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 					A							:


   def __init__(self					:						List[Any]	,  __UpperCAmelCase					:						Optional[int]	,  __UpperCAmelCase					:						List[Any]=1_3	,  __UpperCAmelCase					:						Tuple=3_0	,  __UpperCAmelCase					:						Dict=2	,  __UpperCAmelCase					:						str=3	,  __UpperCAmelCase					:						List[str]=True	,  __UpperCAmelCase					:						str=True	,  __UpperCAmelCase					:						str=3_2	,  __UpperCAmelCase					:						List[Any]=2	,  __UpperCAmelCase					:						List[str]=4	,  __UpperCAmelCase					:						List[str]=3_7	,  __UpperCAmelCase					:						List[Any]="gelu"	,  __UpperCAmelCase					:						int=0.1	,  __UpperCAmelCase					:						Dict=0.1	,  __UpperCAmelCase					:						Optional[int]=1_0	,  __UpperCAmelCase					:						Tuple=0.02	,  __UpperCAmelCase					:						Tuple=3	,  __UpperCAmelCase					:						List[Any]=0.6	,  __UpperCAmelCase					:						Any=None	,  )						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  parent
         UpperCAmelCase__								=  batch_size
         UpperCAmelCase__								=  image_size
         UpperCAmelCase__								=  patch_size
         UpperCAmelCase__								=  num_channels
         UpperCAmelCase__								=  is_training
         UpperCAmelCase__								=  use_labels
         UpperCAmelCase__								=  hidden_size
         UpperCAmelCase__								=  num_hidden_layers
         UpperCAmelCase__								=  num_attention_heads
         UpperCAmelCase__								=  intermediate_size
         UpperCAmelCase__								=  hidden_act
         UpperCAmelCase__								=  hidden_dropout_prob
         UpperCAmelCase__								=  attention_probs_dropout_prob
         UpperCAmelCase__								=  type_sequence_label_size
         UpperCAmelCase__								=  initializer_range
         UpperCAmelCase__								=  mask_ratio
         UpperCAmelCase__								=  scope

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


   def       lowercase_				(self					:						List[Any]		)						->    List[str]:
         """simple docstring"""



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

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

         UpperCAmelCase__								=  self.get_config()

         return config, pixel_values, labels


   def       lowercase_				(self					:						Any		)						->    Union[str, Any]:
         """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=__UpperCAmelCase	,  initializer_range=self.initializer_range	,  mask_ratio=self.mask_ratio	,  )


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						Optional[Any]	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						Union[str, Any]		)						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  TFViTMAEModel(config=__UpperCAmelCase		)
         UpperCAmelCase__								=  model(__UpperCAmelCase	,  training=__UpperCAmelCase		)
         self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, self.seq_length, self.hidden_size)		)


   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						Optional[Any]	,  __UpperCAmelCase					:						Optional[int]		)						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  TFViTMAEForPreTraining(__UpperCAmelCase		)
         UpperCAmelCase__								=  model(__UpperCAmelCase	,  training=__UpperCAmelCase		)
         # expected sequence length = num_patches
         UpperCAmelCase__								=  (self.image_size // self.patch_size) ** 2
         UpperCAmelCase__								=  self.patch_size**2 * self.num_channels
         self.parent.assertEqual(result.logits.shape	,  (self.batch_size, num_patches, expected_num_channels)		)

         # test greyscale images
         UpperCAmelCase__								=  1
         UpperCAmelCase__								=  TFViTMAEForPreTraining(__UpperCAmelCase		)

         UpperCAmelCase__								=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
         UpperCAmelCase__								=  model(__UpperCAmelCase	,  training=__UpperCAmelCase		)
         UpperCAmelCase__								=  self.patch_size**2
         self.parent.assertEqual(result.logits.shape	,  (self.batch_size, num_patches, expected_num_channels)		)





   def       lowercase_				(self					:						List[Any]		)						->    int:
         """simple docstring"""



