





"""simple docstring"""




import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   import PIL

   from transformers import FlavaImageProcessor
   from transformers.image_utils import PILImageResampling
   from transformers.models.flava.image_processing_flava import (
       FLAVA_CODEBOOK_MEAN,
       FLAVA_CODEBOOK_STD,
       FLAVA_IMAGE_MEAN,
       FLAVA_IMAGE_STD,
   )
else:
   lowerCamelCase_	:		Optional[Any]         =   None

class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_=7   , snake_case_=3   , snake_case_=3_0   , snake_case_=4_0_0   , snake_case_=True   , snake_case_=None   , snake_case_=True   , snake_case_=None   , snake_case_=None   , snake_case_=True   , snake_case_=1 / 2_5_5   , snake_case_=True   , snake_case_=FLAVA_IMAGE_MEAN   , snake_case_=FLAVA_IMAGE_STD   , snake_case_=1_4   , snake_case_=7_5   , snake_case_=None   , snake_case_=1_6   , snake_case_=0.3   , snake_case_=None   , snake_case_=True   , snake_case_=None   , snake_case_=None   , snake_case_=True   , snake_case_=None   , snake_case_=True   , snake_case_=True   , snake_case_=FLAVA_CODEBOOK_MEAN   , snake_case_=FLAVA_CODEBOOK_STD   , ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
        A_    :			Union[str, Any]									=    crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
        A_    :			Optional[Any]									=    codebook_size if codebook_size is not None else {'height': 1_1_2, 'width': 1_1_2}
        A_    :			List[str]									=    codebook_crop_size if codebook_crop_size is not None else {'height': 1_1_2, 'width': 1_1_2}

        A_    :			Optional[Any]									=    parent
        A_    :			Union[str, Any]									=    batch_size
        A_    :			List[str]									=    num_channels
        A_    :			Union[str, Any]									=    do_resize
        A_    :			Union[str, Any]									=    do_rescale
        A_    :			List[Any]									=    rescale_factor
        A_    :			List[str]									=    min_resolution
        A_    :			Optional[Any]									=    max_resolution
        A_    :			List[Any]									=    size
        A_    :			Optional[Any]									=    resample if resample is not None else PILImageResampling.BICUBIC
        A_    :			Dict									=    do_normalize
        A_    :			List[Any]									=    image_mean
        A_    :			Union[str, Any]									=    image_std
        A_    :			Union[str, Any]									=    do_center_crop
        A_    :			Union[str, Any]									=    crop_size

        A_    :			Optional[Any]									=    input_size_patches
        A_    :			List[Any]									=    total_mask_patches
        A_    :			Dict									=    mask_group_max_patches
        A_    :			List[str]									=    mask_group_min_patches
        A_    :			int									=    mask_group_min_aspect_ratio
        A_    :			Union[str, Any]									=    mask_group_max_aspect_ratio

        A_    :			Optional[int]									=    codebook_do_resize
        A_    :			Union[str, Any]									=    codebook_size
        A_    :			Union[str, Any]									=    codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
        A_    :			Dict									=    codebook_do_center_crop
        A_    :			List[Any]									=    codebook_crop_size
        A_    :			Tuple									=    codebook_do_map_pixels
        A_    :			Dict									=    codebook_do_normalize
        A_    :			Tuple									=    codebook_image_mean
        A_    :			Optional[Any]									=    codebook_image_std

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "resample": self.resample,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "input_size_patches": self.input_size_patches,
            "total_mask_patches": self.total_mask_patches,
            "mask_group_max_patches": self.mask_group_max_patches,
            "mask_group_min_patches": self.mask_group_min_patches,
            "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
            "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
            "codebook_do_resize": self.codebook_do_resize,
            "codebook_size": self.codebook_size,
            "codebook_resample": self.codebook_resample,
            "codebook_do_center_crop": self.codebook_do_center_crop,
            "codebook_crop_size": self.codebook_crop_size,
            "codebook_do_map_pixels": self.codebook_do_map_pixels,
            "codebook_do_normalize": self.codebook_do_normalize,
            "codebook_image_mean": self.codebook_image_mean,
            "codebook_image_std": self.codebook_image_std,
        }

