

"""simple docstring"""





import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()




class    _lowerCAmelCase    (      unittest.TestCase  ):







 """simple docstring"""







 @property
 def    _lowercase					(     self				:     Dict      ):
    __lowercase								=		1
    __lowercase								=		3
    __lowercase								=		(3_2, 3_2)

    __lowercase								=		floats_tensor((batch_size, num_channels) + sizes,   rng=random.Random(0      )      ).to(UpperCAmelCase__      )
    return image


 @property
 def    _lowercase					(     self				:     List[Any]      ):
    torch.manual_seed(0      )
    __lowercase								=		UNetaDModel(
        block_out_channels=(3_2, 6_4),   layers_per_block=2,   sample_size=3_2,   in_channels=6,   out_channels=3,   down_block_types=("DownBlock2D", "AttnDownBlock2D"),   up_block_types=("AttnUpBlock2D", "UpBlock2D"),   )
    return model


 @property
 def    _lowercase					(     self				:     Optional[int]      ):
    torch.manual_seed(0      )
    __lowercase								=		VQModel(
        block_out_channels=[3_2, 6_4],   in_channels=3,   out_channels=3,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],   latent_channels=3,   )
    return model


 def    _lowercase					(     self				:     Dict      ):
    __lowercase								=		"cpu"
    __lowercase								=		self.dummy_uncond_unet
    __lowercase								=		DDIMScheduler()
    __lowercase								=		self.dummy_vq_model

    __lowercase								=		LDMSuperResolutionPipeline(unet=UpperCAmelCase__,   vqvae=UpperCAmelCase__,   scheduler=UpperCAmelCase__      )
    ldm.to(UpperCAmelCase__      )
    ldm.set_progress_bar_config(disable=UpperCAmelCase__      )

    __lowercase								=		self.dummy_image.to(UpperCAmelCase__      )

    __lowercase								=		torch.Generator(device=UpperCAmelCase__      ).manual_seed(0      )
    __lowercase								=		ldm(image=UpperCAmelCase__,   generator=UpperCAmelCase__,   num_inference_steps=2,   output_type="numpy"      ).images

    __lowercase								=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 6_4, 6_4, 3)
    __lowercase								=		np.array([0.8_678, 0.8_245, 0.6_381, 0.6_830, 0.4_385, 0.5_599, 0.4_641, 0.6_201, 0.5_150]      )

    assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2



 @unittest.skipIf(torch_device != "cuda",   "This test requires a GPU"      )
 def    _lowercase					(     self				:     Optional[int]      ):
    __lowercase								=		self.dummy_uncond_unet
    __lowercase								=		DDIMScheduler()
    __lowercase								=		self.dummy_vq_model

    # put models in fp16
    __lowercase								=		unet.half()
    __lowercase								=		vqvae.half()

    __lowercase								=		LDMSuperResolutionPipeline(unet=UpperCAmelCase__,   vqvae=UpperCAmelCase__,   scheduler=UpperCAmelCase__      )
    ldm.to(UpperCAmelCase__      )
    ldm.set_progress_bar_config(disable=UpperCAmelCase__      )

    __lowercase								=		self.dummy_image.to(UpperCAmelCase__      )

    __lowercase								=		ldm(UpperCAmelCase__,   num_inference_steps=2,   output_type="numpy"      ).images

    assert image.shape == (1, 6_4, 6_4, 3)







@slow
@require_torch
class    _lowerCAmelCase    (      unittest.TestCase  ):







 """simple docstring"""







 def    _lowercase					(     self				:     Dict      ):
    __lowercase								=		load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/vq_diffusion/teddy_bear_pool.png"      )
    __lowercase								=		init_image.resize((6_4, 6_4),   resample=PIL_INTERPOLATION["lanczos"]      )

    __lowercase								=		LDMSuperResolutionPipeline.from_pretrained("duongna/ldm-super-resolution",   device_map="auto"      )
    ldm.set_progress_bar_config(disable=UpperCAmelCase__      )

