






"""simple docstring"""





import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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 SwinaSRForImageSuperResolution, SwinaSRModel
		from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
		from PIL import Image

		from transformers import SwinaSRImageProcessor







class 		_a       :





			'''simple docstring'''






			def __init__(   self			,				UpperCAmelCase_			,				UpperCAmelCase_=13			,				UpperCAmelCase_=32			,				UpperCAmelCase_=1			,				UpperCAmelCase_=3			,				UpperCAmelCase_=16			,				UpperCAmelCase_=[1, 2, 1]			,				UpperCAmelCase_=[2, 2, 4]			,				UpperCAmelCase_=2			,				UpperCAmelCase_=2.0			,				UpperCAmelCase_=True			,				UpperCAmelCase_=0.0			,				UpperCAmelCase_=0.0			,				UpperCAmelCase_=0.1			,				UpperCAmelCase_="gelu"			,				UpperCAmelCase_=False			,				UpperCAmelCase_=True			,				UpperCAmelCase_=0.02			,				UpperCAmelCase_=1E-5			,				UpperCAmelCase_=True			,				UpperCAmelCase_=None			,				UpperCAmelCase_=False			,				UpperCAmelCase_=2			,				)	-> Optional[Any]:




										'''simple docstring'''




										lowercase__:    Any        =      parent
										lowercase__:    List[str]        =      batch_size
										lowercase__:    Optional[int]        =      image_size
										lowercase__:    Dict        =      patch_size
										lowercase__:    Tuple        =      num_channels
										lowercase__:    Optional[int]        =      embed_dim
										lowercase__:    int        =      depths
										lowercase__:    List[str]        =      num_heads
										lowercase__:    List[str]        =      window_size
										lowercase__:    List[str]        =      mlp_ratio
										lowercase__:    Optional[Any]        =      qkv_bias
										lowercase__:    Any        =      hidden_dropout_prob
										lowercase__:    str        =      attention_probs_dropout_prob
										lowercase__:    Union[str, Any]        =      drop_path_rate
										lowercase__:    Dict        =      hidden_act
										lowercase__:    List[str]        =      use_absolute_embeddings
										lowercase__:    Optional[int]        =      patch_norm
										lowercase__:    Dict        =      layer_norm_eps
										lowercase__:    Union[str, Any]        =      initializer_range
										lowercase__:    Dict        =      is_training
										lowercase__:    List[Any]        =      scope
										lowercase__:    Tuple        =      use_labels
										lowercase__:    Any        =      upscale

										# here we set some attributes to make tests pass
										lowercase__:    List[str]        =      len(UpperCAmelCase_)
										lowercase__:    Union[str, Any]        =      embed_dim
										lowercase__:    List[Any]        =      (image_size // patch_size) ** 2





			def __lowercase    (   self)	-> int:




										'''simple docstring'''




										lowercase__:    Optional[Any]        =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

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

										return config, pixel_values, labels





			def __lowercase    (   self)	-> Any:




										'''simple docstring'''




										return SwinaSRConfig(
										    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			,				upscale=self.upscale			,				)





			def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> Any:




										'''simple docstring'''




										lowercase__:    str        =      SwinaSRModel(config=UpperCAmelCase_)
										model.to(UpperCAmelCase_)
										model.eval()
										lowercase__:    int        =      model(UpperCAmelCase_)

										self.parent.assertEqual(
										    result.last_hidden_state.shape			,				(self.batch_size, self.embed_dim, self.image_size, self.image_size))





			def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> int:




										'''simple docstring'''




										lowercase__:    List[str]        =      SwinaSRForImageSuperResolution(UpperCAmelCase_)
										model.to(UpperCAmelCase_)
										model.eval()
										lowercase__:    str        =      model(UpperCAmelCase_)

										lowercase__:    Union[str, Any]        =      self.image_size * self.upscale
										self.parent.assertEqual(
										    result.reconstruction.shape			,				(self.batch_size, self.num_channels, expected_image_size, expected_image_size))





			def __lowercase    (   self)	-> Optional[Any]:




										'''simple docstring'''




										lowercase__:    Optional[Any]        =      self.prepare_config_and_inputs()
										lowercase__:    Union[str, Any]        =      config_and_inputs
										lowercase__:    str        =      {"pixel_values": pixel_values}
										return config, inputs_dict







@require_torch
class 		_a       (						lowercase_    ,					lowercase_    ,					unittest.TestCase   ):





			'''simple docstring'''






			UpperCamelCase__      					=    (SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
			UpperCamelCase__      					=    {"""feature-extraction""": SwinaSRModel} if is_torch_available() else {}

			UpperCamelCase__      					=    False
			UpperCamelCase__      					=    False
			UpperCamelCase__      					=    False
			UpperCamelCase__      					=    False
			UpperCamelCase__      					=    False





			def __lowercase    (   self)	-> List[str]:




										'''simple docstring'''




										lowercase__:    Dict        =      SwinaSRModelTester(self)
										lowercase__:    List[str]        =      ConfigTester(self			,				config_class=UpperCAmelCase_			,				embed_dim=37)





			def __lowercase    (   self)	-> int:




										'''simple docstring'''




										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 __lowercase    (   self)	-> Optional[int]:




										'''simple docstring'''




										lowercase__:    List[Any]        =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCAmelCase_)





			def __lowercase    (   self)	-> List[str]:




										'''simple docstring'''




										lowercase__:    List[str]        =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_super_resolution(*UpperCAmelCase_)





			@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0.")
			def __lowercase    (   self)	-> Any:




										'''simple docstring'''




										pass





			@unittest.skip(reason="Swin2SR does not use inputs_embeds")
			def __lowercase    (   self)	-> List[str]:




										'''simple docstring'''




										pass





			@unittest.skip(reason="Swin2SR does not support training yet")
			def __lowercase    (   self)	-> Dict:




										'''simple docstring'''




										pass





			@unittest.skip(reason="Swin2SR does not support training yet")
			def __lowercase    (   self)	-> Dict:




										'''simple docstring'''




										pass





			def __lowercase    (   self)	-> str:




										'''simple docstring'''




										lowercase__:    List[str]        =      self.model_tester.prepare_config_and_inputs_for_common()

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





			def __lowercase    (   self)	-> Tuple:




										'''simple docstring'''




										lowercase__:    Union[str, Any]        =      self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowercase__:    str        =      ["pixel_values"]
																	self.assertListEqual(arg_names[:1]			,				UpperCAmelCase_)





			@slow
			def __lowercase    (   self)	-> Union[str, Any]:




										'''simple docstring'''




										for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase__:    List[Any]        =      SwinaSRModel.from_pretrained(UpperCAmelCase_)
																	self.assertIsNotNone(UpperCAmelCase_)





			def __lowercase    (   self)	-> List[str]:




										'''simple docstring'''




										lowercase__:    Optional[Any]        =      self.model_tester.prepare_config_and_inputs_for_common()

										lowercase__:    Tuple        =      _config_zero_init(UpperCAmelCase_)
										for model_class in self.all_model_classes:
																	lowercase__:    Dict        =      model_class(config=UpperCAmelCase_)
																	for name, param in model.named_parameters():
																								if "logit_scale" in name:
																															continue
																								if 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"""			,				)





			def __lowercase    (   self)	-> List[str]:




										'''simple docstring'''




										lowercase__:    int        =      self.model_tester.prepare_config_and_inputs_for_common()
										lowercase__:    Optional[int]        =      True

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

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

																	self.assertListEqual(
																	    list(attentions[0].shape[-3:])			,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,				)
																	lowercase__:    int        =      len(UpperCAmelCase_)

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

																	self.assertEqual(out_len + 1			,				len(UpperCAmelCase_))

																	lowercase__:    List[Any]        =      outputs.attentions

																	self.assertEqual(len(UpperCAmelCase_)			,				UpperCAmelCase_)

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







@require_vision
@require_torch
@slow
class 		_a       (						unittest.TestCase   ):





			'''simple docstring'''






			def __lowercase    (   self)	-> str:




										'''simple docstring'''




										lowercase__:    Union[str, Any]        =      SwinaSRImageProcessor()
										lowercase__:    Union[str, Any]        =      SwinaSRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64").to(UpperCAmelCase_)

										lowercase__:    Dict        =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
										lowercase__:    Dict        =      processor(images=UpperCAmelCase_			,				return_tensors="pt").to(UpperCAmelCase_)

										# forward pass
										with torch.no_grad():
																	lowercase__:    Optional[int]        =      model(**UpperCAmelCase_)

										# verify the logits
										lowercase__:    Union[str, Any]        =      torch.Size([1, 3, 976, 1_296])
										self.assertEqual(outputs.reconstruction.shape			,				UpperCAmelCase_)
										lowercase__:    Union[str, Any]        =      torch.tensor(
										    [[0.54_58, 0.55_46, 0.56_38], [0.55_26, 0.55_65, 0.56_51], [0.53_96, 0.54_26, 0.56_21]]).to(UpperCAmelCase_)
										self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]			,				UpperCAmelCase_			,				atol=1E-4))














"""simple docstring"""





import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      LayoutLMForMaskedLM,
      LayoutLMForQuestionAnswering,
      LayoutLMForSequenceClassification,
      LayoutLMForTokenClassification,
      LayoutLMModel,
  )







class 		_a       :





   '''simple docstring'''






   def __init__(   self			,				UpperCAmelCase_			,				UpperCAmelCase_=13			,				UpperCAmelCase_=7			,				UpperCAmelCase_=True			,				UpperCAmelCase_=True			,				UpperCAmelCase_=True			,				UpperCAmelCase_=True			,				UpperCAmelCase_=99			,				UpperCAmelCase_=32			,				UpperCAmelCase_=5			,				UpperCAmelCase_=4			,				UpperCAmelCase_=37			,				UpperCAmelCase_="gelu"			,				UpperCAmelCase_=0.1			,				UpperCAmelCase_=0.1			,				UpperCAmelCase_=512			,				UpperCAmelCase_=16			,				UpperCAmelCase_=2			,				UpperCAmelCase_=0.02			,				UpperCAmelCase_=3			,				UpperCAmelCase_=4			,				UpperCAmelCase_=None			,				UpperCAmelCase_=1_000			,				)	-> Optional[int]:




          '''simple docstring'''




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





   def __lowercase    (   self)	-> Optional[int]:




          '''simple docstring'''




          lowercase__:    int        =      ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size)

          lowercase__:    Tuple        =      ids_tensor([self.batch_size, self.seq_length, 4]			,				self.range_bbox)
          # Ensure that bbox is legal
          for i in range(bbox.shape[0]):
                 for j in range(bbox.shape[1]):
                        if bbox[i, j, 3] < bbox[i, j, 1]:
                               lowercase__:    List[str]        =      bbox[i, j, 3]
                               lowercase__:    List[str]        =      bbox[i, j, 1]
                               lowercase__:    Union[str, Any]        =      t
                        if bbox[i, j, 2] < bbox[i, j, 0]:
                               lowercase__:    Optional[int]        =      bbox[i, j, 2]
                               lowercase__:    Tuple        =      bbox[i, j, 0]
                               lowercase__:    Optional[Any]        =      t

          lowercase__:    Dict        =      None
          if self.use_input_mask:
                 lowercase__:    str        =      ids_tensor([self.batch_size, self.seq_length]			,				vocab_size=2)

          lowercase__:    str        =      None
          if self.use_token_type_ids:
                 lowercase__:    List[Any]        =      ids_tensor([self.batch_size, self.seq_length]			,				self.type_vocab_size)

          lowercase__:    Tuple        =      None
          lowercase__:    Dict        =      None
          lowercase__:    Any        =      None
          if self.use_labels:
                 lowercase__:    List[Any]        =      ids_tensor([self.batch_size]			,				self.type_sequence_label_size)
                 lowercase__:    Any        =      ids_tensor([self.batch_size, self.seq_length]			,				self.num_labels)
                 lowercase__:    List[str]        =      ids_tensor([self.batch_size]			,				self.num_choices)

          lowercase__:    int        =      self.get_config()

          return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





   def __lowercase    (   self)	-> Optional[int]:




          '''simple docstring'''




          return LayoutLMConfig(
              vocab_size=self.vocab_size			,				hidden_size=self.hidden_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			,				)





