





from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor







class   __lowerCAmelCase			:





   def __init__( self					,			snake_case					,			snake_case=13					,			snake_case=30					,			snake_case=2					,			snake_case=3					,			snake_case=True					,			snake_case=True					,			snake_case=32					,			snake_case=2					,			snake_case=4					,			snake_case=37					,			snake_case="gelu"					,			snake_case=0.1					,			snake_case=0.1					,			snake_case=10					,			snake_case=0.02					,			snake_case=3					,			snake_case=0.6					,			snake_case=None					,			)				->  List[str]:
      """simple docstring"""





      a__	:						Tuple      		=  parent
      a__	:						Any      		=  batch_size
      a__	:						List[Any]      		=  image_size
      a__	:						Optional[int]      		=  patch_size
      a__	:						Dict      		=  num_channels
      a__	:						Optional[int]      		=  is_training
      a__	:						List[str]      		=  use_labels
      a__	:						int      		=  hidden_size
      a__	:						Optional[int]      		=  num_hidden_layers
      a__	:						Optional[int]      		=  num_attention_heads
      a__	:						List[str]      		=  intermediate_size
      a__	:						Tuple      		=  hidden_act
      a__	:						Tuple      		=  hidden_dropout_prob
      a__	:						Tuple      		=  attention_probs_dropout_prob
      a__	:						int      		=  type_sequence_label_size
      a__	:						int      		=  initializer_range
      a__	:						Union[str, Any]      		=  mask_ratio
      a__	:						Optional[Any]      		=  scope

      # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
      # (we add 1 for the [CLS] token)
      a__	:						Tuple      		=  (image_size // patch_size) ** 2
      a__	:						List[str]      		=  int(math.ceil((1 - mask_ratio) * (num_patches + 1)      )      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

      a__	:						Dict      		=  self.get_config()

      return config, pixel_values, labels





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      return ViTMAEConfig(
          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					,			decoder_hidden_size=self.hidden_size					,			decoder_num_hidden_layers=self.num_hidden_layers					,			decoder_num_attention_heads=self.num_attention_heads					,			decoder_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=snake_case					,			initializer_range=self.initializer_range					,			mask_ratio=self.mask_ratio					,			)





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case      )				->  Dict:
      """simple docstring"""





      a__	:						List[str]      		=  TFViTMAEModel(config=snake_case      )
      a__	:						int      		=  model(snake_case					,			training=snake_case      )
      self.parent.assertEqual(result.last_hidden_state.shape					,			(self.batch_size, self.seq_length, self.hidden_size)      )





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case      )				->  Dict:
      """simple docstring"""





      a__	:						Optional[int]      		=  TFViTMAEForPreTraining(snake_case      )
      a__	:						Dict      		=  model(snake_case					,			training=snake_case      )
      # expected sequence length = num_patches
      a__	:						Union[str, Any]      		=  (self.image_size // self.patch_size) ** 2
      a__	:						Any      		=  self.patch_size**2 * self.num_channels
      self.parent.assertEqual(result.logits.shape					,			(self.batch_size, num_patches, expected_num_channels)      )

      # test greyscale images
      a__	:						Any      		=  1
      a__	:						Any      		=  TFViTMAEForPreTraining(snake_case      )

      a__	:						int      		=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
      a__	:						Dict      		=  model(snake_case					,			training=snake_case      )
      a__	:						Dict      		=  self.patch_size**2
      self.parent.assertEqual(result.logits.shape					,			(self.batch_size, num_patches, expected_num_channels)      )





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      a__	:						Any      		=  self.prepare_config_and_inputs()
      ((a__)		,     (a__)		,     (a__))	:						Dict      		=  config_and_inputs
      a__	:						List[str]      		=  {"pixel_values": pixel_values}
      return config, inputs_dict







@require_tf
class   __lowerCAmelCase			(					_UpperCamelCase		,_UpperCamelCase		,unittest.TestCase  ):
   _UpperCamelCase :						List[Any]     		=				(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
   _UpperCamelCase :						Optional[int]     		=				{"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

