





'''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 			UpperCAmelCase					:




      def __init__(      self      :			int      ,      __snake_case      :			Any      ,      __snake_case      :			List[Any]=2      ,      __snake_case      :			List[str]=3      ,      __snake_case      :			List[str]=4      ,      __snake_case      :			Union[str, Any]=2      ,      __snake_case      :			Union[str, Any]=7      ,      __snake_case      :			Union[str, Any]=True      ,      __snake_case      :			Dict=True      ,      __snake_case      :			Optional[int]=True      ,      __snake_case      :			Optional[int]=True      ,      __snake_case      :			int=99      ,      __snake_case      :			Optional[int]=36      ,      __snake_case      :			Dict=3      ,      __snake_case      :			Optional[Any]=4      ,      __snake_case      :			Union[str, Any]=37      ,      __snake_case      :			str="gelu"      ,      __snake_case      :			int=0.1      ,      __snake_case      :			Optional[int]=0.1      ,      __snake_case      :			Dict=5_12      ,      __snake_case      :			int=16      ,      __snake_case      :			int=2      ,      __snake_case      :			List[str]=0.02      ,      __snake_case      :			Optional[int]=6      ,      __snake_case      :			Optional[int]=6      ,      __snake_case      :			Any=3      ,      __snake_case      :			Union[str, Any]=4      ,      __snake_case      :			Any=None      ,      __snake_case      :			str=10_00      ,      )    ->				Union[str, Any]:
            _lowerCAmelCase												=							parent
            _lowerCAmelCase												=							batch_size
            _lowerCAmelCase												=							num_channels
            _lowerCAmelCase												=							image_size
            _lowerCAmelCase												=							patch_size
            _lowerCAmelCase												=							text_seq_length
            _lowerCAmelCase												=							is_training
            _lowerCAmelCase												=							use_input_mask
            _lowerCAmelCase												=							use_token_type_ids
            _lowerCAmelCase												=							use_labels
            _lowerCAmelCase												=							vocab_size
            _lowerCAmelCase												=							hidden_size
            _lowerCAmelCase												=							num_hidden_layers
            _lowerCAmelCase												=							num_attention_heads
            _lowerCAmelCase												=							intermediate_size
            _lowerCAmelCase												=							hidden_act
            _lowerCAmelCase												=							hidden_dropout_prob
            _lowerCAmelCase												=							attention_probs_dropout_prob
            _lowerCAmelCase												=							max_position_embeddings
            _lowerCAmelCase												=							type_vocab_size
            _lowerCAmelCase												=							type_sequence_label_size
            _lowerCAmelCase												=							initializer_range
            _lowerCAmelCase												=							coordinate_size
            _lowerCAmelCase												=							shape_size
            _lowerCAmelCase												=							num_labels
            _lowerCAmelCase												=							num_choices
            _lowerCAmelCase												=							scope
            _lowerCAmelCase												=							range_bbox

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




      def     lowercase__   (      self      :			List[str]  )    ->				Any:
            _lowerCAmelCase												=							ids_tensor([self.batch_size, self.text_seq_length]      ,      self.vocab_size  )

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

            _lowerCAmelCase												=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

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

            _lowerCAmelCase												=							None
            _lowerCAmelCase												=							None
            if self.use_labels:
                  _lowerCAmelCase												=							ids_tensor([self.batch_size]      ,      self.type_sequence_label_size  )
                  _lowerCAmelCase												=							ids_tensor([self.batch_size, self.text_seq_length]      ,      self.num_labels  )

            _lowerCAmelCase												=							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     lowercase__   (      self      :			Union[str, Any]      ,      __snake_case      :			Dict      ,      __snake_case      :			List[Any]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			str      ,      __snake_case      :			List[str]      ,      __snake_case      :			str      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			List[str]  )    ->				Any:
            _lowerCAmelCase												=							LayoutLMvaModel(config=__snake_case  )
            model.to(__snake_case  )
            model.eval()

            # text + image
            _lowerCAmelCase												=							model(__snake_case      ,      pixel_values=__snake_case  )
            _lowerCAmelCase												=							model(
                __snake_case      ,      bbox=__snake_case      ,      pixel_values=__snake_case      ,      attention_mask=__snake_case      ,      token_type_ids=__snake_case  )
            _lowerCAmelCase												=							model(__snake_case      ,      bbox=__snake_case      ,      pixel_values=__snake_case      ,      token_type_ids=__snake_case  )
            _lowerCAmelCase												=							model(__snake_case      ,      bbox=__snake_case      ,      pixel_values=__snake_case  )

