




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()
lowerCamelCase							:						str					       =  logging.get_logger(__name__)

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			str      ,				lowercase      :			str=False	):


      '''simple docstring'''



      lowerCamelCase_							=       []
      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"
            lowerCamelCase_							=       [(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      _SCREAMING_SNAKE_CASE     (				lowercase      :			Dict      ,				lowercase      :			Tuple      ,				lowercase      :			Any=False	):


      '''simple docstring'''



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

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			List[str]      ,				lowercase      :			Any      ,				lowercase      :			Union[str, Any]	):


      '''simple docstring'''



      lowerCamelCase_							=       dct.pop(lowercase	)
      lowerCamelCase_							=       val

def      _SCREAMING_SNAKE_CASE     (				):


      '''simple docstring'''



      lowerCamelCase_							=       'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowerCamelCase_							=       Image.open(requests.get(lowercase      ,				stream=lowercase	).raw	)
      return im

@torch.no_grad()
def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Union[str, Any]      ,				lowercase      :			List[Any]	):


      '''simple docstring'''



      lowerCamelCase_							=       DeiTConfig()
      # all deit models have fine-tuned heads
      lowerCamelCase_							=       False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      lowerCamelCase_							=       10_00
      lowerCamelCase_							=       'huggingface/label-files'
      lowerCamelCase_							=       'imagenet-1k-id2label.json'
      lowerCamelCase_							=       json.load(open(hf_hub_download(lowercase      ,				lowercase      ,				repo_type='dataset'	)      ,				'r'	)	)
      lowerCamelCase_							=       {int(lowercase	): v for k, v in idalabel.items()}
      lowerCamelCase_							=       idalabel
      lowerCamelCase_							=       {v: k for k, v in idalabel.items()}
      lowerCamelCase_							=       int(deit_name[-6:-4]	)
      lowerCamelCase_							=       int(deit_name[-3:]	)
      # size of the architecture
      if deit_name[9:].startswith('tiny'	):
            lowerCamelCase_							=       1_92
            lowerCamelCase_							=       7_68
            lowerCamelCase_							=       12
            lowerCamelCase_							=       3
      elif deit_name[9:].startswith('small'	):
            lowerCamelCase_							=       3_84
            lowerCamelCase_							=       15_36
            lowerCamelCase_							=       12
            lowerCamelCase_							=       6
      if deit_name[9:].startswith('base'	):
            pass
      elif deit_name[4:].startswith('large'	):
            lowerCamelCase_							=       10_24
            lowerCamelCase_							=       40_96
            lowerCamelCase_							=       24
            lowerCamelCase_							=       16

      # load original model from timm
      lowerCamelCase_							=       timm.create_model(lowercase      ,				pretrained=lowercase	)
      timm_model.eval()

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

      # load HuggingFace model
      lowerCamelCase_							=       DeiTForImageClassificationWithTeacher(lowercase	).eval()
      model.load_state_dict(lowercase	)

      # Check outputs on an image, prepared by DeiTImageProcessor
      lowerCamelCase_							=       int(
          (2_56 / 2_24) * config.image_size	)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      lowerCamelCase_							=       DeiTImageProcessor(size=lowercase      ,				crop_size=config.image_size	)
      lowerCamelCase_							=       image_processor(images=prepare_img()      ,				return_tensors='pt'	)
      lowerCamelCase_							=       encoding['pixel_values']
      lowerCamelCase_							=       model(lowercase	)

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

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


if __name__ == "__main__":
 lowerCamelCase							:						str					       =  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."
 )

 lowerCamelCase							:						List[Any]					       =  parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)





'''simple docstring'''

import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
     import jax

     from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class    lowerCAmelCase       (     unittest.TestCase  ):


 def __init__(    self  : Optional[Any]			,							a__  : List[Any]			,							a__  : Any=13			,							a__  : Optional[int]=30			,							a__  : List[Any]=2			,							a__  : Union[str, Any]=3			,							a__  : Tuple=True			,							a__  : Tuple=True			,							a__  : List[str]=32			,							a__  : Tuple=5			,							a__  : List[str]=4			,							a__  : str=37			,							a__  : Dict="gelu"			,							a__  : Union[str, Any]=0.1			,							a__  : Union[str, Any]=0.1			,							a__  : List[Any]=10			,							a__  : List[Any]=0.02			,							):
  '''simple docstring'''

