



'''simple docstring'''




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase							:Union[str, Any]       = logging.get_logger(__name__)

lowerCamelCase							:Dict       = {
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			for attribute in key.split("""."""   ):
						A_     :						Union[str, Any]     =					getattr(lowerCamelCase__						,				lowerCamelCase__   )

			if weight_type is not None:
						A_     :						List[Any]     =					getattr(lowerCamelCase__						,				lowerCamelCase__   ).shape
			else:
						A_     :						Optional[int]     =					hf_pointer.shape

			assert hf_shape == value.shape, (
			    f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
			    f' {value.shape} for {full_name}'
			)

			if weight_type == "weight":
						A_     :						Optional[int]     =					value
			elif weight_type == "weight_g":
						A_     :						Optional[Any]     =					value
			elif weight_type == "weight_v":
						A_     :						Dict     =					value
			elif weight_type == "bias":
						A_     :						str     =					value
			else:
						A_     :						List[str]     =					value

			logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'   )



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						int     =					[]
			A_     :						Union[str, Any]     =					fairseq_model.state_dict()

			A_     :						List[Any]     =					hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						A_     :						Union[str, Any]     =					False
						if "conv_layers" in name:
									load_conv_layer(
									    lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				hf_model.config.feat_extract_norm == """group"""						,				)
									A_     :						str     =					True
						else:
									for key, mapped_key in MAPPING.items():
												A_     :						Tuple     =					"""sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

												if key in name or key.split("""w2v_model."""   )[-1] == name.split("""."""   )[0]:
															A_     :						Any     =					True
															if "*" in mapped_key:
																		A_     :						int     =					name.split(lowerCamelCase__   )[0].split("""."""   )[-2]
																		A_     :						Optional[int]     =					mapped_key.replace("""*"""						,				lowerCamelCase__   )
															if "weight_g" in name:
																		A_     :						Any     =					"""weight_g"""
															elif "weight_v" in name:
																		A_     :						Any     =					"""weight_v"""
															elif "weight" in name:
																		A_     :						Dict     =					"""weight"""
															elif "bias" in name:
																		A_     :						Dict     =					"""bias"""
															else:
																		A_     :						Tuple     =					None
															set_recursively(lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   )
												continue
						if not is_used:
									unused_weights.append(lowerCamelCase__   )

			logger.warning(f'Unused weights: {unused_weights}'   )



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Optional[int]     =					full_name.split("""conv_layers."""   )[-1]
			A_     :						List[str]     =					name.split("""."""   )
			A_     :						int     =					int(items[0]   )
			A_     :						str     =					int(items[1]   )

			if type_id == 0:
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    f'{full_name} has size {value.shape}, but'
									    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
									)
									A_     :						List[Any]     =					value
									logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'   )
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
									    f'{full_name} has size {value.shape}, but'
									    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
									)
									A_     :						str     =					value
									logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'   )
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
									    " found."
									)
									A_     :						Tuple     =					value
									logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'   )
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    f'{full_name} has size {value.shape}, but'
									    f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
									)
									A_     :						List[Any]     =					value
									logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'   )
			else:
						unused_weights.append(lowerCamelCase__   )



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Dict     =					SEWConfig()
			if is_finetuned:
						A_     :						str     =					model.wav_encoder.wav_model.cfg
			else:
						A_     :						List[Any]     =					model.cfg

			A_     :						int     =					fs_config.conv_bias
			A_     :						List[str]     =					eval(fs_config.conv_feature_layers   )
			A_     :						Tuple     =					[x[0] for x in conv_layers]
			A_     :						int     =					[x[1] for x in conv_layers]
			A_     :						Optional[int]     =					[x[2] for x in conv_layers]
			A_     :						List[Any]     =					"""gelu"""
			A_     :						Any     =					"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
			A_     :						Optional[Any]     =					0.0
			A_     :						List[Any]     =					fs_config.activation_fn.name
			A_     :						Optional[int]     =					fs_config.encoder_embed_dim
			A_     :						List[Any]     =					0.02
			A_     :						Tuple     =					fs_config.encoder_ffn_embed_dim
			A_     :						str     =					1E-5
			A_     :						Any     =					fs_config.encoder_layerdrop
			A_     :						Optional[Any]     =					fs_config.encoder_attention_heads
			A_     :						Optional[int]     =					fs_config.conv_pos_groups
			A_     :						Optional[Any]     =					fs_config.conv_pos
			A_     :						str     =					len(lowerCamelCase__   )
			A_     :						Any     =					fs_config.encoder_layers
			A_     :						Dict     =					fs_config.squeeze_factor

