






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     _SCREAMING_SNAKE_CASE       :





	def __init__(		self				:					Union[str, Any] ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					Optional[Any]=13 ,  __lowerCamelCase				:					List[Any]=32 ,  __lowerCamelCase				:					Tuple=2 ,  __lowerCamelCase				:					Optional[int]=3 ,  __lowerCamelCase				:					Optional[int]=16 ,  __lowerCamelCase				:					Optional[int]=[32, 64, 128] ,  __lowerCamelCase				:					int=[1, 2, 1] ,  __lowerCamelCase				:					int=[2, 2, 4] ,  __lowerCamelCase				:					Any=2 ,  __lowerCamelCase				:					Tuple=2.0 ,  __lowerCamelCase				:					Tuple=True ,  __lowerCamelCase				:					List[Any]=0.0 ,  __lowerCamelCase				:					Dict=0.0 ,  __lowerCamelCase				:					Any=0.1 ,  __lowerCamelCase				:					List[str]="gelu" ,  __lowerCamelCase				:					Tuple=False ,  __lowerCamelCase				:					str=True ,  __lowerCamelCase				:					List[Any]=0.02 ,  __lowerCamelCase				:					Optional[Any]=1E-5 ,  __lowerCamelCase				:					int=True ,  __lowerCamelCase				:					Optional[Any]=None ,  __lowerCamelCase				:					int=True ,  __lowerCamelCase				:					str=10 ,  __lowerCamelCase				:					Tuple=8 ,  __lowerCamelCase				:					List[Any]=["stage1", "stage2"] ,  __lowerCamelCase				:					Union[str, Any]=[1, 2] ,  ):
		UpperCamelCase     :str          =	parent
		UpperCamelCase     :Dict          =	batch_size
		UpperCamelCase     :str          =	image_size
		UpperCamelCase     :List[Any]          =	patch_size
		UpperCamelCase     :Union[str, Any]          =	num_channels
		UpperCamelCase     :str          =	embed_dim
		UpperCamelCase     :int          =	hidden_sizes
		UpperCamelCase     :Any          =	depths
		UpperCamelCase     :Union[str, Any]          =	num_heads
		UpperCamelCase     :List[str]          =	window_size
		UpperCamelCase     :int          =	mlp_ratio
		UpperCamelCase     :int          =	qkv_bias
		UpperCamelCase     :Optional[int]          =	hidden_dropout_prob
		UpperCamelCase     :Optional[int]          =	attention_probs_dropout_prob
		UpperCamelCase     :Tuple          =	drop_path_rate
		UpperCamelCase     :Optional[Any]          =	hidden_act
		UpperCamelCase     :List[Any]          =	use_absolute_embeddings
		UpperCamelCase     :List[Any]          =	patch_norm
		UpperCamelCase     :int          =	layer_norm_eps
		UpperCamelCase     :Union[str, Any]          =	initializer_range
		UpperCamelCase     :Union[str, Any]          =	is_training
		UpperCamelCase     :Tuple          =	scope
		UpperCamelCase     :int          =	use_labels
		UpperCamelCase     :Optional[int]          =	type_sequence_label_size
		UpperCamelCase     :Union[str, Any]          =	encoder_stride
		UpperCamelCase     :List[str]          =	out_features
		UpperCamelCase     :List[Any]          =	out_indices





	def 						_A (		self				:					List[str]	):
		UpperCamelCase     :int          =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

		UpperCamelCase     :Dict          =	self.get_config()

		return config, pixel_values, labels





	def 						_A (		self				:					Optional[int]	):
		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 						_A (		self				:					Dict ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					Optional[int]	):
		UpperCamelCase     :Union[str, Any]          =	FocalNetModel(config=__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()
		UpperCamelCase     :Optional[Any]          =	model(__lowerCamelCase	)

		UpperCamelCase     :Optional[Any]          =	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths	) - 1))
		UpperCamelCase     :Tuple          =	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 						_A (		self				:					Dict ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					List[str]	):
		UpperCamelCase     :List[str]          =	FocalNetBackbone(config=__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()
		UpperCamelCase     :List[str]          =	model(__lowerCamelCase	)

		# 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
		UpperCamelCase     :List[Any]          =	None
		UpperCamelCase     :Dict          =	FocalNetBackbone(config=__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()
		UpperCamelCase     :Dict          =	model(__lowerCamelCase	)

		# 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 						_A (		self				:					Optional[int] ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					int	):
		UpperCamelCase     :Tuple          =	FocalNetForMaskedImageModeling(config=__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()
		UpperCamelCase     :Optional[Any]          =	model(__lowerCamelCase	)
		self.parent.assertEqual(
		    result.reconstruction.shape ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)	)