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return (self.size["height"], self.size["width"])

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return (
            (self.input_size_patches, self.input_size_patches)
            if not isinstance(self.input_size_patches   , snake_case_    )
            else self.input_size_patches
        )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return (self.codebook_size["height"], self.codebook_size["width"])


@require_torch
@require_vision
class 					_UpperCAmelCase    (  UpperCAmelCase__     ,  unittest.TestCase						):



      '''simple docstring'''
      lowercase_ :					List[Any]             =  FlavaImageProcessor if is_vision_available() else None
      lowercase_ :					Any             =  None

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Tuple									=    FlavaImageProcessingTester(self    )

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return self.image_processor_tester.prepare_image_processor_dict()

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[str]									=    self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(snake_case_   , 'image_mean'    )    )
        self.assertTrue(hasattr(snake_case_   , 'image_std'    )    )
        self.assertTrue(hasattr(snake_case_   , 'do_normalize'    )    )
        self.assertTrue(hasattr(snake_case_   , 'do_resize'    )    )
        self.assertTrue(hasattr(snake_case_   , 'resample'    )    )
        self.assertTrue(hasattr(snake_case_   , 'crop_size'    )    )
        self.assertTrue(hasattr(snake_case_   , 'do_center_crop'    )    )
        self.assertTrue(hasattr(snake_case_   , 'do_rescale'    )    )
        self.assertTrue(hasattr(snake_case_   , 'rescale_factor'    )    )
        self.assertTrue(hasattr(snake_case_   , 'masking_generator'    )    )
        self.assertTrue(hasattr(snake_case_   , 'codebook_do_resize'    )    )
        self.assertTrue(hasattr(snake_case_   , 'codebook_size'    )    )
        self.assertTrue(hasattr(snake_case_   , 'codebook_resample'    )    )
        self.assertTrue(hasattr(snake_case_   , 'codebook_do_center_crop'    )    )
        self.assertTrue(hasattr(snake_case_   , 'codebook_crop_size'    )    )
        self.assertTrue(hasattr(snake_case_   , 'codebook_do_map_pixels'    )    )
        self.assertTrue(hasattr(snake_case_   , 'codebook_do_normalize'    )    )
        self.assertTrue(hasattr(snake_case_   , 'codebook_image_mean'    )    )
        self.assertTrue(hasattr(snake_case_   , 'codebook_image_std'    )    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    self.image_processing_class.from_dict(self.image_processor_dict    )
        self.assertEqual(image_processor.size   , {'height': 2_2_4, 'width': 2_2_4}    )
        self.assertEqual(image_processor.crop_size   , {'height': 2_2_4, 'width': 2_2_4}    )
        self.assertEqual(image_processor.codebook_size   , {'height': 1_1_2, 'width': 1_1_2}    )
        self.assertEqual(image_processor.codebook_crop_size   , {'height': 1_1_2, 'width': 1_1_2}    )

        A_    :			Optional[int]									=    self.image_processing_class.from_dict(
            self.image_processor_dict   , size=4_2   , crop_size=8_4   , codebook_size=3_3   , codebook_crop_size=6_6    )
        self.assertEqual(image_processor.size   , {'height': 4_2, 'width': 4_2}    )
        self.assertEqual(image_processor.crop_size   , {'height': 8_4, 'width': 8_4}    )
        self.assertEqual(image_processor.codebook_size   , {'height': 3_3, 'width': 3_3}    )
        self.assertEqual(image_processor.codebook_crop_size   , {'height': 6_6, 'width': 6_6}    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        pass

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        A_    :			Dict									=    prepare_image_inputs(self.image_processor_tester   , equal_resolution=snake_case_    )
        for image in image_inputs:
          self.assertIsInstance(snake_case_   , PIL.Image.Image    )

        # Test not batched input
        A_    :			Any									=    image_processing(image_inputs[0]   , return_tensors='pt'    )

