






"""simple docstring"""



import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        LayoutLMvaConfig,
        LayoutLMvaForQuestionAnswering,
        LayoutLMvaForSequenceClassification,
        LayoutLMvaForTokenClassification,
        LayoutLMvaModel,
    )
    from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import LayoutLMvaImageProcessor

class        a     :







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase=2							,		_lowerCamelCase=3							,		_lowerCamelCase=4							,		_lowerCamelCase=2							,		_lowerCamelCase=7							,		_lowerCamelCase=True							,		_lowerCamelCase=True							,		_lowerCamelCase=True							,		_lowerCamelCase=True							,		_lowerCamelCase=9_9							,		_lowerCamelCase=3_6							,		_lowerCamelCase=3							,		_lowerCamelCase=4							,		_lowerCamelCase=3_7							,		_lowerCamelCase="gelu"							,		_lowerCamelCase=0.1							,		_lowerCamelCase=0.1							,		_lowerCamelCase=5_1_2							,		_lowerCamelCase=1_6							,		_lowerCamelCase=2							,		_lowerCamelCase=0.0_2							,		_lowerCamelCase=6							,		_lowerCamelCase=6							,		_lowerCamelCase=3							,		_lowerCamelCase=4							,		_lowerCamelCase=None							,		_lowerCamelCase=1_0_0_0							,		):
      lowercase							    =       parent
      lowercase							    =       batch_size
      lowercase							    =       num_channels
      lowercase							    =       image_size
      lowercase							    =       patch_size
      lowercase							    =       text_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							    =       coordinate_size
      lowercase							    =       shape_size
      lowercase							    =       num_labels
      lowercase							    =       num_choices
      lowercase							    =       scope
      lowercase							    =       range_bbox

      # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
      lowercase							    =       text_seq_length
      lowercase							    =       (image_size // patch_size) ** 2 + 1
      lowercase							    =       self.text_seq_length + self.image_seq_length







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       ids_tensor([self.batch_size, self.text_seq_length]							,		self.vocab_size       )

      lowercase							    =       ids_tensor([self.batch_size, self.text_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							    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

      lowercase							    =       None
      if self.use_input_mask:
        lowercase							    =       random_attention_mask([self.batch_size, self.text_seq_length]       )

      lowercase							    =       None
      if self.use_token_type_ids:
        lowercase							    =       ids_tensor([self.batch_size, self.text_seq_length]							,		self.type_vocab_size       )

      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.text_seq_length]							,		self.num_labels       )

      lowercase							    =       LayoutLMvaConfig(
          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							,		coordinate_size=self.coordinate_size							,		shape_size=self.shape_size							,		input_size=self.image_size							,		patch_size=self.patch_size							,		)

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







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       LayoutLMvaModel(config=_lowerCamelCase       )
      model.to(_lowerCamelCase       )
      model.eval()

      # text + image
      lowercase							    =       model(_lowerCamelCase							,		pixel_values=_lowerCamelCase       )
      lowercase							    =       model(
          _lowerCamelCase							,		bbox=_lowerCamelCase							,		pixel_values=_lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase       )
      lowercase							    =       model(_lowerCamelCase							,		bbox=_lowerCamelCase							,		pixel_values=_lowerCamelCase							,		token_type_ids=_lowerCamelCase       )
      lowercase							    =       model(_lowerCamelCase							,		bbox=_lowerCamelCase							,		pixel_values=_lowerCamelCase       )

      self.parent.assertEqual(result.last_hidden_state.shape							,		(self.batch_size, self.seq_length, self.hidden_size)       )

      # text only
      lowercase							    =       model(_lowerCamelCase       )
      self.parent.assertEqual(
          result.last_hidden_state.shape							,		(self.batch_size, self.text_seq_length, self.hidden_size)       )

      # image only
      lowercase							    =       model(pixel_values=_lowerCamelCase       )
      self.parent.assertEqual(
          result.last_hidden_state.shape							,		(self.batch_size, self.image_seq_length, self.hidden_size)       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       self.num_labels
      lowercase							    =       LayoutLMvaForSequenceClassification(_lowerCamelCase       )
      model.to(_lowerCamelCase       )
      model.eval()
      lowercase							    =       model(
          _lowerCamelCase							,		bbox=_lowerCamelCase							,		pixel_values=_lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase							,		labels=_lowerCamelCase							,		)
      self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.num_labels)       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       self.num_labels
      lowercase							    =       LayoutLMvaForTokenClassification(config=_lowerCamelCase       )
      model.to(_lowerCamelCase       )
      model.eval()
      lowercase							    =       model(
          _lowerCamelCase							,		bbox=_lowerCamelCase							,		pixel_values=_lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase							,		labels=_lowerCamelCase							,		)
      self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.text_seq_length, self.num_labels)       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       LayoutLMvaForQuestionAnswering(config=_lowerCamelCase       )
      model.to(_lowerCamelCase       )
      model.eval()
      lowercase							    =       model(
          _lowerCamelCase							,		bbox=_lowerCamelCase							,		pixel_values=_lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase							,		start_positions=_lowerCamelCase							,		end_positions=_lowerCamelCase							,		)
      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       ):
      lowercase							    =       self.prepare_config_and_inputs()
      (
          (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     
      )							    =       config_and_inputs
      lowercase							    =       {
          'input_ids': input_ids,
          'bbox': bbox,
          'pixel_values': pixel_values,
          'token_type_ids': token_type_ids,
          'attention_mask': input_mask,
      }
      return config, inputs_dict


