

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       __magic_name__       :





			def __init__(						self   ,    __snake_case   ,    __snake_case=2   ,    __snake_case=3   ,    __snake_case=4   ,    __snake_case=2   ,    __snake_case=7   ,    __snake_case=True   ,    __snake_case=True   ,    __snake_case=True   ,    __snake_case=True   ,    __snake_case=99   ,    __snake_case=36   ,    __snake_case=3   ,    __snake_case=4   ,    __snake_case=37   ,    __snake_case="gelu"   ,    __snake_case=0.1   ,    __snake_case=0.1   ,    __snake_case=512   ,    __snake_case=16   ,    __snake_case=2   ,    __snake_case=0.02   ,    __snake_case=6   ,    __snake_case=6   ,    __snake_case=3   ,    __snake_case=4   ,    __snake_case=None   ,    __snake_case=1000   ,    )  ->					Tuple:




						'''simple docstring'''
						__a     =parent
						__a     =batch_size
						__a     =num_channels
						__a     =image_size
						__a     =patch_size
						__a     =text_seq_length
						__a     =is_training
						__a     =use_input_mask
						__a     =use_token_type_ids
						__a     =use_labels
						__a     =vocab_size
						__a     =hidden_size
						__a     =num_hidden_layers
						__a     =num_attention_heads
						__a     =intermediate_size
						__a     =hidden_act
						__a     =hidden_dropout_prob
						__a     =attention_probs_dropout_prob
						__a     =max_position_embeddings
						__a     =type_vocab_size
						__a     =type_sequence_label_size
						__a     =initializer_range
						__a     =coordinate_size
						__a     =shape_size
						__a     =num_labels
						__a     =num_choices
						__a     =scope
						__a     =range_bbox

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





			def 						__magic_name__       (						self   )  ->					Dict:




						'''simple docstring'''
						__a     =ids_tensor([self.batch_size, self.text_seq_length]   ,    self.vocab_size   )

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

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

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

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

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

						__a     =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 						__magic_name__       (						self   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   )  ->					List[Any]:




						'''simple docstring'''
						__a     =LayoutLMvaModel(config=__snake_case   )
						model.to(__snake_case   )
						model.eval()

						# text + image
						__a     =model(__snake_case   ,    pixel_values=__snake_case   )
						__a     =model(
						    __snake_case   ,    bbox=__snake_case   ,    pixel_values=__snake_case   ,    attention_mask=__snake_case   ,    token_type_ids=__snake_case   )
						__a     =model(__snake_case   ,    bbox=__snake_case   ,    pixel_values=__snake_case   ,    token_type_ids=__snake_case   )
						__a     =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
						__a     =model(__snake_case   )
						self.parent.assertEqual(
						    result.last_hidden_state.shape   ,    (self.batch_size, self.text_seq_length, self.hidden_size)   )

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





			def 						__magic_name__       (						self   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   )  ->					str:




						'''simple docstring'''
						__a     =self.num_labels
						__a     =LayoutLMvaForSequenceClassification(__snake_case   )
						model.to(__snake_case   )
						model.eval()
						__a     =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 						__magic_name__       (						self   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   )  ->					Optional[Any]:




						'''simple docstring'''
						__a     =self.num_labels
						__a     =LayoutLMvaForTokenClassification(config=__snake_case   )
						model.to(__snake_case   )
						model.eval()
						__a     =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 						__magic_name__       (						self   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   )  ->					str:




						'''simple docstring'''
						__a     =LayoutLMvaForQuestionAnswering(config=__snake_case   )
						model.to(__snake_case   )
						model.eval()
						__a     =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 						__magic_name__       (						self   )  ->					Optional[int]:




						'''simple docstring'''
						__a     =self.prepare_config_and_inputs()
						(
						    (
						    __a       
						) ,       (
						    __a       
						) ,       (
						    __a       
						) ,       (
						    __a       
						) ,       (
						    __a       
						) ,       (
						    __a       
						) ,       (
						    __a       
						) ,       (
						    __a       
						) ,              
						)     =config_and_inputs
						__a     ={
						    '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       __magic_name__       (     __UpperCamelCase    ,       __UpperCamelCase    ,       unittest.TestCase ):
			SCREAMING_SNAKE_CASE				  =		False
			SCREAMING_SNAKE_CASE				  =		False
			SCREAMING_SNAKE_CASE				  =		False

