




'''simple docstring'''





import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import 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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
       from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor






class     lowercase  :






      def __init__(self			,	a__			,	a__=1_3			,	a__=3_2			,	a__=2			,	a__=3			,	a__=1_6			,	a__=[1, 2, 1]			,	a__=[2, 2, 4]			,	a__=2			,	a__=2.0			,	a__=True			,	a__=0.0			,	a__=0.0			,	a__=0.1			,	a__="gelu"			,	a__=False			,	a__=True			,	a__=0.02			,	a__=1E-5			,	a__=True			,	a__=None			,	a__=True			,	a__=1_0			,	a__=8			,	a__=["stage1", "stage2"]			,	a__=[1, 2]			,	)							-> Dict:
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			parent
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			batch_size
         SCREAMING_SNAKE_CASE__      :					str							    =			image_size
         SCREAMING_SNAKE_CASE__      :					Any							    =			patch_size
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			num_channels
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			embed_dim
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			depths
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			num_heads
         SCREAMING_SNAKE_CASE__      :					Dict							    =			window_size
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			mlp_ratio
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			qkv_bias
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			hidden_dropout_prob
         SCREAMING_SNAKE_CASE__      :					Dict							    =			attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			drop_path_rate
         SCREAMING_SNAKE_CASE__      :					Any							    =			hidden_act
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			use_absolute_embeddings
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			patch_norm
         SCREAMING_SNAKE_CASE__      :					Any							    =			layer_norm_eps
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			initializer_range
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			is_training
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			scope
         SCREAMING_SNAKE_CASE__      :					Dict							    =			use_labels
         SCREAMING_SNAKE_CASE__      :					Dict							    =			type_sequence_label_size
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			encoder_stride
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			out_features
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			out_indices






      def  A__(self       )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE__      :					Any							    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

         SCREAMING_SNAKE_CASE__      :					Any							    =			None
         if self.use_labels:
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			ids_tensor([self.batch_size]			,	self.type_sequence_label_size       )

         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.get_config()

         return config, pixel_values, labels






      def  A__(self       )							-> List[str]:
         return SwinConfig(
             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			,	window_size=self.window_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			,	use_absolute_embeddings=self.use_absolute_embeddings			,	path_norm=self.patch_norm			,	layer_norm_eps=self.layer_norm_eps			,	initializer_range=self.initializer_range			,	encoder_stride=self.encoder_stride			,	out_features=self.out_features			,	out_indices=self.out_indices			,	)






      def  A__(self			,	a__			,	a__			,	a__       )							-> List[str]:
         SCREAMING_SNAKE_CASE__      :					Dict							    =			SwinModel(config=a__       )
         model.to(a__       )
         model.eval()
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			model(a__       )

         SCREAMING_SNAKE_CASE__      :					str							    =			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
         SCREAMING_SNAKE_CASE__      :					Dict							    =			int(config.embed_dim * 2 ** (len(config.depths       ) - 1)       )

         self.parent.assertEqual(result.last_hidden_state.shape			,	(self.batch_size, expected_seq_len, expected_dim)       )






      def  A__(self			,	a__			,	a__			,	a__       )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE__      :					int							    =			SwinBackbone(config=a__       )
         model.to(a__       )
         model.eval()
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			model(a__       )

         # 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
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			None
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			SwinBackbone(config=a__       )
         model.to(a__       )
         model.eval()
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			model(a__       )

         # 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  A__(self			,	a__			,	a__			,	a__       )							-> Dict:
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			SwinForMaskedImageModeling(config=a__       )
         model.to(a__       )
         model.eval()
         SCREAMING_SNAKE_CASE__      :					int							    =			model(a__       )
         self.parent.assertEqual(
             result.logits.shape			,	(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

         # test greyscale images
         SCREAMING_SNAKE_CASE__      :					Any							    =			1
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			SwinForMaskedImageModeling(a__       )
         model.to(a__       )
         model.eval()