        # Test no bool masked pos
        self.assertFalse('bool_masked_pos' in encoded_images    )

        A_		,			A_    :			Optional[Any]									=    self.image_processor_tester.get_expected_image_size()

        self.assertEqual(
            encoded_images.pixel_values.shape   , (1, self.image_processor_tester.num_channels, expected_height, expected_width)   , )

        # Test batched
        A_    :			int									=    image_processing(snake_case_   , return_tensors='pt'    )
        A_		,			A_    :			int									=    self.image_processor_tester.get_expected_image_size()

        # Test no bool masked pos
        self.assertFalse('bool_masked_pos' in encoded_images    )

        self.assertEqual(
            encoded_images.pixel_values.shape   , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )   , )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_    ):

        """simple docstring"""

        A_    :			str									=    self.image_processing_class(**self.image_processor_dict    )
        # create random tensors
        A_    :			str									=    prepare_image_inputs(self.image_processor_tester   , equal_resolution=snake_case_   , **snake_case_    )
        for image in image_inputs:
          self.assertIsInstance(snake_case_   , snake_case_    )

        # Test not batched input
        A_    :			Optional[int]									=    image_processing(image_inputs[0]   , return_tensors='pt'    )

        A_		,			A_    :			str									=    self.image_processor_tester.get_expected_image_size()
        self.assertEqual(
            encoded_images.pixel_values.shape   , (1, self.image_processor_tester.num_channels, expected_height, expected_width)   , )

        A_    :			Tuple									=    image_processing(snake_case_   , return_image_mask=snake_case_   , return_tensors='pt'    )

        A_		,			A_    :			Any									=    self.image_processor_tester.get_expected_image_size()
        self.assertEqual(
            encoded_images.pixel_values.shape   , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )   , )

        A_		,			A_    :			Optional[int]									=    self.image_processor_tester.get_expected_mask_size()
        self.assertEqual(
            encoded_images.bool_masked_pos.shape   , (
                self.image_processor_tester.batch_size,
                expected_height,
                expected_width,
            )   , )

        # Test batched
        A_    :			Dict									=    image_processing(snake_case_   , return_tensors='pt'    ).pixel_values

        A_		,			A_    :			int									=    self.image_processor_tester.get_expected_image_size()
        self.assertEqual(
            encoded_images.shape   , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )   , )

        # Test masking
        A_    :			int									=    image_processing(snake_case_   , return_image_mask=snake_case_   , return_tensors='pt'    )

        A_		,			A_    :			Any									=    self.image_processor_tester.get_expected_image_size()
        self.assertEqual(
            encoded_images.pixel_values.shape   , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )   , )

        A_		,			A_    :			Optional[Any]									=    self.image_processor_tester.get_expected_mask_size()
        self.assertEqual(
            encoded_images.bool_masked_pos.shape   , (
                self.image_processor_tester.batch_size,
                expected_height,
                expected_width,
            )   , )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self._test_call_framework(np.ndarray   , prepare_kwargs={'numpify': True}    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self._test_call_framework(torch.Tensor   , prepare_kwargs={'torchify': True}    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        random.seed(1_2_3_4    )
        A_    :			List[Any]									=    self.image_processing_class(**self.image_processor_dict    )
        A_    :			Optional[int]									=    prepare_image_inputs(self.image_processor_tester   , equal_resolution=snake_case_   , torchify=snake_case_    )

        # Test not batched input
        A_    :			str									=    image_processing(image_inputs[0]   , return_image_mask=snake_case_   , return_tensors='pt'    )
        self.assertEqual(encoded_images.bool_masked_pos.sum().item()   , 7_5    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[Any]									=    self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        A_    :			Optional[int]									=    prepare_image_inputs(self.image_processor_tester   , equal_resolution=snake_case_    )
        for image in image_inputs:
          self.assertIsInstance(snake_case_   , PIL.Image.Image    )

        # Test not batched input
        A_    :			Optional[int]									=    image_processing(image_inputs[0]   , return_codebook_pixels=snake_case_   , return_tensors='pt'    )
        A_		,			A_    :			Any									=    self.image_processor_tester.get_expected_codebook_image_size()
        self.assertEqual(
            encoded_images.codebook_pixel_values.shape   , (1, self.image_processor_tester.num_channels, expected_height, expected_width)   , )

