



'''simple docstring'''




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     :    Optional[Any]      						=					logging.get_logger(__name__)



def    __lowerCamelCase						(      _lowercase			,       _lowercase=False	)   ->    str:
							UpperCAmelCase  :	Tuple							  =	[]
							for i in range(config.num_hidden_layers	):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''deit.encoder.layer.{i}.layernorm_before.weight''')	)
														rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''')	)
														rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''')	)
														rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''')	)
														rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''')	)
														rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''')	)
														rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''')	)
														rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''')	)
														rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''')	)
														rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''')	)

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ("""cls_token""", """deit.embeddings.cls_token"""),
							        ("""dist_token""", """deit.embeddings.distillation_token"""),
							        ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
							        ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
							        ("""pos_embed""", """deit.embeddings.position_embeddings"""),
							    ]	)

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ("""norm.weight""", """layernorm.weight"""),
														        ("""norm.bias""", """layernorm.bias"""),
														        ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
														        ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
														    ]	)

														# if just the base model, we should remove "deit" from all keys that start with "deit"
														UpperCAmelCase  :	Optional[int]							  =	[(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						(      _lowercase			,       _lowercase			,       _lowercase=False	)   ->    List[Any]:
							for i in range(config.num_hidden_layers	):
														if base_model:
																					UpperCAmelCase  :	Optional[Any]							  =	""""""
														else:
																					UpperCAmelCase  :	Optional[Any]							  =	"""deit."""
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														UpperCAmelCase  :	str							  =	state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''	)
														UpperCAmelCase  :	Dict							  =	state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''	)
														# next, add query, keys and values (in that order) to the state dict
														UpperCAmelCase  :	int							  =	in_proj_weight[
														    : config.hidden_size, :
														]
														UpperCAmelCase  :	Optional[Any]							  =	in_proj_bias[: config.hidden_size]
														UpperCAmelCase  :	List[Any]							  =	in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														UpperCAmelCase  :	List[str]							  =	in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														UpperCAmelCase  :	Optional[Any]							  =	in_proj_weight[
														    -config.hidden_size :, :
														]
														UpperCAmelCase  :	str							  =	in_proj_bias[-config.hidden_size :]



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Optional[int]:
							UpperCAmelCase  :	Optional[Any]							  =	dct.pop(_lowercase	)
							UpperCAmelCase  :	Any							  =	val



def    __lowerCamelCase						(      )   ->    Tuple:
							UpperCAmelCase  :	Union[str, Any]							  =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase  :	int							  =	Image.open(requests.get(_lowercase			,       stream=_lowercase	).raw	)
							return im



@torch.no_grad()
def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Tuple:
							UpperCAmelCase  :	List[str]							  =	DeiTConfig()
							# all deit models have fine-tuned heads
							UpperCAmelCase  :	Any							  =	False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							UpperCAmelCase  :	Optional[Any]							  =	1_0_0_0
							UpperCAmelCase  :	int							  =	"""huggingface/label-files"""
							UpperCAmelCase  :	List[str]							  =	"""imagenet-1k-id2label.json"""
							UpperCAmelCase  :	Optional[Any]							  =	json.load(open(hf_hub_download(_lowercase			,       _lowercase			,       repo_type="""dataset"""	)			,       """r"""	)	)
							UpperCAmelCase  :	Dict							  =	{int(_lowercase	): v for k, v in idalabel.items()}
							UpperCAmelCase  :	Any							  =	idalabel
							UpperCAmelCase  :	Optional[int]							  =	{v: k for k, v in idalabel.items()}
							UpperCAmelCase  :	Dict							  =	int(deit_name[-6:-4]	)
							UpperCAmelCase  :	str							  =	int(deit_name[-3:]	)
							# size of the architecture
							if deit_name[9:].startswith("""tiny"""	):
														UpperCAmelCase  :	Tuple							  =	1_9_2
														UpperCAmelCase  :	List[Any]							  =	7_6_8
														UpperCAmelCase  :	Tuple							  =	1_2
														UpperCAmelCase  :	Optional[int]							  =	3
							elif deit_name[9:].startswith("""small"""	):
														UpperCAmelCase  :	Dict							  =	3_8_4
														UpperCAmelCase  :	Tuple							  =	1_5_3_6
														UpperCAmelCase  :	List[Any]							  =	1_2
														UpperCAmelCase  :	List[str]							  =	6
							if deit_name[9:].startswith("""base"""	):
														pass
							elif deit_name[4:].startswith("""large"""	):
														UpperCAmelCase  :	List[Any]							  =	1_0_2_4
														UpperCAmelCase  :	Tuple							  =	4_0_9_6
														UpperCAmelCase  :	Union[str, Any]							  =	2_4
														UpperCAmelCase  :	Any							  =	1_6

