


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 	_lowercase				:






				'''simple docstring'''



				def __init__(	self      :str      ,      lowerCAmelCase__      :List[str]      ,      lowerCAmelCase__      :Dict=13      ,      lowerCAmelCase__      :int=64      ,      lowerCAmelCase__      :Optional[Any]=4      ,      lowerCAmelCase__      :int=3      ,      lowerCAmelCase__      :Union[str, Any]=16      ,      lowerCAmelCase__      :List[str]=[1, 2, 1]      ,      lowerCAmelCase__      :List[Any]=[2, 4, 8]      ,      lowerCAmelCase__      :Optional[Any]=3      ,      lowerCAmelCase__      :Optional[Any]=2.0      ,      lowerCAmelCase__      :Any=True      ,      lowerCAmelCase__      :Optional[int]=0.0      ,      lowerCAmelCase__      :Union[str, Any]=0.0      ,      lowerCAmelCase__      :int=0.1      ,      lowerCAmelCase__      :List[str]="gelu"      ,      lowerCAmelCase__      :List[str]=True      ,      lowerCAmelCase__      :Tuple=0.02      ,      lowerCAmelCase__      :Any=1E-5      ,      lowerCAmelCase__      :Tuple=True      ,      lowerCAmelCase__      :Union[str, Any]=None      ,      lowerCAmelCase__      :int=True      ,      lowerCAmelCase__      :Any=10      ,      lowerCAmelCase__      :Optional[Any]=["stage1", "stage2"]      ,      lowerCAmelCase__      :List[Any]=[1, 2]      ,      )		->      Tuple:
										__SCREAMING_SNAKE_CASE						:					int		      =		parent
										__SCREAMING_SNAKE_CASE						:					Dict		      =		batch_size
										__SCREAMING_SNAKE_CASE						:					Dict		      =		image_size
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		patch_size
										__SCREAMING_SNAKE_CASE						:					Dict		      =		num_channels
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		embed_dim
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		depths
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		num_heads
										__SCREAMING_SNAKE_CASE						:					Dict		      =		kernel_size
										__SCREAMING_SNAKE_CASE						:					Any		      =		mlp_ratio
										__SCREAMING_SNAKE_CASE						:					Dict		      =		qkv_bias
										__SCREAMING_SNAKE_CASE						:					str		      =		hidden_dropout_prob
										__SCREAMING_SNAKE_CASE						:					Dict		      =		attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE						:					str		      =		drop_path_rate
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		hidden_act
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		patch_norm
										__SCREAMING_SNAKE_CASE						:					Dict		      =		layer_norm_eps
										__SCREAMING_SNAKE_CASE						:					Dict		      =		initializer_range
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		is_training
										__SCREAMING_SNAKE_CASE						:					Dict		      =		scope
										__SCREAMING_SNAKE_CASE						:					Any		      =		use_labels
										__SCREAMING_SNAKE_CASE						:					Any		      =		num_labels
										__SCREAMING_SNAKE_CASE						:					str		      =		out_features
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		out_indices


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

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

										__SCREAMING_SNAKE_CASE						:					str		      =		self.get_config()

										return config, pixel_values, labels


				def 		__magic_name__(	self      :List[str]     )		->      int:
										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 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :str      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :Dict     )		->      Optional[int]:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		NatModel(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										__SCREAMING_SNAKE_CASE						:					Any		      =		model(lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					int		      =		(config.image_size // config.patch_size) // (2 ** (len(config.depths     ) - 1))
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		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 		__magic_name__(	self      :Tuple      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Tuple     )		->      int:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		NatForImageClassification(lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										__SCREAMING_SNAKE_CASE						:					Any		      =		model(lowerCAmelCase__      ,      labels=lowerCAmelCase__     )
										self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)     )

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

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


				def 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Dict     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					int		      =		NatBackbone(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										__SCREAMING_SNAKE_CASE						:					Dict		      =		model(lowerCAmelCase__     )

										# 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
										__SCREAMING_SNAKE_CASE						:					int		      =		None
										__SCREAMING_SNAKE_CASE						:					int		      =		NatBackbone(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										__SCREAMING_SNAKE_CASE						:					str		      =		model(lowerCAmelCase__     )

										# 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 		__magic_name__(	self      :int     )		->      int:
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.prepare_config_and_inputs()
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		config_and_inputs
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		{'''pixel_values''': pixel_values}
										return config, inputs_dict




@require_natten
@require_torch
class 	_lowercase				(							A__						,      A__						,      unittest.TestCase	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      Any      =		(
				    (
				        NatModel,
				        NatForImageClassification,
				        NatBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE__    :      Optional[Any]      =		(
				    {'''feature-extraction''': NatModel, '''image-classification''': NatForImageClassification}
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE__    :      Optional[Any]      =		False

				SCREAMING_SNAKE_CASE__    :      int      =		False
				SCREAMING_SNAKE_CASE__    :      Optional[Any]      =		False
				SCREAMING_SNAKE_CASE__    :      Tuple      =		False
				SCREAMING_SNAKE_CASE__    :      Tuple      =		False


				def 		__magic_name__(	self      :Optional[Any]     )		->      Optional[Any]:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		NatModelTester(self     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		ConfigTester(self      ,      config_class=lowerCAmelCase__      ,      embed_dim=37     )


				def 		__magic_name__(	self      :List[str]     )		->      Dict:
										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 		__magic_name__(	self      :Optional[int]     )		->      List[str]:
										return


				def 		__magic_name__(	self      :Union[str, Any]     )		->      Any:
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :List[Any]     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :List[Any]     )		->      List[Any]:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*lowerCAmelCase__     )