        # Test batched
        A_    :			Optional[int]									=    image_processing(snake_case_   , return_codebook_pixels=snake_case_   , return_tensors='pt'    )
        A_		,			A_    :			int									=    self.image_processor_tester.get_expected_codebook_image_size()
        self.assertEqual(
            encoded_images.codebook_pixel_values.shape   , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )   , )


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  UpperCamelCase_      :


  """simple docstring"""



  def __init__(						self     :					Tuple  ,	A_     :					List[str]  ,	A_     :					List[Any]=13  ,	A_     :					Optional[int]=7  ,	A_     :					List[Any]=True  ,	A_     :					str=True  ,	A_     :					str=True  ,	A_     :					str=True  ,	A_     :					List[Any]=99  ,	A_     :					str=32  ,	A_     :					Dict=5  ,	A_     :					Tuple=4  ,	A_     :					Optional[Any]=37  ,	A_     :					List[Any]="gelu"  ,	A_     :					Any=0.1  ,	A_     :					Union[str, Any]=0.1  ,	A_     :					List[Any]=512  ,	A_     :					Dict=16  ,	A_     :					List[Any]=2  ,	A_     :					Optional[Any]=0.02  ,	A_     :					List[Any]=3  ,	A_     :					Optional[Any]=4  ,	A_     :					Dict=None  ,	A_     :					str=1000  ,	)       ->			str:
      SCREAMING_SNAKE_CASE__         =    parent
      SCREAMING_SNAKE_CASE__         =    batch_size
      SCREAMING_SNAKE_CASE__         =    seq_length
      SCREAMING_SNAKE_CASE__         =    is_training
      SCREAMING_SNAKE_CASE__         =    use_input_mask
      SCREAMING_SNAKE_CASE__         =    use_token_type_ids
      SCREAMING_SNAKE_CASE__         =    use_labels
      SCREAMING_SNAKE_CASE__         =    vocab_size
      SCREAMING_SNAKE_CASE__         =    hidden_size
      SCREAMING_SNAKE_CASE__         =    num_hidden_layers
      SCREAMING_SNAKE_CASE__         =    num_attention_heads
      SCREAMING_SNAKE_CASE__         =    intermediate_size
      SCREAMING_SNAKE_CASE__         =    hidden_act
      SCREAMING_SNAKE_CASE__         =    hidden_dropout_prob
      SCREAMING_SNAKE_CASE__         =    attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE__         =    max_position_embeddings
      SCREAMING_SNAKE_CASE__         =    type_vocab_size
      SCREAMING_SNAKE_CASE__         =    type_sequence_label_size
      SCREAMING_SNAKE_CASE__         =    initializer_range
      SCREAMING_SNAKE_CASE__         =    num_labels
      SCREAMING_SNAKE_CASE__         =    num_choices
      SCREAMING_SNAKE_CASE__         =    scope
      SCREAMING_SNAKE_CASE__         =    range_bbox



  def   _UpperCamelCase     (						self     :					List[str]						)       ->			int:
      SCREAMING_SNAKE_CASE__         =    ids_tensor([self.batch_size, self.seq_length]  ,	self.vocab_size						)

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

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

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

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

      SCREAMING_SNAKE_CASE__         =    self.get_config()

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



  def   _UpperCamelCase     (						self     :					List[str]						)       ->			int:
      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   _UpperCamelCase     (						self     :					List[Any]  ,	A_     :					Any  ,	A_     :					List[str]  ,	A_     :					Optional[int]  ,	A_     :					Dict  ,	A_     :					int  ,	A_     :					List[str]  ,	A_     :					Any  ,	A_     :					Tuple						)       ->			List[str]:
      SCREAMING_SNAKE_CASE__         =    LayoutLMModel(config=A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	A_  ,	attention_mask=A_  ,	token_type_ids=A_						)
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	A_  ,	token_type_ids=A_						)
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	A_						)
      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   _UpperCamelCase     (						self     :					int  ,	A_     :					int  ,	A_     :					Any  ,	A_     :					Tuple  ,	A_     :					Dict  ,	A_     :					int  ,	A_     :					Any  ,	A_     :					int  ,	A_     :					Union[str, Any]						)       ->			Tuple:
      SCREAMING_SNAKE_CASE__         =    LayoutLMForMaskedLM(config=A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	labels=A_						)
      self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.seq_length, self.vocab_size)						)