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





			def 						__magic_name__       (						self   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   )  ->					Dict:




						'''simple docstring'''
						# `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 						__magic_name__       (						self   )  ->					Union[str, Any]:




						'''simple docstring'''
						__a     =LayoutLMvaModelTester(self   )
						__a     =ConfigTester(self   ,    config_class=__snake_case   ,    hidden_size=37   )





			def 						__magic_name__       (						self   ,    __snake_case   ,    __snake_case   ,    __snake_case=False   )  ->					Union[str, Any]:




						'''simple docstring'''
						__a     =copy.deepcopy(__snake_case   )
						if model_class in get_values(__snake_case   ):
									__a     ={
									    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   ):
												__a     =torch.ones(self.model_tester.batch_size   ,    dtype=torch.long   ,    device=__snake_case   )
									elif model_class in get_values(__snake_case   ):
												__a     =torch.zeros(
												    self.model_tester.batch_size   ,    dtype=torch.long   ,    device=__snake_case   )
												__a     =torch.zeros(
												    self.model_tester.batch_size   ,    dtype=torch.long   ,    device=__snake_case   )
									elif model_class in [
									    *get_values(__snake_case   ),
									]:
												__a     =torch.zeros(
												    self.model_tester.batch_size   ,    dtype=torch.long   ,    device=__snake_case   )
									elif model_class in [
									    *get_values(__snake_case   ),
									]:
												__a     =torch.zeros(
												    (self.model_tester.batch_size, self.model_tester.text_seq_length)   ,    dtype=torch.long   ,    device=__snake_case   ,    )

						return inputs_dict





			def 						__magic_name__       (						self   )  ->					Any:




						'''simple docstring'''
						self.config_tester.run_common_tests()





			def 						__magic_name__       (						self   )  ->					Optional[Any]:




						'''simple docstring'''
						__a     =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__snake_case   )





			def 						__magic_name__       (						self   )  ->					Union[str, Any]:




						'''simple docstring'''
						__a     =self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
									__a     =type
									self.model_tester.create_and_check_model(*__snake_case   )





			def 						__magic_name__       (						self   )  ->					List[Any]:




						'''simple docstring'''
						__a     =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*__snake_case   )





			def 						__magic_name__       (						self   )  ->					Optional[Any]:




						'''simple docstring'''
						__a     =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*__snake_case   )





			def 						__magic_name__       (						self   )  ->					Tuple:




						'''simple docstring'''
						__a     =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*__snake_case   )





			@slow
			def 						__magic_name__       (						self   )  ->					Optional[Any]:




						'''simple docstring'''
						for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__a     =LayoutLMvaModel.from_pretrained(__snake_case   )
									self.assertIsNotNone(__snake_case   )






def 			UpperCamelCase_(				):




			"""simple docstring"""






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






@require_torch
class       __magic_name__       (     unittest.TestCase ):





			@cached_property
			def 						__magic_name__       (						self   )  ->					Any:




						'''simple docstring'''
						return LayoutLMvaImageProcessor(apply_ocr=__snake_case   ) if is_vision_available() else None





			@slow
			def 						__magic_name__       (						self   )  ->					Union[str, Any]:




						'''simple docstring'''
						__a     =LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'   ).to(__snake_case   )

						__a     =self.default_image_processor
						__a     =prepare_img()
						__a     =image_processor(images=__snake_case   ,    return_tensors='pt'   ).pixel_values.to(__snake_case   )

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

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

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

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

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








def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if any(not isinstance(__lowerCAmelCase     ,		__lowerCAmelCase				) or x < 0 for x in sequence				):
        raise TypeError("Sequence must be list of non-negative integers"				)
    for _ in range(len(__lowerCAmelCase				)				):
        for i, (rod_upper, rod_lower) in enumerate(zip(__lowerCAmelCase     ,		sequence[1:]				)				):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
     assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]



