



'''simple docstring'''







import subprocess
import sys
import warnings
from argparse import ArgumentParser
from pathlib import Path

from packaging import version

from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
from ..utils import logging
from ..utils.import_utils import is_optimum_available
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import get_preprocessor


SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """1.5.0"""

SCREAMING_SNAKE_CASE__     : Any				=   ["""vision-encoder-decoder"""]







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		if is_optimum_available():
				from optimum.version import __version__ as optimum_version

				SCREAMING_SNAKE_CASE_						:Any										=      version.parse(SCREAMING_SNAKE_CASE     )
				if parsed_optimum_version < version.parse(SCREAMING_SNAKE_CASE     ):
						raise RuntimeError(
						    F'transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You '
						    'can upgrade optimum by running: pip install -U optimum[exporters]'     )
		else:
				raise RuntimeError(
				    'transformers.onnx requires optimum to run, you can install the library by running: pip install '
				    'optimum[exporters]'     )
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      [
		    sys.executable,
		    '-m',
		    'optimum.exporters.onnx',
		    F'--model {args.model}',
		    F'--task {args.feature}',
		    F'--framework {args.framework}' if args.framework is not None else '',
		    F'{args.output}',
		]
		SCREAMING_SNAKE_CASE_						:Tuple										=      subprocess.Popen(' '.join(SCREAMING_SNAKE_CASE     ) ,		stdout=subprocess.PIPE ,		shell=SCREAMING_SNAKE_CASE     )
		proc.wait()

		logger.info(
		    'The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as '
		    'transformers.onnx is deprecated, and will be removed in v5. You can find more information here: '
		    'https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model.'     )







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:int										=      args.output if args.output.is_file() else args.output.joinpath('model.onnx'     )
		if not args.output.parent.exists():
				args.output.parent.mkdir(parents=SCREAMING_SNAKE_CASE     )

		# Allocate the model
		SCREAMING_SNAKE_CASE_						:Any										=      FeaturesManager.get_model_from_feature(
		    args.feature ,		args.model ,		framework=args.framework ,		cache_dir=args.cache_dir     )

		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      FeaturesManager.check_supported_model_or_raise(SCREAMING_SNAKE_CASE ,		feature=args.feature     )
		SCREAMING_SNAKE_CASE_						:Tuple										=      model_onnx_config(model.config     )

		if model_kind in ENCODER_DECODER_MODELS:
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      model.get_encoder()
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      model.get_decoder()

				SCREAMING_SNAKE_CASE_						:Dict										=      onnx_config.get_encoder_config(encoder_model.config     )
				SCREAMING_SNAKE_CASE_						:str										=      onnx_config.get_decoder_config(
				    encoder_model.config ,		decoder_model.config ,		feature=args.feature     )

				if args.opset is None:
						SCREAMING_SNAKE_CASE_						:int										=      max(encoder_onnx_config.default_onnx_opset ,		decoder_onnx_config.default_onnx_opset     )

				if args.opset < min(encoder_onnx_config.default_onnx_opset ,		decoder_onnx_config.default_onnx_opset     ):
						raise ValueError(
						    F'Opset {args.opset} is not sufficient to export {model_kind}. At least '
						    F' {min(encoder_onnx_config.default_onnx_opset ,		decoder_onnx_config.default_onnx_opset     )} is required.'     )

				SCREAMING_SNAKE_CASE_						:Dict										=      AutoFeatureExtractor.from_pretrained(args.model     )

				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:str										=      export(
				    SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		args.opset ,		args.output.parent.joinpath('encoder_model.onnx'     ) ,		)

				validate_model_outputs(
				    SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		args.output.parent.joinpath('encoder_model.onnx'     ) ,		SCREAMING_SNAKE_CASE ,		args.atol if args.atol else encoder_onnx_config.atol_for_validation ,		)

				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      AutoTokenizer.from_pretrained(args.model     )