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





@require_tf
class 					A							(    UpperCAmelCase_		,    UpperCAmelCase_		,    unittest.TestCase					):
   __UpperCAmelCase			:			Union[str, Any]              =      (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
   __UpperCAmelCase			:			List[Any]              =      {'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

   __UpperCAmelCase			:			Optional[Any]              =      False
   __UpperCAmelCase			:			Union[str, Any]              =      False
   __UpperCAmelCase			:			List[str]              =      False
   __UpperCAmelCase			:			List[Any]              =      False


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



         UpperCAmelCase__								=  TFViTMAEModelTester(self		)
         UpperCAmelCase__								=  ConfigTester(self	,  config_class=__UpperCAmelCase	,  has_text_modality=__UpperCAmelCase	,  hidden_size=3_7		)


   def       lowercase_				(self					:						List[Any]		)						->    int:
         """simple docstring"""



         self.config_tester.run_common_tests()


   @unittest.skip(reason="ViTMAE does not use inputs_embeds"		)
   def       lowercase_				(self					:						List[str]		)						->    Union[str, Any]:
         """simple docstring"""



         pass


   def       lowercase_				(self					:						List[str]		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__		,				UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs_for_common()

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


   def       lowercase_				(self					:						List[str]		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__		,				UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs_for_common()

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

               UpperCAmelCase__								=  ["pixel_values"]
               self.assertListEqual(arg_names[:1]	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						List[Any]		)						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__UpperCAmelCase		)


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



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase		)


   def       lowercase_				(self					:						Optional[Any]		)						->    List[str]:
         """simple docstring"""



         np.random.seed(2		)

         UpperCAmelCase__		,				UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs_for_common()

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

         for model_class in self.all_model_classes:
               UpperCAmelCase__								=  model_class(__UpperCAmelCase		)
               UpperCAmelCase__								=  self._prepare_for_class(__UpperCAmelCase	,  __UpperCAmelCase		)

               UpperCAmelCase__								=  model(__UpperCAmelCase	,  noise=__UpperCAmelCase		)

               UpperCAmelCase__								=  copy.deepcopy(self._prepare_for_class(__UpperCAmelCase	,  __UpperCAmelCase		)		)
               UpperCAmelCase__								=  model(**__UpperCAmelCase	,  noise=__UpperCAmelCase		)
               UpperCAmelCase__								=  outputs_dict[0].numpy()
               UpperCAmelCase__								=  outputs_keywords[0].numpy()

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


   def       lowercase_				(self					:						str		)						->    Any:
         """simple docstring"""



         np.random.seed(2		)

         UpperCAmelCase__		,				UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs_for_common()

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

         def prepare_numpy_arrays(__UpperCAmelCase					:						Union[str, Any]		):
               UpperCAmelCase__								=  {}
               for k, v in inputs_dict.items():
                     if tf.is_tensor(__UpperCAmelCase		):
                           UpperCAmelCase__								=  v.numpy()
                     else:
                           UpperCAmelCase__								=  np.array(__UpperCAmelCase		)

               return inputs_np_dict

         for model_class in self.all_model_classes:
               UpperCAmelCase__								=  model_class(__UpperCAmelCase		)

               UpperCAmelCase__								=  self._prepare_for_class(__UpperCAmelCase	,  __UpperCAmelCase		)
               UpperCAmelCase__								=  prepare_numpy_arrays(__UpperCAmelCase		)

               UpperCAmelCase__								=  model(__UpperCAmelCase	,  noise=__UpperCAmelCase		)
               UpperCAmelCase__								=  model(**__UpperCAmelCase	,  noise=__UpperCAmelCase		)
               self.assert_outputs_same(__UpperCAmelCase	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						List[Any]	,  __UpperCAmelCase					:						List[str]		)						->    Dict:
         """simple docstring"""



         np.random.seed(2		)

         UpperCAmelCase__								=  int((tf_model.config.image_size // tf_model.config.patch_size) ** 2		)
         UpperCAmelCase__								=  np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
         UpperCAmelCase__								=  tf.constant(__UpperCAmelCase		)