				@unittest.skip(reason='''Nat does not use inputs_embeds'''     )
				def 		__magic_name__(	self      :List[str]     )		->      Optional[int]:
										pass


				@unittest.skip(reason='''Nat does not use feedforward chunking'''     )
				def 		__magic_name__(	self      :Union[str, Any]     )		->      Union[str, Any]:
										pass


				def 		__magic_name__(	self      :Optional[Any]     )		->      Any:
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.model_tester.prepare_config_and_inputs_for_common()

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


				def 		__magic_name__(	self      :Union[str, Any]     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.model_tester.prepare_config_and_inputs_for_common()

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

																__SCREAMING_SNAKE_CASE						:					int		      =		['''pixel_values''']
																self.assertListEqual(arg_names[:1]      ,      lowerCAmelCase__     )


				def 		__magic_name__(	self      :Optional[int]     )		->      List[Any]:
										self.skipTest('''Nat\'s attention operation is handled entirely by NATTEN.'''     )


				def 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Any     )		->      List[Any]:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		model_class(lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()

										with torch.no_grad():
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		model(**self._prepare_for_class(lowerCAmelCase__      ,      lowerCAmelCase__     )     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		outputs.hidden_states

										__SCREAMING_SNAKE_CASE						:					Dict		      =		getattr(
										    self.model_tester      ,      '''expected_num_hidden_layers'''      ,      len(self.model_tester.depths     ) + 1     )
										self.assertEqual(len(lowerCAmelCase__     )      ,      lowerCAmelCase__     )

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

										__SCREAMING_SNAKE_CASE						:					int		      =		image_size[0] // patch_size[0]
										__SCREAMING_SNAKE_CASE						:					List[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":
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		outputs.reshaped_hidden_states
																self.assertEqual(len(lowerCAmelCase__     )      ,      lowerCAmelCase__     )

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


				def 		__magic_name__(	self      :str     )		->      int:
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.model_tester.prepare_config_and_inputs_for_common()

										__SCREAMING_SNAKE_CASE						:					Union[str, 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:
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		True
																self.check_hidden_states_output(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__     )

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

																self.check_hidden_states_output(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__     )


				@slow
				def 		__magic_name__(	self      :Optional[Any]     )		->      List[str]:
										for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		NatModel.from_pretrained(lowerCAmelCase__     )
																self.assertIsNotNone(lowerCAmelCase__     )



				def 		__magic_name__(	self      :Optional[int]     )		->      str:
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.model_tester.prepare_config_and_inputs_for_common()

										__SCREAMING_SNAKE_CASE						:					Dict		      =		_config_zero_init(lowerCAmelCase__     )
										for model_class in self.all_model_classes:
																__SCREAMING_SNAKE_CASE						:					Any		      =		model_class(config=lowerCAmelCase__     )
																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 	_lowercase				(							unittest.TestCase	):






				'''simple docstring'''



				@cached_property
				def 		__magic_name__(	self      :str     )		->      List[Any]:
										return AutoImageProcessor.from_pretrained('''shi-labs/nat-mini-in1k-224'''     ) if is_vision_available() else None



				@slow
				def 		__magic_name__(	self      :Optional[int]     )		->      Tuple:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		NatForImageClassification.from_pretrained('''shi-labs/nat-mini-in1k-224'''     ).to(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.default_image_processor

										__SCREAMING_SNAKE_CASE						:					Dict		      =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		image_processor(images=lowerCAmelCase__      ,      return_tensors='''pt'''     ).to(lowerCAmelCase__     )

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

										# verify the logits
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		torch.Size((1, 1_000)     )
										self.assertEqual(outputs.logits.shape      ,      lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		torch.tensor([0.3805, -0.8676, -0.3912]     ).to(lowerCAmelCase__     )
										self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,      lowerCAmelCase__      ,      atol=1E-4     )     )




@require_torch
@require_natten
class 	_lowercase				(							unittest.TestCase						,      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      Union[str, Any]      =		(NatBackbone,) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE__    :      str      =		NatConfig


				def 		__magic_name__(	self      :int     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					Any		      =		NatModelTester(self     )




from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
						from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
class 	_lowercase				(      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      int      =		c_float


				@property
				def 		__magic_name__(	self      :int     )		->      int:
										return sizeof(self.value     )




def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__ = 0				):
						# < 0 is possible if using a dynamic axis
						if dimension <= 0:
												__SCREAMING_SNAKE_CASE						:					int		      =		fixed_dimension

						dimension -= num_token_to_add
						return dimension


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						return num_parameters * dtype.size


def 	_UpperCamelCase							(				lowercase__				):
						from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

						try:
												return AutoProcessor.from_pretrained(lowercase__				)
						except (ValueError, OSError, KeyError):
												__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Tuple		      =		None, None
												try:
																		__SCREAMING_SNAKE_CASE						:					List[Any]		      =		AutoTokenizer.from_pretrained(lowercase__				)
												except (OSError, KeyError):
																		pass
												try:
																		__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		AutoFeatureExtractor.from_pretrained(lowercase__				)
												except (OSError, KeyError):
																		pass

												if tokenizer is not None and feature_extractor is not None:
																		raise ValueError(
																		    F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
												elif tokenizer is None and feature_extractor is None:
																		return None
												elif tokenizer is not None:
																		return tokenizer
												else:
																		return feature_extractor