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> Optional[Any]:




          '''simple docstring'''




          lowercase__:    Any        =      LayoutLMModel(config=UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    Tuple        =      model(UpperCAmelCase_			,				UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)
          lowercase__:    Dict        =      model(UpperCAmelCase_			,				UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)
          lowercase__:    Any        =      model(UpperCAmelCase_			,				UpperCAmelCase_)
          self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size))
          self.parent.assertEqual(result.pooler_output.shape			,				(self.batch_size, self.hidden_size))





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> List[Any]:




          '''simple docstring'''




          lowercase__:    int        =      LayoutLMForMaskedLM(config=UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    str        =      model(UpperCAmelCase_			,				UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				labels=UpperCAmelCase_)
          self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.vocab_size))





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> Optional[int]:




          '''simple docstring'''




          lowercase__:    Union[str, Any]        =      self.num_labels
          lowercase__:    Optional[Any]        =      LayoutLMForSequenceClassification(UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    Optional[Any]        =      model(
              UpperCAmelCase_			,				UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				labels=UpperCAmelCase_)
          self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels))





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> List[str]:




          '''simple docstring'''




          lowercase__:    List[str]        =      self.num_labels
          lowercase__:    List[str]        =      LayoutLMForTokenClassification(config=UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    List[Any]        =      model(UpperCAmelCase_			,				UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				labels=UpperCAmelCase_)
          self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.num_labels))





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> str:




          '''simple docstring'''




          lowercase__:    Dict        =      LayoutLMForQuestionAnswering(config=UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    List[Any]        =      model(
              UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				start_positions=UpperCAmelCase_			,				end_positions=UpperCAmelCase_			,				)
          self.parent.assertEqual(result.start_logits.shape			,				(self.batch_size, self.seq_length))
          self.parent.assertEqual(result.end_logits.shape			,				(self.batch_size, self.seq_length))





   def __lowercase    (   self)	-> Tuple:




          '''simple docstring'''




          lowercase__:    int        =      self.prepare_config_and_inputs()
          (
              (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     
          ):    int        =      config_and_inputs
          lowercase__:    Optional[int]        =      {
              "input_ids": input_ids,
              "bbox": bbox,
              "token_type_ids": token_type_ids,
              "attention_mask": input_mask,
          }
          return config, inputs_dict







@require_torch
class 		_a       (						lowercase_    ,					lowercase_    ,					unittest.TestCase   ):





   '''simple docstring'''






   UpperCamelCase__      					=    (
       (
           LayoutLMModel,
           LayoutLMForMaskedLM,
           LayoutLMForSequenceClassification,
           LayoutLMForTokenClassification,
           LayoutLMForQuestionAnswering,
       )
       if is_torch_available()
       else None
   )
   UpperCamelCase__      					=    (
       {
           """document-question-answering""": LayoutLMForQuestionAnswering,
           """feature-extraction""": LayoutLMModel,
           """fill-mask""": LayoutLMForMaskedLM,
           """text-classification""": LayoutLMForSequenceClassification,
           """token-classification""": LayoutLMForTokenClassification,
           """zero-shot""": LayoutLMForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   UpperCamelCase__      					=    True





   def __lowercase    (   self)	-> Optional[int]:




          '''simple docstring'''




          lowercase__:    List[str]        =      LayoutLMModelTester(self)
          lowercase__:    List[str]        =      ConfigTester(self			,				config_class=UpperCAmelCase_			,				hidden_size=37)





   def __lowercase    (   self)	-> Optional[Any]:




          '''simple docstring'''




          self.config_tester.run_common_tests()





   def __lowercase    (   self)	-> List[str]:




          '''simple docstring'''




          lowercase__:    int        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCAmelCase_)





   def __lowercase    (   self)	-> List[str]:




          '''simple docstring'''




          lowercase__:    List[Any]        =      self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
                 lowercase__:    Union[str, Any]        =      type
                 self.model_tester.create_and_check_model(*UpperCAmelCase_)





   def __lowercase    (   self)	-> List[str]:




          '''simple docstring'''




          lowercase__:    Any        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_)





   def __lowercase    (   self)	-> Dict:




          '''simple docstring'''




          lowercase__:    List[Any]        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_)





   def __lowercase    (   self)	-> int:




          '''simple docstring'''




          lowercase__:    Dict        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_)





   def __lowercase    (   self)	-> str:




          '''simple docstring'''




          lowercase__:    Optional[Any]        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_)





   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
   def __lowercase    (   self)	-> List[Any]:




          '''simple docstring'''




          pass







def     A(  ):
       """simple docstring"""





       lowercase__:    Any        =      torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]						,       device=snake_case_		)  # noqa: E231
       lowercase__:    Dict        =      torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]						,       device=snake_case_		)  # noqa: E231
       lowercase__:    Optional[Any]        =      torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]						,       device=snake_case_		)  # noqa: E231
       lowercase__:    Dict        =      torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]						,       device=snake_case_		)  # noqa: E231
       # these are sequence labels (i.e. at the token level)
       lowercase__:    int        =      torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]						,       device=snake_case_		)  # noqa: E231
       # fmt: on

       return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class 		_a       (						unittest.TestCase   ):





   '''simple docstring'''






   @slow
   def __lowercase    (   self)	-> Any:




          '''simple docstring'''




          lowercase__:    str        =      LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased").to(UpperCAmelCase_)

          lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__:    Dict        =      prepare_layoutlm_batch_inputs()

          # forward pass
          lowercase__:    str        =      model(input_ids=UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)

          # test the sequence output on [0, :3, :3]
          lowercase__:    Optional[int]        =      torch.tensor(
              [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]			,				device=UpperCAmelCase_			,				)

          self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]			,				UpperCAmelCase_			,				atol=1E-3))

          # test the pooled output on [1, :3]
          lowercase__:    List[Any]        =      torch.tensor([-0.65_80, -0.02_14, 0.85_52]			,				device=UpperCAmelCase_)

          self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]			,				UpperCAmelCase_			,				atol=1E-3))





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




          '''simple docstring'''




          lowercase__:    int        =      LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"			,				num_labels=2).to(
              UpperCAmelCase_)

          lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__:    List[Any]        =      prepare_layoutlm_batch_inputs()

          # forward pass
          lowercase__:    str        =      model(
              input_ids=UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				labels=torch.tensor([1, 1]			,				device=UpperCAmelCase_)			,				)

          # test whether we get a loss as a scalar
          lowercase__:    Dict        =      outputs.loss
          lowercase__:    int        =      torch.Size([])
          self.assertEqual(loss.shape			,				UpperCAmelCase_)

          # test the shape of the logits
          lowercase__:    str        =      outputs.logits
          lowercase__:    Any        =      torch.Size((2, 2))
          self.assertEqual(logits.shape			,				UpperCAmelCase_)





   @slow
   def __lowercase    (   self)	-> Union[str, Any]:




          '''simple docstring'''




          lowercase__:    Tuple        =      LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"			,				num_labels=13).to(
              UpperCAmelCase_)

          lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__:    List[Any]        =      prepare_layoutlm_batch_inputs()

          # forward pass
          lowercase__:    Any        =      model(
              input_ids=UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				labels=UpperCAmelCase_)

          # test the loss calculation to be around 2.65
          # expected_loss = torch.tensor(2.65, device=torch_device)

          # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
          # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

          # test the shape of the logits
          lowercase__:    int        =      outputs.logits
          lowercase__:    int        =      torch.Size((2, 25, 13))
          self.assertEqual(logits.shape			,				UpperCAmelCase_)





   @slow
   def __lowercase    (   self)	-> Tuple:




          '''simple docstring'''




          lowercase__:    Tuple        =      LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased").to(UpperCAmelCase_)

          lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__:    Tuple        =      prepare_layoutlm_batch_inputs()

          # forward pass
          lowercase__:    str        =      model(input_ids=UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)

          # test the shape of the logits
          lowercase__:    str        =      torch.Size((2, 25))
          self.assertEqual(outputs.start_logits.shape			,				UpperCAmelCase_)
          self.assertEqual(outputs.end_logits.shape			,				UpperCAmelCase_)