							# load original model from timm
							UpperCAmelCase  :	Optional[int]							  =	timm.create_model(_lowercase			,       pretrained=_lowercase	)
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							UpperCAmelCase  :	Dict							  =	timm_model.state_dict()
							UpperCAmelCase  :	Union[str, Any]							  =	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
							UpperCAmelCase  :	List[Any]							  =	DeiTForImageClassificationWithTeacher(_lowercase	).eval()
							model.load_state_dict(_lowercase	)

							# Check outputs on an image, prepared by DeiTImageProcessor
							UpperCAmelCase  :	List[Any]							  =	int(
							    (2_5_6 / 2_2_4) * config.image_size	)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
							UpperCAmelCase  :	List[str]							  =	DeiTImageProcessor(size=_lowercase			,       crop_size=config.image_size	)
							UpperCAmelCase  :	str							  =	image_processor(images=prepare_img()			,       return_tensors="""pt"""	)
							UpperCAmelCase  :	Tuple							  =	encoding["""pixel_values"""]
							UpperCAmelCase  :	Optional[int]							  =	model(_lowercase	)

							UpperCAmelCase  :	str							  =	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__":
							a     :    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."""
							)

							a     :    List[str]      						=					parser.parse_args()
							convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






'''simple docstring'''




import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
							from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class     UpperCamelCase_				:





	def __init__(						self	,	A	,	A=13	,	A=32	,	A=3	,	A=4	,	A=[10, 20, 30, 40]	,	A=[2, 2, 3, 2]	,	A=True	,	A=True	,	A=37	,	A="gelu"	,	A=10	,	A=0.0_2	,	A=["stage2", "stage3", "stage4"]	,	A=[2, 3, 4]	,	A=None	,	)    ->    int:
								UpperCAmelCase  :	str							  =	parent
								UpperCAmelCase  :	List[Any]							  =	batch_size
								UpperCAmelCase  :	Dict							  =	image_size
								UpperCAmelCase  :	Tuple							  =	num_channels
								UpperCAmelCase  :	Union[str, Any]							  =	num_stages
								UpperCAmelCase  :	Any							  =	hidden_sizes
								UpperCAmelCase  :	str							  =	depths
								UpperCAmelCase  :	Optional[int]							  =	is_training
								UpperCAmelCase  :	Union[str, Any]							  =	use_labels
								UpperCAmelCase  :	Any							  =	intermediate_size
								UpperCAmelCase  :	str							  =	hidden_act
								UpperCAmelCase  :	List[str]							  =	num_labels
								UpperCAmelCase  :	Tuple							  =	initializer_range
								UpperCAmelCase  :	Optional[Any]							  =	out_features
								UpperCAmelCase  :	List[str]							  =	out_indices
								UpperCAmelCase  :	Any							  =	scope





	def 				_lowercase(						self					)    ->    Union[str, Any]:
								UpperCAmelCase  :	Tuple							  =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

								UpperCAmelCase  :	List[Any]							  =	None
								if self.use_labels:
															UpperCAmelCase  :	Dict							  =	ids_tensor([self.batch_size]	,	self.num_labels					)

