





"""simple docstring"""


import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
		import torch

		from transformers import (
		    LayoutLMForMaskedLM,
		    LayoutLMForQuestionAnswering,
		    LayoutLMForSequenceClassification,
		    LayoutLMForTokenClassification,
		    LayoutLMModel,
		)






class        lowercase__ :

			"""simple docstring"""







			def __init__(			self	,      _A	,      _A=1_3	,      _A=7	,      _A=True	,      _A=True	,      _A=True	,      _A=True	,      _A=9_9	,      _A=3_2	,      _A=5	,      _A=4	,      _A=3_7	,      _A="gelu"	,      _A=0.1	,      _A=0.1	,      _A=5_1_2	,      _A=1_6	,      _A=2	,      _A=0.02	,      _A=3	,      _A=4	,      _A=None	,      _A=1_0_0_0	,      ):






									'''simple docstring'''







									UpperCamelCase							:		Optional[Any]							      = parent
									UpperCamelCase							:		Tuple							      = batch_size
									UpperCamelCase							:		Dict							      = seq_length
									UpperCamelCase							:		Tuple							      = is_training
									UpperCamelCase							:		List[Any]							      = use_input_mask
									UpperCamelCase							:		List[str]							      = use_token_type_ids
									UpperCamelCase							:		str							      = use_labels
									UpperCamelCase							:		Optional[int]							      = vocab_size
									UpperCamelCase							:		Dict							      = hidden_size
									UpperCamelCase							:		List[Any]							      = num_hidden_layers
									UpperCamelCase							:		List[str]							      = num_attention_heads
									UpperCamelCase							:		Optional[Any]							      = intermediate_size
									UpperCamelCase							:		Any							      = hidden_act
									UpperCamelCase							:		Union[str, Any]							      = hidden_dropout_prob
									UpperCamelCase							:		int							      = attention_probs_dropout_prob
									UpperCamelCase							:		Optional[int]							      = max_position_embeddings
									UpperCamelCase							:		Dict							      = type_vocab_size
									UpperCamelCase							:		List[Any]							      = type_sequence_label_size
									UpperCamelCase							:		List[str]							      = initializer_range
									UpperCamelCase							:		List[str]							      = num_labels
									UpperCamelCase							:		List[str]							      = num_choices
									UpperCamelCase							:		List[str]							      = scope
									UpperCamelCase							:		Dict							      = range_bbox







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		str							      = ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size						)

									UpperCamelCase							:		Union[str, Any]							      = ids_tensor([self.batch_size, self.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]:
																											UpperCamelCase							:		Union[str, Any]							      = bbox[i, j, 3]
																											UpperCamelCase							:		List[Any]							      = bbox[i, j, 1]
																											UpperCamelCase							:		Tuple							      = t
																					if bbox[i, j, 2] < bbox[i, j, 0]:
																											UpperCamelCase							:		str							      = bbox[i, j, 2]
																											UpperCamelCase							:		Dict							      = bbox[i, j, 0]
																											UpperCamelCase							:		Optional[int]							      = t

									UpperCamelCase							:		List[str]							      = None
									if self.use_input_mask:
															UpperCamelCase							:		List[Any]							      = ids_tensor([self.batch_size, self.seq_length]	,      vocab_size=2						)

									UpperCamelCase							:		str							      = None
									if self.use_token_type_ids:
															UpperCamelCase							:		Dict							      = ids_tensor([self.batch_size, self.seq_length]	,      self.type_vocab_size						)

									UpperCamelCase							:		Tuple							      = None
									UpperCamelCase							:		List[str]							      = None
									UpperCamelCase							:		Tuple							      = None
									if self.use_labels:
															UpperCamelCase							:		List[str]							      = ids_tensor([self.batch_size]	,      self.type_sequence_label_size						)
															UpperCamelCase							:		Dict							      = ids_tensor([self.batch_size, self.seq_length]	,      self.num_labels						)
															UpperCamelCase							:		int							      = ids_tensor([self.batch_size]	,      self.num_choices						)

									UpperCamelCase							:		Dict							      = self.get_config()

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







			def      _a			(			self						):






									'''simple docstring'''







									return LayoutLMConfig(
									    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	,      )







			def      _a			(			self	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A						):






									'''simple docstring'''







									UpperCamelCase							:		Optional[Any]							      = LayoutLMModel(config=_A						)
									model.to(_A						)
									model.eval()
									UpperCamelCase							:		Any							      = model(_A	,      _A	,      attention_mask=_A	,      token_type_ids=_A						)
									UpperCamelCase							:		List[Any]							      = model(_A	,      _A	,      token_type_ids=_A						)
									UpperCamelCase							:		Union[str, Any]							      = model(_A	,      _A						)
									self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)						)
									self.parent.assertEqual(result.pooler_output.shape	,      (self.batch_size, self.hidden_size)						)







			def      _a			(			self	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A						):






									'''simple docstring'''







