"""simple docstring"""







import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import (
           FocalNetBackbone,
           FocalNetForImageClassification,
           FocalNetForMaskedImageModeling,
           FocalNetModel,
       )
       from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor







class        UpperCamelCase		:
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase=13     ,__UpperCamelCase=32     ,__UpperCamelCase=2     ,__UpperCamelCase=3     ,__UpperCamelCase=16     ,__UpperCamelCase=[32, 64, 128]     ,__UpperCamelCase=[1, 2, 1]     ,__UpperCamelCase=[2, 2, 4]     ,__UpperCamelCase=2     ,__UpperCamelCase=2.0     ,__UpperCamelCase=True     ,__UpperCamelCase=0.0     ,__UpperCamelCase=0.0     ,__UpperCamelCase=0.1     ,__UpperCamelCase="gelu"     ,__UpperCamelCase=False     ,__UpperCamelCase=True     ,__UpperCamelCase=0.02     ,__UpperCamelCase=1e-5     ,__UpperCamelCase=True     ,__UpperCamelCase=None     ,__UpperCamelCase=True     ,__UpperCamelCase=10     ,__UpperCamelCase=8     ,__UpperCamelCase=["stage1", "stage2"]     ,__UpperCamelCase=[1, 2]     ,)	->      Any:


        '''simple docstring'''


        lowercase_	:				Any													=	parent
        lowercase_	:				Dict													=	batch_size
        lowercase_	:				Tuple													=	image_size
        lowercase_	:				Union[str, Any]													=	patch_size
        lowercase_	:				str													=	num_channels
        lowercase_	:				Optional[Any]													=	embed_dim
        lowercase_	:				Optional[int]													=	hidden_sizes
        lowercase_	:				Union[str, Any]													=	depths
        lowercase_	:				List[Any]													=	num_heads
        lowercase_	:				Union[str, Any]													=	window_size
        lowercase_	:				Optional[Any]													=	mlp_ratio
        lowercase_	:				Optional[int]													=	qkv_bias
        lowercase_	:				int													=	hidden_dropout_prob
        lowercase_	:				int													=	attention_probs_dropout_prob
        lowercase_	:				Optional[Any]													=	drop_path_rate
        lowercase_	:				int													=	hidden_act
        lowercase_	:				Dict													=	use_absolute_embeddings
        lowercase_	:				List[Any]													=	patch_norm
        lowercase_	:				Tuple													=	layer_norm_eps
        lowercase_	:				Tuple													=	initializer_range
        lowercase_	:				List[str]													=	is_training
        lowercase_	:				Optional[Any]													=	scope
        lowercase_	:				Any													=	use_labels
        lowercase_	:				Union[str, Any]													=	type_sequence_label_size
        lowercase_	:				Union[str, Any]													=	encoder_stride
        lowercase_	:				Tuple													=	out_features
        lowercase_	:				int													=	out_indices
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				List[str]													=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

        lowercase_	:				Dict													=	None
        if self.use_labels:
             lowercase_	:				Tuple													=	ids_tensor([self.batch_size]     ,self.type_sequence_label_size			)

        lowercase_	:				Tuple													=	self.get_config()

        return config, pixel_values, labels
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        return FocalNetConfig(
            image_size=self.image_size     ,patch_size=self.patch_size     ,num_channels=self.num_channels     ,embed_dim=self.embed_dim     ,hidden_sizes=self.hidden_sizes     ,depths=self.depths     ,num_heads=self.num_heads     ,window_size=self.window_size     ,mlp_ratio=self.mlp_ratio     ,qkv_bias=self.qkv_bias     ,hidden_dropout_prob=self.hidden_dropout_prob     ,attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,drop_path_rate=self.drop_path_rate     ,hidden_act=self.hidden_act     ,use_absolute_embeddings=self.use_absolute_embeddings     ,path_norm=self.patch_norm     ,layer_norm_eps=self.layer_norm_eps     ,initializer_range=self.initializer_range     ,encoder_stride=self.encoder_stride     ,out_features=self.out_features     ,out_indices=self.out_indices     ,)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	FocalNetModel(config=_UpperCamelCase			)
        model.to(_UpperCamelCase			)
        model.eval()
        lowercase_	:				List[Any]													=	model(_UpperCamelCase			)