  def   _UpperCamelCase     (						self     :					Dict  ,	A_     :					Union[str, Any]  ,	A_     :					Union[str, Any]  ,	A_     :					Dict  ,	A_     :					Optional[int]  ,	A_     :					Tuple  ,	A_     :					Optional[int]  ,	A_     :					Optional[int]  ,	A_     :					Optional[Any]						)       ->			Optional[Any]:
      SCREAMING_SNAKE_CASE__         =    self.num_labels
      SCREAMING_SNAKE_CASE__         =    LayoutLMForSequenceClassification(A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(
          A_  ,	A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	labels=A_						)
      self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.num_labels)						)



  def   _UpperCamelCase     (						self     :					Any  ,	A_     :					Any  ,	A_     :					Any  ,	A_     :					str  ,	A_     :					Any  ,	A_     :					Optional[Any]  ,	A_     :					Tuple  ,	A_     :					Tuple  ,	A_     :					Any						)       ->			Any:
      SCREAMING_SNAKE_CASE__         =    self.num_labels
      SCREAMING_SNAKE_CASE__         =    LayoutLMForTokenClassification(config=A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	labels=A_						)
      self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.seq_length, self.num_labels)						)



  def   _UpperCamelCase     (						self     :					Optional[int]  ,	A_     :					str  ,	A_     :					Optional[Any]  ,	A_     :					int  ,	A_     :					str  ,	A_     :					Optional[Any]  ,	A_     :					List[str]  ,	A_     :					Union[str, Any]  ,	A_     :					Optional[Any]						)       ->			Any:
      SCREAMING_SNAKE_CASE__         =    LayoutLMForQuestionAnswering(config=A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(
          A_  ,	bbox=A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	start_positions=A_  ,	end_positions=A_  ,	)
      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   _UpperCamelCase     (						self     :					Optional[Any]						)       ->			List[str]:
      SCREAMING_SNAKE_CASE__         =    self.prepare_config_and_inputs()
      (
          (
          SCREAMING_SNAKE_CASE__
      )  ,		(
          SCREAMING_SNAKE_CASE__
      )  ,		(
          SCREAMING_SNAKE_CASE__
      )  ,		(
          SCREAMING_SNAKE_CASE__
      )  ,		(
          SCREAMING_SNAKE_CASE__
      )  ,		(
          SCREAMING_SNAKE_CASE__
      )  ,		(
          SCREAMING_SNAKE_CASE__
      )  ,		(
          SCREAMING_SNAKE_CASE__
      )  ,		
      )         =    config_and_inputs
      SCREAMING_SNAKE_CASE__         =    {
          "input_ids": input_ids,
          "bbox": bbox,
          "token_type_ids": token_type_ids,
          "attention_mask": input_mask,
      }
      return config, inputs_dict







@require_torch
class  UpperCamelCase_      (	_snake_case		,  _snake_case		,  unittest.TestCase      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Optional[int]					    =   (
      (
          LayoutLMModel,
          LayoutLMForMaskedLM,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMForQuestionAnswering,
      )
      if is_torch_available()
      else None
  )
  SCREAMING_SNAKE_CASE__					:			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 {}
  )
  SCREAMING_SNAKE_CASE__					:			Any					    =   True



  def   _UpperCamelCase     (						self     :					int						)       ->			Optional[Any]:
      SCREAMING_SNAKE_CASE__         =    LayoutLMModelTester(self						)
      SCREAMING_SNAKE_CASE__         =    ConfigTester(self  ,	config_class=A_  ,	hidden_size=37						)



  def   _UpperCamelCase     (						self     :					Tuple						)       ->			Tuple:
      self.config_tester.run_common_tests()