		# test greyscale images
		UpperCamelCase     :str          =	1
		UpperCamelCase     :List[str]          =	FocalNetForMaskedImageModeling(__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()

		UpperCamelCase     :str          =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
		UpperCamelCase     :Tuple          =	model(__lowerCamelCase	)
		self.parent.assertEqual(result.reconstruction.shape ,  (self.batch_size, 1, self.image_size, self.image_size)	)





	def 						_A (		self				:					Optional[Any] ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Any ,  __lowerCamelCase				:					Optional[Any]	):
		UpperCamelCase     :Optional[int]          =	self.type_sequence_label_size
		UpperCamelCase     :Optional[Any]          =	FocalNetForImageClassification(__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()
		UpperCamelCase     :Tuple          =	model(__lowerCamelCase ,  labels=__lowerCamelCase	)
		self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.type_sequence_label_size)	)

		# test greyscale images
		UpperCamelCase     :List[str]          =	1
		UpperCamelCase     :Union[str, Any]          =	FocalNetForImageClassification(__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()

		UpperCamelCase     :Optional[int]          =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
		UpperCamelCase     :Union[str, Any]          =	model(__lowerCamelCase	)
		self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.type_sequence_label_size)	)





	def 						_A (		self				:					str	):
		UpperCamelCase     :Optional[Any]          =	self.prepare_config_and_inputs()

		UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :Tuple          =	config_and_inputs
		UpperCamelCase     :str          =	{"""pixel_values""": pixel_values}
		return config, inputs_dict

@require_torch
class     _SCREAMING_SNAKE_CASE       (			_a		,   _a		,   unittest.TestCase      ):
	snake_case__							:     str     	=  (
	    (
	        FocalNetModel,
	        FocalNetForImageClassification,
	        FocalNetForMaskedImageModeling,
	        FocalNetBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	snake_case__							:     List[str]     	=  (
	    {"""feature-extraction""": FocalNetModel, """image-classification""": FocalNetForImageClassification}
	    if is_torch_available()
	    else {}
	)
	snake_case__							:     Optional[int]     	=  False

	snake_case__							:     Tuple     	=  False
	snake_case__							:     Optional[Any]     	=  False
	snake_case__							:     Optional[int]     	=  False
	snake_case__							:     Union[str, Any]     	=  False





	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     :Dict          =	FocalNetModelTester(self	)
		UpperCamelCase     :Optional[int]          =	ConfigTester(self ,  config_class=__lowerCamelCase ,  embed_dim=37 ,  has_text_modality=__lowerCamelCase	)





	def 						_A (		self				:					Union[str, Any]	):
		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 						_A (		self				:					List[Any]	):
		return





	def 						_A (		self				:					Dict	):
		UpperCamelCase     :List[Any]          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*__lowerCamelCase	)





	def 						_A (		self				:					Optional[int]	):
		UpperCamelCase     :List[str]          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_backbone(*__lowerCamelCase	)





	def 						_A (		self				:					Any	):
		UpperCamelCase     :List[str]          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCamelCase	)





	def 						_A (		self				:					Optional[int]	):
		UpperCamelCase     :List[str]          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase	)





	@unittest.skip(reason="""FocalNet does not use inputs_embeds"""	)
	def 						_A (		self				:					Optional[int]	):
		pass





	@unittest.skip(reason="""FocalNet does not use feedforward chunking"""	)
	def 						_A (		self				:					Optional[Any]	):
		pass





	def 						_A (		self				:					List[str]	):
		UpperCamelCase     ,      UpperCamelCase     :Tuple          =	self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes[:-1]:
			UpperCamelCase     :Optional[Any]          =	model_class(__lowerCamelCase	)
			self.assertIsInstance(model.get_input_embeddings() ,  (nn.Module)	)
			UpperCamelCase     :Tuple          =	model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(__lowerCamelCase ,  nn.Linear	)	)





	def 						_A (		self				:					int	):
		UpperCamelCase     ,      UpperCamelCase     :int          =	self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes[:-1]:
			UpperCamelCase     :Dict          =	model_class(__lowerCamelCase	)
			UpperCamelCase     :Optional[int]          =	inspect.signature(model.forward	)
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			UpperCamelCase     :Union[str, Any]          =	[*signature.parameters.keys()]

			UpperCamelCase     :str          =	["""pixel_values"""]
			self.assertListEqual(arg_names[:1] ,  __lowerCamelCase	)





	def 						_A (		self				:					Any ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					Dict	):
		UpperCamelCase     :Optional[Any]          =	model_class(__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()

		with torch.no_grad():
			UpperCamelCase     :Optional[int]          =	model(**self._prepare_for_class(__lowerCamelCase ,  __lowerCamelCase	)	)