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

         super().check_pt_tf_models(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						Optional[Any]		)						->    Union[str, Any]:
         """simple docstring"""



         np.random.seed(2		)

         UpperCAmelCase__		,				UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs_for_common()

         UpperCAmelCase__								=  {
             module_member
             for model_class in self.all_model_classes
             for module in (import_module(model_class.__module__		),)
             for module_member_name in dir(__UpperCAmelCase		)
             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(__UpperCAmelCase	,  __UpperCAmelCase		),)
             if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		)
             and tf.keras.layers.Layer in module_member.__bases__
             and getattr(__UpperCAmelCase	,  "_keras_serializable"	,  __UpperCAmelCase		)
         }

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

         for main_layer_class in tf_main_layer_classes:
               UpperCAmelCase__								=  main_layer_class(__UpperCAmelCase		)

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

               UpperCAmelCase__								=  tf.keras.Model(__UpperCAmelCase	,  outputs=main_layer(__UpperCAmelCase		)		)
               UpperCAmelCase__								=  model(__UpperCAmelCase		)

               with tempfile.TemporaryDirectory() as tmpdirname:
                     UpperCAmelCase__								=  os.path.join(__UpperCAmelCase	,  "keras_model.h5"		)
                     model.save(__UpperCAmelCase		)
                     UpperCAmelCase__								=  tf.keras.models.load_model(
                         __UpperCAmelCase	,  custom_objects={main_layer_class.__name__: main_layer_class}		)
                     assert isinstance(__UpperCAmelCase	,  tf.keras.Model		)
                     UpperCAmelCase__								=  model(__UpperCAmelCase		)
                     self.assert_outputs_same(__UpperCAmelCase	,  __UpperCAmelCase		)


   @slow
   def       lowercase_				(self					:						Union[str, Any]		)						->    Dict:
         """simple docstring"""



         np.random.seed(2		)

         UpperCAmelCase__		,				UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs_for_common()

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

         for model_class in self.all_model_classes:
               UpperCAmelCase__								=  model_class(__UpperCAmelCase		)
               UpperCAmelCase__								=  self._prepare_for_class(__UpperCAmelCase	,  __UpperCAmelCase		)
               UpperCAmelCase__								=  model(__UpperCAmelCase	,  noise=__UpperCAmelCase		)

               if model_class.__name__ == "TFViTMAEModel":
                     UpperCAmelCase__								=  outputs.last_hidden_state.numpy()
                     UpperCAmelCase__								=  0
               else:
                     UpperCAmelCase__								=  outputs.logits.numpy()
                     UpperCAmelCase__								=  0

               with tempfile.TemporaryDirectory() as tmpdirname:
                     model.save_pretrained(__UpperCAmelCase	,  saved_model=__UpperCAmelCase		)
                     UpperCAmelCase__								=  model_class.from_pretrained(__UpperCAmelCase		)
                     UpperCAmelCase__								=  model(__UpperCAmelCase	,  noise=__UpperCAmelCase		)

                     if model_class.__name__ == "TFViTMAEModel":
                           UpperCAmelCase__								=  after_outputs["last_hidden_state"].numpy()
                           UpperCAmelCase__								=  0
                     else:
                           UpperCAmelCase__								=  after_outputs["logits"].numpy()
                           UpperCAmelCase__								=  0

                     UpperCAmelCase__								=  np.amax(np.abs(out_a - out_a		)		)
                     self.assertLessEqual(__UpperCAmelCase	,  1E-5		)


   def       lowercase_				(self					:						List[Any]		)						->    Any:
         """simple docstring"""



         np.random.seed(2		)

         UpperCAmelCase__		,				UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs_for_common()

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

         for model_class in self.all_model_classes:
               UpperCAmelCase__								=  model_class(__UpperCAmelCase		)
               UpperCAmelCase__								=  self._prepare_for_class(__UpperCAmelCase	,  __UpperCAmelCase		)

