

'''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 lowerCamelCase					:

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

				def  _lowerCamelCase  (    self      )  ->				str:
					snake_case							       = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

					snake_case							       = self.get_config()

					return config, pixel_values, labels

				def  _lowerCamelCase  (    self      )  ->				Tuple:
					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,		lowercase_,		lowercase_,		lowercase_      )  ->				Optional[Any]:
					snake_case							       = NatModel(config=lowerCAmelCase__      )
					model.to(lowerCAmelCase__      )
					model.eval()
					snake_case							       = model(lowerCAmelCase__      )

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

					# test greyscale images
					snake_case							       = 1
					snake_case							       = NatForImageClassification(lowerCAmelCase__      )
					model.to(lowerCAmelCase__      )
					model.eval()

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

				def  _lowerCamelCase  (    self,		lowercase_,		lowercase_,		lowercase_      )  ->				Tuple:
					snake_case							       = NatBackbone(config=lowerCAmelCase__      )
					model.to(lowerCAmelCase__      )
					model.eval()
					snake_case							       = 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
					snake_case							       = None
					snake_case							       = NatBackbone(config=lowerCAmelCase__      )
					model.to(lowerCAmelCase__      )
					model.eval()
					snake_case							       = 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  _lowerCamelCase  (    self      )  ->				Optional[int]:
					snake_case							       = self.prepare_config_and_inputs()
					snake_case							       = config_and_inputs
					snake_case							       = {'''pixel_values''': pixel_values}
					return config, inputs_dict







@require_natten
@require_torch
class lowerCamelCase					(  _lowerCAmelCase							,							_lowerCAmelCase							,							unittest.TestCase							):
				snake_case_							=      (
				    (
				        NatModel,
				        NatForImageClassification,
				        NatBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				snake_case_							=      (
				    {'''feature-extraction''': NatModel, '''image-classification''': NatForImageClassification}
				    if is_torch_available()
				    else {}
				)
				snake_case_							=      False

				snake_case_							=      False
				snake_case_							=      False
				snake_case_							=      False
				snake_case_							=      False

				def  _lowerCamelCase  (    self      )  ->				Union[str, Any]:
					snake_case							       = NatModelTester(self      )
					snake_case							       = ConfigTester(self,		config_class=lowerCAmelCase__,		embed_dim=37      )

				def  _lowerCamelCase  (    self      )  ->				Tuple:
					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      )  ->				List[Any]:
					return

				def  _lowerCamelCase  (    self      )  ->				Any:
					snake_case							       = self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowerCAmelCase__      )

				def  _lowerCamelCase  (    self      )  ->				str:
					snake_case							       = self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__      )

				def  _lowerCamelCase  (    self      )  ->				List[str]:
					snake_case							       = 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  _lowerCamelCase  (    self      )  ->				Dict:
					pass

				@unittest.skip(reason='Nat does not use feedforward chunking'      )
				def  _lowerCamelCase  (    self      )  ->				Dict:
					pass

				def  _lowerCamelCase  (    self      )  ->				Tuple:
					snake_case							       = self.model_tester.prepare_config_and_inputs_for_common()

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

				def  _lowerCamelCase  (    self      )  ->				Union[str, Any]:
					snake_case							       = self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

				def  _lowerCamelCase  (    self,		lowercase_,		lowercase_,		lowercase_,		lowercase_      )  ->				Tuple:
					snake_case							       = model_class(lowerCAmelCase__      )
					model.to(lowerCAmelCase__      )
					model.eval()

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

					snake_case							       = outputs.hidden_states

					snake_case							       = 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
					snake_case							       = (
					    config.patch_size
					    if isinstance(config.patch_size,		collections.abc.Iterable      )
					    else (config.patch_size, config.patch_size)
					)

					snake_case							       = image_size[0] // patch_size[0]
					snake_case							       = 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":
						snake_case							       = outputs.reshaped_hidden_states
						self.assertEqual(len(lowerCAmelCase__      ),		lowerCAmelCase__      )

						snake_case							       = reshaped_hidden_states[0].shape
						snake_case							       = (
						    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  _lowerCamelCase  (    self      )  ->				int:
					snake_case							       = self.model_tester.prepare_config_and_inputs_for_common()

					snake_case							       = (
					    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:
						snake_case							       = 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"]
						snake_case							       = True

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

				@slow
				def  _lowerCamelCase  (    self      )  ->				Dict:



					for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						snake_case							       = NatModel.from_pretrained(lowerCAmelCase__      )
						self.assertIsNotNone(lowerCAmelCase__      )






				def  _lowerCamelCase  (    self      )  ->				Union[str, Any]:
					snake_case							       = self.model_tester.prepare_config_and_inputs_for_common()

					snake_case							       = _config_zero_init(lowerCAmelCase__      )
					for model_class in self.all_model_classes:
						snake_case							       = 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 lowerCamelCase					(  unittest.TestCase							):

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






				@slow
				def  _lowerCamelCase  (    self      )  ->				str:
					snake_case							       = NatForImageClassification.from_pretrained('shi-labs/nat-mini-in1k-224'      ).to(lowerCAmelCase__      )
					snake_case							       = self.default_image_processor

					snake_case							       = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
					snake_case							       = image_processor(images=lowerCAmelCase__,		return_tensors='pt'      ).to(lowerCAmelCase__      )

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

					# verify the logits
					snake_case							       = torch.Size((1, 1000)      )
					self.assertEqual(outputs.logits.shape,		lowerCAmelCase__      )
					snake_case							       = torch.tensor([0.3_805, -0.8_676, -0.3_912]      ).to(lowerCAmelCase__      )
					self.assertTrue(torch.allclose(outputs.logits[0, :3],		lowerCAmelCase__,		atol=1E-4      )      )