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

								return config, pixel_values, labels





	def 				_lowercase(						self					)    ->    Optional[Any]:
								return ConvNextVaConfig(
								    num_channels=self.num_channels	,	hidden_sizes=self.hidden_sizes	,	depths=self.depths	,	num_stages=self.num_stages	,	hidden_act=self.hidden_act	,	is_decoder=A	,	initializer_range=self.initializer_range	,	out_features=self.out_features	,	out_indices=self.out_indices	,	num_labels=self.num_labels	,	)





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    Optional[Any]:
								UpperCAmelCase  :	int							  =	ConvNextVaModel(config=A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	List[Any]							  =	model(A					)
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape	,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,	)





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    Any:
								UpperCAmelCase  :	List[str]							  =	ConvNextVaForImageClassification(A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	int							  =	model(A	,	labels=A					)
								self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.num_labels)					)





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    Any:
								UpperCAmelCase  :	Optional[Any]							  =	ConvNextVaBackbone(config=A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	Any							  =	model(A					)

								# verify hidden states
								self.parent.assertEqual(len(result.feature_maps					)	,	len(config.out_features					)					)
								self.parent.assertListEqual(list(result.feature_maps[0].shape					)	,	[self.batch_size, self.hidden_sizes[1], 4, 4]					)

								# 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  :	Any							  =	None
								UpperCAmelCase  :	Optional[int]							  =	ConvNextVaBackbone(config=A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	int							  =	model(A					)

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps					)	,	1					)
								self.parent.assertListEqual(list(result.feature_maps[0].shape					)	,	[self.batch_size, self.hidden_sizes[-1], 1, 1]					)

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





	def 				_lowercase(						self					)    ->    List[str]:
								UpperCAmelCase  :	Dict							  =	self.prepare_config_and_inputs()
								UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  :	List[Any]							  =	config_and_inputs
								UpperCAmelCase  :	str							  =	{"""pixel_values""": pixel_values}
								return config, inputs_dict






	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	List[str]							  =	self.prepare_config_and_inputs()
								UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  :	Optional[Any]							  =	config_and_inputs
								UpperCAmelCase  :	List[str]							  =	{"""pixel_values""": pixel_values, """labels""": labels}
								return config, inputs_dict




@require_torch
class     UpperCamelCase_				(							__magic_name__   ,  __magic_name__   ,  unittest.TestCase    ):
	lowercase    =				(
	    (
	        ConvNextVaModel,
	        ConvNextVaForImageClassification,
	        ConvNextVaBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	lowercase    =				(
	    {'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
	    if is_torch_available()
	    else {}
	)

	lowercase    =				False
	lowercase    =				False
	lowercase    =				False
	lowercase    =				False
	lowercase    =				False





	def 				_lowercase(						self					)    ->    Optional[int]:
								UpperCAmelCase  :	Dict							  =	ConvNextVaModelTester(self					)
								UpperCAmelCase  :	List[str]							  =	ConfigTester(self	,	config_class=A	,	has_text_modality=A	,	hidden_size=37					)





	def 				_lowercase(						self					)    ->    int:
								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 				_lowercase(						self					)    ->    List[str]:
								return





	@unittest.skip(reason="""ConvNextV2 does not use inputs_embeds"""					)
	def 				_lowercase(						self					)    ->    Dict:
								pass





	@unittest.skip(reason="""ConvNextV2 does not support input and output embeddings"""					)
	def 				_lowercase(						self					)    ->    Any:
								pass





	@unittest.skip(reason="""ConvNextV2 does not use feedforward chunking"""					)
	def 				_lowercase(						self					)    ->    int:
								pass





	def 				_lowercase(						self					)    ->    Dict:
								if not self.model_tester.is_training:
															return

								for model_class in self.all_model_classes:
															UpperCAmelCase  ,  UpperCAmelCase  :	Optional[Any]							  =	self.model_tester.prepare_config_and_inputs_with_labels()
															UpperCAmelCase  :	Optional[int]							  =	True

															if model_class.__name__ in [
															    *get_values(A					),
															    *get_values(A					),
															]:
																						continue