         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			model(a__       )
         self.parent.assertEqual(result.logits.shape			,	(self.batch_size, 1, self.image_size, self.image_size)       )






      def  A__(self			,	a__			,	a__			,	a__       )							-> List[str]:
         SCREAMING_SNAKE_CASE__      :					Dict							    =			self.type_sequence_label_size
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			SwinForImageClassification(a__       )
         model.to(a__       )
         model.eval()
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			model(a__			,	labels=a__       )
         self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.type_sequence_label_size)       )

         # test greyscale images
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			1
         SCREAMING_SNAKE_CASE__      :					int							    =			SwinForImageClassification(a__       )
         model.to(a__       )
         model.eval()

         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			model(a__       )
         self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.type_sequence_label_size)       )






      def  A__(self       )							-> List[str]:
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.prepare_config_and_inputs()
         (
             (
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					
         )      :					Any							    =			config_and_inputs
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_torch
class     lowercase  ( UpperCAmelCase_					,      UpperCAmelCase_					,      unittest.TestCase):
      __lowercase					=	(
          (
              SwinModel,
              SwinBackbone,
              SwinForImageClassification,
              SwinForMaskedImageModeling,
          )
          if is_torch_available()
          else ()
      )
      __lowercase					=	(
          {'''feature-extraction''': SwinModel, '''image-classification''': SwinForImageClassification}
          if is_torch_available()
          else {}
      )
      __lowercase					=	True

      __lowercase					=	False
      __lowercase					=	False
      __lowercase					=	False






      def  A__(self       )							-> List[Any]:
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			SwinModelTester(self       )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			ConfigTester(self			,	config_class=a__			,	embed_dim=3_7       )






      def  A__(self       )							-> Any:
         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  A__(self       )							-> int:
         return






      def  A__(self       )							-> List[Any]:
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__       )






      @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''       )
      def  A__(self       )							-> int:
         pass






      def  A__(self       )							-> Any:
         super().test_training_gradient_checkpointing()






      def  A__(self       )							-> Dict:
         SCREAMING_SNAKE_CASE__      :					str							    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*a__       )






      def  A__(self       )							-> int:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*a__       )






      def  A__(self       )							-> int:
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*a__       )






      @unittest.skip(reason='''Swin does not use inputs_embeds'''       )
      def  A__(self       )							-> Optional[Any]:
         pass






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






      def  A__(self       )							-> str:
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					str							    =			self.model_tester.prepare_config_and_inputs_for_common()

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






      def  A__(self       )							-> Dict:
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.model_tester.prepare_config_and_inputs_for_common()

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

            SCREAMING_SNAKE_CASE__      :					int							    =			['''pixel_values''']
            self.assertListEqual(arg_names[:1]			,	a__       )






      def  A__(self       )							-> str:
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Any							    =			self.model_tester.prepare_config_and_inputs_for_common()
         SCREAMING_SNAKE_CASE__      :					int							    =			True

         for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			True
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			False
            SCREAMING_SNAKE_CASE__      :					Any							    =			True
            SCREAMING_SNAKE_CASE__      :					Any							    =			model_class(a__       )
            model.to(a__       )
            model.eval()
            with torch.no_grad():
               SCREAMING_SNAKE_CASE__      :					List[str]							    =			model(**self._prepare_for_class(a__			,	a__       )       )
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			outputs.attentions
            SCREAMING_SNAKE_CASE__      :					int							    =			len(self.model_tester.depths       )
            self.assertEqual(len(a__       )			,	a__       )

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			True
            SCREAMING_SNAKE_CASE__      :					int							    =			config.window_size**2
            SCREAMING_SNAKE_CASE__      :					int							    =			model_class(a__       )
            model.to(a__       )
            model.eval()
            with torch.no_grad():
               SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			model(**self._prepare_for_class(a__			,	a__       )       )
            SCREAMING_SNAKE_CASE__      :					Any							    =			outputs.attentions
            self.assertEqual(len(a__       )			,	a__       )

            self.assertListEqual(
                list(attentions[0].shape[-3:]       )			,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,	)
            SCREAMING_SNAKE_CASE__      :					Dict							    =			len(a__       )