				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[Any]										=      export(
				    SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		args.opset ,		args.output.parent.joinpath('decoder_model.onnx'     ) ,		)

				validate_model_outputs(
				    SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		args.output.parent.joinpath('decoder_model.onnx'     ) ,		SCREAMING_SNAKE_CASE ,		args.atol if args.atol else decoder_onnx_config.atol_for_validation ,		)
				logger.info(
				    F'All good, model saved at: {args.output.parent.joinpath("encoder_model.onnx"     ).as_posix()},'
				    F' {args.output.parent.joinpath("decoder_model.onnx"     ).as_posix()}'     )

		else:
				# Instantiate the appropriate preprocessor
				if args.preprocessor == "auto":
						SCREAMING_SNAKE_CASE_						:Dict										=      get_preprocessor(args.model     )
				elif args.preprocessor == "tokenizer":
						SCREAMING_SNAKE_CASE_						:int										=      AutoTokenizer.from_pretrained(args.model     )
				elif args.preprocessor == "image_processor":
						SCREAMING_SNAKE_CASE_						:Any										=      AutoImageProcessor.from_pretrained(args.model     )
				elif args.preprocessor == "feature_extractor":
						SCREAMING_SNAKE_CASE_						:Optional[Any]										=      AutoFeatureExtractor.from_pretrained(args.model     )
				elif args.preprocessor == "processor":
						SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      AutoProcessor.from_pretrained(args.model     )
				else:
						raise ValueError(F'Unknown preprocessor type \'{args.preprocessor}\''     )

				# Ensure the requested opset is sufficient
				if args.opset is None:
						SCREAMING_SNAKE_CASE_						:Tuple										=      onnx_config.default_onnx_opset

				if args.opset < onnx_config.default_onnx_opset:
						raise ValueError(
						    F'Opset {args.opset} is not sufficient to export {model_kind}. '
						    F'At least  {onnx_config.default_onnx_opset} is required.'     )

				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:str										=      export(
				    SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		args.opset ,		args.output ,		)

				if args.atol is None:
						SCREAMING_SNAKE_CASE_						:Tuple										=      onnx_config.atol_for_validation

				validate_model_outputs(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		args.output ,		SCREAMING_SNAKE_CASE ,		args.atol     )
				logger.info(F'All good, model saved at: {args.output.as_posix()}'     )
				warnings.warn(
				    'The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend'
				    ' using optimum.exporters.onnx in future. You can find more information here:'
				    ' https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model.' ,		SCREAMING_SNAKE_CASE ,		)







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:Dict										=      ArgumentParser('Hugging Face Transformers ONNX exporter'     )
		parser.add_argument(
		    '-m' ,		'--model' ,		type=SCREAMING_SNAKE_CASE ,		required=SCREAMING_SNAKE_CASE ,		help='Model ID on huggingface.co or path on disk to load model from.'     )
		parser.add_argument(
		    '--feature' ,		default='default' ,		help='The type of features to export the model with.' ,		)
		parser.add_argument('--opset' ,		type=SCREAMING_SNAKE_CASE ,		default=SCREAMING_SNAKE_CASE ,		help='ONNX opset version to export the model with.'     )
		parser.add_argument(
		    '--atol' ,		type=SCREAMING_SNAKE_CASE ,		default=SCREAMING_SNAKE_CASE ,		help='Absolute difference tolerance when validating the model.'     )
		parser.add_argument(
		    '--framework' ,		type=SCREAMING_SNAKE_CASE ,		choices=['pt', 'tf'] ,		default=SCREAMING_SNAKE_CASE ,		help=(
		        'The framework to use for the ONNX export.'
		        ' If not provided, will attempt to use the local checkpoint\'s original framework'
		        ' or what is available in the environment.'
		    ) ,		)
		parser.add_argument('output' ,		type=SCREAMING_SNAKE_CASE ,		help='Path indicating where to store generated ONNX model.'     )
		parser.add_argument('--cache_dir' ,		type=SCREAMING_SNAKE_CASE ,		default=SCREAMING_SNAKE_CASE ,		help='Path indicating where to store cache.'     )
		parser.add_argument(
		    '--preprocessor' ,		type=SCREAMING_SNAKE_CASE ,		choices=['auto', 'tokenizer', 'feature_extractor', 'image_processor', 'processor'] ,		default='auto' ,		help='Which type of preprocessor to use. \'auto\' tries to automatically detect it.' ,		)
		parser.add_argument(
		    '--export_with_transformers' ,		action='store_true' ,		help=(
		        'Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be '
		        'useful when exporting a model supported in transformers but not in optimum, otherwise it is not '
		        'recommended.'
		    ) ,		)