    __lowercase								=		torch.manual_seed(0      )
    __lowercase								=		ldm(image=UpperCAmelCase__,   generator=UpperCAmelCase__,   num_inference_steps=2_0,   output_type="numpy"      ).images

    __lowercase								=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 2_5_6, 2_5_6, 3)
    __lowercase								=		np.array([0.7_644, 0.7_679, 0.7_642, 0.7_633, 0.7_666, 0.7_560, 0.7_425, 0.7_257, 0.6_907]      )

    assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2







"""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    _lowerCAmelCase    :







 """simple docstring"""







 def __init__(     self				:     Optional[int],   UpperCAmelCase__				:     Any,   UpperCAmelCase__				:     Dict=1_3,   UpperCAmelCase__				:     Tuple=7,   UpperCAmelCase__				:     Union[str, Any]=True,   UpperCAmelCase__				:     Tuple=True,   UpperCAmelCase__				:     Dict=True,   UpperCAmelCase__				:     List[str]=True,   UpperCAmelCase__				:     Optional[int]=9_9,   UpperCAmelCase__				:     Union[str, Any]=3_2,   UpperCAmelCase__				:     str=5,   UpperCAmelCase__				:     int=4,   UpperCAmelCase__				:     Any=3_7,   UpperCAmelCase__				:     Tuple="gelu",   UpperCAmelCase__				:     str=0.1,   UpperCAmelCase__				:     Tuple=0.1,   UpperCAmelCase__				:     Any=5_1_2,   UpperCAmelCase__				:     Optional[Any]=1_6,   UpperCAmelCase__				:     Optional[Any]=2,   UpperCAmelCase__				:     Tuple=0.02,   UpperCAmelCase__				:     List[str]=3,   UpperCAmelCase__				:     Any=4,   UpperCAmelCase__				:     Dict=None,   UpperCAmelCase__				:     Dict=1_0_0_0,   ):
    __lowercase								=		parent
    __lowercase								=		batch_size
    __lowercase								=		seq_length
    __lowercase								=		is_training
    __lowercase								=		use_input_mask
    __lowercase								=		use_token_type_ids
    __lowercase								=		use_labels
    __lowercase								=		vocab_size
    __lowercase								=		hidden_size
    __lowercase								=		num_hidden_layers
    __lowercase								=		num_attention_heads
    __lowercase								=		intermediate_size
    __lowercase								=		hidden_act
    __lowercase								=		hidden_dropout_prob
    __lowercase								=		attention_probs_dropout_prob
    __lowercase								=		max_position_embeddings
    __lowercase								=		type_vocab_size
    __lowercase								=		type_sequence_label_size
    __lowercase								=		initializer_range
    __lowercase								=		num_labels
    __lowercase								=		num_choices
    __lowercase								=		scope
    __lowercase								=		range_bbox


 def    _lowercase					(     self				:     Union[str, Any]      ):
    __lowercase								=		ids_tensor([self.batch_size, self.seq_length],   self.vocab_size      )

    __lowercase								=		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								=		bbox[i, j, 3]
             __lowercase								=		bbox[i, j, 1]
             __lowercase								=		t
          if bbox[i, j, 2] < bbox[i, j, 0]:
             __lowercase								=		bbox[i, j, 2]
             __lowercase								=		bbox[i, j, 0]
             __lowercase								=		t

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

    __lowercase								=		None
    if self.use_token_type_ids:
       __lowercase								=		ids_tensor([self.batch_size, self.seq_length],   self.type_vocab_size      )

