





import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFEfficientFormerForImageClassification,
      TFEfficientFormerForImageClassificationWithTeacher,
      TFEfficientFormerModel,
  )
  from transformers.models.efficientformer.modeling_tf_efficientformer import (
      TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  )


if is_vision_available():
  from PIL import Image

  from transformers import EfficientFormerImageProcessor





class  lowerCAmelCase							:







  def __init__(      self  :Union[str, Any]   ,							_lowercase  :int   ,							_lowercase  :int = 13   ,							_lowercase  :int = 64   ,							_lowercase  :int = 2   ,							_lowercase  :int = 3   ,							_lowercase  :int = 3   ,							_lowercase  :bool = True   ,							_lowercase  :bool = True   ,							_lowercase  :int = 1_28   ,							_lowercase  :int=[16, 32, 64, 1_28]   ,							_lowercase  :int = 7   ,							_lowercase  :int = 4   ,							_lowercase  :int = 37   ,							_lowercase  :str = "gelu"   ,							_lowercase  :float = 0.1   ,							_lowercase  :float = 0.1   ,							_lowercase  :int = 10   ,							_lowercase  :float = 0.02   ,							_lowercase  :int = 2   ,							_lowercase  :int = 1   ,							_lowercase  :int = 1_28   ,							_lowercase  :List[int] = [2, 2, 2, 2]   ,							_lowercase  :int = 2   ,							_lowercase  :int = 2   ,							):






     '''simple docstring'''





     lowercase__			       =  parent
     lowercase__			       =  batch_size
     lowercase__			       =  image_size
     lowercase__			       =  patch_size
     lowercase__			       =  num_channels
     lowercase__			       =  is_training
     lowercase__			       =  use_labels
     lowercase__			       =  hidden_size
     lowercase__			       =  num_hidden_layers
     lowercase__			       =  num_attention_heads
     lowercase__			       =  intermediate_size
     lowercase__			       =  hidden_act
     lowercase__			       =  hidden_dropout_prob
     lowercase__			       =  attention_probs_dropout_prob
     lowercase__			       =  type_sequence_label_size
     lowercase__			       =  initializer_range
     lowercase__			       =  encoder_stride
     lowercase__			       =  num_attention_outputs
     lowercase__			       =  embed_dim
     lowercase__			       =  embed_dim + 1
     lowercase__			       =  resolution
     lowercase__			       =  depths
     lowercase__			       =  hidden_sizes
     lowercase__			       =  dim
     lowercase__			       =  mlp_expansion_ratio







  def   UpperCAmelCase					(      self  :List[str] ):






     '''simple docstring'''





     lowercase__			       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

     lowercase__			       =  None
     if self.use_labels:
        lowercase__			       =  ids_tensor([self.batch_size]   ,							self.type_sequence_label_size )

     lowercase__			       =  self.get_config()

     return config, pixel_values, labels







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     return EfficientFormerConfig(
         image_size=self.image_size   ,							patch_size=self.patch_size   ,							num_channels=self.num_channels   ,							hidden_size=self.hidden_size   ,							num_hidden_layers=self.num_hidden_layers   ,							num_attention_heads=self.num_attention_heads   ,							intermediate_size=self.intermediate_size   ,							hidden_act=self.hidden_act   ,							hidden_dropout_prob=self.hidden_dropout_prob   ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,							is_decoder=_lowerCamelCase   ,							initializer_range=self.initializer_range   ,							encoder_stride=self.encoder_stride   ,							resolution=self.resolution   ,							depths=self.depths   ,							hidden_sizes=self.hidden_sizes   ,							dim=self.dim   ,							mlp_expansion_ratio=self.mlp_expansion_ratio   ,							)







  def   UpperCAmelCase					(      self  :Dict   ,							_lowercase  :Dict   ,							_lowercase  :Optional[Any]   ,							_lowercase  :List[Any] ):






     '''simple docstring'''





     lowercase__			       =  TFEfficientFormerModel(config=_lowerCamelCase )
     lowercase__			       =  model(_lowerCamelCase   ,							training=_lowerCamelCase )
     self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.seq_length, self.hidden_size) )







  def   UpperCAmelCase					(      self  :List[Any]   ,							_lowercase  :str   ,							_lowercase  :Optional[int]   ,							_lowercase  :Tuple ):