   _UpperCamelCase :						str     		=				False
   _UpperCamelCase :						Tuple     		=				False
   _UpperCamelCase :						Optional[Any]     		=				False
   _UpperCamelCase :						List[Any]     		=				False





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      a__	:						List[Any]      		=  TFViTMAEModelTester(self      )
      a__	:						Optional[Any]      		=  ConfigTester(self					,			config_class=snake_case					,			has_text_modality=snake_case					,			hidden_size=37      )





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      self.config_tester.run_common_tests()





   @unittest.skip(reason="ViTMAE does not use inputs_embeds"      )
   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      pass





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      a__		,     a__	:						List[str]      		=  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         a__	:						int      		=  model_class(snake_case      )
         self.assertIsInstance(model.get_input_embeddings()					,			(tf.keras.layers.Layer)      )
         a__	:						Any      		=  model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(snake_case					,			tf.keras.layers.Layer      )      )





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      a__		,     a__	:						int      		=  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         a__	:						Union[str, Any]      		=  model_class(snake_case      )
         a__	:						str      		=  inspect.signature(model.call      )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         a__	:						Optional[int]      		=  [*signature.parameters.keys()]

         a__	:						Any      		=  ["pixel_values"]
         self.assertListEqual(arg_names[:1]					,			snake_case      )





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__	:						Optional[int]      		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						Tuple      		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*snake_case      )





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      np.random.seed(2      )

      a__		,     a__	:						Any      		=  self.model_tester.prepare_config_and_inputs_for_common()

      a__	:						List[Any]      		=  int((config.image_size // config.patch_size) ** 2      )
      a__	:						str      		=  np.random.uniform(size=(self.model_tester.batch_size, num_patches)      )

      for model_class in self.all_model_classes:
         a__	:						Optional[Any]      		=  model_class(snake_case      )
         a__	:						Any      		=  self._prepare_for_class(snake_case					,			snake_case      )

         a__	:						Union[str, Any]      		=  model(snake_case					,			noise=snake_case      )

         a__	:						str      		=  copy.deepcopy(self._prepare_for_class(snake_case					,			snake_case      )      )
         a__	:						str      		=  model(**snake_case					,			noise=snake_case      )
         a__	:						List[str]      		=  outputs_dict[0].numpy()
         a__	:						int      		=  outputs_keywords[0].numpy()

         self.assertLess(np.sum(np.abs(output_dict - output_keywords      )      )					,			1E-6      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      np.random.seed(2      )

      a__		,     a__	:						List[str]      		=  self.model_tester.prepare_config_and_inputs_for_common()

      a__	:						Any      		=  int((config.image_size // config.patch_size) ** 2      )
      a__	:						List[str]      		=  np.random.uniform(size=(self.model_tester.batch_size, num_patches)      )

      def prepare_numpy_arrays(snake_case      ):
         a__	:						int      		=  {}
         for k, v in inputs_dict.items():
            if tf.is_tensor(snake_case      ):
               a__	:						Optional[int]      		=  v.numpy()
            else:
               a__	:						str      		=  np.array(snake_case      )

         return inputs_np_dict

      for model_class in self.all_model_classes:
         a__	:						Tuple      		=  model_class(snake_case      )

         a__	:						Dict      		=  self._prepare_for_class(snake_case					,			snake_case      )
         a__	:						Tuple      		=  prepare_numpy_arrays(snake_case      )

         a__	:						str      		=  model(snake_case					,			noise=snake_case      )
         a__	:						Tuple      		=  model(**snake_case					,			noise=snake_case      )
         self.assert_outputs_same(snake_case					,			snake_case      )





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case      )				->  int:
      """simple docstring"""





      np.random.seed(2      )

      a__	:						Optional[Any]      		=  int((tf_model.config.image_size // tf_model.config.patch_size) ** 2      )
      a__	:						int      		=  np.random.uniform(size=(self.model_tester.batch_size, num_patches)      )
      a__	:						List[str]      		=  tf.constant(snake_case      )

