConfigTester(self						,  config_class=lowerCamelCase_						,  has_text_modality=lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    str     )      -> List[str]:
      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()



  def        lowerCAmelCase_				(		self   :    int     )      -> Tuple:
      return



  @unittest.skip(reason="""ResNet does not use inputs_embeds"""     )
  def        lowerCAmelCase_				(		self   :    List[str]     )      -> Dict:
      pass



  @unittest.skip(reason="""ResNet does not support input and output embeddings"""     )
  def        lowerCAmelCase_				(		self   :    List[Any]     )      -> List[Any]:
      pass



  def        lowerCAmelCase_				(		self   :    Tuple     )      -> str:
      __a      ,  __a			      =						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          __a			      =						model_class(lowerCamelCase_     )
          __a			      =						inspect.signature(model.forward     )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          __a			      =						[*signature.parameters.keys()]

          __a			      =						["""pixel_values"""]
          self.assertListEqual(arg_names[:1]						,  lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    int     )      -> Any:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Tuple     )      -> Optional[int]:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]     )      -> Optional[int]:
      __a      ,  __a			      =						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          __a			      =						model_class(config=lowerCamelCase_     )
          for name, module in model.named_modules():
              if isinstance(lowerCamelCase_						,  (nn.BatchNormad, nn.GroupNorm)     ):
                  self.assertTrue(
                      torch.all(module.weight == 1     )						,  msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""						,  )
                  self.assertTrue(
                      torch.all(module.bias == 0     )						,  msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""						,  )



  def        lowerCAmelCase_				(		self   :    List[str]     )      -> int:
      def check_hidden_states_output(lowerCamelCase_   :    Any						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Tuple     ):
          __a			      =						model_class(lowerCamelCase_     )
          model.to(lowerCamelCase_     )
          model.eval()

          with torch.no_grad():
              __a			      =						model(**self._prepare_for_class(lowerCamelCase_						,  lowerCamelCase_     )     )

          __a			      =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          __a			      =						self.model_tester.num_stages
          self.assertEqual(len(lowerCamelCase_     )						,  expected_num_stages + 1     )

          # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:]     )						,  [self.model_tester.image_size // 4, self.model_tester.image_size // 4]						,  )

      __a      ,  __a			      =						self.model_tester.prepare_config_and_inputs_for_common()
      __a			      =						["""basic""", """bottleneck"""]
      for model_class in self.all_model_classes:
          for layer_type in layers_type:
              __a			      =						layer_type
              __a			      =						True
              check_hidden_states_output(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              __a			      =						True

              check_hidden_states_output(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )



  @unittest.skip(reason="""ResNet does not use feedforward chunking"""     )
  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Tuple:
      pass



  def        lowerCAmelCase_				(		self   :    int     )      -> Dict:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_     )



  @slow
  def        lowerCAmelCase_				(		self   :    Dict     )      -> Any:
      for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __a			      =						ResNetModel.from_pretrained(lowerCamelCase_     )
          self.assertIsNotNone(lowerCamelCase_     )

def 						UpperCamelCase				(	):
    __a			      =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
    return image






@require_torch
@require_vision
class 					a					(				unittest.TestCase							):



  @cached_property
  def        lowerCAmelCase_				(		self   :    str     )      -> Optional[int]:
      return (
          AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
          if is_vision_available()
          else None
      )



  @slow
  def        lowerCAmelCase_				(		self   :    Dict     )      -> Optional[Any]:
      __a			      =						ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(lowerCamelCase_     )

      __a			      =						self.default_image_processor
      __a			      =						prepare_img()
      __a			      =						image_processor(images=lowerCamelCase_						,  return_tensors="""pt"""     ).to(lowerCamelCase_     )

      # forward pass
      with torch.no_grad():
          __a			      =						model(**lowerCamelCase_     )

      # verify the logits
      __a			      =						torch.Size((1, 10_00)     )
      self.assertEqual(outputs.logits.shape						,  lowerCamelCase_     )

      __a			      =						torch.tensor([-11.10_69, -9.78_77, -8.37_77]     ).to(lowerCamelCase_     )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]						,  lowerCamelCase_						,  atol=1E-4     )     )