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

    __lowercase								=		self.get_config()

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


 def    _lowercase					(     self				:     Tuple      ):
    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				:     Optional[Any],   UpperCAmelCase__				:     Any,   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     Optional[int],   UpperCAmelCase__				:     List[Any],   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     Any      ):
    __lowercase								=		LayoutLMModel(config=UpperCAmelCase__      )
    model.to(UpperCAmelCase__      )
    model.eval()
    __lowercase								=		model(UpperCAmelCase__,   UpperCAmelCase__,   attention_mask=UpperCAmelCase__,   token_type_ids=UpperCAmelCase__      )
    __lowercase								=		model(UpperCAmelCase__,   UpperCAmelCase__,   token_type_ids=UpperCAmelCase__      )
    __lowercase								=		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				:     Tuple,   UpperCAmelCase__				:     Tuple,   UpperCAmelCase__				:     List[Any],   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     Dict,   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     Any,   UpperCAmelCase__				:     Optional[int],   UpperCAmelCase__				:     Dict      ):
    __lowercase								=		LayoutLMForMaskedLM(config=UpperCAmelCase__      )
    model.to(UpperCAmelCase__      )
    model.eval()
    __lowercase								=		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				:     Tuple,   UpperCAmelCase__				:     int,   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     Optional[int],   UpperCAmelCase__				:     List[Any],   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     Dict,   UpperCAmelCase__				:     Optional[int]      ):
    __lowercase								=		self.num_labels
    __lowercase								=		LayoutLMForSequenceClassification(UpperCAmelCase__      )
    model.to(UpperCAmelCase__      )
    model.eval()
    __lowercase								=		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				:     Any,   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     Any,   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     List[Any],   UpperCAmelCase__				:     Dict,   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     Union[str, Any],   UpperCAmelCase__				:     Any      ):
    __lowercase								=		self.num_labels
    __lowercase								=		LayoutLMForTokenClassification(config=UpperCAmelCase__      )
    model.to(UpperCAmelCase__      )
    model.eval()
    __lowercase								=		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				:     Optional[Any],   UpperCAmelCase__				:     int,   UpperCAmelCase__				:     Dict,   UpperCAmelCase__				:     Optional[int],   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     List[Any],   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     int,   UpperCAmelCase__				:     Dict      ):
    __lowercase								=		LayoutLMForQuestionAnswering(config=UpperCAmelCase__      )
    model.to(UpperCAmelCase__      )
    model.eval()
    __lowercase								=		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				:     Optional[int]      ):
    __lowercase								=		self.prepare_config_and_inputs()
    (
        (
        __lowercase
    )						,(
        __lowercase
    )						,(
        __lowercase
    )						,(
        __lowercase
    )						,(
        __lowercase
    )						,(
        __lowercase
    )						,(
        __lowercase
    )						,(
        __lowercase
    )						,
    )								=		config_and_inputs
    __lowercase								=		{
        "input_ids": input_ids,
        "bbox": bbox,
        "token_type_ids": token_type_ids,
        "attention_mask": input_mask,
    }
    return config, inputs_dict







@require_torch
class    _lowerCAmelCase    (      lowercase	,lowercase	,unittest.TestCase  ):







 """simple docstring"""







 __UpperCAmelCase      :   Dict  						=      (
     (
         LayoutLMModel,
         LayoutLMForMaskedLM,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMForQuestionAnswering,
     )
     if is_torch_available()
     else None
 )
 __UpperCAmelCase      :   Union[str, Any]  						=      (
     {
         "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      :   int  						=      True


 def    _lowercase					(     self				:     str      ):
    __lowercase								=		LayoutLMModelTester(self      )
    __lowercase								=		ConfigTester(self,   config_class=UpperCAmelCase__,   hidden_size=3_7      )


 def    _lowercase					(     self				:     int      ):
    self.config_tester.run_common_tests()


 def    _lowercase					(     self				:     List[str]      ):
    __lowercase								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCAmelCase__      )


 def    _lowercase					(     self				:     Optional[int]      ):
    __lowercase								=		self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
       __lowercase								=		type
       self.model_tester.create_and_check_model(*UpperCAmelCase__      )


 def    _lowercase					(     self				:     List[Any]      ):
    __lowercase								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__      )


 def    _lowercase					(     self				:     Optional[Any]      ):
    __lowercase								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__      )


 def    _lowercase					(     self				:     Union[str, Any]      ):
    __lowercase								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__      )


 def    _lowercase					(     self				:     Optional[int]      ):
    __lowercase								=		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				:     Tuple      ):
    pass







def     _A    (    )      ->		Optional[int]:
   '''simple docstring'''