            # Check attention is always last and order is fine
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			True
            SCREAMING_SNAKE_CASE__      :					str							    =			True
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			model_class(a__       )
            model.to(a__       )
            model.eval()
            with torch.no_grad():
               SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			model(**self._prepare_for_class(a__			,	a__       )       )

            # also another +1 for reshaped_hidden_states
            SCREAMING_SNAKE_CASE__      :					str							    =			1 if model_class.__name__ == '''SwinBackbone''' else 2
            self.assertEqual(out_len + added_hidden_states			,	len(a__       )       )

            SCREAMING_SNAKE_CASE__      :					Any							    =			outputs.attentions

            self.assertEqual(len(a__       )			,	a__       )

            self.assertListEqual(
                list(self_attentions[0].shape[-3:]       )			,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,	)






      def  A__(self			,	a__			,	a__			,	a__			,	a__       )							-> Tuple:
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			model_class(a__       )
         model.to(a__       )
         model.eval()

         with torch.no_grad():
            SCREAMING_SNAKE_CASE__      :					Dict							    =			model(**self._prepare_for_class(a__			,	a__       )       )

         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			outputs.hidden_states

         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			getattr(
             self.model_tester			,	'''expected_num_hidden_layers'''			,	len(self.model_tester.depths       ) + 1       )
         self.assertEqual(len(a__       )			,	a__       )

         # Swin has a different seq_length
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			(
             config.patch_size
             if isinstance(config.patch_size			,	collections.abc.Iterable       )
             else (config.patch_size, config.patch_size)
         )

         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]       )			,	[num_patches, self.model_tester.embed_dim]			,	)

         if not model_class.__name__ == "SwinBackbone":
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			outputs.reshaped_hidden_states
            self.assertEqual(len(a__       )			,	a__       )

            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Dict							    =			reshaped_hidden_states[0].shape
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			(
                reshaped_hidden_states[0].view(a__			,	a__			,	height * width       ).permute(0			,	2			,	1       )
            )
            self.assertListEqual(
                list(reshaped_hidden_states.shape[-2:]       )			,	[num_patches, self.model_tester.embed_dim]			,	)






      def  A__(self       )							-> int:
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.model_tester.prepare_config_and_inputs_for_common()

         SCREAMING_SNAKE_CASE__      :					int							    =			(
             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[Any]							    =			True
            self.check_hidden_states_output(a__			,	a__			,	a__			,	a__       )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			True

            self.check_hidden_states_output(a__			,	a__			,	a__			,	a__       )






      def  A__(self       )							-> Tuple:
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Dict							    =			self.model_tester.prepare_config_and_inputs_for_common()
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			3

         SCREAMING_SNAKE_CASE__      :					Optional[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)
         )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			(
             config.patch_size
             if isinstance(config.patch_size			,	collections.abc.Iterable       )
             else (config.patch_size, config.patch_size)
         )

         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			True
            self.check_hidden_states_output(a__			,	a__			,	a__			,	(padded_height, padded_width)       )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			True
            self.check_hidden_states_output(a__			,	a__			,	a__			,	(padded_height, padded_width)       )






      @slow
      def  A__(self       )							-> str:
         for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			SwinModel.from_pretrained(a__       )
            self.assertIsNotNone(a__       )






      def  A__(self       )							-> Dict:
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			self.model_tester.prepare_config_and_inputs_for_common()

         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			_config_zero_init(a__       )
         for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			model_class(config=a__       )
            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_vision
@require_torch
class     lowercase  ( unittest.TestCase):






      @cached_property
      def  A__(self       )							-> str:
         return (
             AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''       )
             if is_vision_available()
             else None
         )






      @slow
      def  A__(self       )							-> str:
         SCREAMING_SNAKE_CASE__      :					str							    =			SwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''       ).to(a__       )
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.default_image_processor

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			image_processor(images=a__			,	return_tensors='''pt'''       ).to(a__       )

         # forward pass
         with torch.no_grad():
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			model(**a__       )