  def   _UpperCamelCase     (						self     :					Dict						)       ->			Tuple:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A_						)



  def   _UpperCamelCase     (						self     :					Optional[Any]						)       ->			Dict:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
          SCREAMING_SNAKE_CASE__         =    type
          self.model_tester.create_and_check_model(*A_						)



  def   _UpperCamelCase     (						self     :					Tuple						)       ->			Tuple:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*A_						)



  def   _UpperCamelCase     (						self     :					Dict						)       ->			Optional[Any]:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*A_						)



  def   _UpperCamelCase     (						self     :					List[str]						)       ->			List[str]:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*A_						)



  def   _UpperCamelCase     (						self     :					int						)       ->			Optional[Any]:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*A_						)







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





def 				__a							(       ):
    # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
    # fmt: off
    SCREAMING_SNAKE_CASE__         =    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=__A						)  # noqa: E231
    SCREAMING_SNAKE_CASE__         =    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=__A						)  # noqa: E231
    SCREAMING_SNAKE_CASE__         =    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=__A						)  # noqa: E231
    SCREAMING_SNAKE_CASE__         =    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=__A						)  # noqa: E231
    # these are sequence labels (i.e. at the token level)
    SCREAMING_SNAKE_CASE__         =    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=__A						)  # noqa: E231
    # fmt: on

    return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class  UpperCamelCase_      (	unittest.TestCase      ):


  """simple docstring"""



  @slow
  def   _UpperCamelCase     (						self     :					List[str]						)       ->			List[Any]:
      SCREAMING_SNAKE_CASE__         =    LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"						).to(A_						)

      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    prepare_layoutlm_batch_inputs()

      # forward pass
      SCREAMING_SNAKE_CASE__         =    model(input_ids=A_  ,	bbox=A_  ,	attention_mask=A_  ,	token_type_ids=A_						)

      # test the sequence output on [0, :3, :3]
      SCREAMING_SNAKE_CASE__         =    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=A_  ,	)

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

      # test the pooled output on [1, :3]
      SCREAMING_SNAKE_CASE__         =    torch.tensor([-0.6_580, -0.0_214, 0.8_552]  ,	device=A_						)

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



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

      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    prepare_layoutlm_batch_inputs()

      # forward pass
      SCREAMING_SNAKE_CASE__         =    model(
          input_ids=A_  ,	bbox=A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	labels=torch.tensor([1, 1]  ,	device=A_						)  ,	)

      # test whether we get a loss as a scalar
      SCREAMING_SNAKE_CASE__         =    outputs.loss
      SCREAMING_SNAKE_CASE__         =    torch.Size([]						)
      self.assertEqual(loss.shape  ,	A_						)

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



  @slow
  def   _UpperCamelCase     (						self     :					List[str]						)       ->			List[Any]:
      # initialize model with randomly initialized token classification head
      SCREAMING_SNAKE_CASE__         =    LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"  ,	num_labels=13						).to(
          A_						)

      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    prepare_layoutlm_batch_inputs()

      # forward pass
      SCREAMING_SNAKE_CASE__         =    model(
          input_ids=A_  ,	bbox=A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	labels=A_						)

      # 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
      SCREAMING_SNAKE_CASE__         =    outputs.logits
      SCREAMING_SNAKE_CASE__         =    torch.Size((2, 25, 13)						)
      self.assertEqual(logits.shape  ,	A_						)







  @slow
  def   _UpperCamelCase     (						self     :					Dict						)       ->			List[Any]:
      # initialize model with randomly initialized token classification head
      SCREAMING_SNAKE_CASE__         =    LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"						).to(A_						)

      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    prepare_layoutlm_batch_inputs()

      # forward pass
      SCREAMING_SNAKE_CASE__         =    model(input_ids=A_  ,	bbox=A_  ,	attention_mask=A_  ,	token_type_ids=A_						)

      # test the shape of the logits
      SCREAMING_SNAKE_CASE__         =    torch.Size((2, 25)						)
      self.assertEqual(outputs.start_logits.shape  ,	A_						)
      self.assertEqual(outputs.end_logits.shape  ,	A_						)


