

"""simple docstring"""

import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

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

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor


class       _snake_case  :







						def __init__(self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=1_3  ,				UpperCAmelCase_=6_4  ,				UpperCAmelCase_=4  ,				UpperCAmelCase_=3  ,				UpperCAmelCase_=1_6  ,				UpperCAmelCase_=[1, 2, 1]  ,				UpperCAmelCase_=[2, 4, 8]  ,				UpperCAmelCase_=3  ,				UpperCAmelCase_=[[3], [1, 2], [1]]  ,				UpperCAmelCase_=2.0  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=0.0  ,				UpperCAmelCase_=0.0  ,				UpperCAmelCase_=0.1  ,				UpperCAmelCase_="gelu"  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=0.0_2  ,				UpperCAmelCase_=1e-5  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=1_0  ,				UpperCAmelCase_=["stage1", "stage2"]  ,				UpperCAmelCase_=[1, 2]  ,				)      ->    Optional[int]:
											__lowercase        =      parent
											__lowercase        =      batch_size
											__lowercase        =      image_size
											__lowercase        =      patch_size
											__lowercase        =      num_channels
											__lowercase        =      embed_dim
											__lowercase        =      depths
											__lowercase        =      num_heads
											__lowercase        =      kernel_size
											__lowercase        =      dilations
											__lowercase        =      mlp_ratio
											__lowercase        =      qkv_bias
											__lowercase        =      hidden_dropout_prob
											__lowercase        =      attention_probs_dropout_prob
											__lowercase        =      drop_path_rate
											__lowercase        =      hidden_act
											__lowercase        =      patch_norm
											__lowercase        =      layer_norm_eps
											__lowercase        =      initializer_range
											__lowercase        =      is_training
											__lowercase        =      scope
											__lowercase        =      use_labels
											__lowercase        =      num_labels
											__lowercase        =      out_features
											__lowercase        =      out_indices







						def 							_UpperCamelCase     (self				)      ->    List[str]:
											__lowercase        =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

											__lowercase        =      self.get_config()

											return config, pixel_values, labels







						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											return DinatConfig(
											    num_labels=self.num_labels  ,				image_size=self.image_size  ,				patch_size=self.patch_size  ,				num_channels=self.num_channels  ,				embed_dim=self.embed_dim  ,				depths=self.depths  ,				num_heads=self.num_heads  ,				kernel_size=self.kernel_size  ,				dilations=self.dilations  ,				mlp_ratio=self.mlp_ratio  ,				qkv_bias=self.qkv_bias  ,				hidden_dropout_prob=self.hidden_dropout_prob  ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,				drop_path_rate=self.drop_path_rate  ,				hidden_act=self.hidden_act  ,				patch_norm=self.patch_norm  ,				layer_norm_eps=self.layer_norm_eps  ,				initializer_range=self.initializer_range  ,				out_features=self.out_features  ,				out_indices=self.out_indices  ,				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    List[str]:
											__lowercase        =      DinatModel(config=UpperCAmelCase_				)
											model.to(UpperCAmelCase_				)
											model.eval()
											__lowercase        =      model(UpperCAmelCase_				)

											__lowercase        =      __lowercase        =      (config.image_size // config.patch_size) // (2 ** (len(config.depths				) - 1))
											__lowercase        =      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 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    Any:
											__lowercase        =      DinatForImageClassification(UpperCAmelCase_				)
											model.to(UpperCAmelCase_				)
											model.eval()
											__lowercase        =      model(UpperCAmelCase_  ,				labels=UpperCAmelCase_				)
											self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.num_labels)				)

											# test greyscale images
											__lowercase        =      1
											__lowercase        =      DinatForImageClassification(UpperCAmelCase_				)
											model.to(UpperCAmelCase_				)
											model.eval()

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







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    Optional[int]:
											__lowercase        =      DinatBackbone(config=UpperCAmelCase_				)
											model.to(UpperCAmelCase_				)
											model.eval()
											__lowercase        =      model(UpperCAmelCase_				)

											# 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], 1_6, 1_6]				)

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

											# verify backbone works with out_features=None
											__lowercase        =      None
											__lowercase        =      DinatBackbone(config=UpperCAmelCase_				)
											model.to(UpperCAmelCase_				)
											model.eval()
											__lowercase        =      model(UpperCAmelCase_				)

											# 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 							_UpperCamelCase     (self				)      ->    Union[str, Any]:
											__lowercase        =      self.prepare_config_and_inputs()
											__lowercase    ,			__lowercase    ,			__lowercase        =      config_and_inputs
											__lowercase        =      {'''pixel_values''': pixel_values}
											return config, inputs_dict



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

						__snake_case			      =  False
						__snake_case			      =  False
						__snake_case			      =  False
						__snake_case			      =  False







						def 							_UpperCamelCase     (self				)      ->    Tuple:
											__lowercase        =      DinatModelTester(self				)
											__lowercase        =      ConfigTester(self  ,				config_class=UpperCAmelCase_  ,				embed_dim=3_7				)







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







						def 							_UpperCamelCase     (self				)      ->    Tuple:
											__lowercase        =      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCAmelCase_				)







						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											__lowercase        =      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_				)







						def 							_UpperCamelCase     (self				)      ->    Optional[Any]:
											__lowercase        =      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*UpperCAmelCase_				)







						@unittest.skip(reason='''Dinat does not use inputs_embeds'''				)
						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											pass







