





'''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_     :

    '''simple docstring'''


    def __init__(		self ,		A ,		A=2 ,		A=3 ,		A=4 ,		A=2 ,		A=7 ,		A=True ,		A=True ,		A=True ,		A=True ,		A=99 ,		A=36 ,		A=3 ,		A=4 ,		A=37 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=512 ,		A=16 ,		A=2 ,		A=0.02 ,		A=6 ,		A=6 ,		A=3 ,		A=4 ,		A=None ,		A=1000 ,		)		->   Dict:
        _SCREAMING_SNAKE_CASE									=			parent
        _SCREAMING_SNAKE_CASE									=			batch_size
        _SCREAMING_SNAKE_CASE									=			num_channels
        _SCREAMING_SNAKE_CASE									=			image_size
        _SCREAMING_SNAKE_CASE									=			patch_size
        _SCREAMING_SNAKE_CASE									=			text_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									=			coordinate_size
        _SCREAMING_SNAKE_CASE									=			shape_size
        _SCREAMING_SNAKE_CASE									=			num_labels
        _SCREAMING_SNAKE_CASE									=			num_choices
        _SCREAMING_SNAKE_CASE									=			scope
        _SCREAMING_SNAKE_CASE									=			range_bbox

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


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			ids_tensor([self.batch_size, self.text_seq_length] ,		self.vocab_size       )

        _SCREAMING_SNAKE_CASE									=			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]:
                    _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									=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

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

        _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.text_seq_length] ,		self.num_labels       )

        _SCREAMING_SNAKE_CASE									=			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 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			LayoutLMvaModel(config=A       )
        model.to(A       )
        model.eval()

        # text + image
        _SCREAMING_SNAKE_CASE									=			model(A ,		pixel_values=A       )
        _SCREAMING_SNAKE_CASE									=			model(
            A ,		bbox=A ,		pixel_values=A ,		attention_mask=A ,		token_type_ids=A       )
        _SCREAMING_SNAKE_CASE									=			model(A ,		bbox=A ,		pixel_values=A ,		token_type_ids=A       )
        _SCREAMING_SNAKE_CASE									=			model(A ,		bbox=A ,		pixel_values=A       )

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

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

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


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			self.num_labels
        _SCREAMING_SNAKE_CASE									=			LayoutLMvaForSequenceClassification(A       )
        model.to(A       )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			model(
            A ,		bbox=A ,		pixel_values=A ,		attention_mask=A ,		token_type_ids=A ,		labels=A ,		)
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.num_labels)       )


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A ,		A ,		A       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self.num_labels
        _SCREAMING_SNAKE_CASE									=			LayoutLMvaForTokenClassification(config=A       )
        model.to(A       )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			model(
            A ,		bbox=A ,		pixel_values=A ,		attention_mask=A ,		token_type_ids=A ,		labels=A ,		)
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.text_seq_length, self.num_labels)       )


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			LayoutLMvaForQuestionAnswering(config=A       )
        model.to(A       )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			model(
            A ,		bbox=A ,		pixel_values=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 			snake_case_(		self       )		->   Any:
        _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,
            """pixel_values""": pixel_values,
            """token_type_ids""": token_type_ids,
            """attention_mask""": input_mask,
        }
        return config, inputs_dict




@require_torch
class 					a_     (				snake_case_      ,    snake_case_      ,    unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = False
    UpperCamelCase     = False
    UpperCamelCase     = False

    UpperCamelCase     = (
        (
            LayoutLMvaModel,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    UpperCamelCase     = (
        {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
        if is_torch_available()
        else {}
    )


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A       )		->   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 			snake_case_(		self       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			LayoutLMvaModelTester(self       )
        _SCREAMING_SNAKE_CASE									=			ConfigTester(self ,		config_class=A ,		hidden_size=37       )


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

        return inputs_dict


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


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A       )


    def 			snake_case_(		self       )		->   str:
        _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 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*A       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*A       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*A       )





    @slow
    def 			snake_case_(		self       )		->   List[str]:
        for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _SCREAMING_SNAKE_CASE									=			LayoutLMvaModel.from_pretrained(A       )
            self.assertIsNotNone(A       )





def lowerCamelCase						(     )						->Dict:
    _SCREAMING_SNAKE_CASE									=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
    return image






@require_torch
class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''


    @cached_property
    def 			snake_case_(		self       )		->   List[str]:
        return LayoutLMvaImageProcessor(apply_ocr=A       ) if is_vision_available() else None





    @slow
    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""       ).to(A       )

        _SCREAMING_SNAKE_CASE									=			self.default_image_processor
        _SCREAMING_SNAKE_CASE									=			prepare_img()
        _SCREAMING_SNAKE_CASE									=			image_processor(images=A ,		return_tensors="""pt"""       ).pixel_values.to(A       )

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

        # forward pass
        _SCREAMING_SNAKE_CASE									=			model(
            input_ids=input_ids.to(A       ) ,		bbox=bbox.to(A       ) ,		pixel_values=pixel_values.to(A       ) ,		)

        # verify the logits
        _SCREAMING_SNAKE_CASE									=			torch.Size((1, 199, 768)       )
        self.assertEqual(outputs.last_hidden_state.shape ,		A       )

        _SCREAMING_SNAKE_CASE									=			torch.tensor(
            [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]       ).to(A       )

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










'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_					= {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= ["""MBartTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= ["""MBartTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= [
          """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MBartForCausalLM""",
          """MBartForConditionalGeneration""",
          """MBartForQuestionAnswering""",
          """MBartForSequenceClassification""",
          """MBartModel""",
          """MBartPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= [
          """TFMBartForConditionalGeneration""",
          """TFMBartModel""",
          """TFMBartPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= [
          """FlaxMBartForConditionalGeneration""",
          """FlaxMBartForQuestionAnswering""",
          """FlaxMBartForSequenceClassification""",
          """FlaxMBartModel""",
          """FlaxMBartPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart import MBartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart_fast import MBartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mbart import (
                MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                MBartForCausalLM,
                MBartForConditionalGeneration,
                MBartForQuestionAnswering,
                MBartForSequenceClassification,
                MBartModel,
                MBartPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mbart import (
                FlaxMBartForConditionalGeneration,
                FlaxMBartForQuestionAnswering,
                FlaxMBartForSequenceClassification,
                FlaxMBartModel,
                FlaxMBartPreTrainedModel,
            )

else:
      import sys

      lowercase_					= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