     '''simple docstring'''





     lowercase__			       =  self.type_sequence_label_size
     lowercase__			       =  TFEfficientFormerForImageClassification(_lowerCamelCase )
     lowercase__			       =  model(_lowerCamelCase   ,							labels=_lowerCamelCase   ,							training=_lowerCamelCase )
     self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.type_sequence_label_size) )

     # test greyscale images
     lowercase__			       =  1
     lowercase__			       =  TFEfficientFormerForImageClassification(_lowerCamelCase )

     lowercase__			       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
     lowercase__			       =  model(_lowerCamelCase   ,							labels=_lowerCamelCase )
     self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.type_sequence_label_size) )







  def   UpperCAmelCase					(      self  :Any ):






     '''simple docstring'''





     lowercase__			       =  self.prepare_config_and_inputs()
     lowercase__       ,							lowercase__       ,							lowercase__			       =  config_and_inputs
     lowercase__			       =  {"pixel_values": pixel_values}
     return config, inputs_dict





@require_tf
class  lowerCAmelCase							(       A_							,							A_							,							unittest.TestCase ):
  __lowerCamelCase		=      (
      (
          TFEfficientFormerModel,
          TFEfficientFormerForImageClassificationWithTeacher,
          TFEfficientFormerForImageClassification,
      )
      if is_tf_available()
      else ()
  )
  __lowerCamelCase		=      (
      {
          'feature-extraction': TFEfficientFormerModel,
          'image-classification': (
              TFEfficientFormerForImageClassification,
              TFEfficientFormerForImageClassificationWithTeacher,
          ),
      }
      if is_tf_available()
      else {}
  )

  __lowerCamelCase		=      False

  __lowerCamelCase		=      False
  __lowerCamelCase		=      False
  __lowerCamelCase		=      False
  __lowerCamelCase		=      False







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     lowercase__			       =  TFEfficientFormerModelTester(self )
     lowercase__			       =  ConfigTester(
         self   ,							config_class=_lowerCamelCase   ,							has_text_modality=_lowerCamelCase   ,							hidden_size=37 )







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     self.config_tester.run_common_tests()







  @unittest.skip(reason="EfficientFormer does not use inputs_embeds" )
  def   UpperCAmelCase					(      self  :Any ):






     '''simple docstring'''





     pass







  @unittest.skip(reason="EfficientFormer does not support input and output embeddings" )
  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     pass







  def   UpperCAmelCase					(      self  :Union[str, Any] ):






     '''simple docstring'''





     lowercase__       ,							lowercase__			       =  self.model_tester.prepare_config_and_inputs_for_common()

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

        lowercase__			       =  ["pixel_values"]
        self.assertListEqual(arg_names[:1]   ,							_lowerCamelCase )







  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     def check_hidden_states_output(_lowercase  :List[Any]   ,							_lowercase  :Optional[Any]   ,							_lowercase  :Dict ):
        lowercase__			       =  model_class(_lowerCamelCase )

        lowercase__			       =  model(**self._prepare_for_class(_lowerCamelCase   ,							_lowerCamelCase )   ,							training=_lowerCamelCase )
        lowercase__			       =  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        lowercase__			       =  getattr(
            self.model_tester   ,							"expected_num_hidden_layers"   ,							self.model_tester.num_hidden_layers + 1 )
        self.assertEqual(len(_lowerCamelCase )   ,							_lowerCamelCase )

        if hasattr(self.model_tester   ,							"encoder_seq_length" ):
           lowercase__			       =  self.model_tester.encoder_seq_length
           if hasattr(self.model_tester   ,							"chunk_length" ) and self.model_tester.chunk_length > 1:
              lowercase__			       =  seq_length * self.model_tester.chunk_length
        else:
           lowercase__			       =  self.model_tester.seq_length

        self.assertListEqual(
            list(hidden_states[-1].shape[-2:] )   ,							[seq_length, self.model_tester.hidden_size]   ,							)