			# take care of any params that are overridden by the Wav2VecCtc model
			if is_finetuned:
						A_     :						Optional[Any]     =					model.cfg
						A_     :						List[str]     =					fs_config.final_dropout
						A_     :						Tuple     =					fs_config.layerdrop
			A_     :						List[str]     =					fs_config.activation_dropout
			A_     :						List[Any]     =					fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
			A_     :						str     =					fs_config.attention_dropout
			A_     :						Optional[Any]     =					fs_config.dropout_input
			A_     :						Optional[Any]     =					fs_config.dropout
			A_     :						Dict     =					fs_config.mask_channel_length
			A_     :						List[str]     =					fs_config.mask_channel_prob
			A_     :						Tuple     =					fs_config.mask_length
			A_     :						Union[str, Any]     =					fs_config.mask_prob

			A_     :						str     =					"""Wav2Vec2FeatureExtractor"""
			A_     :						str     =					"""Wav2Vec2CTCTokenizer"""

			return config



@torch.no_grad()
def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__=None						,				lowerCamelCase__=None						,				lowerCamelCase__=True   ):
			'''simple docstring'''





			if is_finetuned:
						A_,   A_,   A_     :						Tuple     =					fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]						,				arg_overrides={"""data""": """/""".join(dict_path.split("""/"""   )[:-1]   )}   )
			else:
						A_,   A_,   A_     :						str     =					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

			if config_path is not None:
						A_     :						Any     =					SEWConfig.from_pretrained(lowerCamelCase__   )
			else:
						A_     :						Optional[Any]     =					convert_config(model[0]						,				lowerCamelCase__   )
			A_     :						Optional[Any]     =					model[0].eval()

			A_     :						Dict     =					True if config.feat_extract_norm == """layer""" else False
			A_     :						Any     =					WavaVecaFeatureExtractor(
			    feature_size=1						,				sampling_rate=1_60_00						,				padding_value=0						,				do_normalize=lowerCamelCase__						,				return_attention_mask=lowerCamelCase__						,				)

			if is_finetuned:
						if dict_path:
									A_     :						List[Any]     =					Dictionary.load(lowerCamelCase__   )

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									A_     :						Dict     =					target_dict.pad_index
									A_     :						Dict     =					target_dict.bos_index
									A_     :						str     =					target_dict.pad_index
									A_     :						Optional[int]     =					target_dict.bos_index
									A_     :						str     =					target_dict.eos_index
									A_     :						Optional[Any]     =					len(target_dict.symbols   )
									A_     :						List[Any]     =					os.path.join(lowerCamelCase__						,				"""vocab.json"""   )
									if not os.path.isdir(lowerCamelCase__   ):
												logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCamelCase__   )   )
												return
									os.makedirs(lowerCamelCase__						,				exist_ok=lowerCamelCase__   )
									with open(lowerCamelCase__						,				"""w"""						,				encoding="""utf-8"""   ) as vocab_handle:
												json.dump(target_dict.indices						,				lowerCamelCase__   )
									A_     :						Union[str, Any]     =					WavaVecaCTCTokenizer(
									    lowerCamelCase__						,				unk_token=target_dict.unk_word						,				pad_token=target_dict.pad_word						,				bos_token=target_dict.bos_word						,				eos_token=target_dict.eos_word						,				word_delimiter_token="""|"""						,				do_lower_case=lowerCamelCase__						,				)
									A_     :						List[Any]     =					WavaVecaProcessor(feature_extractor=lowerCamelCase__						,				tokenizer=lowerCamelCase__   )
									processor.save_pretrained(lowerCamelCase__   )

						A_     :						Tuple     =					SEWForCTC(lowerCamelCase__   )
			else:
						A_     :						Tuple     =					SEWModel(lowerCamelCase__   )
						feature_extractor.save_pretrained(lowerCamelCase__   )

			recursively_load_weights(lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   )

			hf_model.save_pretrained(lowerCamelCase__   )


if __name__ == "__main__":
						lowerCamelCase							:Dict       = argparse.ArgumentParser()
						parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
						parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
						parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
						parser.add_argument(
						    '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
						)
						lowerCamelCase							:Any       = parser.parse_args()
						convert_sew_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
						)