      # Add `noise` argument.
      # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
      a__	:						str      		=  tf_noise

      super().check_pt_tf_models(snake_case					,			snake_case					,			snake_case      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      np.random.seed(2      )

      a__		,     a__	:						Tuple      		=  self.model_tester.prepare_config_and_inputs_for_common()

      a__	:						Dict      		=  {
          module_member
          for model_class in self.all_model_classes
          for module in (import_module(model_class.__module__      ),)
          for module_member_name in dir(snake_case      )
          if module_member_name.endswith("MainLayer"      )
          # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
          and module_member_name[: -len("MainLayer"      )] == model_class.__name__[: -len("Model"      )]
          for module_member in (getattr(snake_case					,			snake_case      ),)
          if isinstance(snake_case					,			snake_case      )
          and tf.keras.layers.Layer in module_member.__bases__
          and getattr(snake_case					,			"_keras_serializable"					,			snake_case      )
      }

      a__	:						Dict      		=  int((config.image_size // config.patch_size) ** 2      )
      a__	:						int      		=  np.random.uniform(size=(self.model_tester.batch_size, num_patches)      )
      a__	:						Tuple      		=  tf.convert_to_tensor(snake_case      )
      inputs_dict.update({"noise": noise}      )

      for main_layer_class in tf_main_layer_classes:
         a__	:						str      		=  main_layer_class(snake_case      )

         a__	:						Union[str, Any]      		=  {
             name: tf.keras.Input(tensor.shape[1:]					,			dtype=tensor.dtype      ) for name, tensor in inputs_dict.items()
         }

         a__	:						Union[str, Any]      		=  tf.keras.Model(snake_case					,			outputs=main_layer(snake_case      )      )
         a__	:						Optional[int]      		=  model(snake_case      )

         with tempfile.TemporaryDirectory() as tmpdirname:
            a__	:						Dict      		=  os.path.join(snake_case					,			"keras_model.h5"      )
            model.save(snake_case      )
            a__	:						Optional[Any]      		=  tf.keras.models.load_model(
                snake_case					,			custom_objects={main_layer_class.__name__: main_layer_class}      )
            assert isinstance(snake_case					,			tf.keras.Model      )
            a__	:						Any      		=  model(snake_case      )
            self.assert_outputs_same(snake_case					,			snake_case      )





   @slow
   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      np.random.seed(2      )

      a__		,     a__	:						Optional[int]      		=  self.model_tester.prepare_config_and_inputs_for_common()

      a__	:						int      		=  int((config.image_size // config.patch_size) ** 2      )
      a__	:						List[Any]      		=  np.random.uniform(size=(self.model_tester.batch_size, num_patches)      )

      for model_class in self.all_model_classes:
         a__	:						Union[str, Any]      		=  model_class(snake_case      )
         a__	:						str      		=  self._prepare_for_class(snake_case					,			snake_case      )
         a__	:						List[Any]      		=  model(snake_case					,			noise=snake_case      )

         if model_class.__name__ == "TFViTMAEModel":
            a__	:						Tuple      		=  outputs.last_hidden_state.numpy()
            a__	:						Any      		=  0
         else:
            a__	:						Dict      		=  outputs.logits.numpy()
            a__	:						Union[str, Any]      		=  0

         with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(snake_case					,			saved_model=snake_case      )
            a__	:						List[str]      		=  model_class.from_pretrained(snake_case      )
            a__	:						Optional[Any]      		=  model(snake_case					,			noise=snake_case      )

            if model_class.__name__ == "TFViTMAEModel":
               a__	:						int      		=  after_outputs["last_hidden_state"].numpy()
               a__	:						List[Any]      		=  0
            else:
               a__	:						Union[str, Any]      		=  after_outputs["logits"].numpy()
               a__	:						Optional[int]      		=  0

            a__	:						str      		=  np.amax(np.abs(out_a - out_a      )      )
            self.assertLessEqual(snake_case					,			1E-5      )