               UpperCAmelCase__								=  model(__UpperCAmelCase	,  noise=__UpperCAmelCase		)
               UpperCAmelCase__								=  model.get_config()
               # make sure that returned config is jsonifiable, which is required by keras
               json.dumps(__UpperCAmelCase		)
               UpperCAmelCase__								=  model_class.from_config(model.get_config()		)
               # make sure it also accepts a normal config
               UpperCAmelCase__								=  model_class.from_config(model.config		)
               UpperCAmelCase__								=  new_model(__UpperCAmelCase		)  # Build model
               new_model.set_weights(model.get_weights()		)
               UpperCAmelCase__								=  new_model(__UpperCAmelCase	,  noise=__UpperCAmelCase		)

               self.assert_outputs_same(__UpperCAmelCase	,  __UpperCAmelCase		)


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



         pass


   @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"		)
   def       lowercase_				(self					:						Tuple		)						->    Optional[Any]:
         """simple docstring"""



         pass





   @slow
   def       lowercase_				(self					:						List[Any]		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"		)
         self.assertIsNotNone(__UpperCAmelCase		)




def        lowerCAmelCase_			(					)					->    List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
      return image







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


   @cached_property
   def       lowercase_				(self					:						List[Any]		)						->    Union[str, Any]:
         """simple docstring"""



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





   @slow
   def       lowercase_				(self					:						List[Any]		)						->    str:
         """simple docstring"""



         np.random.seed(2		)

         UpperCAmelCase__								=  TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"		)

         UpperCAmelCase__								=  self.default_image_processor
         UpperCAmelCase__								=  prepare_img()
         UpperCAmelCase__								=  image_processor(images=__UpperCAmelCase	,  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)
         UpperCAmelCase__								=  ViTMAEConfig()
         UpperCAmelCase__								=  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2		)
         UpperCAmelCase__								=  np.random.uniform(size=(1, num_patches)		)

         # forward pass
         UpperCAmelCase__								=  model(**__UpperCAmelCase	,  noise=__UpperCAmelCase		)

         # verify the logits
         UpperCAmelCase__								=  tf.convert_to_tensor([1, 1_9_6, 7_6_8]		)
         self.assertEqual(outputs.logits.shape	,  __UpperCAmelCase		)

         UpperCAmelCase__								=  tf.convert_to_tensor(
             [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]		)

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




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()
UpperCamelCase__    							= logging.get_logger(__name__)




def        lowerCAmelCase_			(					__A,  __A=False							)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  []
      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"
            UpperCAmelCase__								=  [(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        lowerCAmelCase_			(					__A,  __A,  __A=False							)					->    Tuple:







      '''simple docstring'''


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




def        lowerCAmelCase_			(					__A,  __A,  __A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  dct.pop(__A							)
      UpperCAmelCase__								=  val




def        lowerCAmelCase_			(					)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  "http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase__								=  Image.open(requests.get(__A,  stream=__A							).raw							)
      return im




@torch.no_grad()
def        lowerCAmelCase_			(					__A,  __A							)					->    Optional[Any]:







      '''simple docstring'''


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

      # load original model from timm
      UpperCAmelCase__								=  timm.create_model(__A,  pretrained=__A							)
      timm_model.eval()

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

      # load HuggingFace model
      UpperCAmelCase__								=  DeiTForImageClassificationWithTeacher(__A							).eval()
      model.load_state_dict(__A							)

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

      UpperCAmelCase__								=  timm_model(__A							)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(__A,  outputs.logits,  atol=1e-3							)

      Path(__A							).mkdir(exist_ok=__A							)
      print(f"""Saving model {deit_name} to {pytorch_dump_folder_path}"""							)
      model.save_pretrained(__A							)
      print(f"""Saving image processor to {pytorch_dump_folder_path}"""							)
      image_processor.save_pretrained(__A							)


if __name__ == "__main__":
      UpperCamelCase__    							= 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.'
      )

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