@require_torch
class 					a					(				A_   ,		unittest.TestCase							):
  A_ :				Tuple 			=							(ResNetBackbone,) if is_torch_available() else ()
  A_ :				Tuple 			=							False
  A_ :				List[str] 			=							ResNetConfig



  def        lowerCAmelCase_				(		self   :    Dict     )      -> str:
      __a			      =						ResNetModelTester(self     )

"""simple docstring"""


import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)

def 						UpperCamelCase				(	_lowerCAmelCase   :							List[str]				, _lowerCAmelCase   :							Optional[int]=False				, _lowerCAmelCase   :							Union[str, Any]=False				, _lowerCAmelCase   :							Any="data2vec."						):
    __a			      =						"""backbone.""" if is_semantic else """"""

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

    # projection layer + position embeddings
    rename_keys.extend(
        [
            (f"""{prefix}cls_token""", f"""{hf_prefix}embeddings.cls_token"""),
            (f"""{prefix}patch_embed.proj.weight""", f"""{hf_prefix}embeddings.patch_embeddings.projection.weight"""),
            (f"""{prefix}patch_embed.proj.bias""", f"""{hf_prefix}embeddings.patch_embeddings.projection.bias"""),
        ]						)

    if has_lm_head:
        # mask token + shared relative position bias + layernorm
        rename_keys.extend(
            [
                ("""mask_token""", f"""{hf_prefix}embeddings.mask_token"""),
                (
                    """rel_pos_bias.relative_position_bias_table""",
                    f"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
                ),
                (
                    """rel_pos_bias.relative_position_index""",
                    f"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
                ),
                ("""norm.weight""", """layernorm.weight"""),
                ("""norm.bias""", """layernorm.bias"""),
            ]						)
    elif is_semantic:
        # semantic segmentation classification heads
        rename_keys.extend(
            [
                ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
                ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
                ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
                ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
            ]						)
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ("""fc_norm.weight""", f"""{hf_prefix}pooler.layernorm.weight"""),
                ("""fc_norm.bias""", f"""{hf_prefix}pooler.layernorm.bias"""),
                ("""head.weight""", """classifier.weight"""),
                ("""head.bias""", """classifier.bias"""),
            ]						)

    return rename_keys

def 						UpperCamelCase				(	_lowerCAmelCase   :							str				, _lowerCAmelCase   :							Tuple				, _lowerCAmelCase   :							List[Any]=False				, _lowerCAmelCase   :							Optional[int]=False				, _lowerCAmelCase   :							Tuple="data2vec_vision."						):
    for i in range(config.num_hidden_layers						):
        __a			      =						"""backbone.""" if is_semantic else """"""
        # queries, keys and values
        __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""						)
        __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""						)
        __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""						)

        __a			      =						in_proj_weight[
            : config.hidden_size, :
        ]
        __a			      =						q_bias
        __a			      =						in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        __a			      =						in_proj_weight[
            -config.hidden_size :, :
        ]
        __a			      =						v_bias

        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""						)
        __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""						)

        __a			      =						gamma_a
        __a			      =						gamma_a

        # relative_position bias table + index
        if not has_lm_head:
            # each layer has its own relative position bias
            __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""						)
            __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""						)

            __a			      =						table
            __a			      =						index

def 						UpperCamelCase				(	):
    __a			      =						argparse.ArgumentParser(
        """Convert Data2VecVision to HF for image classification and pretraining"""				, add_help=_lowerCAmelCase						)
    parser.add_argument("""--hf_checkpoint_name"""				, type=_lowerCAmelCase						)
    parser.add_argument("""--input_size"""				, default=224				, type=_lowerCAmelCase				, help="""images input size"""						)
    parser.add_argument("""--beit_checkpoint"""				, default=""""""				, help="""beit checkpoint"""						)

    return parser.parse_args()