									UpperCamelCase							:		Dict							      = LayoutLMForMaskedLM(config=_A						)
									model.to(_A						)
									model.eval()
									UpperCamelCase							:		str							      = model(_A	,      _A	,      attention_mask=_A	,      token_type_ids=_A	,      labels=_A						)
									self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.vocab_size)						)







			def      _a			(			self	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A						):






									'''simple docstring'''







									UpperCamelCase							:		int							      = self.num_labels
									UpperCamelCase							:		List[str]							      = LayoutLMForSequenceClassification(_A						)
									model.to(_A						)
									model.eval()
									UpperCamelCase							:		int							      = model(
									    _A	,      _A	,      attention_mask=_A	,      token_type_ids=_A	,      labels=_A						)
									self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.num_labels)						)







			def      _a			(			self	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A						):






									'''simple docstring'''







									UpperCamelCase							:		Any							      = self.num_labels
									UpperCamelCase							:		Optional[int]							      = LayoutLMForTokenClassification(config=_A						)
									model.to(_A						)
									model.eval()
									UpperCamelCase							:		Dict							      = model(_A	,      _A	,      attention_mask=_A	,      token_type_ids=_A	,      labels=_A						)
									self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.num_labels)						)







			def      _a			(			self	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      _A						):






									'''simple docstring'''







									UpperCamelCase							:		List[str]							      = LayoutLMForQuestionAnswering(config=_A						)
									model.to(_A						)
									model.eval()
									UpperCamelCase							:		int							      = model(
									    _A	,      bbox=_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      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Dict							      = self.prepare_config_and_inputs()
									(
									    (
									    UpperCamelCase
									)      ,	(
									    UpperCamelCase
									)      ,	(
									    UpperCamelCase
									)      ,	(
									    UpperCamelCase
									)      ,	(
									    UpperCamelCase
									)      ,	(
									    UpperCamelCase
									)      ,	(
									    UpperCamelCase
									)      ,	(
									    UpperCamelCase
									)      ,	
									)							:		int							      = config_and_inputs
									UpperCamelCase							:		Optional[int]							      = {
									    """input_ids""": input_ids,
									    """bbox""": bbox,
									    """token_type_ids""": token_type_ids,
									    """attention_mask""": input_mask,
									}
									return config, inputs_dict






@require_torch
class        lowercase__ (      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    unittest.TestCase  ):

			"""simple docstring"""
			__lowerCAmelCase    :							List[str]								=     (
			    (
			        LayoutLMModel,
			        LayoutLMForMaskedLM,
			        LayoutLMForSequenceClassification,
			        LayoutLMForTokenClassification,
			        LayoutLMForQuestionAnswering,
			    )
			    if is_torch_available()
			    else None
			)
			__lowerCAmelCase    :							List[Any]								=     (
			    {
			        """document-question-answering""": LayoutLMForQuestionAnswering,
			        """feature-extraction""": LayoutLMModel,
			        """fill-mask""": LayoutLMForMaskedLM,
			        """text-classification""": LayoutLMForSequenceClassification,
			        """token-classification""": LayoutLMForTokenClassification,
			        """zero-shot""": LayoutLMForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			__lowerCAmelCase    :							Any								=     True







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Tuple							      = LayoutLMModelTester(self						)
									UpperCamelCase							:		int							      = ConfigTester(self	,      config_class=_A	,      hidden_size=3_7						)







			def      _a			(			self						):






									'''simple docstring'''







									self.config_tester.run_common_tests()







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Tuple							      = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_A						)







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		List[str]							      = self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
															UpperCamelCase							:		Optional[int]							      = type
															self.model_tester.create_and_check_model(*_A						)







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Any							      = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_A						)







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Optional[Any]							      = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_A						)







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Union[str, Any]							      = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_A						)







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		List[str]							      = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_A						)







			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""						)
			def      _a			(			self						):






									'''simple docstring'''







									pass


def     UpperCamelCase       ():
						# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
						# fmt: off
						UpperCamelCase							:		Tuple							      = torch.tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]       ,    device=SCREAMING_SNAKE_CASE    )  # noqa: E231
						UpperCamelCase							:		List[Any]							      = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]       ,    device=SCREAMING_SNAKE_CASE    )  # noqa: E231
						UpperCamelCase							:		List[str]							      = torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]       ,    device=SCREAMING_SNAKE_CASE    )  # noqa: E231
						UpperCamelCase							:		List[str]							      = torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]       ,    device=SCREAMING_SNAKE_CASE    )  # noqa: E231
						# these are sequence labels (i.e. at the token level)
						UpperCamelCase							:		Dict							      = torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]       ,    device=SCREAMING_SNAKE_CASE    )  # noqa: E231
						# fmt: on

						return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class        lowercase__ (      unittest.TestCase  ):

			"""simple docstring"""







			@slow
			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		List[Any]							      = LayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""						).to(_A						)

									UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase							:		int							      = prepare_layoutlm_batch_inputs()