		SCREAMING_SNAKE_CASE_						:List[str]										=      parser.parse_args()
		if args.export_with_transformers or not is_optimum_available():
				export_with_transformers(SCREAMING_SNAKE_CASE     )
		else:
				export_with_optimum(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__     : int				=   logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
							logger.setLevel(logging.INFO)
							main()


'''simple docstring'''







import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, 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 DinatBackbone, DinatForImageClassification, DinatModel
							from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor






class 					lowerCAmelCase__  :






					def __init__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE=13							,				__SCREAMING_SNAKE_CASE=64							,				__SCREAMING_SNAKE_CASE=4							,				__SCREAMING_SNAKE_CASE=3							,				__SCREAMING_SNAKE_CASE=16							,				__SCREAMING_SNAKE_CASE=[1, 2, 1]							,				__SCREAMING_SNAKE_CASE=[2, 4, 8]							,				__SCREAMING_SNAKE_CASE=3							,				__SCREAMING_SNAKE_CASE=[[3], [1, 2], [1]]							,				__SCREAMING_SNAKE_CASE=2.0							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=0.0							,				__SCREAMING_SNAKE_CASE=0.0							,				__SCREAMING_SNAKE_CASE=0.1							,				__SCREAMING_SNAKE_CASE="gelu"							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=0.02							,				__SCREAMING_SNAKE_CASE=1E-5							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=10							,				__SCREAMING_SNAKE_CASE=["stage1", "stage2"]							,				__SCREAMING_SNAKE_CASE=[1, 2]							,				):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			parent
											lowercase_      :	Optional[Any]						=			batch_size
											lowercase_      :	str						=			image_size
											lowercase_      :	Optional[Any]						=			patch_size
											lowercase_      :	Union[str, Any]						=			num_channels
											lowercase_      :	List[Any]						=			embed_dim
											lowercase_      :	Optional[int]						=			depths
											lowercase_      :	Any						=			num_heads
											lowercase_      :	Any						=			kernel_size
											lowercase_      :	str						=			dilations
											lowercase_      :	Optional[Any]						=			mlp_ratio
											lowercase_      :	Optional[Any]						=			qkv_bias
											lowercase_      :	Union[str, Any]						=			hidden_dropout_prob
											lowercase_      :	str						=			attention_probs_dropout_prob
											lowercase_      :	List[Any]						=			drop_path_rate
											lowercase_      :	str						=			hidden_act
											lowercase_      :	Optional[int]						=			patch_norm
											lowercase_      :	Optional[int]						=			layer_norm_eps
											lowercase_      :	str						=			initializer_range
											lowercase_      :	Optional[Any]						=			is_training
											lowercase_      :	Any						=			scope
											lowercase_      :	Dict						=			use_labels
											lowercase_      :	Union[str, Any]						=			num_labels
											lowercase_      :	List[str]						=			out_features
											lowercase_      :	str						=			out_indices






					def       _snake_case       (  self						):


											"""simple docstring"""





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

											lowercase_      :	List[str]						=			None
											if self.use_labels:
																	lowercase_      :	List[str]						=			ids_tensor([self.batch_size]							,				self.num_labels						)

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

											return config, pixel_values, labels






					def       _snake_case       (  self						):