'''simple docstring'''




import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 torch import nn

						from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
						from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor







class      _lowerCAmelCase							:


	def __init__(self	,				lowercase	,				lowercase=13	,				lowercase=30	,				lowercase=2	,				lowercase=3	,				lowercase=True	,				lowercase=True	,				lowercase=32	,				lowercase=5	,				lowercase=4	,				lowercase=37	,				lowercase="gelu"	,				lowercase=0.1	,				lowercase=0.1	,				lowercase=10	,				lowercase=0.02	,				lowercase=None	,				lowercase=2	,				):
				A_     :						List[str]     =					parent
				A_     :						str     =					batch_size
				A_     :						Optional[Any]     =					image_size
				A_     :						List[str]     =					patch_size
				A_     :						List[str]     =					num_channels
				A_     :						List[str]     =					is_training
				A_     :						str     =					use_labels
				A_     :						List[str]     =					hidden_size
				A_     :						List[Any]     =					num_hidden_layers
				A_     :						Any     =					num_attention_heads
				A_     :						Any     =					intermediate_size
				A_     :						Optional[Any]     =					hidden_act
				A_     :						Optional[int]     =					hidden_dropout_prob
				A_     :						str     =					attention_probs_dropout_prob
				A_     :						Optional[int]     =					type_sequence_label_size
				A_     :						Any     =					initializer_range
				A_     :						int     =					scope
				A_     :						str     =					encoder_stride

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


	def    _a     (self							):
				A_     :						str     =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

				A_     :						Union[str, Any]     =					self.get_config()

				return config, pixel_values, labels


	def    _a     (self							):
				return ViTConfig(
				    image_size=self.image_size	,				patch_size=self.patch_size	,				num_channels=self.num_channels	,				hidden_size=self.hidden_size	,				num_hidden_layers=self.num_hidden_layers	,				num_attention_heads=self.num_attention_heads	,				intermediate_size=self.intermediate_size	,				hidden_act=self.hidden_act	,				hidden_dropout_prob=self.hidden_dropout_prob	,				attention_probs_dropout_prob=self.attention_probs_dropout_prob	,				is_decoder=lowercase	,				initializer_range=self.initializer_range	,				encoder_stride=self.encoder_stride	,				)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):
				A_     :						List[str]     =					ViTModel(config=lowercase							)
				model.to(lowercase							)
				model.eval()
				A_     :						List[str]     =					model(lowercase							)
				self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):
				A_     :						List[str]     =					ViTForMaskedImageModeling(config=lowercase							)
				model.to(lowercase							)
				model.eval()
				A_     :						Tuple     =					model(lowercase							)
				self.parent.assertEqual(
				    result.reconstruction.shape	,				(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

				# test greyscale images
				A_     :						Union[str, Any]     =					1
				A_     :						Any     =					ViTForMaskedImageModeling(lowercase							)
				model.to(lowercase							)
				model.eval()

				A_     :						str     =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
				A_     :						Optional[int]     =					model(lowercase							)
				self.parent.assertEqual(result.reconstruction.shape	,				(self.batch_size, 1, self.image_size, self.image_size)							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):
				A_     :						Dict     =					self.type_sequence_label_size
				A_     :						str     =					ViTForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()
				A_     :						List[str]     =					model(lowercase	,				labels=lowercase							)
				self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.type_sequence_label_size)							)

				# test greyscale images
				A_     :						Any     =					1
				A_     :						str     =					ViTForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()

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


	def    _a     (self							):
				A_     :						str     =					self.prepare_config_and_inputs()
				(
				    (
				    A_
				),   (
				    A_
				),   (
				    A_
				),   
				)     :						Optional[int]     =					config_and_inputs
				A_     :						Tuple     =					{"""pixel_values""": pixel_values}
				return config, inputs_dict







@require_torch
class      _lowerCAmelCase							(      __UpperCAmelCase				,      __UpperCAmelCase				,      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		Any		      = (
	    (
	        ViTModel,
	        ViTForImageClassification,
	        ViTForMaskedImageModeling,
	    )
	    if is_torch_available()
	    else ()
	)
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = (
	    {'feature-extraction': ViTModel, 'image-classification': ViTForImageClassification}
	    if is_torch_available()
	    else {}
	)
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = True

