
"""simple docstring"""







import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


lowerCamelCase__     =						torch.cuda.is_available()
lowerCamelCase__     =						[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
lowerCamelCase__     =						["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
lowerCamelCase__     =						{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		Dict		     = '\n'.join(_UpperCamelCase						)
	Path(_UpperCamelCase						).open('w'						).writelines(_UpperCamelCase						)
def        __lowerCAmelCase							(_UpperCamelCase						):
	for split in ["train", "val", "test"]:
		_dump_articles(os.path.join(_UpperCamelCase			,    F"{split}.source"						)			,    _UpperCamelCase						)
		_dump_articles(os.path.join(_UpperCamelCase			,    F"{split}.target"						)			,    _UpperCamelCase						)
	return tmp_dir




class 					A__			(      _lowerCamelCase):

		@classmethod
		def 				__lowerCamelCase				( cls					):
			return cls

		@require_torch_multi_gpu
		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		str		     = {
			    'no_teacher': True,
			    'freeze_encoder': True,
			    'gpus': 2,
			    'overwrite_output_dir': True,
			    'sortish_sampler': True,
			}
			self._test_distiller_cli_fork(_SCREAMING_SNAKE_CASE			,						check_contents=_SCREAMING_SNAKE_CASE					)







		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=True					):
			__lowerCAmelCase     :		Optional[int]		     = {
			    'label_smoothing': 0.0,
			    'early_stopping_patience': -1,
			    'train_batch_size': 1,
			    'eval_batch_size': 2,
			    'max_epochs': 2,
			    'alpha_mlm': 0.2,
			    'alpha_ce': 0.8,
			    'do_predict': True,
			    'model_name_or_path': 'sshleifer/tinier_bart',
			    'teacher': CHEAP_ARGS['model_name_or_path'],
			    'val_check_interval': 0.5,
			}
			default_updates.update(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		dict		     = CHEAP_ARGS.copy()
			__lowerCAmelCase     :		int		     = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
			__lowerCAmelCase     :		Dict		     = self.get_auto_remove_tmp_dir()
			args_d.update(data_dir=_SCREAMING_SNAKE_CASE			,						output_dir=_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)

			def convert(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
				if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
					return ""
				if v is False or v is None:
					return ""
				if v is True:  # or len(str(v))==0:
					return f"--{k}"
				return f"--{k}={v}"

			__lowerCAmelCase     :		Union[str, Any]		     = [x for x in (convert(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					) for k, v in args_d.items()) if len(_SCREAMING_SNAKE_CASE					)]
			__lowerCAmelCase     :		int		     = [sys.executable, f"{self.test_file_dir}/distillation.py"] + cli_args
			execute_subprocess_async(_SCREAMING_SNAKE_CASE			,						env=self.get_env()					)

			__lowerCAmelCase     :		List[str]		     = os.listdir(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Union[str, Any]		     = {os.path.basename(_SCREAMING_SNAKE_CASE					) for p in contents}
			__lowerCAmelCase     :		Any		     = [p for p in contents if p.endswith('ckpt'					)]
			assert len(_SCREAMING_SNAKE_CASE					) > 0

			self.assertIn('test_generations.txt'			,						_SCREAMING_SNAKE_CASE					)
			self.assertIn('test_results.txt'			,						_SCREAMING_SNAKE_CASE					)

			# get the following from the module, (we don't have access to `model` here)
			__lowerCAmelCase     :		Any		     = os.path.join(_SCREAMING_SNAKE_CASE			,						'metrics.json'					)
			__lowerCAmelCase     :		Tuple		     = 'rouge2'

			__lowerCAmelCase     :		int		     = load_json(_SCREAMING_SNAKE_CASE					)
			# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
			print(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Union[str, Any]		     = metrics['val'][-1]
			self.assertGreaterEqual(last_step_stats['val_avg_gen_time']			,						0.01					)
			self.assertIsInstance(last_step_stats[f"val_avg_{val_metric}"]			,						_SCREAMING_SNAKE_CASE					)
			self.assertEqual(len(metrics['test']					)			,						1					)
			__lowerCAmelCase     :		Union[str, Any]		     = int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1					)
			self.assertEqual(len(metrics['val']					)			,						_SCREAMING_SNAKE_CASE					)

"""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					)