   __lowercase								=		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=UpperCamelCase_)  # noqa: E231
   __lowercase								=		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=UpperCamelCase_)  # noqa: E231
   __lowercase								=		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=UpperCamelCase_)  # noqa: E231
   __lowercase								=		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=UpperCamelCase_)  # noqa: E231
   # these are sequence labels (i.e. at the token level)
   __lowercase								=		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=UpperCamelCase_)  # noqa: E231
   # fmt: on

   return input_ids, attention_mask, bbox, token_type_ids, labels




@require_torch
class    _lowerCAmelCase    (      unittest.TestCase  ):







 """simple docstring"""







 @slow
 def    _lowercase					(     self				:     Dict      ):
    __lowercase								=		LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"      ).to(UpperCAmelCase__      )

    __lowercase						,__lowercase						,__lowercase						,__lowercase						,__lowercase								=		prepare_layoutlm_batch_inputs()

    # forward pass
    __lowercase								=		model(input_ids=UpperCAmelCase__,   bbox=UpperCAmelCase__,   attention_mask=UpperCAmelCase__,   token_type_ids=UpperCAmelCase__      )

    # test the sequence output on [0, :3, :3]
    __lowercase								=		torch.tensor(
        [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]],   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								=		torch.tensor([-0.6_580, -0.0_214, 0.8_552],   device=UpperCAmelCase__      )

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


 @slow
 def    _lowercase					(     self				:     str      ):
    # initialize model with randomly initialized sequence classification head
    __lowercase								=		LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased",   num_labels=2      ).to(
        UpperCAmelCase__      )

    __lowercase						,__lowercase						,__lowercase						,__lowercase						,__lowercase								=		prepare_layoutlm_batch_inputs()

    # forward pass
    __lowercase								=		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								=		outputs.loss
    __lowercase								=		torch.Size([]      )
    self.assertEqual(loss.shape,   UpperCAmelCase__      )

    # test the shape of the logits
    __lowercase								=		outputs.logits
    __lowercase								=		torch.Size((2, 2)      )
    self.assertEqual(logits.shape,   UpperCAmelCase__      )


 @slow
 def    _lowercase					(     self				:     List[str]      ):
    # initialize model with randomly initialized token classification head
    __lowercase								=		LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased",   num_labels=1_3      ).to(
        UpperCAmelCase__      )

    __lowercase						,__lowercase						,__lowercase						,__lowercase						,__lowercase								=		prepare_layoutlm_batch_inputs()

    # forward pass
    __lowercase								=		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								=		outputs.logits
    __lowercase								=		torch.Size((2, 2_5, 1_3)      )
    self.assertEqual(logits.shape,   UpperCAmelCase__      )



 @slow
 def    _lowercase					(     self				:     Dict      ):
    # initialize model with randomly initialized token classification head
    __lowercase								=		LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"      ).to(UpperCAmelCase__      )

    __lowercase						,__lowercase						,__lowercase						,__lowercase						,__lowercase								=		prepare_layoutlm_batch_inputs()

    # forward pass
    __lowercase								=		model(input_ids=UpperCAmelCase__,   bbox=UpperCAmelCase__,   attention_mask=UpperCAmelCase__,   token_type_ids=UpperCAmelCase__      )

    # test the shape of the logits
    __lowercase								=		torch.Size((2, 2_5)      )
    self.assertEqual(outputs.start_logits.shape,   UpperCAmelCase__      )
    self.assertEqual(outputs.end_logits.shape,   UpperCAmelCase__      )




