






import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
snake_case_   :	str							      =logging.get_logger(__name__)

snake_case_   :	Tuple							      ={
    '''post_extract_proj''': '''feature_projection.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.layer_norm''': '''encoder.layer_norm''',
    '''mask_emb''': '''masked_spec_embed''',
}



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		for attribute in key.split("."	):
				__A    =							getattr(lowerCAmelCase__					,     lowerCAmelCase__	)

		if weight_type is not None:
				__A    =							getattr(lowerCAmelCase__					,     lowerCAmelCase__	).shape
		else:
				__A    =							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    =							value
		elif weight_type == "weight_g":
				__A    =							value
		elif weight_type == "weight_v":
				__A    =							value
		elif weight_type == "bias":
				__A    =							value
		else:
				__A    =							value

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							[]
		__A    =							fairseq_model.state_dict()

		__A    =							hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				__A    =							False
				if "conv_layers" in name:
						load_conv_layer(
						    lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     hf_model.config.feat_extract_norm == "group"					,     )
						__A    =							True
				else:
						for key, mapped_key in MAPPING.items():
								__A    =							mapped_key

								if key in name:
										__A    =							True
										if "*" in mapped_key:
												__A    =							name.split(lowerCAmelCase__	)[0].split("."	)[-2]
												__A    =							mapped_key.replace("*"					,     lowerCAmelCase__	)
										if "weight_g" in name:
												__A    =							"weight_g"
										elif "weight_v" in name:
												__A    =							"weight_v"
										elif "weight" in name:
												__A    =							"weight"
										elif "bias" in name:
												__A    =							"bias"
										else:
												__A    =							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      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							full_name.split("conv_layers."	)[-1]
		__A    =							name.split("."	)
		__A    =							int(items[0]	)
		__A    =							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    =							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    =							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    =							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    =							value
						logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
		else:
				unused_weights.append(lowerCAmelCase__	)



def      UpperCAmelCase						( lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							HubertConfig()
		__A    =							model.config

		__A    =							fs_config.activation_dropout
		__A    =							False
		__A    =							fs_config.attention_dropout
		__A    =							False
		__A    =							eval(fs_config.extractor_conv_feature_layers	)
		__A    =							[x[0] for x in conv_layers]
		__A    =							[x[1] for x in conv_layers]
		__A    =							[x[2] for x in conv_layers]
		__A    =							"gelu"
		__A    =							"layer" if fs_config.extractor_mode == "layer_norm" else "group"
		__A    =							False
		__A    =							0.0
		__A    =							0.0
		__A    =							fs_config.activation_fn
		__A    =							fs_config.dropout
		__A    =							fs_config.encoder_embed_dim
		__A    =							0.02
		__A    =							fs_config.encoder_ffn_embed_dim
		__A    =							1E-5
		__A    =							0.0
		__A    =							fs_config.encoder_attention_heads
		__A    =							fs_config.conv_pos_groups
		__A    =							fs_config.conv_pos
		__A    =							len(lowerCAmelCase__	)
		__A    =							fs_config.encoder_layers

		return config



@torch.no_grad()
def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__=None	):


		'''simple docstring'''

		__A    =							distilhubert().model.model

		if config_path is not None:
				__A    =							HubertConfig.from_pretrained(lowerCAmelCase__	)
		else:
				__A    =							convert_config(lowerCAmelCase__	)
		__A    =							model.eval()

		__A    =							WavaVecaFeatureExtractor(
		    feature_size=1					,     sampling_rate=1_6000					,     padding_value=0					,     do_normalize=lowerCAmelCase__					,     return_attention_mask=lowerCAmelCase__					,     )
		__A    =							HubertModel(lowerCAmelCase__	)

		recursively_load_weights(lowerCAmelCase__					,     lowerCAmelCase__	)

		feature_extractor.save_pretrained(lowerCAmelCase__	)
		hf_model.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
					snake_case_   :	str							      =argparse.ArgumentParser()
					parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
					parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
					snake_case_   :	str							      =parser.parse_args()
					convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)



"""simple docstring"""







import collections
import inspect
import unittest

from transformers import NatConfig
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 NatBackbone, NatForImageClassification, NatModel
							from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor




class 					A__			:

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

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Dict		     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

			__lowerCAmelCase     :		Union[str, Any]		     = None
			if self.use_labels:
				__lowerCAmelCase     :		int		     = ids_tensor([self.batch_size]			,						self.num_labels					)

			__lowerCAmelCase     :		Tuple		     = self.get_config()

			return config, pixel_values, labels

		def 				__lowerCamelCase				( self					):
			return NatConfig(
			    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			,						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 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		Any		     = NatModel(config=_SCREAMING_SNAKE_CASE					)
			model.to(_SCREAMING_SNAKE_CASE					)
			model.eval()
			__lowerCAmelCase     :		Union[str, Any]		     = model(_SCREAMING_SNAKE_CASE					)

			__lowerCAmelCase     :		str		     = (config.image_size // config.patch_size) // (2 ** (len(config.depths					) - 1))
			__lowerCAmelCase     :		Any		     = 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 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		Dict		     = NatForImageClassification(_SCREAMING_SNAKE_CASE					)
			model.to(_SCREAMING_SNAKE_CASE					)
			model.eval()
			__lowerCAmelCase     :		Dict		     = model(_SCREAMING_SNAKE_CASE			,						labels=_SCREAMING_SNAKE_CASE					)
			self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)					)

			# test greyscale images
			__lowerCAmelCase     :		Union[str, Any]		     = 1
			__lowerCAmelCase     :		Any		     = NatForImageClassification(_SCREAMING_SNAKE_CASE					)
			model.to(_SCREAMING_SNAKE_CASE					)
			model.eval()

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

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		Tuple		     = NatBackbone(config=_SCREAMING_SNAKE_CASE					)
			model.to(_SCREAMING_SNAKE_CASE					)
			model.eval()
			__lowerCAmelCase     :		Dict		     = 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
			__lowerCAmelCase     :		List[Any]		     = None
			__lowerCAmelCase     :		List[Any]		     = NatBackbone(config=_SCREAMING_SNAKE_CASE					)
			model.to(_SCREAMING_SNAKE_CASE					)
			model.eval()
			__lowerCAmelCase     :		Dict		     = 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 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Dict		     = self.prepare_config_and_inputs()
			__lowerCAmelCase     ,  __lowerCAmelCase     ,  __lowerCAmelCase     :		str		     = config_and_inputs
			__lowerCAmelCase     :		Tuple		     = {'pixel_values': pixel_values}
			return config, inputs_dict




@require_natten
@require_torch
class 					A__			(      _lowerCamelCase			,	_lowerCamelCase			,	unittest.TestCase):
		A_		:			str							  =      (
		    (
		        NatModel,
		        NatForImageClassification,
		        NatBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		A_		:			int							  =      (
		    {'feature-extraction': NatModel, 'image-classification': NatForImageClassification}
		    if is_torch_available()
		    else {}
		)
		A_		:			Union[str, Any]							  =      False

		A_		:			List[Any]							  =      False
		A_		:			Optional[Any]							  =      False
		A_		:			Union[str, Any]							  =      False
		A_		:			int							  =      False

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		int		     = NatModelTester(self					)
			__lowerCAmelCase     :		Dict		     = ConfigTester(self			,						config_class=_SCREAMING_SNAKE_CASE			,						embed_dim=37					)

		def 				__lowerCamelCase				( self					):
			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 				__lowerCamelCase				( self					):
			return

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		List[Any]		     = self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Optional[int]		     = self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		str		     = self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_backbone(*_SCREAMING_SNAKE_CASE					)

		@unittest.skip(reason='Nat does not use inputs_embeds'					)
		def 				__lowerCamelCase				( self					):
			pass

		@unittest.skip(reason='Nat does not use feedforward chunking'					)
		def 				__lowerCamelCase				( self					):
			pass

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     ,  __lowerCAmelCase     :		Dict		     = self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__lowerCAmelCase     :		Optional[Any]		     = model_class(_SCREAMING_SNAKE_CASE					)
				self.assertIsInstance(model.get_input_embeddings()			,						(nn.Module)					)
				__lowerCAmelCase     :		List[str]		     = model.get_output_embeddings()
				self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE			,						nn.Linear					)					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     ,  __lowerCAmelCase     :		Optional[int]		     = self.model_tester.prepare_config_and_inputs_for_common()