															UpperCAmelCase  :	Any							  =	model_class(A					)
															model.to(A					)
															model.train()
															UpperCAmelCase  :	List[str]							  =	self._prepare_for_class(A	,	A	,	return_labels=A					)
															UpperCAmelCase  :	List[str]							  =	model(**A					).loss
															loss.backward()





	def 				_lowercase(						self					)    ->    Tuple:
								if not self.model_tester.is_training:
															return

								for model_class in self.all_model_classes:
															UpperCAmelCase  ,  UpperCAmelCase  :	Any							  =	self.model_tester.prepare_config_and_inputs_with_labels()
															UpperCAmelCase  :	List[str]							  =	False
															UpperCAmelCase  :	int							  =	True

															if (
															    model_class.__name__
															    in [*get_values(A					), *get_values(A					)]
															    or not model_class.supports_gradient_checkpointing
															):
																						continue

															UpperCAmelCase  :	Dict							  =	model_class(A					)
															model.to(A					)
															model.gradient_checkpointing_enable()
															model.train()
															UpperCAmelCase  :	Any							  =	self._prepare_for_class(A	,	A	,	return_labels=A					)
															UpperCAmelCase  :	Any							  =	model(**A					).loss
															loss.backward()





	def 				_lowercase(						self					)    ->    Tuple:
								UpperCAmelCase  ,  UpperCAmelCase  :	List[Any]							  =	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															UpperCAmelCase  :	int							  =	model_class(A					)
															UpperCAmelCase  :	List[str]							  =	inspect.signature(model.forward					)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															UpperCAmelCase  :	Tuple							  =	[*signature.parameters.keys()]

															UpperCAmelCase  :	Optional[Any]							  =	["""pixel_values"""]
															self.assertListEqual(arg_names[:1]	,	A					)





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	Tuple							  =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A					)





	def 				_lowercase(						self					)    ->    List[str]:
								def check_hidden_states_output(A	,	A	,	A					):
															UpperCAmelCase  :	Optional[Any]							  =	model_class(A					)
															model.to(A					)
															model.eval()

															with torch.no_grad():
																						UpperCAmelCase  :	Dict							  =	model(**self._prepare_for_class(A	,	A					)					)

															UpperCAmelCase  :	Tuple							  =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															UpperCAmelCase  :	Optional[Any]							  =	self.model_tester.num_stages
															self.assertEqual(len(A					)	,	expected_num_stages + 1					)

															# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]					)	,	[self.model_tester.image_size // 4, self.model_tester.image_size // 4]	,	)

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

								for model_class in self.all_model_classes:
															UpperCAmelCase  :	str							  =	True
															check_hidden_states_output(A	,	A	,	A					)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															UpperCAmelCase  :	int							  =	True

															check_hidden_states_output(A	,	A	,	A					)





	def 				_lowercase(						self					)    ->    Tuple:
								UpperCAmelCase  :	Dict							  =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*A					)






	@slow
	def 				_lowercase(						self					)    ->    Any:
								for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase  :	Tuple							  =	ConvNextVaModel.from_pretrained(A					)
															self.assertIsNotNone(A					)




def    __lowerCamelCase						(      )   ->    Optional[int]:
							UpperCAmelCase  :	Optional[int]							  =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
							return image


@require_torch
@require_vision
class     UpperCamelCase_				(							unittest.TestCase    ):





	@cached_property
	def 				_lowercase(						self					)    ->    str:
								return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224"""					) if is_vision_available() else None






	@slow
	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	Any							  =	ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224"""					).to(A					)

								UpperCAmelCase  :	List[Any]							  =	self.default_image_processor
								UpperCAmelCase  :	Any							  =	prepare_img()
								UpperCAmelCase  :	Tuple							  =	preprocessor(images=A	,	return_tensors="""pt"""					).to(A					)

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

								# verify the logits
								UpperCAmelCase  :	Dict							  =	torch.Size((1, 1000)					)
								self.assertEqual(outputs.logits.shape	,	A					)

								UpperCAmelCase  :	Dict							  =	torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6]					).to(A					)
								self.assertTrue(torch.allclose(outputs.logits[0, :3]	,	A	,	atol=1e-4					)					)