						@unittest.skip(reason='''Dinat does not use feedforward chunking'''				)
						def 							_UpperCamelCase     (self				)      ->    Optional[Any]:
											pass







						def 							_UpperCamelCase     (self				)      ->    Optional[int]:
											__lowercase    ,			__lowercase        =      self.model_tester.prepare_config_and_inputs_for_common()

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







						def 							_UpperCamelCase     (self				)      ->    str:
											__lowercase    ,			__lowercase        =      self.model_tester.prepare_config_and_inputs_for_common()

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

																__lowercase        =      ['''pixel_values''']
																self.assertListEqual(arg_names[:1]  ,				UpperCAmelCase_				)







						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											self.skipTest('''Dinat\'s attention operation is handled entirely by NATTEN.'''				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    Union[str, Any]:
											__lowercase        =      model_class(UpperCAmelCase_				)
											model.to(UpperCAmelCase_				)
											model.eval()

											with torch.no_grad():
																__lowercase        =      model(**self._prepare_for_class(UpperCAmelCase_  ,				UpperCAmelCase_				)				)

											__lowercase        =      outputs.hidden_states

											__lowercase        =      getattr(
											    self.model_tester  ,				'''expected_num_hidden_layers'''  ,				len(self.model_tester.depths				) + 1				)
											self.assertEqual(len(UpperCAmelCase_				)  ,				UpperCAmelCase_				)

											# Dinat has a different seq_length
											__lowercase        =      (
											    config.patch_size
											    if isinstance(config.patch_size  ,				collections.abc.Iterable				)
											    else (config.patch_size, config.patch_size)
											)

											__lowercase        =      image_size[0] // patch_size[0]
											__lowercase        =      image_size[1] // patch_size[1]

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

											if model_class.__name__ != "DinatBackbone":
																__lowercase        =      outputs.reshaped_hidden_states
																self.assertEqual(len(UpperCAmelCase_				)  ,				UpperCAmelCase_				)

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







						def 							_UpperCamelCase     (self				)      ->    Tuple:
											__lowercase    ,			__lowercase        =      self.model_tester.prepare_config_and_inputs_for_common()

											__lowercase        =      (
											    self.model_tester.image_size
											    if isinstance(self.model_tester.image_size  ,				collections.abc.Iterable				)
											    else (self.model_tester.image_size, self.model_tester.image_size)
											)

											for model_class in self.all_model_classes:
																__lowercase        =      True
																self.check_hidden_states_output(UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																__lowercase        =      True

																self.check_hidden_states_output(UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)







						@slow
						def 							_UpperCamelCase     (self				)      ->    Optional[int]:
											for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowercase        =      DinatModel.from_pretrained(UpperCAmelCase_				)
																self.assertIsNotNone(UpperCAmelCase_				)







						def 							_UpperCamelCase     (self				)      ->    int:
											__lowercase    ,			__lowercase        =      self.model_tester.prepare_config_and_inputs_for_common()

											__lowercase        =      _config_zero_init(UpperCAmelCase_				)
											for model_class in self.all_model_classes:
																__lowercase        =      model_class(config=UpperCAmelCase_				)
																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       _snake_case  (    unittest.TestCase							):







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







						@slow
						def 							_UpperCamelCase     (self				)      ->    List[str]:
											__lowercase        =      DinatForImageClassification.from_pretrained('''shi-labs/dinat-mini-in1k-224'''				).to(UpperCAmelCase_				)
											__lowercase        =      self.default_image_processor

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

											# forward pass
											with torch.no_grad():
																__lowercase        =      model(**UpperCAmelCase_				)

											# verify the logits
											__lowercase        =      torch.Size((1, 1_0_0_0)				)
											self.assertEqual(outputs.logits.shape  ,				UpperCAmelCase_				)
											__lowercase        =      torch.tensor([-0.1_5_4_5, -0.7_6_6_7, 0.4_6_4_2]				).to(UpperCAmelCase_				)
											self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,				UpperCAmelCase_  ,				atol=1e-4				)				)



@require_torch
@require_natten
class       _snake_case  (    unittest.TestCase    ,      UpperCamelCase							):
						__snake_case			      =  (DinatBackbone,) if is_torch_available() else ()
						__snake_case			      =  DinatConfig







						def 							_UpperCamelCase     (self				)      ->    str:
											__lowercase        =      DinatModelTester(self				)





"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a__			:						Tuple							=			{
    """configuration_segformer""": ["""SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SegformerConfig""", """SegformerOnnxConfig"""]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						List[str]							=			["""SegformerFeatureExtractor"""]
							a__			:						str							=			["""SegformerImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						Optional[Any]							=			[
							    """SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """SegformerDecodeHead""",
							    """SegformerForImageClassification""",
							    """SegformerForSemanticSegmentation""",
							    """SegformerLayer""",
							    """SegformerModel""",
							    """SegformerPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						List[Any]							=			[
							    """TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFSegformerDecodeHead""",
							    """TFSegformerForImageClassification""",
							    """TFSegformerForSemanticSegmentation""",
							    """TFSegformerModel""",
							    """TFSegformerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_segformer import SegformerFeatureExtractor
														from .image_processing_segformer import SegformerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_segformer import (
														    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SegformerDecodeHead,
														    SegformerForImageClassification,
														    SegformerForSemanticSegmentation,
														    SegformerLayer,
														    SegformerModel,
														    SegformerPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_segformer import (
														    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSegformerDecodeHead,
														    TFSegformerForImageClassification,
														    TFSegformerForSemanticSegmentation,
														    TFSegformerModel,
														    TFSegformerPreTrainedModel,
														)

else:
							import sys

							a__			:						Any							=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