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

				__lowerCAmelCase     :		Dict		     = ['pixel_values']
				self.assertListEqual(arg_names[:1]			,						_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self					):
			self.skipTest('Nat\'s attention operation is handled entirely by NATTEN.'					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		List[str]		     = model_class(_SCREAMING_SNAKE_CASE					)
			model.to(_SCREAMING_SNAKE_CASE					)
			model.eval()

			with torch.no_grad():
				__lowerCAmelCase     :		Any		     = model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)					)

			__lowerCAmelCase     :		str		     = outputs.hidden_states

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

			# Nat has a different seq_length
			__lowerCAmelCase     :		Dict		     = (
			    config.patch_size
			    if isinstance(config.patch_size			,						collections.abc.Iterable					)
			    else (config.patch_size, config.patch_size)
			)

			__lowerCAmelCase     :		str		     = image_size[0] // patch_size[0]
			__lowerCAmelCase     :		Union[str, 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__ != "NatBackbone":
				__lowerCAmelCase     :		Optional[Any]		     = outputs.reshaped_hidden_states
				self.assertEqual(len(_SCREAMING_SNAKE_CASE					)			,						_SCREAMING_SNAKE_CASE					)

				__lowerCAmelCase     ,  __lowerCAmelCase     ,  __lowerCAmelCase     ,  __lowerCAmelCase     :		str		     = reshaped_hidden_states[0].shape
				__lowerCAmelCase     :		List[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 				__lowerCamelCase				( self					):
			__lowerCAmelCase     ,  __lowerCAmelCase     :		Optional[int]		     = self.model_tester.prepare_config_and_inputs_for_common()

			__lowerCAmelCase     :		List[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:
				__lowerCAmelCase     :		Union[str, Any]		     = 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"]
				__lowerCAmelCase     :		Dict		     = True

				self.check_hidden_states_output(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)

		@slow
		def 				__lowerCamelCase				( self					):
			for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__lowerCAmelCase     :		List[Any]		     = NatModel.from_pretrained(_SCREAMING_SNAKE_CASE					)
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)







		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     ,  __lowerCAmelCase     :		Optional[Any]		     = self.model_tester.prepare_config_and_inputs_for_common()

			__lowerCAmelCase     :		List[Any]		     = _config_zero_init(_SCREAMING_SNAKE_CASE					)
			for model_class in self.all_model_classes:
				__lowerCAmelCase     :		int		     = 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 					A__			(      unittest.TestCase):

		@cached_property
		def 				__lowerCamelCase				( self					):
			return AutoImageProcessor.from_pretrained('shi-labs/nat-mini-in1k-224'					) if is_vision_available() else None







		@slow
		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Optional[int]		     = NatForImageClassification.from_pretrained('shi-labs/nat-mini-in1k-224'					).to(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		str		     = self.default_image_processor

			__lowerCAmelCase     :		str		     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
			__lowerCAmelCase     :		Dict		     = image_processor(images=_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).to(_SCREAMING_SNAKE_CASE					)

			# forward pass
			with torch.no_grad():
				__lowerCAmelCase     :		Dict		     = model(**_SCREAMING_SNAKE_CASE					)

			# verify the logits
			__lowerCAmelCase     :		Optional[int]		     = torch.Size((1, 10_00)					)
			self.assertEqual(outputs.logits.shape			,						_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Dict		     = torch.tensor([0.3805, -0.8676, -0.3912]					).to(_SCREAMING_SNAKE_CASE					)
			self.assertTrue(torch.allclose(outputs.logits[0, :3]			,						_SCREAMING_SNAKE_CASE			,						atol=1E-4					)					)




@require_torch
@require_natten
class 					A__			(      unittest.TestCase			,	_lowerCamelCase):
		A_		:			str							  =      (NatBackbone,) if is_torch_available() else ()
		A_		:			Any							  =      NatConfig

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Union[str, Any]		     = NatModelTester(self					)