        lowercase_	:				Optional[Any]													=	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths			) - 1))
        lowercase_	:				Dict													=	int(config.embed_dim * 2 ** (len(config.depths			) - 1)			)

        self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, expected_seq_len, expected_dim)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      int:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	FocalNetBackbone(config=_UpperCamelCase			)
        model.to(_UpperCamelCase			)
        model.eval()
        lowercase_	:				Any													=	model(_UpperCamelCase			)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps			)     ,len(config.out_features			)			)
        self.parent.assertListEqual(list(result.feature_maps[0].shape			)     ,[self.batch_size, self.image_size, 8, 8]			)

        # verify channels
        self.parent.assertEqual(len(model.channels			)     ,len(config.out_features			)			)
        self.parent.assertListEqual(model.channels     ,config.hidden_sizes[:-1]			)

        # verify backbone works with out_features=None
        lowercase_	:				str													=	None
        lowercase_	:				Union[str, Any]													=	FocalNetBackbone(config=_UpperCamelCase			)
        model.to(_UpperCamelCase			)
        model.eval()
        lowercase_	:				List[str]													=	model(_UpperCamelCase			)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps			)     ,1			)
        self.parent.assertListEqual(list(result.feature_maps[0].shape			)     ,[self.batch_size, self.image_size * 2, 4, 4]			)

        # verify channels
        self.parent.assertEqual(len(model.channels			)     ,1			)
        self.parent.assertListEqual(model.channels     ,[config.hidden_sizes[-1]]			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				Any													=	FocalNetForMaskedImageModeling(config=_UpperCamelCase			)
        model.to(_UpperCamelCase			)
        model.eval()
        lowercase_	:				str													=	model(_UpperCamelCase			)
        self.parent.assertEqual(
            result.reconstruction.shape     ,(self.batch_size, self.num_channels, self.image_size, self.image_size)			)

        # test greyscale images
        lowercase_	:				Dict													=	1
        lowercase_	:				List[Any]													=	FocalNetForMaskedImageModeling(_UpperCamelCase			)
        model.to(_UpperCamelCase			)
        model.eval()

        lowercase_	:				int													=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase_	:				Optional[Any]													=	model(_UpperCamelCase			)
        self.parent.assertEqual(result.reconstruction.shape     ,(self.batch_size, 1, self.image_size, self.image_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Any													=	self.type_sequence_label_size
        lowercase_	:				Union[str, Any]													=	FocalNetForImageClassification(_UpperCamelCase			)
        model.to(_UpperCamelCase			)
        model.eval()
        lowercase_	:				Tuple													=	model(_UpperCamelCase     ,labels=_UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)			)

        # test greyscale images
        lowercase_	:				Tuple													=	1
        lowercase_	:				Any													=	FocalNetForImageClassification(_UpperCamelCase			)
        model.to(_UpperCamelCase			)
        model.eval()

        lowercase_	:				int													=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase_	:				List[str]													=	model(_UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)			)







   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				str													=	self.prepare_config_and_inputs()

        lowercase_							,  lowercase_							,  lowercase_	:				List[str]													=	config_and_inputs
        lowercase_	:				str													=	{'pixel_values': pixel_values}
        return config, inputs_dict







@require_torch
class        UpperCamelCase		(			__a						,      __a						,      unittest.TestCase  ):
   lowercase							=  (
       (
           FocalNetModel,
           FocalNetForImageClassification,
           FocalNetForMaskedImageModeling,
           FocalNetBackbone,
       )
       if is_torch_available()
       else ()
   )
   lowercase							=  (
       {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
       if is_torch_available()
       else {}
   )
   lowercase							=  False