@require_torch
@require_natten
class lowerCamelCase					(  unittest.TestCase							,							_lowerCAmelCase							):
				snake_case_							=      (NatBackbone,) if is_torch_available() else ()
				snake_case_							=      NatConfig

				def  _lowerCamelCase  (    self      )  ->				str:
					snake_case							       = NatModelTester(self      )












"""simple docstring"""




import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						from transformers.models.electra.modeling_flax_electra import (
						    FlaxElectraForCausalLM,
						    FlaxElectraForMaskedLM,
						    FlaxElectraForMultipleChoice,
						    FlaxElectraForPreTraining,
						    FlaxElectraForQuestionAnswering,
						    FlaxElectraForSequenceClassification,
						    FlaxElectraForTokenClassification,
						    FlaxElectraModel,
						)




class  _a						(      unittest.TestCase				):






				def __init__( self		:    str, lowerCAmelCase__		:    Tuple, lowerCAmelCase__		:    List[str]=1_3, lowerCAmelCase__		:    Optional[int]=7, lowerCAmelCase__		:    Optional[Any]=True, lowerCAmelCase__		:    Optional[int]=True, lowerCAmelCase__		:    Any=True, lowerCAmelCase__		:    str=True, lowerCAmelCase__		:    Any=9_9, lowerCAmelCase__		:    Union[str, Any]=2_4, lowerCAmelCase__		:    Optional[Any]=3_2, lowerCAmelCase__		:    Tuple=5, lowerCAmelCase__		:    int=4, lowerCAmelCase__		:    List[Any]=3_7, lowerCAmelCase__		:    Dict="gelu", lowerCAmelCase__		:    Any=0.1, lowerCAmelCase__		:    Any=0.1, lowerCAmelCase__		:    str=5_1_2, lowerCAmelCase__		:    Tuple=1_6, lowerCAmelCase__		:    int=2, lowerCAmelCase__		:    int=0.02, lowerCAmelCase__		:    Optional[int]=4, )	->      Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Dict     =    parent
										_UpperCamelCase			:					Tuple     =    batch_size
										_UpperCamelCase			:					str     =    seq_length
										_UpperCamelCase			:					Any     =    is_training
										_UpperCamelCase			:					Union[str, Any]     =    use_attention_mask
										_UpperCamelCase			:					List[Any]     =    use_token_type_ids
										_UpperCamelCase			:					str     =    use_labels
										_UpperCamelCase			:					Union[str, Any]     =    vocab_size
										_UpperCamelCase			:					int     =    hidden_size
										_UpperCamelCase			:					Optional[int]     =    embedding_size
										_UpperCamelCase			:					int     =    num_hidden_layers
										_UpperCamelCase			:					int     =    num_attention_heads
										_UpperCamelCase			:					Tuple     =    intermediate_size
										_UpperCamelCase			:					Optional[int]     =    hidden_act
										_UpperCamelCase			:					Any     =    hidden_dropout_prob
										_UpperCamelCase			:					Union[str, Any]     =    attention_probs_dropout_prob
										_UpperCamelCase			:					Any     =    max_position_embeddings
										_UpperCamelCase			:					List[str]     =    type_vocab_size
										_UpperCamelCase			:					Union[str, Any]     =    type_sequence_label_size
										_UpperCamelCase			:					str     =    initializer_range
										_UpperCamelCase			:					Union[str, Any]     =    num_choices






				def 				snake_case	( self		:    Optional[Any]							)	->      Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    ids_tensor([self.batch_size, self.seq_length], self.vocab_size							)

										_UpperCamelCase			:					Dict     =    None
										if self.use_attention_mask:
																_UpperCamelCase			:					Union[str, Any]     =    random_attention_mask([self.batch_size, self.seq_length]							)

										_UpperCamelCase			:					Optional[Any]     =    None
										if self.use_token_type_ids:
																_UpperCamelCase			:					Union[str, Any]     =    ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size							)

										_UpperCamelCase			:					Optional[Any]     =    ElectraConfig(
										    vocab_size=self.vocab_size, hidden_size=self.hidden_size, embedding_size=self.embedding_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, )

										return config, input_ids, token_type_ids, attention_mask






				def 				snake_case	( self		:    Any							)	->      Optional[int]:




										'''simple docstring'''



										_UpperCamelCase			:					Dict     =    self.prepare_config_and_inputs()
										_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase			:					Tuple     =    config_and_inputs
										_UpperCamelCase			:					List[Any]     =    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
										return config, inputs_dict




@require_flax
class  _a						(      _lowerCAmelCase	,				unittest.TestCase				):
				UpperCamelCase								=    True

				UpperCamelCase								=    (
				    (
				        FlaxElectraModel,
				        FlaxElectraForCausalLM,
				        FlaxElectraForMaskedLM,
				        FlaxElectraForPreTraining,
				        FlaxElectraForTokenClassification,
				        FlaxElectraForQuestionAnswering,
				        FlaxElectraForMultipleChoice,
				        FlaxElectraForSequenceClassification,
				    )
				    if is_flax_available()
				    else ()
				)






				def 				snake_case	( self		:    Union[str, Any]							)	->      Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    FlaxElectraModelTester(self							)






				@slow
				def 				snake_case	( self		:    Optional[Any]							)	->      str:




										'''simple docstring'''



										for model_class_name in self.all_model_classes:
																if model_class_name == FlaxElectraForMaskedLM:
																						_UpperCamelCase			:					int     =    model_class_name.from_pretrained('''google/electra-small-generator'''							)
																else:
																						_UpperCamelCase			:					Any     =    model_class_name.from_pretrained('''google/electra-small-discriminator'''							)
																_UpperCamelCase			:					List[str]     =    model(np.ones((1, 1)							)							)
																self.assertIsNotNone(lowerCAmelCase__							)