		UpperCamelCase     :Tuple          =	outputs.hidden_states

		UpperCamelCase     :Union[str, Any]          =	getattr(
		    self.model_tester ,  """expected_num_hidden_layers""" ,  len(self.model_tester.depths	) + 1	)
		self.assertEqual(len(__lowerCamelCase	) ,  __lowerCamelCase	)

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

		UpperCamelCase     :Any          =	(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] ,  )

		UpperCamelCase     :Union[str, Any]          =	outputs.reshaped_hidden_states
		self.assertEqual(len(__lowerCamelCase	) ,  __lowerCamelCase	)

		UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :List[Any]          =	reshaped_hidden_states[0].shape
		UpperCamelCase     :List[Any]          =	(
		    reshaped_hidden_states[0].view(__lowerCamelCase ,  __lowerCamelCase ,  height * width	).permute(0 ,  2 ,  1	)
		)
		self.assertListEqual(
		    list(reshaped_hidden_states.shape[-2:]	) ,  [num_patches, self.model_tester.embed_dim] ,  )





	def 						_A (		self				:					List[Any]	):
		UpperCamelCase     ,      UpperCamelCase     :Any          =	self.model_tester.prepare_config_and_inputs_for_common()

		UpperCamelCase     :Optional[Any]          =	(
		    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]:
			UpperCamelCase     :List[Any]          =	True
			self.check_hidden_states_output(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			UpperCamelCase     :Optional[Any]          =	True

			self.check_hidden_states_output(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)





	def 						_A (		self				:					int	):
		UpperCamelCase     ,      UpperCamelCase     :Optional[Any]          =	self.model_tester.prepare_config_and_inputs_for_common()
		UpperCamelCase     :List[str]          =	3

		UpperCamelCase     :Optional[Any]          =	(
		    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)
		)
		UpperCamelCase     :List[str]          =	(
		    config.patch_size
		    if isinstance(config.patch_size ,  collections.abc.Iterable	)
		    else (config.patch_size, config.patch_size)
		)

		UpperCamelCase     :Optional[Any]          =	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
		UpperCamelCase     :Dict          =	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

		for model_class in self.all_model_classes[:-1]:
			UpperCamelCase     :Optional[Any]          =	True
			self.check_hidden_states_output(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  (padded_height, padded_width)	)

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			UpperCamelCase     :Dict          =	True
			self.check_hidden_states_output(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  (padded_height, padded_width)	)





	@slow
	def 						_A (		self				:					Tuple	):
		for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			UpperCamelCase     :Union[str, Any]          =	FocalNetModel.from_pretrained(__lowerCamelCase	)
			self.assertIsNotNone(__lowerCamelCase	)





	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     ,      UpperCamelCase     :Dict          =	self.model_tester.prepare_config_and_inputs_for_common()

		UpperCamelCase     :Dict          =	_config_zero_init(__lowerCamelCase	)
		for model_class in self.all_model_classes:
			UpperCamelCase     :Optional[Any]          =	model_class(config=__lowerCamelCase	)
			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     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	@cached_property
	def 						_A (		self				:					List[str]	):
		# TODO update organization
		return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""	) if is_vision_available() else None





	@slow
	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     :Union[str, Any]          =	FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""	).to(__lowerCamelCase	)
		UpperCamelCase     :Optional[Any]          =	self.default_image_processor

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

		# forward pass
		with torch.no_grad():
			UpperCamelCase     :int          =	model(**__lowerCamelCase	)

		# verify the logits
		UpperCamelCase     :str          =	torch.Size((1, 1_000)	)
		self.assertEqual(outputs.logits.shape ,  __lowerCamelCase	)
		UpperCamelCase     :Tuple          =	torch.tensor([0.2166, -0.4368, 0.2191]	).to(__lowerCamelCase	)
		self.assertTrue(torch.allclose(outputs.logits[0, :3] ,  __lowerCamelCase ,  atol=1E-4	)	)
		self.assertTrue(outputs.logits.argmax(dim=-1	).item() ,  281	)

@require_torch
class     _SCREAMING_SNAKE_CASE       (			_a		,   unittest.TestCase      ):
	snake_case__							:     Optional[Any]     	=  (FocalNetBackbone,) if is_torch_available() else ()
	snake_case__							:     str     	=  FocalNetConfig

	snake_case__							:     int     	=  False





	def 						_A (		self				:					List[str]	):
		UpperCamelCase     :Optional[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)