@require_torch
class        a     (   a_,				a_,				unittest.TestCase	):
    UpperCAmelCase_ :     List[Any]	     =False
    UpperCAmelCase_ :     Any	     =False
    UpperCAmelCase_ :     Optional[int]	     =False

    UpperCAmelCase_ :     Union[str, Any]	     =(
        (
            LayoutLMvaModel,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase_ :     Dict	     =(
        {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
        if is_torch_available()
        else {}
    )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
      # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
      # the sequence dimension of the text embedding only.
      # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
      return True







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       LayoutLMvaModelTester(self       )
      lowercase							    =       ConfigTester(self							,		config_class=_lowerCamelCase							,		hidden_size=3_7       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase=False       ):
      lowercase							    =       copy.deepcopy(_lowerCamelCase       )
      if model_class in get_values(_lowerCamelCase       ):
        lowercase							    =       {
            k: v.unsqueeze(1       ).expand(-1							,		self.model_tester.num_choices							,		-1       ).contiguous()
            if isinstance(_lowerCamelCase							,		torch.Tensor       ) and v.ndim > 1
            else v
            for k, v in inputs_dict.items()
        }
      if return_labels:
        if model_class in get_values(_lowerCamelCase       ):
          lowercase							    =       torch.ones(self.model_tester.batch_size							,		dtype=torch.long							,		device=_lowerCamelCase       )
        elif model_class in get_values(_lowerCamelCase       ):
          lowercase							    =       torch.zeros(
              self.model_tester.batch_size							,		dtype=torch.long							,		device=_lowerCamelCase       )
          lowercase							    =       torch.zeros(
              self.model_tester.batch_size							,		dtype=torch.long							,		device=_lowerCamelCase       )
        elif model_class in [
            *get_values(_lowerCamelCase       ),
        ]:
          lowercase							    =       torch.zeros(
              self.model_tester.batch_size							,		dtype=torch.long							,		device=_lowerCamelCase       )
        elif model_class in [
            *get_values(_lowerCamelCase       ),
        ]:
          lowercase							    =       torch.zeros(
              (self.model_tester.batch_size, self.model_tester.text_seq_length)							,		dtype=torch.long							,		device=_lowerCamelCase							,		)

      return inputs_dict







    def 			UpperCamelCase_      (			self       ):
      self.config_tester.run_common_tests()







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      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(*_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase       )







    @slow
    def 			UpperCamelCase_      (			self       ):
      for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase							    =       LayoutLMvaModel.from_pretrained(_lowerCamelCase       )
        self.assertIsNotNone(_lowerCamelCase       )


def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
  return image



@require_torch
class        a     (   unittest.TestCase	):







    @cached_property
    def 			UpperCamelCase_      (			self       ):
      return LayoutLMvaImageProcessor(apply_ocr=_lowerCamelCase       ) if is_vision_available() else None







    @slow
    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'       ).to(_lowerCamelCase       )

      lowercase							    =       self.default_image_processor
      lowercase							    =       prepare_img()
      lowercase							    =       image_processor(images=_lowerCamelCase							,		return_tensors='pt'       ).pixel_values.to(_lowerCamelCase       )

      lowercase							    =       torch.tensor([[1, 2]]       )
      lowercase							    =       torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]       ).unsqueeze(0       )

      # forward pass
      lowercase							    =       model(
          input_ids=input_ids.to(_lowerCamelCase       )							,		bbox=bbox.to(_lowerCamelCase       )							,		pixel_values=pixel_values.to(_lowerCamelCase       )							,		)

      # verify the logits
      lowercase							    =       torch.Size((1, 1_9_9, 7_6_8)       )
      self.assertEqual(outputs.last_hidden_state.shape							,		_lowerCamelCase       )

      lowercase							    =       torch.tensor(
          [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]       ).to(_lowerCamelCase       )

      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]							,		_lowerCamelCase							,		atol=1e-4       )       )










"""simple docstring"""



from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

_UpperCamelCase		:  Any						=		TypeVar('T')

class        a     (   Generic[T]	):







    def __init__(			self							,		_lowerCamelCase       ):
      lowercase							    =       data
      lowercase							    =       None







    def __str__(			self       ):
      return F'{self.data}'


class        a     (   Generic[T]	):







    def __init__(			self       ):
      lowercase							    =       None







    def __iter__(			self       ):
      lowercase							    =       self.top
      while node:
        yield node.data
        lowercase							    =       node.next







    def __str__(			self       ):
      return "->".join([str(_lowerCamelCase       ) for item in self]       )







    def __len__(			self       ):
      return len(tuple(iter(self       )       )       )







    def 			UpperCamelCase_      (			self       ):
      return self.top is None







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       Node(_lowerCamelCase       )
      if not self.is_empty():
        lowercase							    =       self.top
      lowercase							    =       node







    def 			UpperCamelCase_      (			self       ):
      if self.is_empty():
        raise IndexError('pop from empty stack'       )
      assert isinstance(self.top							,		_lowerCamelCase       )
      lowercase							    =       self.top
      lowercase							    =       self.top.next
      return pop_node.data







    def 			UpperCamelCase_      (			self       ):
      if self.is_empty():
        raise IndexError('peek from empty stack'       )

      assert self.top is not None
      return self.top.data







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       None


if __name__ == "__main__":
    from doctest import testmod

    testmod()