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

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

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




      def     lowercase__   (      self      :			int      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			List[str]      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			List[str]      ,      __snake_case      :			List[str]      ,      __snake_case      :			Dict      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			Optional[Any]  )    ->				Optional[int]:
            _lowerCAmelCase												=							self.num_labels
            _lowerCAmelCase												=							LayoutLMvaForSequenceClassification(__snake_case  )
            model.to(__snake_case  )
            model.eval()
            _lowerCAmelCase												=							model(
                __snake_case      ,      bbox=__snake_case      ,      pixel_values=__snake_case      ,      attention_mask=__snake_case      ,      token_type_ids=__snake_case      ,      labels=__snake_case      ,      )
            self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)  )




      def     lowercase__   (      self      :			Union[str, Any]      ,      __snake_case      :			List[str]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			List[Any]      ,      __snake_case      :			Any      ,      __snake_case      :			Tuple      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Dict  )    ->				Any:
            _lowerCAmelCase												=							self.num_labels
            _lowerCAmelCase												=							LayoutLMvaForTokenClassification(config=__snake_case  )
            model.to(__snake_case  )
            model.eval()
            _lowerCAmelCase												=							model(
                __snake_case      ,      bbox=__snake_case      ,      pixel_values=__snake_case      ,      attention_mask=__snake_case      ,      token_type_ids=__snake_case      ,      labels=__snake_case      ,      )
            self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.text_seq_length, self.num_labels)  )




      def     lowercase__   (      self      :			int      ,      __snake_case      :			Dict      ,      __snake_case      :			int      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			str      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Tuple      ,      __snake_case      :			Union[str, Any]  )    ->				Dict:
            _lowerCAmelCase												=							LayoutLMvaForQuestionAnswering(config=__snake_case  )
            model.to(__snake_case  )
            model.eval()
            _lowerCAmelCase												=							model(
                __snake_case      ,      bbox=__snake_case      ,      pixel_values=__snake_case      ,      attention_mask=__snake_case      ,      token_type_ids=__snake_case      ,      start_positions=__snake_case      ,      end_positions=__snake_case      ,      )
            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      :			Union[str, Any]  )    ->				Optional[int]:
            _lowerCAmelCase												=							self.prepare_config_and_inputs()
            (
                (
                _lowerCAmelCase
            )					,							(
                _lowerCAmelCase
            )					,							(
                _lowerCAmelCase
            )					,							(
                _lowerCAmelCase
            )					,							(
                _lowerCAmelCase
            )					,							(
                _lowerCAmelCase
            )					,							(
                _lowerCAmelCase
            )					,							(
                _lowerCAmelCase
            )					,							
            )												=							config_and_inputs
            _lowerCAmelCase												=							{
                """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 			UpperCAmelCase					(      snake_case_							,		snake_case_							,		unittest.TestCase       ):
      _lowercase:      Optional[int]											=       False
      _lowercase:      Tuple											=       False
      _lowercase:      Dict											=       False

      _lowercase:      Dict											=       (
          (
              LayoutLMvaModel,
              LayoutLMvaForSequenceClassification,
              LayoutLMvaForTokenClassification,
              LayoutLMvaForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      _lowercase:      Optional[int]											=       (
          {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
          if is_torch_available()
          else {}
      )




      def     lowercase__   (      self      :			int      ,      __snake_case      :			Tuple      ,      __snake_case      :			str      ,      __snake_case      :			Dict      ,      __snake_case      :			str      ,      __snake_case      :			str  )    ->				int:
            # `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     lowercase__   (      self      :			List[Any]  )    ->				str:
            _lowerCAmelCase												=							LayoutLMvaModelTester(self  )
            _lowerCAmelCase												=							ConfigTester(self      ,      config_class=__snake_case      ,      hidden_size=37  )




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

            return inputs_dict




      def     lowercase__   (      self      :			List[str]  )    ->				List[Any]:
            self.config_tester.run_common_tests()




      def     lowercase__   (      self      :			str  )    ->				Tuple:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__snake_case  )




      def     lowercase__   (      self      :			Optional[int]  )    ->				List[str]:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                  _lowerCAmelCase												=							type
                  self.model_tester.create_and_check_model(*__snake_case  )




      def     lowercase__   (      self      :			Tuple  )    ->				List[str]:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*__snake_case  )




      def     lowercase__   (      self      :			List[Any]  )    ->				Optional[Any]:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*__snake_case  )




      def     lowercase__   (      self      :			Dict  )    ->				Any:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*__snake_case  )