def 						UpperCamelCase				(	_lowerCAmelCase   :							Optional[int]				, _lowerCAmelCase   :							Union[str, Any]				, _lowerCAmelCase   :							Union[str, Any]						):
    def load_state_dict(_lowerCAmelCase   :							Tuple				, _lowerCAmelCase   :							str				, _lowerCAmelCase   :							Union[str, Any]=""				, _lowerCAmelCase   :							Union[str, Any]="relative_position_index"						):
        __a			      =						[]
        __a			      =						[]
        __a			      =						[]
        # copy state_dict so _load_from_state_dict can modify it
        __a			      =						getattr(_lowerCAmelCase				, """_metadata"""				, _lowerCAmelCase						)
        __a			      =						state_dict.copy()
        if metadata is not None:
            __a			      =						metadata

        def load(_lowerCAmelCase   :							List[Any]				, _lowerCAmelCase   :							List[str]=""						):
            __a			      =						{} if metadata is None else metadata.get(prefix[:-1]				, {}						)
            module._load_from_state_dict(
                _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase						)
            for name, child in module._modules.items():
                if child is not None:
                    load(_lowerCAmelCase				, prefix + name + """."""						)

        load(_lowerCAmelCase				, prefix=_lowerCAmelCase						)

        __a			      =						[]
        __a			      =						[]
        for key in missing_keys:
            __a			      =						True
            for ignore_key in ignore_missing.split("""|"""						):
                if ignore_key in key:
                    __a			      =						False
                    break
            if keep_flag:
                warn_missing_keys.append(_lowerCAmelCase						)
            else:
                ignore_missing_keys.append(_lowerCAmelCase						)

        __a			      =						warn_missing_keys

        if len(_lowerCAmelCase						) > 0:
            print(
                """Weights of {} not initialized from pretrained model: {}""".format(
                    model.__class__.__name__				, _lowerCAmelCase						)						)
        if len(_lowerCAmelCase						) > 0:
            print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__				, _lowerCAmelCase						)						)
        if len(_lowerCAmelCase						) > 0:
            print(
                """Ignored weights of {} not initialized from pretrained model: {}""".format(
                    model.__class__.__name__				, _lowerCAmelCase						)						)
        if len(_lowerCAmelCase						) > 0:
            print("""\n""".join(_lowerCAmelCase						)						)

    __a			      =						{
        """pretrained""": False,
        """use_shared_rel_pos_bias""": True,
        """use_abs_pos_emb""": False,
        """init_values""": 0.1,
    }

    if is_finetuned:
        model_kwargs.update(
            {
                """num_classes""": 1000,
                """use_mean_pooling""": True,
                """init_scale""": 0.001,
                """use_rel_pos_bias""": True,
            }						)

    __a			      =						create_model(
        """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""				, **_lowerCAmelCase				, )
    __a			      =						model.patch_embed.patch_size
    __a			      =						(args.input_size // patch_size[0], args.input_size // patch_size[1])
    __a			      =						torch.load(args.beit_checkpoint				, map_location="""cpu"""						)

    print(f"""Load ckpt from {args.beit_checkpoint}"""						)
    __a			      =						None
    for model_key in ("model", "module"):
        if model_key in checkpoint:
            __a			      =						checkpoint[model_key]
            print(f"""Load state_dict by model_key = {model_key}"""						)
            break

    __a			      =						list(checkpoint_model.keys()						)
    for key in all_keys:
        if "relative_position_index" in key:
            checkpoint_model.pop(_lowerCAmelCase						)

        if "relative_position_bias_table" in key:
            __a			      =						checkpoint_model[key]
            __a      ,  __a			      =						rel_pos_bias.size()
            __a      ,  __a			      =						model.state_dict()[key].size()
            __a			      =						model.patch_embed.patch_shape
            if dst_patch_shape[0] != dst_patch_shape[1]:
                raise NotImplementedError()

    load_state_dict(_lowerCAmelCase				, _lowerCAmelCase				, prefix=""""""						)

    return model

def 						UpperCamelCase				(	):
    __a			      =						get_args()

    __a			      =						"""ft1k""" in args.hf_checkpoint_name
    __a			      =						"""large""" in args.hf_checkpoint_name

    if is_finetuned:
        # To convert Beit's data2vec_vision to HF you need to copy
        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
        # into this folder.
        import modeling_finetune  # noqa: F401
    else:
        # To convert Beit's data2vec_vision to HF you need to copy
        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
        # into this folder
        # IMPORTANT: Note that for now we've only converted the down-stream
        # model and not the full pretrained model. This means for the integration
        # test you need to add a `return x` after the following line:
        # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
        # to make the integration test pass.
        import modeling_cyclical  # noqa: F401