        if config.is_encoder_decoder:
           lowercase__			       =  outputs.decoder_hidden_states

           self.asseretIsInstance(_lowerCamelCase   ,							(list, tuple) )
           self.assertEqual(len(_lowerCamelCase )   ,							_lowerCamelCase )
           lowercase__			       =  getattr(self.model_tester   ,							"seq_length"   ,							_lowerCamelCase )
           lowercase__			       =  getattr(self.model_tester   ,							"decoder_seq_length"   ,							_lowerCamelCase )

           self.assertListEqual(
               list(hidden_states[-1].shape[-2:] )   ,							[decoder_seq_length, self.model_tester.hidden_size]   ,							)

     lowercase__       ,							lowercase__			       =  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        lowercase__			       =  True
        check_hidden_states_output(_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase )

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

        check_hidden_states_output(_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase )







  def   UpperCAmelCase					(      self  :Dict   ,							_lowercase  :int   ,							_lowercase  :Optional[int]   ,							_lowercase  :List[Any]=False ):






     '''simple docstring'''





     lowercase__			       =  super()._prepare_for_class(_lowerCamelCase   ,							_lowerCamelCase   ,							return_labels=_lowerCamelCase )

     if return_labels:
        if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
           del inputs_dict["labels"]

     return inputs_dict







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCamelCase )







  @unittest.skip(reason="EfficientFormer does not implement masked image modeling yet" )
  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     lowercase__			       =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCamelCase )







  def   UpperCAmelCase					(      self  :Union[str, Any] ):






     '''simple docstring'''





     lowercase__			       =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )







  @slow
  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase__			       =  TFEfficientFormerModel.from_pretrained(_lowerCamelCase )
        self.assertIsNotNone(_lowerCamelCase )







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     lowercase__       ,							lowercase__			       =  self.model_tester.prepare_config_and_inputs_for_common()
     lowercase__			       =  True

     lowercase__			       =  getattr(self.model_tester   ,							"seq_length"   ,							_lowerCamelCase )
     lowercase__			       =  getattr(self.model_tester   ,							"encoder_seq_length"   ,							_lowerCamelCase )
     lowercase__			       =  getattr(self.model_tester   ,							"key_length"   ,							_lowerCamelCase )
     lowercase__			       =  getattr(self.model_tester   ,							"chunk_length"   ,							_lowerCamelCase )

     if chunk_length is not None and hasattr(self.model_tester   ,							"num_hashes" ):
        lowercase__			       =  encoder_seq_length * self.model_tester.num_hashes

     for model_class in self.all_model_classes:
        lowercase__			       =  True
        lowercase__			       =  False
        lowercase__			       =  True
        lowercase__			       =  model_class(_lowerCamelCase )

        lowercase__			       =  model(**self._prepare_for_class(_lowerCamelCase   ,							_lowerCamelCase )   ,							training=_lowerCamelCase )
        lowercase__			       =  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
        self.assertEqual(len(_lowerCamelCase )   ,							self.model_tester.num_attention_outputs )

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        lowercase__			       =  True
        lowercase__			       =  model_class(_lowerCamelCase )
        lowercase__			       =  model(**self._prepare_for_class(_lowerCamelCase   ,							_lowerCamelCase )   ,							training=_lowerCamelCase )

        lowercase__			       =  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
        self.assertEqual(len(_lowerCamelCase )   ,							self.model_tester.num_attention_outputs )

        if chunk_length is not None:
           self.assertListEqual(
               list(attentions[0].shape[-4:] )   ,							[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]   ,							)
        else:
           self.assertListEqual(
               list(attentions[0].shape[-3:] )   ,							[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]   ,							)







  def   UpperCAmelCase					(      self  :Any ):