											"""simple docstring"""





											return DinatConfig(
											    num_labels=self.num_labels							,				image_size=self.image_size							,				patch_size=self.patch_size							,				num_channels=self.num_channels							,				embed_dim=self.embed_dim							,				depths=self.depths							,				num_heads=self.num_heads							,				kernel_size=self.kernel_size							,				dilations=self.dilations							,				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							,				patch_norm=self.patch_norm							,				layer_norm_eps=self.layer_norm_eps							,				initializer_range=self.initializer_range							,				out_features=self.out_features							,				out_indices=self.out_indices							,				)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			DinatModel(config=__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	Union[str, Any]						=			model(__SCREAMING_SNAKE_CASE						)

											lowercase_      :	int						=			(config.image_size // config.patch_size) // (2 ** (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_height, expected_width, expected_dim)						)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	int						=			DinatForImageClassification(__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	Union[str, Any]						=			model(__SCREAMING_SNAKE_CASE							,				labels=__SCREAMING_SNAKE_CASE						)
											self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_labels)						)

											# test greyscale images
											lowercase_      :	Union[str, Any]						=			1
											lowercase_      :	List[str]						=			DinatForImageClassification(__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()

											lowercase_      :	str						=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
											lowercase_      :	Optional[int]						=			model(__SCREAMING_SNAKE_CASE						)
											self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_labels)						)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Dict						=			DinatBackbone(config=__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	Union[str, Any]						=			model(__SCREAMING_SNAKE_CASE						)

											# 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, model.channels[0], 16, 16]						)

											# verify channels
											self.parent.assertEqual(len(model.channels						)							,				len(config.out_features						)						)

											# verify backbone works with out_features=None
											lowercase_      :	str						=			None
											lowercase_      :	Optional[int]						=			DinatBackbone(config=__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	str						=			model(__SCREAMING_SNAKE_CASE						)

											# verify feature maps
											self.parent.assertEqual(len(result.feature_maps						)							,				1						)
											self.parent.assertListEqual(list(result.feature_maps[0].shape						)							,				[self.batch_size, model.channels[-1], 4, 4]						)

											# verify channels
											self.parent.assertEqual(len(model.channels						)							,				1						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Any						=			self.prepare_config_and_inputs()
											lowercase_		,					lowercase_		,					lowercase_      :	int						=			config_and_inputs
											lowercase_      :	Any						=			{'''pixel_values''': pixel_values}
											return config, inputs_dict







@require_natten
@require_torch
class 					lowerCAmelCase__  (    lowerCamelCase_       ,		lowerCamelCase_       ,		unittest.TestCase	):
					lowerCAmelCase_	  =	(
					    (
					        DinatModel,
					        DinatForImageClassification,
					        DinatBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					lowerCAmelCase_	  =	(
					    {'''feature-extraction''': DinatModel, '''image-classification''': DinatForImageClassification}
					    if is_torch_available()
					    else {}
					)
					lowerCAmelCase_	  =	False

					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			DinatModelTester(self						)
											lowercase_      :	Optional[Any]						=			ConfigTester(self							,				config_class=__SCREAMING_SNAKE_CASE							,				embed_dim=37						)






					def       _snake_case       (  self						):


											"""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       _snake_case       (  self						):


											"""simple docstring"""





											return






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Any						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE						)






					@unittest.skip(reason='''Dinat does not use inputs_embeds'''						)
					def       _snake_case       (  self						):


											"""simple docstring"""





											pass






					@unittest.skip(reason='''Dinat does not use feedforward chunking'''						)
					def       _snake_case       (  self						):


											"""simple docstring"""





											pass






					def       _snake_case       (  self						):


											"""simple docstring"""





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

											for model_class in self.all_model_classes:
																	lowercase_      :	str						=			model_class(__SCREAMING_SNAKE_CASE						)
																	self.assertIsInstance(model.get_input_embeddings()							,				(nn.Module)						)
																	lowercase_      :	int						=			model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE							,				nn.Linear						)						)






					def       _snake_case       (  self						):


											"""simple docstring"""