      @slow
      def     lowercase__   (      self      :			int  )    ->				Any:
            for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _lowerCAmelCase												=							LayoutLMvaModel.from_pretrained(__snake_case  )
                  self.assertIsNotNone(__snake_case  )




def        UpperCamelCase__				(		):





      """simple docstring"""


      _lowerCAmelCase												=							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
      return image





@require_torch
class 			UpperCAmelCase					(      unittest.TestCase       ):




      @cached_property
      def     lowercase__   (      self      :			List[str]  )    ->				Optional[int]:
            return LayoutLMvaImageProcessor(apply_ocr=__snake_case  ) if is_vision_available() else None




      @slow
      def     lowercase__   (      self      :			int  )    ->				Optional[Any]:
            _lowerCAmelCase												=							LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""  ).to(__snake_case  )

            _lowerCAmelCase												=							self.default_image_processor
            _lowerCAmelCase												=							prepare_img()
            _lowerCAmelCase												=							image_processor(images=__snake_case      ,      return_tensors="""pt"""  ).pixel_values.to(__snake_case  )

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

            # forward pass
            _lowerCAmelCase												=							model(
                input_ids=input_ids.to(__snake_case  )      ,      bbox=bbox.to(__snake_case  )      ,      pixel_values=pixel_values.to(__snake_case  )      ,      )

            # verify the logits
            _lowerCAmelCase												=							torch.Size((1, 1_99, 7_68)  )
            self.assertEqual(outputs.last_hidden_state.shape      ,      __snake_case  )

            _lowerCAmelCase												=							torch.tensor(
                [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]  ).to(__snake_case  )

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













'''simple docstring'''






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


A__    :       str              =5_00_00
A__    :       Optional[int]              =50_00

A__       ,						A__    :       Optional[int]              =os.path.split(__file__)
A__    :       Tuple              =os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      for i in range(lowerCAmelCase  ):
            _lowerCAmelCase												=							dataset[i]
@get_duration
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      for i in range(0							,     len(lowerCAmelCase  )							,     lowerCAmelCase  ):
            _lowerCAmelCase												=							dataset[i : i + batch_size]
@get_duration
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      with dataset.formatted_as(type=lowerCAmelCase  ):
            for i in range(lowerCAmelCase  ):
                  _lowerCAmelCase												=							dataset[i]
@get_duration
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      with dataset.formatted_as(type=lowerCAmelCase  ):
            for i in range(0							,     lowerCAmelCase							,     lowerCAmelCase  ):
                  _lowerCAmelCase												=							dataset[i : i + batch_size]
def        UpperCamelCase__				(		):





      """simple docstring"""


      _lowerCAmelCase												=							{"""num examples""": SPEED_TEST_N_EXAMPLES}
      _lowerCAmelCase												=							[
          (read, {"""length""": SMALL_TEST}),
          (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
          (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
          (read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
          (read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
          (read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
          (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
          (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
      ]

      _lowerCAmelCase												=							[
          (read, {"""length""": SMALL_TEST}),
          (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
          (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
          (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
          (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
      ]
      with tempfile.TemporaryDirectory() as tmp_dir:
            print("""generating dataset"""  )
            _lowerCAmelCase												=							datasets.Features(
                {"""list""": datasets.Sequence(datasets.Value("""float32"""  )  ), """numbers""": datasets.Value("""float32"""  )}  )
            _lowerCAmelCase												=							generate_example_dataset(
                os.path.join(lowerCAmelCase							,     """dataset.arrow"""  )							,     lowerCAmelCase							,     num_examples=lowerCAmelCase							,     seq_shapes={"""list""": (1_00,)}							,     )
            print("""first set of iterations"""  )
            for func, kwargs in functions:
                  print(func.__name__							,     str(lowerCAmelCase  )  )
                  _lowerCAmelCase												=							func(lowerCAmelCase							,     **lowerCAmelCase  )

            print("""shuffling dataset"""  )
            _lowerCAmelCase												=							dataset.shuffle()
            print("""Second set of iterations (after shuffling"""  )
            for func, kwargs in functions_shuffled:
                  print("""shuffled """							,     func.__name__							,     str(lowerCAmelCase  )  )
                  _lowerCAmelCase												=							func(
                      lowerCAmelCase							,     **lowerCAmelCase  )

      with open(lowerCAmelCase							,     """wb"""  ) as f:
            f.write(json.dumps(lowerCAmelCase  ).encode("""utf-8"""  )  )


if __name__ == "__main__":  # useful to run the profiler
      benchmark_iterating()