     '''simple docstring'''





     lowercase__       ,							lowercase__			       =  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        # Prepare our model
        lowercase__			       =  model_class(_lowerCamelCase )
        # These are maximally general inputs for the model, with multiple None dimensions
        # Hopefully this will catch any conditionals that fail for flexible shapes
        lowercase__			       =  {
            key: tf.keras.Input(shape=val.shape[1:]   ,							dtype=val.dtype   ,							name=_lowerCamelCase )
            for key, val in model.input_signature.items()
            if key in model.dummy_inputs
        }
        lowercase__			       =  model(_lowerCamelCase )
        self.assertTrue(outputs_dict is not None )


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







@require_tf
@require_vision
class  lowerCAmelCase							(       unittest.TestCase ):







  @cached_property
  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     return (
         EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300" )
         if is_vision_available()
         else None
     )







  @slow
  def   UpperCAmelCase					(      self  :Union[str, Any] ):






     '''simple docstring'''





     lowercase__			       =  TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300" )
     lowercase__			       =  self.default_image_processor
     lowercase__			       =  prepare_img()
     lowercase__			       =  image_processor(images=_lowerCamelCase   ,							return_tensors="tf" )
     # forward pass
     lowercase__			       =  model(**_lowerCamelCase   ,							training=_lowerCamelCase )
     # verify the logits
     lowercase__			       =  tf.TensorShape((1, 10_00) )
     self.assertEqual(outputs.logits.shape   ,							_lowerCamelCase )
     lowercase__			       =  tf.constant([-0.0555, 0.4825, -0.0852] )
     self.assertTrue(np.allclose(outputs.logits[0, :3]   ,							_lowerCamelCase   ,							atol=1e-4 ) )







  @slow
  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
         "snap-research/efficientformer-l1-300" )
     lowercase__			       =  self.default_image_processor
     lowercase__			       =  prepare_img()
     lowercase__			       =  image_processor(images=_lowerCamelCase   ,							return_tensors="tf" )
     # forward pass
     lowercase__			       =  model(**_lowerCamelCase   ,							training=_lowerCamelCase )
     # verify the logits
     lowercase__			       =  tf.TensorShape((1, 10_00) )
     self.assertEqual(outputs.logits.shape   ,							_lowerCamelCase )
     lowercase__			       =  tf.constant([-0.1312, 0.4353, -1.0499] )
     self.assertTrue(np.allclose(outputs.logits[0, :3]   ,							_lowerCamelCase   ,							atol=1e-4 ) )











import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)


def _A       (   __magic_name__  , __magic_name__=False	):
   lowercase__			       =  []
   for i in range(config.num_hidden_layers	):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((f'''module.blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''')	)
      rename_keys.append((f'''module.blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''')	)
      rename_keys.append(
          (f'''module.blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''')	)
      rename_keys.append((f'''module.blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''')	)
      rename_keys.append((f'''module.blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''')	)
      rename_keys.append((f'''module.blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''')	)
      rename_keys.append((f'''module.blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''')	)
      rename_keys.append((f'''module.blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''')	)
      rename_keys.append((f'''module.blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''')	)
      rename_keys.append((f'''module.blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''')	)

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

   if base_model:
      # layernorm + pooler
      rename_keys.extend(
          [
              ("module.norm.weight", "layernorm.weight"),
              ("module.norm.bias", "layernorm.bias"),
          ]	)

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

   return rename_keys


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


def _A       (   __magic_name__	):
   lowercase__			       =  ["head.weight", "head.bias"]
   for k in ignore_keys:
      state_dict.pop(__magic_name__  , __magic_name__	)


def _A       (   __magic_name__	):
   # projection head is used in the self-supervised pre-training in MSN,
   # for downstream task it's not needed.
   lowercase__			       =  [
       "module.fc.fc1.weight",
       "module.fc.fc1.bias",
       "module.fc.bn1.weight",
       "module.fc.bn1.bias",
       "module.fc.bn1.running_mean",
       "module.fc.bn1.running_var",
       "module.fc.bn1.num_batches_tracked",
       "module.fc.fc2.weight",
       "module.fc.fc2.bias",
       "module.fc.bn2.weight",
       "module.fc.bn2.bias",
       "module.fc.bn2.running_mean",
       "module.fc.bn2.running_var",
       "module.fc.bn2.num_batches_tracked",
       "module.fc.fc3.weight",
       "module.fc.fc3.bias",
   ]
   for k in ignore_keys:
      state_dict.pop(__magic_name__  , __magic_name__	)


