


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		      =							logging.get_logger(__name__)




def        snake_case_					(  snake_case      ,							snake_case=False					)  ->    Tuple:
       lowercase__:       Optional[Any]        =       []
       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"
              lowercase__:       Tuple        =       [(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        snake_case_					(  snake_case      ,							snake_case      ,							snake_case=False					)  ->    str:



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




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    List[Any]:
       lowercase__:       Any        =       dct.pop(A__					)
       lowercase__:       Optional[Any]        =       val




def        snake_case_					(  )  ->    str:
       lowercase__:       List[Any]        =       'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowercase__:       Optional[Any]        =       Image.open(requests.get(A__      ,							stream=A__					).raw					)
       return im




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

       # load original model from timm
       lowercase__:       int        =       timm.create_model(A__      ,							pretrained=A__					)
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       lowercase__:       str        =       timm_model.state_dict()
       lowercase__:       Optional[int]        =       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
       lowercase__:       int        =       DeiTForImageClassificationWithTeacher(A__					).eval()
       model.load_state_dict(A__					)

       # Check outputs on an image, prepared by DeiTImageProcessor
       lowercase__:       Tuple        =       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
       lowercase__:       Tuple        =       DeiTImageProcessor(size=A__      ,							crop_size=config.image_size					)
       lowercase__:       str        =       image_processor(images=prepare_img()      ,							return_tensors='pt'					)
       lowercase__:       str        =       encoding['pixel_values']
       lowercase__:       Tuple        =       model(A__					)

       lowercase__:       Optional[Any]        =       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__":
 __lowerCAmelCase		      =							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		      =							parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
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 (
     TFDeiTForImageClassification,
     TFDeiTForImageClassificationWithTeacher,
     TFDeiTForMaskedImageModeling,
     TFDeiTModel,
 )
 from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import DeiTImageProcessor





class   __a      :
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=13    ,			lowerCAmelCase__=30    ,			lowerCAmelCase__=2    ,			lowerCAmelCase__=3    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=32    ,			lowerCAmelCase__=2    ,			lowerCAmelCase__=4    ,			lowerCAmelCase__=37    ,			lowerCAmelCase__="gelu"    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=10    ,			lowerCAmelCase__=0.0_2    ,			lowerCAmelCase__=3    ,			lowerCAmelCase__=None    ,			lowerCAmelCase__=2    ,			) ->    List[str]:




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

        # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
        lowercase__:       List[str]        =       (image_size // patch_size) ** 2
        lowercase__:       Tuple        =       num_patches + 2
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       List[Any]        =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

        lowercase__:       List[str]        =       self.get_config()

        return config, pixel_values, labels
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        return DeiTConfig(
            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    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    int:




        '''simple docstring'''
        lowercase__:       List[str]        =       TFDeiTModel(config=lowerCAmelCase__			)
        lowercase__:       Any        =       model(lowerCAmelCase__			)
        self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, self.seq_length, self.hidden_size)			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Any        =       TFDeiTForMaskedImageModeling(config=lowerCAmelCase__			)
        lowercase__:       int        =       model(lowerCAmelCase__			)
        self.parent.assertEqual(
            result.reconstruction.shape    ,			(self.batch_size, self.num_channels, self.image_size, self.image_size)			)

        # test greyscale images
        lowercase__:       Tuple        =       1
        lowercase__:       Optional[int]        =       TFDeiTForMaskedImageModeling(lowerCAmelCase__			)

        lowercase__:       int        =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase__:       str        =       model(lowerCAmelCase__			)
        self.parent.assertEqual(result.reconstruction.shape    ,			(self.batch_size, 1, self.image_size, self.image_size)			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Any        =       self.type_sequence_label_size
        lowercase__:       Optional[Any]        =       TFDeiTForImageClassification(lowerCAmelCase__			)
        lowercase__:       List[Any]        =       model(lowerCAmelCase__    ,			labels=lowerCAmelCase__			)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size)			)

        # test greyscale images
        lowercase__:       Optional[Any]        =       1
        lowercase__:       Union[str, Any]        =       TFDeiTForImageClassification(lowerCAmelCase__			)

        lowercase__:       List[str]        =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase__:       str        =       model(lowerCAmelCase__    ,			labels=lowerCAmelCase__			)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Dict        =       self.prepare_config_and_inputs()
        lowercase__      ,						lowercase__      ,						lowercase__:       Dict        =       config_and_inputs
        lowercase__:       Optional[Any]        =       {'pixel_values': pixel_values}
        return config, inputs_dict





@require_tf
class   __a      (   __UpperCamelCase	,	__UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Union[str, Any]       					=			(
     (
         TFDeiTModel,
         TFDeiTForImageClassification,
         TFDeiTForImageClassificationWithTeacher,
         TFDeiTForMaskedImageModeling,
     )
     if is_tf_available()
     else ()
 )
 __lowercase			:							Union[str, Any]       					=			(
     {
         'feature-extraction': TFDeiTModel,
         'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
     }
     if is_tf_available()
     else {}
 )

 __lowercase			:							Union[str, Any]       					=			False
 __lowercase			:							Tuple       					=			False
 __lowercase			:							int       					=			False
 __lowercase			:							Optional[int]       					=			False
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       List[Any]        =       TFDeiTModelTester(self			)
        lowercase__:       int        =       ConfigTester(self    ,			config_class=lowerCAmelCase__    ,			has_text_modality=lowerCAmelCase__    ,			hidden_size=37			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        self.config_tester.run_common_tests()
 @unittest.skip(reason='DeiT does not use inputs_embeds'			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__      ,						lowercase__:       int        =       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowercase__:       Tuple        =       model_class(lowerCAmelCase__			)
               self.assertIsInstance(model.get_input_embeddings()    ,			(tf.keras.layers.Layer)			)
               lowercase__:       Union[str, Any]        =       model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCAmelCase__    ,			tf.keras.layers.Dense			)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__      ,						lowercase__:       Union[str, Any]        =       self.model_tester.prepare_config_and_inputs_for_common()

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

               lowercase__:       Any        =       ['pixel_values']
               self.assertListEqual(arg_names[:1]    ,			lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Dict        =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       Tuple        =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       List[Any]        =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__=False			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       super()._prepare_for_class(lowerCAmelCase__    ,			lowerCAmelCase__    ,			return_labels=lowerCAmelCase__			)

        if return_labels:
               if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call			).parameters:
                      del inputs_dict["labels"]

        return inputs_dict
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__:       Union[str, Any]        =       TFDeiTModel.from_pretrained(lowerCAmelCase__			)
               self.assertIsNotNone(lowerCAmelCase__			)





def        snake_case_					(  )  ->    Tuple:
       lowercase__:       Any        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
       return image





@require_tf
@require_vision
class   __a      (   unittest.TestCase				):
 @cached_property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        return (
            DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'			)
            if is_vision_available()
            else None
        )
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       str        =       TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'			)

        lowercase__:       str        =       self.default_image_processor
        lowercase__:       Dict        =       prepare_img()
        lowercase__:       Optional[int]        =       image_processor(images=lowerCAmelCase__    ,			return_tensors='tf'			)

        # forward pass
        lowercase__:       int        =       model(**lowerCAmelCase__			)

        # verify the logits
        lowercase__:       Tuple        =       tf.TensorShape((1, 1_000)			)
        self.assertEqual(outputs.logits.shape    ,			lowerCAmelCase__			)

        lowercase__:       List[Any]        =       tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]			)

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