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      np.random.seed(2      )

      a__		,     a__	:						List[Any]      		=  self.model_tester.prepare_config_and_inputs_for_common()

      a__	:						Tuple      		=  int((config.image_size // config.patch_size) ** 2      )
      a__	:						str      		=  np.random.uniform(size=(self.model_tester.batch_size, num_patches)      )

      for model_class in self.all_model_classes:
         a__	:						Optional[int]      		=  model_class(snake_case      )
         a__	:						Tuple      		=  self._prepare_for_class(snake_case					,			snake_case      )

         a__	:						List[str]      		=  model(snake_case					,			noise=snake_case      )
         a__	:						str      		=  model.get_config()
         # make sure that returned config is jsonifiable, which is required by keras
         json.dumps(snake_case      )
         a__	:						str      		=  model_class.from_config(model.get_config()      )
         # make sure it also accepts a normal config
         a__	:						str      		=  model_class.from_config(model.config      )
         a__	:						Any      		=  new_model(snake_case      )  # Build model
         new_model.set_weights(model.get_weights()      )
         a__	:						Union[str, Any]      		=  new_model(snake_case					,			noise=snake_case      )

         self.assert_outputs_same(snake_case					,			snake_case      )





   @unittest.skip(
       reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."      )
   def 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





      pass





   @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"      )
   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      pass





   @slow
   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						str      		=  TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"      )
      self.assertIsNotNone(snake_case      )







def       _A  (      ):
   a__	:						Optional[Any]      		=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
   return image







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





   @cached_property
   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"      ) if is_vision_available() else None





   @slow
   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      np.random.seed(2      )

      a__	:						Optional[int]      		=  TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"      )

      a__	:						List[Any]      		=  self.default_image_processor
      a__	:						int      		=  prepare_img()
      a__	:						int      		=  image_processor(images=snake_case					,			return_tensors="tf"      )

      # prepare a noise vector that will be also used for testing the TF model
      # (this way we can ensure that the PT and TF models operate on the same inputs)
      a__	:						Dict      		=  ViTMAEConfig()
      a__	:						Union[str, Any]      		=  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2      )
      a__	:						Any      		=  np.random.uniform(size=(1, num_patches)      )

      # forward pass
      a__	:						List[str]      		=  model(**snake_case					,			noise=snake_case      )

      # verify the logits
      a__	:						Any      		=  tf.convert_to_tensor([1, 196, 768]      )
      self.assertEqual(outputs.logits.shape					,			snake_case      )

      a__	:						Union[str, Any]      		=  tf.convert_to_tensor(
          [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]      )

      tf.debugging.assert_near(outputs.logits[0, :3, :3]					,			snake_case					,			atol=1E-4      )








import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Dict      		=  old_name

   if "patch_embed" in old_name:
      a__		,     a__		,     a__	:						Union[str, Any]      		=  old_name.split("."   )

      if layer == "0":
         a__	:						Union[str, Any]      		=  old_name.replace("0"							, "convolution1"   )
      elif layer == "1":
         a__	:						Dict      		=  old_name.replace("1"							, "batchnorm_before"   )
      elif layer == "3":
         a__	:						List[str]      		=  old_name.replace("3"							, "convolution2"   )
      else:
         a__	:						Optional[Any]      		=  old_name.replace("4"							, "batchnorm_after"   )