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

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

																	lowercase_      :	Dict						=			['''pixel_values''']
																	self.assertListEqual(arg_names[:1]							,				__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											self.skipTest('''Dinat\'s attention operation is handled entirely by NATTEN.'''						)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Union[str, Any]						=			model_class(__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()

											with torch.no_grad():
																	lowercase_      :	Optional[Any]						=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)						)

											lowercase_      :	int						=			outputs.hidden_states

											lowercase_      :	List[str]						=			getattr(
											    self.model_tester							,				'''expected_num_hidden_layers'''							,				len(self.model_tester.depths						) + 1						)
											self.assertEqual(len(__SCREAMING_SNAKE_CASE						)							,				__SCREAMING_SNAKE_CASE						)

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

											lowercase_      :	Union[str, Any]						=			image_size[0] // patch_size[0]
											lowercase_      :	Any						=			image_size[1] // patch_size[1]

											self.assertListEqual(
											    list(hidden_states[0].shape[-3:]						)							,				[height, width, self.model_tester.embed_dim]							,				)

											if model_class.__name__ != "DinatBackbone":
																	lowercase_      :	Any						=			outputs.reshaped_hidden_states
																	self.assertEqual(len(__SCREAMING_SNAKE_CASE						)							,				__SCREAMING_SNAKE_CASE						)

																	lowercase_		,					lowercase_		,					lowercase_		,					lowercase_      :	int						=			reshaped_hidden_states[0].shape
																	lowercase_      :	str						=			(
																	    reshaped_hidden_states[0].view(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						).permute(0							,				2							,				3							,				1						)
																	)
																	self.assertListEqual(
																	    list(reshaped_hidden_states.shape[-3:]						)							,				[height, width, self.model_tester.embed_dim]							,				)






					def       _snake_case       (  self						):


											"""simple docstring"""





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

											lowercase_      :	List[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)
											)

											for model_class in self.all_model_classes:
																	lowercase_      :	Dict						=			True
																	self.check_hidden_states_output(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)

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

																	self.check_hidden_states_output(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)






					@slow
					def       _snake_case       (  self						):


											"""simple docstring"""





											for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase_      :	Optional[Any]						=			DinatModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
																	self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





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

											lowercase_      :	Tuple						=			_config_zero_init(__SCREAMING_SNAKE_CASE						)
											for model_class in self.all_model_classes:
																	lowercase_      :	List[str]						=			model_class(config=__SCREAMING_SNAKE_CASE						)
																	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_natten
@require_vision
@require_torch
class 					lowerCAmelCase__  (    unittest.TestCase	):






					@cached_property
					def       _snake_case       (  self						):


											"""simple docstring"""





											return AutoImageProcessor.from_pretrained('''shi-labs/dinat-mini-in1k-224'''						) if is_vision_available() else None






					@slow
					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			DinatForImageClassification.from_pretrained('''shi-labs/dinat-mini-in1k-224'''						).to(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	str						=			self.default_image_processor

											lowercase_      :	Tuple						=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
											lowercase_      :	Tuple						=			image_processor(images=__SCREAMING_SNAKE_CASE							,				return_tensors='''pt'''						).to(__SCREAMING_SNAKE_CASE						)

											# forward pass
											with torch.no_grad():
																	lowercase_      :	str						=			model(**__SCREAMING_SNAKE_CASE						)

											# verify the logits
											lowercase_      :	List[Any]						=			torch.Size((1, 10_00)						)
											self.assertEqual(outputs.logits.shape							,				__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Union[str, Any]						=			torch.tensor([-0.1_545, -0.7_667, 0.4_642]						).to(__SCREAMING_SNAKE_CASE						)
											self.assertTrue(torch.allclose(outputs.logits[0, :3]							,				__SCREAMING_SNAKE_CASE							,				atol=1E-4						)						)







@require_torch
@require_natten
class 					lowerCAmelCase__  (    unittest.TestCase       ,		lowerCamelCase_	):
					lowerCAmelCase_	  =	(DinatBackbone,) if is_torch_available() else ()
					lowerCAmelCase_	  =	DinatConfig






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			DinatModelTester(self						)