    # 1. Create model config
    __a			      =						DataaVecVisionConfig()
    if is_finetuned:
        __a			      =						True
        __a			      =						False
        __a			      =						True
        __a			      =						1000

        __a			      =						"""huggingface/label-files"""
        __a			      =						"""imagenet-1k-id2label.json"""
        __a			      =						json.load(open(hf_hub_download(_lowerCAmelCase				, _lowerCAmelCase				, repo_type="""dataset"""						)				, """r"""						)						)
        __a			      =						{int(_lowerCAmelCase						): v for k, v in idalabel.items()}
        __a			      =						idalabel
        __a			      =						{v: k for k, v in idalabel.items()}
    else:
        __a			      =						False
        __a			      =						True
        __a			      =						False

    if is_large:
        __a			      =						1024
        __a			      =						4096
        __a			      =						24
        __a			      =						16

    # 2. Load Beit model
    __a			      =						load_beit_model(_lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase						)
    orig_model.eval()

    # 3. Forward Beit model
    __a			      =						BeitImageProcessor(size=config.image_size				, do_center_crop=_lowerCAmelCase						)
    __a			      =						Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""						)
    __a			      =						image_processor(images=_lowerCAmelCase				, return_tensors="""pt"""						)
    __a			      =						encoding["""pixel_values"""]

    __a			      =						(pixel_values,) if is_finetuned else (pixel_values, None)
    with torch.no_grad():
        __a			      =						orig_model(*_lowerCAmelCase						)

    # 4. Load HF Data2VecVision model
    if is_finetuned:
        __a			      =						DataaVecVisionForImageClassification(_lowerCAmelCase						)
        hf_model.eval()
        __a			      =						False
        __a			      =						"""data2vec_vision."""
    else:
        __a			      =						DataaVecVisionModel(_lowerCAmelCase						)
        hf_model.eval()
        __a			      =						True
        __a			      =						""""""

    __a			      =						create_rename_keys(_lowerCAmelCase				, hf_prefix=_lowerCAmelCase				, has_lm_head=_lowerCAmelCase						)
    __a			      =						orig_model.state_dict()
    for src, dest in rename_keys:
        __a			      =						state_dict.pop(_lowerCAmelCase						)
        __a			      =						val

    read_in_q_k_v(_lowerCAmelCase				, _lowerCAmelCase				, hf_prefix=_lowerCAmelCase				, has_lm_head=_lowerCAmelCase						)
    __a      ,  __a			      =						hf_model.load_state_dict(_lowerCAmelCase				, strict=_lowerCAmelCase						)
    print("""HF missing"""				, _lowerCAmelCase						)
    print("""HF unexpected_keys"""				, _lowerCAmelCase						)

    # 5. Forward HF Data2VecVision model
    with torch.no_grad():
        __a			      =						hf_model(_lowerCAmelCase						)

    __a			      =						hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

    # 6. Compare
    __a			      =						torch.max(torch.abs(hf_output - orig_model_output						)						).item()

    print(f"""max_absolute_diff = {max_absolute_diff}"""						)
    __a			      =						torch.allclose(_lowerCAmelCase				, _lowerCAmelCase				, atol=1E-3						)
    print("""Do both models output the same tensors?"""				, """🔥""" if success else """💩"""						)
    if not success:
        raise Exception("""Something went wRoNg"""						)

    # 7. Save
    print(f"""Saving to {args.hf_checkpoint_name}"""						)
    hf_model.save_pretrained(args.hf_checkpoint_name						)
    image_processor.save_pretrained(args.hf_checkpoint_name						)


if __name__ == "__main__":
 main()
 # Run the following to convert checkpoints
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./pretrained_base.pt \
 #          --hf_checkpoint_name "./data2vec-vision-base"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./finetuned_base.pt \
 #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./pretrained_large.pt \
 #          --hf_checkpoint_name "./data2vec-vision-large"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./finetuned_large.pt \
 #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"