   if "network" in old_name and re.search(r"\d\.\d"							, lowerCamelCase   ):
      a__	:						List[str]      		=  r"\b\d{2}\b"
      if bool(re.search(lowerCamelCase							, lowerCamelCase   )   ):
         a__	:						Optional[int]      		=  re.search(r"\d\.\d\d."							, lowerCamelCase   ).group()
      else:
         a__	:						Any      		=  re.search(r"\d\.\d."							, lowerCamelCase   ).group()
      if int(match[0]   ) < 6:
         a__	:						List[Any]      		=  old_name.replace(lowerCamelCase							, ""   )
         a__	:						int      		=  trimmed_name.replace("network"							, match[0] + ".meta4D_layers.blocks." + match[2:-1]   )
         a__	:						List[Any]      		=  "intermediate_stages." + trimmed_name
      else:
         a__	:						Union[str, Any]      		=  old_name.replace(lowerCamelCase							, ""   )
         if int(match[2]   ) < num_meta4D_last_stage:
            a__	:						Optional[Any]      		=  trimmed_name.replace("network"							, "meta4D_layers.blocks." + match[2]   )
         else:
            a__	:						Union[str, Any]      		=  str(int(match[2]   ) - num_meta4D_last_stage   )
            a__	:						str      		=  trimmed_name.replace("network"							, "meta3D_layers.blocks." + layer_index   )
            if "norm1" in old_name:
               a__	:						List[str]      		=  trimmed_name.replace("norm1"							, "layernorm1"   )
            elif "norm2" in old_name:
               a__	:						Optional[int]      		=  trimmed_name.replace("norm2"							, "layernorm2"   )
            elif "fc1" in old_name:
               a__	:						List[str]      		=  trimmed_name.replace("fc1"							, "linear_in"   )
            elif "fc2" in old_name:
               a__	:						Any      		=  trimmed_name.replace("fc2"							, "linear_out"   )

         a__	:						Any      		=  "last_stage." + trimmed_name

   elif "network" in old_name and re.search(r".\d."							, lowerCamelCase   ):
      a__	:						List[str]      		=  old_name.replace("network"							, "intermediate_stages"   )

   if "fc" in new_name:
      a__	:						str      		=  new_name.replace("fc"							, "convolution"   )
   elif ("norm1" in new_name) and ("layernorm1" not in new_name):
      a__	:						str      		=  new_name.replace("norm1"							, "batchnorm_before"   )
   elif ("norm2" in new_name) and ("layernorm2" not in new_name):
      a__	:						Any      		=  new_name.replace("norm2"							, "batchnorm_after"   )
   if "proj" in new_name:
      a__	:						Optional[int]      		=  new_name.replace("proj"							, "projection"   )
   if "dist_head" in new_name:
      a__	:						Tuple      		=  new_name.replace("dist_head"							, "distillation_classifier"   )
   elif "head" in new_name:
      a__	:						Optional[int]      		=  new_name.replace("head"							, "classifier"   )
   elif "patch_embed" in new_name:
      a__	:						Tuple      		=  "efficientformer." + new_name
   elif new_name == "norm.weight" or new_name == "norm.bias":
      a__	:						Union[str, Any]      		=  new_name.replace("norm"							, "layernorm"   )
      a__	:						Optional[int]      		=  "efficientformer." + new_name
   else:
      a__	:						List[Any]      		=  "efficientformer.encoder." + new_name

   return new_name






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   for key in checkpoint.copy().keys():
      a__	:						Optional[Any]      		=  checkpoint.pop(lowerCamelCase   )
      a__	:						Dict      		=  val

   return checkpoint






def       _A  (      ):
   a__	:						Tuple      		=  "http://images.cocodataset.org/val2017/000000039769.jpg"
   a__	:						List[Any]      		=  Image.open(requests.get(lowerCamelCase							, stream=lowerCamelCase   ).raw   )

   return image






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
   a__	:						List[str]      		=  torch.load(lowerCamelCase							, map_location="cpu"   )["model"]
   a__	:						str      		=  EfficientFormerConfig.from_json_file(lowerCamelCase   )
   a__	:						int      		=  EfficientFormerForImageClassificationWithTeacher(lowerCamelCase   )
   a__	:						Optional[Any]      		=  "_".join(checkpoint_path.split("/"   )[-1].split("."   )[0].split("_"   )[:-1]   )

   a__	:						Tuple      		=  config.depths[-1] - config.num_metaad_blocks + 1
   a__	:						Union[str, Any]      		=  convert_torch_checkpoint(lowerCamelCase							, lowerCamelCase   )

   model.load_state_dict(lowerCamelCase   )
   model.eval()

   a__	:						Dict      		=  {
       "bilinear": PILImageResampling.BILINEAR,
       "bicubic": PILImageResampling.BICUBIC,
       "nearest": PILImageResampling.NEAREST,
   }