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

  # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
  lowerCAmelCase__     :   Tuple     		=	(image_size // patch_size) ** 2
  lowerCAmelCase__     :   Dict     		=	num_patches + 1


 def    _A	(    self  : int    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

  lowerCAmelCase__     :   Any     		=	ViTConfig(
      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=a__			,							initializer_range=self.initializer_range			,							)

  return config, pixel_values


 def    _A	(    self  : Optional[Any]			,							a__  : Optional[int]			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	FlaxViTModel(config=a__    )
  lowerCAmelCase__     :   Any     		=	model(a__    )
  # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
  lowerCAmelCase__     :   List[str]     		=	(self.image_size, self.image_size)
  lowerCAmelCase__     :   List[str]     		=	(self.patch_size, self.patch_size)
  lowerCAmelCase__     :   int     		=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
  self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, num_patches + 1, self.hidden_size)    )


 def    _A	(    self  : str			,							a__  : Optional[int]			,							a__  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[Any]     		=	self.type_sequence_label_size
  lowerCAmelCase__     :   Any     		=	FlaxViTForImageClassification(config=a__    )
  lowerCAmelCase__     :   Dict     		=	model(a__    )
  self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)    )

  # test greyscale images
  lowerCAmelCase__     :   Tuple     		=	1
  lowerCAmelCase__     :   List[Any]     		=	FlaxViTForImageClassification(a__    )

  lowerCAmelCase__     :   Union[str, Any]     		=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
  lowerCAmelCase__     :   int     		=	model(a__    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.prepare_config_and_inputs()
  (
      (
      lowerCAmelCase__
  )		,     (
      lowerCAmelCase__
  )		,     
  )     :   Tuple     		=	config_and_inputs
  lowerCAmelCase__     :   Optional[Any]     		=	{"pixel_values": pixel_values}
  return config, inputs_dict




@require_flax
class    lowerCAmelCase       (     UpperCamelCase_    ,  unittest.TestCase  ):
 A_				:				Union[str, Any]	=			(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[Any]     		=	FlaxViTModelTester(self    )
  lowerCAmelCase__     :   Union[str, Any]     		=	ConfigTester(self			,							config_class=a__			,							has_text_modality=a__			,							hidden_size=37    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  self.config_tester.run_common_tests()


 def    _A	(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*a__    )


 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*a__    )


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

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

  for model_class in self.all_model_classes:
   lowerCAmelCase__     :   Dict     		=	model_class(a__    )
   lowerCAmelCase__     :   str     		=	inspect.signature(model.__call__    )
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   lowerCAmelCase__     :   List[Any]     		=	[*signature.parameters.keys()]

   lowerCAmelCase__     :   List[str]     		=	["pixel_values"]
   self.assertListEqual(arg_names[:1]			,							a__    )


 def    _A	(    self  : str    ):
  '''simple docstring'''

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

  for model_class in self.all_model_classes:
   with self.subTest(model_class.__name__    ):
    lowerCAmelCase__     :   List[Any]     		=	self._prepare_for_class(a__			,							a__    )
    lowerCAmelCase__     :   Optional[int]     		=	model_class(a__    )

    @jax.jit
    def model_jitted(a__  : Optional[int]			,							**a__  : Optional[Any]    ):
     return model(pixel_values=a__			,							**a__    )

    with self.subTest("JIT Enabled"    ):
     lowerCAmelCase__     :   Any     		=	model_jitted(**a__    ).to_tuple()

    with self.subTest("JIT Disabled"    ):
     with jax.disable_jit():
      lowerCAmelCase__     :   str     		=	model_jitted(**a__    ).to_tuple()

    self.assertEqual(len(a__    )			,							len(a__    )    )
    for jitted_output, output in zip(a__			,							a__    ):
     self.assertEqual(jitted_output.shape			,							output.shape    )


 @slow
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  for model_class_name in self.all_model_classes:
   lowerCAmelCase__     :   Optional[Any]     		=	model_class_name.from_pretrained("google/vit-base-patch16-224"    )
   lowerCAmelCase__     :   Tuple     		=	model(np.ones((1, 3, 224, 224)    )    )
   self.assertIsNotNone(a__    )