   lowercase							=  False
   lowercase							=  False
   lowercase							=  False
   lowercase							=  False
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	FocalNetModelTester(self			)
        lowercase_	:				Optional[int]													=	ConfigTester(self     ,config_class=_UpperCamelCase     ,embed_dim=37     ,has_text_modality=_UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        return
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				List[str]													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				str													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*_UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Dict													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase			)
   @unittest.skip(reason='FocalNet does not use inputs_embeds'			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        pass
   @unittest.skip(reason='FocalNet does not use feedforward chunking'			)
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        pass
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				str													=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:-1]:
             lowercase_	:				str													=	model_class(_UpperCamelCase			)
             self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module)			)
             lowercase_	:				Tuple													=	model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_UpperCamelCase     ,nn.Linear			)			)
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				Union[str, Any]													=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:-1]:
             lowercase_	:				List[str]													=	model_class(_UpperCamelCase			)
             lowercase_	:				Optional[int]													=	inspect.signature(model.forward			)
             # 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]     ,_UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				int													=	model_class(_UpperCamelCase			)
        model.to(_UpperCamelCase			)
        model.eval()

        with torch.no_grad():
             lowercase_	:				Union[str, Any]													=	model(**self._prepare_for_class(_UpperCamelCase     ,_UpperCamelCase			)			)

        lowercase_	:				Union[str, Any]													=	outputs.hidden_states

        lowercase_	:				Any													=	getattr(
            self.model_tester     ,'expected_num_hidden_layers'     ,len(self.model_tester.depths			) + 1			)
        self.assertEqual(len(_UpperCamelCase			)     ,_UpperCamelCase			)

        # FocalNet has a different seq_length
        lowercase_	:				Union[str, Any]													=	(
            config.patch_size
            if isinstance(config.patch_size     ,collections.abc.Iterable			)
            else (config.patch_size, config.patch_size)
        )

        lowercase_	:				str													=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]			)     ,[num_patches, self.model_tester.embed_dim]     ,)

        lowercase_	:				List[str]													=	outputs.reshaped_hidden_states
        self.assertEqual(len(_UpperCamelCase			)     ,_UpperCamelCase			)

        lowercase_							,  lowercase_							,  lowercase_							,  lowercase_	:				int													=	reshaped_hidden_states[0].shape
        lowercase_	:				Union[str, Any]													=	(
            reshaped_hidden_states[0].view(_UpperCamelCase     ,_UpperCamelCase     ,height * width			).permute(0     ,2     ,1			)
        )
        self.assertListEqual(
            list(reshaped_hidden_states.shape[-2:]			)     ,[num_patches, self.model_tester.embed_dim]     ,)
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


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

        lowercase_	:				Optional[int]													=	(
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size     ,collections.abc.Iterable			)
            else (self.model_tester.image_size, self.model_tester.image_size)
        )

        for model_class in self.all_model_classes[:-1]:
             lowercase_	:				Any													=	True
             self.check_hidden_states_output(_UpperCamelCase     ,_UpperCamelCase     ,_UpperCamelCase     ,_UpperCamelCase			)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowercase_	:				List[str]													=	True

             self.check_hidden_states_output(_UpperCamelCase     ,_UpperCamelCase     ,_UpperCamelCase     ,_UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				List[Any]													=	self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_	:				Optional[Any]													=	3

        lowercase_	:				str													=	(
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size     ,collections.abc.Iterable			)
            else (self.model_tester.image_size, self.model_tester.image_size)
        )
        lowercase_	:				Tuple													=	(
            config.patch_size
            if isinstance(config.patch_size     ,collections.abc.Iterable			)
            else (config.patch_size, config.patch_size)
        )

        lowercase_	:				Optional[int]													=	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        lowercase_	:				Optional[int]													=	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes[:-1]:
             lowercase_	:				Union[str, Any]													=	True
             self.check_hidden_states_output(_UpperCamelCase     ,_UpperCamelCase     ,_UpperCamelCase     ,(padded_height, padded_width)			)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowercase_	:				List[Any]													=	True
             self.check_hidden_states_output(_UpperCamelCase     ,_UpperCamelCase     ,_UpperCamelCase     ,(padded_height, padded_width)			)
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_	:				List[str]													=	FocalNetModel.from_pretrained(_UpperCamelCase			)
             self.assertIsNotNone(_UpperCamelCase			)







   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				Union[str, Any]													=	self.model_tester.prepare_config_and_inputs_for_common()

        lowercase_	:				Optional[int]													=	_config_zero_init(_UpperCamelCase			)
        for model_class in self.all_model_classes:
             lowercase_	:				List[str]													=	model_class(config=_UpperCamelCase			)
             for name, param in model.named_parameters():
                  if "embeddings" not in name and param.requires_grad:
                       self.assertIn(
                           ((param.data.mean() * 1e9).round() / 1e9).item()     ,[0.0, 1.0]     ,msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''     ,)