   # prepare image
   a__	:						str      		=  prepare_img()
   a__	:						Dict      		=  256
   a__	:						Union[str, Any]      		=  224
   a__	:						List[str]      		=  EfficientFormerImageProcessor(
       size={"shortest_edge": image_size}							, crop_size={"height": crop_size, "width": crop_size}							, resample=pillow_resamplings["bicubic"]							, )
   a__	:						List[str]      		=  processor(images=lowerCamelCase							, return_tensors="pt"   ).pixel_values

   # original processing pipeline
   a__	:						List[str]      		=  Compose(
       [
           Resize(lowerCamelCase							, interpolation=pillow_resamplings["bicubic"]   ),
           CenterCrop(lowerCamelCase   ),
           ToTensor(),
           Normalize(lowerCamelCase							, lowerCamelCase   ),
       ]   )
   a__	:						List[Any]      		=  image_transforms(lowerCamelCase   ).unsqueeze(0   )

   assert torch.allclose(lowerCamelCase							, lowerCamelCase   )

   a__	:						Optional[int]      		=  model(lowerCamelCase   )
   a__	:						Any      		=  outputs.logits

   a__	:						Optional[Any]      		=  (1, 1000)

   if "l1" in model_name:
      a__	:						Tuple      		=  torch.Tensor(
          [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]   )
      assert torch.allclose(logits[0, :10]							, lowerCamelCase							, atol=1E-3   )
      assert logits.shape == expected_shape
   elif "l3" in model_name:
      a__	:						int      		=  torch.Tensor(
          [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]   )
      assert torch.allclose(logits[0, :10]							, lowerCamelCase							, atol=1E-3   )
      assert logits.shape == expected_shape
   elif "l7" in model_name:
      a__	:						Optional[Any]      		=  torch.Tensor(
          [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]   )
      assert logits.shape == expected_shape
   else:
      raise ValueError(
          F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""   )

   # Save Checkpoints
   Path(lowerCamelCase   ).mkdir(exist_ok=lowerCamelCase   )
   model.save_pretrained(lowerCamelCase   )
   print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""   )
   processor.save_pretrained(lowerCamelCase   )
   print(F"""Processor successfuly saved at {pytorch_dump_path}"""   )

   if push_to_hub:
      print("Pushing model to the hub..."   )

      model.push_to_hub(
          repo_id=F"""Bearnardd/{pytorch_dump_path}"""							, commit_message="Add model"							, use_temp_dir=lowerCamelCase							, )
      processor.push_to_hub(
          repo_id=F"""Bearnardd/{pytorch_dump_path}"""							, commit_message="Add image processor"							, use_temp_dir=lowerCamelCase							, )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__       :			str			      =						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--pytorch_model_path""",
      default=None,
      type=str,
      required=True,
      help="""Path to EfficientFormer pytorch checkpoint.""",
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help="""The json file for EfficientFormer model config.""",
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )

  parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
  parser.add_argument(
      """--no-push_to_hub""",
      dest="""push_to_hub""",
      action="""store_false""",
      help="""Do not push model and image processor to the hub""",
  )
  parser.set_defaults(push_to_hub=True)

  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						parser.parse_args()
  convert_efficientformer_checkpoint(
      checkpoint_path=args.pytorch_model_path,
      efficientformer_config_file=args.config_file,
      pytorch_dump_path=args.pytorch_dump_path,
      push_to_hub=args.push_to_hub,
  )