	__SCREAMING_SNAKE_CASE       :		Dict		      = False
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = False
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = False


	def    _a     (self							):
				A_     :						Any     =					ViTModelTester(self							)
				A_     :						str     =					ConfigTester(self	,				config_class=lowercase	,				has_text_modality=lowercase	,				hidden_size=37							)


	def    _a     (self							):
				self.config_tester.run_common_tests()


	@unittest.skip(reason="""ViT does not use inputs_embeds"""							)
	def    _a     (self							):
				pass


	def    _a     (self							):
				A_,   A_     :						Union[str, Any]     =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_     :						Union[str, Any]     =					model_class(lowercase							)
							self.assertIsInstance(model.get_input_embeddings()	,				(nn.Module)							)
							A_     :						Tuple     =					model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(lowercase	,				nn.Linear							)							)


	def    _a     (self							):
				A_,   A_     :						List[Any]     =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_     :						Optional[Any]     =					model_class(lowercase							)
							A_     :						Any     =					inspect.signature(model.forward							)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							A_     :						List[str]     =					[*signature.parameters.keys()]

							A_     :						Dict     =					["""pixel_values"""]
							self.assertListEqual(arg_names[:1]	,				lowercase							)


	def    _a     (self							):
				A_     :						Tuple     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase							)


	def    _a     (self							):
				A_     :						List[Any]     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*lowercase							)


	def    _a     (self							):
				A_     :						Dict     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase							)


	@slow
	def    _a     (self							):
				for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A_     :						Dict     =					ViTModel.from_pretrained(lowercase							)
							self.assertIsNotNone(lowercase							)






def      a      (			):
			'''simple docstring'''





			A_     :						List[Any]     =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
			return image







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


	@cached_property
	def    _a     (self							):
				return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224"""							) if is_vision_available() else None


	@slow
	def    _a     (self							):
				A_     :						Optional[int]     =					ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""							).to(lowercase							)

				A_     :						List[str]     =					self.default_image_processor
				A_     :						Tuple     =					prepare_img()
				A_     :						int     =					image_processor(images=lowercase	,				return_tensors="""pt"""							).to(lowercase							)

				# forward pass
				with torch.no_grad():
							A_     :						str     =					model(**lowercase							)

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

				A_     :						str     =					torch.tensor([-0.27_44, 0.82_15, -0.08_36]							).to(lowercase							)

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


	@slow
	def    _a     (self							):
				# ViT models have an `interpolate_pos_encoding` argument in their forward method,
				# allowing to interpolate the pre-trained position embeddings in order to use
				# the model on higher resolutions. The DINO model by Facebook AI leverages this
				# to visualize self-attention on higher resolution images.
				A_     :						Optional[int]     =					ViTModel.from_pretrained("""facebook/dino-vits8"""							).to(lowercase							)

				A_     :						List[Any]     =					ViTImageProcessor.from_pretrained("""facebook/dino-vits8"""	,				size=480							)
				A_     :						Dict     =					prepare_img()
				A_     :						str     =					image_processor(images=lowercase	,				return_tensors="""pt"""							)
				A_     :						int     =					inputs.pixel_values.to(lowercase							)

				# forward pass
				with torch.no_grad():
							A_     :						int     =					model(lowercase	,				interpolate_pos_encoding=lowercase							)

				# verify the logits
				A_     :						int     =					torch.Size((1, 3601, 384)							)
				self.assertEqual(outputs.last_hidden_state.shape	,				lowercase							)

				A_     :						List[Any]     =					torch.tensor(
				    [[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]]							).to(lowercase							)

				self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]	,				lowercase	,				atol=1E-4							)							)


	@slow
	@require_accelerate
	@require_torch_gpu
	def    _a     (self							):
				A_     :						List[Any]     =					ViTModel.from_pretrained("""facebook/dino-vits8"""	,				torch_dtype=torch.floataa	,				device_map="""auto"""							)
				A_     :						int     =					self.default_image_processor

				A_     :						Any     =					prepare_img()
				A_     :						List[str]     =					image_processor(images=lowercase	,				return_tensors="""pt"""							)
				A_     :						Any     =					inputs.pixel_values.to(lowercase							)

				# forward pass to make sure inference works in fp16
				with torch.no_grad():
							A_     :						Optional[Any]     =					model(lowercase							)