def _A       (   __magic_name__  , __magic_name__  , __magic_name__	):
   lowercase__			       =  dct.pop(__magic_name__	)
   lowercase__			       =  val


def _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  ViTMSNConfig()
   lowercase__			       =  1000

   lowercase__			       =  "datasets/huggingface/label-files"
   lowercase__			       =  "imagenet-1k-id2label.json"
   lowercase__			       =  json.load(open(hf_hub_download(__magic_name__  , __magic_name__	)  , "r"	)	)
   lowercase__			       =  {int(__magic_name__	): v for k, v in idalabel.items()}
   lowercase__			       =  idalabel
   lowercase__			       =  {v: k for k, v in idalabel.items()}

   if "s16" in checkpoint_url:
      lowercase__			       =  384
      lowercase__			       =  1536
      lowercase__			       =  6
   elif "l16" in checkpoint_url:
      lowercase__			       =  1024
      lowercase__			       =  4096
      lowercase__			       =  24
      lowercase__			       =  16
      lowercase__			       =  0.1
   elif "b4" in checkpoint_url:
      lowercase__			       =  4
   elif "l7" in checkpoint_url:
      lowercase__			       =  7
      lowercase__			       =  1024
      lowercase__			       =  4096
      lowercase__			       =  24
      lowercase__			       =  16
      lowercase__			       =  0.1

   lowercase__			       =  ViTMSNModel(__magic_name__	)

   lowercase__			       =  torch.hub.load_state_dict_from_url(__magic_name__  , map_location="cpu"	)["target_encoder"]

   lowercase__			       =  ViTImageProcessor(size=config.image_size	)

   remove_projection_head(__magic_name__	)
   lowercase__			       =  create_rename_keys(__magic_name__  , base_model=__magic_name__	)

   for src, dest in rename_keys:
      rename_key(__magic_name__  , __magic_name__  , __magic_name__	)
   read_in_q_k_v(__magic_name__  , __magic_name__  , base_model=__magic_name__	)

   model.load_state_dict(__magic_name__	)
   model.eval()

   lowercase__			       =  "http://images.cocodataset.org/val2017/000000039769.jpg"

   lowercase__			       =  Image.open(requests.get(__magic_name__  , stream=__magic_name__	).raw	)
   lowercase__			       =  ViTImageProcessor(
       size=config.image_size  , image_mean=__magic_name__  , image_std=__magic_name__	)
   lowercase__			       =  image_processor(images=__magic_name__  , return_tensors="pt"	)

   # forward pass
   torch.manual_seed(2	)
   lowercase__			       =  model(**__magic_name__	)
   lowercase__			       =  outputs.last_hidden_state

   # The following Colab Notebook was used to generate these outputs:
   # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
   if "s16" in checkpoint_url:
      lowercase__			       =  torch.tensor([[-1.0_915, -1.4_876, -1.1_809]]	)
   elif "b16" in checkpoint_url:
      lowercase__			       =  torch.tensor([[14.2_889, -18.9_045, 11.7_281]]	)
   elif "l16" in checkpoint_url:
      lowercase__			       =  torch.tensor([[41.5_028, -22.8_681, 45.6_475]]	)
   elif "b4" in checkpoint_url:
      lowercase__			       =  torch.tensor([[-4.3_868, 5.2_932, -0.4_137]]	)
   else:
      lowercase__			       =  torch.tensor([[-0.1_792, -0.6_465, 2.4_263]]	)

   # verify logits
   assert torch.allclose(last_hidden_state[:, 0, :3]  , __magic_name__  , atol=1e-4	)

   print(f'''Saving model to {pytorch_dump_folder_path}'''	)
   model.save_pretrained(__magic_name__	)

   print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
   image_processor.save_pretrained(__magic_name__	)


if __name__ == "__main__":
  _snake_case           =			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar""",
      type=str,
      help="""URL of the checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  _snake_case           =			parser.parse_args()
  convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