         # verify the logits
         SCREAMING_SNAKE_CASE__      :					Dict							    =			torch.Size((1, 1_0_0_0)       )
         self.assertEqual(outputs.logits.shape			,	a__       )
         SCREAMING_SNAKE_CASE__      :					Dict							    =			torch.tensor([-0.09_48, -0.64_54, -0.09_21]       ).to(a__       )
         self.assertTrue(torch.allclose(outputs.logits[0, :3]			,	a__			,	atol=1E-4       )       )






@require_torch
class     lowercase  ( unittest.TestCase					,      UpperCAmelCase_):
      __lowercase					=	(SwinBackbone,) if is_torch_available() else ()
      __lowercase					=	SwinConfig






      def  A__(self       )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE__      :					str							    =			SwinModelTester(self       )







'''simple docstring'''





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''LayoutLMv2ImageProcessor'''
      __lowercase					=	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')






      def __init__(self			,	a__=None			,	a__=None			,	**a__       )							-> Any:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''			,	a__			,	)
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			kwargs.pop('''feature_extractor'''       )

         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.'''       )
         if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.'''       )

         super().__init__(a__			,	a__       )






      def __call__(self			,	a__			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = True			,	a__ = False			,	a__ = False			,	a__ = None			,	a__ = 0			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = True			,	a__ = None			,	**a__			,	)							-> BatchEncoding:
         # verify input
         if self.image_processor.apply_ocr and (boxes is not None):
            raise ValueError(
                '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''       )

         if self.image_processor.apply_ocr and (word_labels is not None):
            raise ValueError(
                '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''       )

         if return_overflowing_tokens is True and return_offsets_mapping is False:
            raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''       )

         # first, apply the image processor
         SCREAMING_SNAKE_CASE__      :					str							    =			self.image_processor(images=a__			,	return_tensors=a__       )

         # second, apply the tokenizer
         if text is not None and self.image_processor.apply_ocr and text_pair is None:
            if isinstance(a__			,	a__       ):
               SCREAMING_SNAKE_CASE__      :					Tuple							    =			[text]  # add batch dimension (as the image processor always adds a batch dimension)
            SCREAMING_SNAKE_CASE__      :					Any							    =			features['''words''']

         SCREAMING_SNAKE_CASE__      :					int							    =			self.tokenizer(
             text=text if text is not None else features['''words''']			,	text_pair=text_pair if text_pair is not None else None			,	boxes=boxes if boxes is not None else features['''boxes''']			,	word_labels=a__			,	add_special_tokens=a__			,	padding=a__			,	truncation=a__			,	max_length=a__			,	stride=a__			,	pad_to_multiple_of=a__			,	return_token_type_ids=a__			,	return_attention_mask=a__			,	return_overflowing_tokens=a__			,	return_special_tokens_mask=a__			,	return_offsets_mapping=a__			,	return_length=a__			,	verbose=a__			,	return_tensors=a__			,	**a__			,	)

         # add pixel values
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			features.pop('''pixel_values'''       )
         if return_overflowing_tokens is True:
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.get_overflowing_images(a__			,	encoded_inputs['''overflow_to_sample_mapping''']       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			images

         return encoded_inputs






      def  A__(self			,	a__			,	a__       )							-> str:
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         SCREAMING_SNAKE_CASE__      :					int							    =			[]
         for sample_idx in overflow_to_sample_mapping:
            images_with_overflow.append(images[sample_idx]       )

         if len(a__       ) != len(a__       ):
            raise ValueError(
                '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                f""" {len(a__       )} and {len(a__       )}"""       )

         return images_with_overflow






      def  A__(self			,	*a__			,	**a__       )							-> List[Any]:
         return self.tokenizer.batch_decode(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Optional[Any]:
         return self.tokenizer.decode(*a__			,	**a__       )






      @property
      def  A__(self       )							-> Tuple:
         return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






      @property
      def  A__(self       )							-> str:
         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,	a__			,	)
         return self.image_processor_class






      @property
      def  A__(self       )							-> Dict:
         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,	a__			,	)
         return self.image_processor