@require_vision
@require_torch
class        UpperCamelCase		(			unittest.TestCase  ):
   @cached_property
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        return AutoImageProcessor.from_pretrained('microsoft/focalnet-tiny'			) if is_vision_available() else None







   @slow
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	FocalNetForImageClassification.from_pretrained('microsoft/focalnet-tiny'			).to(_UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	self.default_image_processor

        lowercase_	:				int													=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
        lowercase_	:				List[str]													=	image_processor(images=_UpperCamelCase     ,return_tensors='pt'			).to(_UpperCamelCase			)

        # forward pass
        with torch.no_grad():
             lowercase_	:				Optional[Any]													=	model(**_UpperCamelCase			)

        # verify the logits
        lowercase_	:				Union[str, Any]													=	torch.Size((1, 1000)			)
        self.assertEqual(outputs.logits.shape     ,_UpperCamelCase			)
        lowercase_	:				Dict													=	torch.tensor([0.2166, -0.4368, 0.2191]			).to(_UpperCamelCase			)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,_UpperCamelCase     ,atol=1e-4			)			)
        self.assertTrue(outputs.logits.argmax(dim=-1			).item()     ,281			)







@require_torch
class        UpperCamelCase		(			__a						,      unittest.TestCase  ):
   lowercase							=  (FocalNetBackbone,) if is_torch_available() else ()
   lowercase							=  FocalNetConfig

   lowercase							=  False
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	FocalNetModelTester(self			)



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()
_A   =     logging.get_logger(__name__)






def    lowerCamelCase__				(				__lowerCAmelCase  :  Tuple    ,							__lowerCAmelCase  :  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    lowerCamelCase__				(				__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  Optional[int]    ,							__lowerCAmelCase  :  str=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    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[Any]    ,							__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  Dict				):




			"""simple docstring"""


			lowerCAmelCase_						=	dct.pop(__lowerCAmelCase				)
			lowerCAmelCase_						=	val






def    lowerCamelCase__				(				):




			"""simple docstring"""


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






@torch.no_grad()
def    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  str				):




			"""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_						=	1000
			lowerCAmelCase_						=	"huggingface/label-files"
			lowerCAmelCase_						=	"imagenet-1k-id2label.json"
			lowerCAmelCase_						=	json.load(open(hf_hub_download(__lowerCAmelCase    ,							__lowerCAmelCase    ,							repo_type="dataset"				)    ,							"r"				)				)
			lowerCAmelCase_						=	{int(__lowerCAmelCase				): 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_						=	192
						lowerCAmelCase_						=	768
						lowerCAmelCase_						=	12
						lowerCAmelCase_						=	3
			elif deit_name[9:].startswith("small"				):
						lowerCAmelCase_						=	384
						lowerCAmelCase_						=	1536
						lowerCAmelCase_						=	12
						lowerCAmelCase_						=	6
			if deit_name[9:].startswith("base"				):
						pass
			elif deit_name[4:].startswith("large"				):
						lowerCAmelCase_						=	1024
						lowerCAmelCase_						=	4096
						lowerCAmelCase_						=	24
						lowerCAmelCase_						=	16

			# load original model from timm
			lowerCAmelCase_						=	timm.create_model(__lowerCAmelCase    ,							pretrained=__lowerCAmelCase				)
			timm_model.eval()

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

			# load HuggingFace model
			lowerCAmelCase_						=	DeiTForImageClassificationWithTeacher(__lowerCAmelCase				).eval()
			model.load_state_dict(__lowerCAmelCase				)

			# Check outputs on an image, prepared by DeiTImageProcessor
			lowerCAmelCase_						=	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
			lowerCAmelCase_						=	DeiTImageProcessor(size=__lowerCAmelCase    ,							crop_size=config.image_size				)
			lowerCAmelCase_						=	image_processor(images=prepare_img()    ,							return_tensors="pt"				)
			lowerCAmelCase_						=	encoding["pixel_values"]
			lowerCAmelCase_						=	model(__lowerCAmelCase				)

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

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


if __name__ == "__main__":
			_A   =     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."
			)

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



