
"""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  __lowerCamelCase  :




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase=2							,						UpperCAmelCase=3							,						UpperCAmelCase=4							,						UpperCAmelCase=2							,						UpperCAmelCase=7							,						UpperCAmelCase=True							,						UpperCAmelCase=True							,						UpperCAmelCase=True							,						UpperCAmelCase=True							,						UpperCAmelCase=99							,						UpperCAmelCase=36							,						UpperCAmelCase=3							,						UpperCAmelCase=4							,						UpperCAmelCase=37							,						UpperCAmelCase="gelu"							,						UpperCAmelCase=0.1							,						UpperCAmelCase=0.1							,						UpperCAmelCase=512							,						UpperCAmelCase=16							,						UpperCAmelCase=2							,						UpperCAmelCase=0.0_2							,						UpperCAmelCase=6							,						UpperCAmelCase=6							,						UpperCAmelCase=3							,						UpperCAmelCase=4							,						UpperCAmelCase=None							,						UpperCAmelCase=1000							,						):
							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        UpperCAmelCase__						(				self   ):
							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        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  LayoutLMvaModel(config=UpperCAmelCase   )
							model.to(UpperCAmelCase   )
							model.eval()

							# text + image
							lowerCamelCase_					=  model(UpperCAmelCase							,						pixel_values=UpperCAmelCase   )
							lowerCamelCase_					=  model(
							    UpperCAmelCase							,						bbox=UpperCAmelCase							,						pixel_values=UpperCAmelCase							,						attention_mask=UpperCAmelCase							,						token_type_ids=UpperCAmelCase   )
							lowerCamelCase_					=  model(UpperCAmelCase							,						bbox=UpperCAmelCase							,						pixel_values=UpperCAmelCase							,						token_type_ids=UpperCAmelCase   )
							lowerCamelCase_					=  model(UpperCAmelCase							,						bbox=UpperCAmelCase							,						pixel_values=UpperCAmelCase   )

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

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

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




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




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




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  LayoutLMvaForQuestionAnswering(config=UpperCAmelCase   )
							model.to(UpperCAmelCase   )
							model.eval()
							lowerCamelCase_					=  model(
							    UpperCAmelCase							,						bbox=UpperCAmelCase							,						pixel_values=UpperCAmelCase							,						attention_mask=UpperCAmelCase							,						token_type_ids=UpperCAmelCase							,						start_positions=UpperCAmelCase							,						end_positions=UpperCAmelCase							,						)
							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   ):
							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  __lowerCamelCase  ( lowerCAmelCase						,	lowerCAmelCase						,	unittest.TestCase			):
				a__:      Optional[int]          =    False
				a__:      List[Any]          =    False
				a__:      Tuple          =    False

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




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
							# `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   ):
							lowerCamelCase_					=  LayoutLMvaModelTester(self   )
							lowerCamelCase_					=  ConfigTester(self							,						config_class=UpperCAmelCase							,						hidden_size=37   )




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

							return inputs_dict




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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							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(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase   )





				@slow
				def        UpperCAmelCase__						(				self   ):
							for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCamelCase_					=  LayoutLMvaModel.from_pretrained(UpperCAmelCase   )
										self.assertIsNotNone(UpperCAmelCase   )






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




@require_torch
class  __lowerCamelCase  ( unittest.TestCase			):




				@cached_property
				def        UpperCAmelCase__						(				self   ):
							return LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase   ) if is_vision_available() else None





				@slow
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''   ).to(UpperCAmelCase   )

							lowerCamelCase_					=  self.default_image_processor
							lowerCamelCase_					=  prepare_img()
							lowerCamelCase_					=  image_processor(images=UpperCAmelCase							,						return_tensors='''pt'''   ).pixel_values.to(UpperCAmelCase   )

							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(UpperCAmelCase   )							,						bbox=bbox.to(UpperCAmelCase   )							,						pixel_values=pixel_values.to(UpperCAmelCase   )							,						)

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

							lowerCamelCase_					=  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(UpperCAmelCase   )

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





"""simple docstring"""


import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


A_    							=					namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			return (abs(source - target						) / target) < 0.01






@pytest.mark.integration
def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  _TestCommandArgs(dataset=lowerCAmelCase__	,all_configs=lowerCAmelCase__	,save_infos=lowerCAmelCase__						)
			lowerCamelCase_					=  TestCommand(*lowerCAmelCase__						)
			test_command.run()
			lowerCamelCase_					=  os.path.join(lowerCAmelCase__	,'''README.md'''						)
			assert os.path.exists(lowerCAmelCase__						)
			lowerCamelCase_					=  DatasetInfosDict.from_directory(lowerCAmelCase__						)
			lowerCamelCase_					=  DatasetInfosDict(
			    {
			        '''default''': DatasetInfo(
			            features=Features(
			                {
			                    '''tokens''': Sequence(Value('''string'''						)						),
			                    '''ner_tags''': Sequence(
			                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']						)						),
			                    '''langs''': Sequence(Value('''string'''						)						),
			                    '''spans''': Sequence(Value('''string'''						)						),
			                }						)	,splits=[
			                {
			                    '''name''': '''train''',
			                    '''num_bytes''': 2_351_563,
			                    '''num_examples''': 10_000,
			                },
			                {
			                    '''name''': '''validation''',
			                    '''num_bytes''': 238_418,
			                    '''num_examples''': 1_000,
			                },
			            ]	,download_size=3_940_680	,dataset_size=2_589_981	,)
			    }						)
			assert dataset_infos.keys() == expected_dataset_infos.keys()
			for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
						lowerCamelCase_		, lowerCamelCase_					=  getattr(dataset_infos['''default''']	,lowerCAmelCase__						), getattr(expected_dataset_infos['''default''']	,lowerCAmelCase__						)
						if key == "num_bytes":
									assert is_apercent_close(lowerCAmelCase__	,lowerCAmelCase__						)
						elif key == "splits":
									assert list(lowerCAmelCase__						) == list(lowerCAmelCase__						)
									for split in result:
												assert result[split].name == expected[split].name
												assert result[split].num_examples == expected[split].num_examples
												assert is_apercent_close(result[split].num_bytes	,expected[split].num_bytes						)
						else:
									result == expected