									# forward pass
									UpperCamelCase							:		List[str]							      = model(input_ids=_A	,      bbox=_A	,      attention_mask=_A	,      token_type_ids=_A						)

									# test the sequence output on [0, :3, :3]
									UpperCamelCase							:		List[Any]							      = torch.tensor(
									    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]	,      device=_A	,      )

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

									# test the pooled output on [1, :3]
									UpperCamelCase							:		Optional[int]							      = torch.tensor([-0.65_80, -0.02_14, 0.85_52]	,      device=_A						)

									self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]	,      _A	,      atol=1e-3						)						)







			@slow
			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Optional[int]							      = LayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""	,      num_labels=2						).to(
									    _A						)

									UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase							:		List[Any]							      = prepare_layoutlm_batch_inputs()

									# forward pass
									UpperCamelCase							:		int							      = model(
									    input_ids=_A	,      bbox=_A	,      attention_mask=_A	,      token_type_ids=_A	,      labels=torch.tensor([1, 1]	,      device=_A						)	,      )

									# test whether we get a loss as a scalar
									UpperCamelCase							:		Optional[Any]							      = outputs.loss
									UpperCamelCase							:		List[Any]							      = torch.Size([]						)
									self.assertEqual(loss.shape	,      _A						)

									# test the shape of the logits
									UpperCamelCase							:		Optional[Any]							      = outputs.logits
									UpperCamelCase							:		int							      = torch.Size((2, 2)						)
									self.assertEqual(logits.shape	,      _A						)







			@slow
			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Optional[Any]							      = LayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""	,      num_labels=1_3						).to(
									    _A						)

									UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase							:		Tuple							      = prepare_layoutlm_batch_inputs()

									# forward pass
									UpperCamelCase							:		Tuple							      = model(
									    input_ids=_A	,      bbox=_A	,      attention_mask=_A	,      token_type_ids=_A	,      labels=_A						)

									# test the loss calculation to be around 2.65
									# expected_loss = torch.tensor(2.65, device=torch_device)

									# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
									# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

									# test the shape of the logits
									UpperCamelCase							:		int							      = outputs.logits
									UpperCamelCase							:		Optional[Any]							      = torch.Size((2, 2_5, 1_3)						)
									self.assertEqual(logits.shape	,      _A						)







			@slow
			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		List[Any]							      = LayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""						).to(_A						)

									UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase							:		Tuple							      = prepare_layoutlm_batch_inputs()

									# forward pass
									UpperCamelCase							:		List[str]							      = model(input_ids=_A	,      bbox=_A	,      attention_mask=_A	,      token_type_ids=_A						)

									# test the shape of the logits
									UpperCamelCase							:		List[str]							      = torch.Size((2, 2_5)						)
									self.assertEqual(outputs.start_logits.shape	,      _A						)
									self.assertEqual(outputs.end_logits.shape	,      _A						)










"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__					:				Any	= logging.get_logger(__name__)

__magic_name__					:				Dict	= {
    """uw-madison/yoso-4096""": """https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json""",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}






class        lowercase__ (      __SCREAMING_SNAKE_CASE  ):

			"""simple docstring"""
			__lowerCAmelCase    :							Optional[int]								=     """yoso"""







			def __init__(			self	,      _A=5_0_2_6_5	,      _A=7_6_8	,      _A=1_2	,      _A=1_2	,      _A=3_0_7_2	,      _A="gelu"	,      _A=0.1	,      _A=0.1	,      _A=4_0_9_6	,      _A=1	,      _A=0.02	,      _A=1e-1_2	,      _A="absolute"	,      _A=True	,      _A=9	,      _A=6_4	,      _A=None	,      _A=True	,      _A=True	,      _A=1	,      _A=0	,      _A=2	,      **_A	,      ):






									'''simple docstring'''







									super().__init__(pad_token_id=_A	,      bos_token_id=_A	,      eos_token_id=_A	,      **_A						)

									UpperCamelCase							:		Any							      = vocab_size
									UpperCamelCase							:		Any							      = max_position_embeddings
									UpperCamelCase							:		int							      = hidden_size
									UpperCamelCase							:		List[Any]							      = num_hidden_layers
									UpperCamelCase							:		Any							      = num_attention_heads
									UpperCamelCase							:		Optional[Any]							      = intermediate_size
									UpperCamelCase							:		Optional[int]							      = hidden_act
									UpperCamelCase							:		List[Any]							      = hidden_dropout_prob
									UpperCamelCase							:		int							      = attention_probs_dropout_prob
									UpperCamelCase							:		Any							      = initializer_range
									UpperCamelCase							:		Optional[Any]							      = type_vocab_size
									UpperCamelCase							:		Any							      = layer_norm_eps
									UpperCamelCase							:		List[Any]							      = position_embedding_type
									UpperCamelCase							:		Optional[Any]							      = use_expectation
									UpperCamelCase							:		Any							      = hash_code_len
									UpperCamelCase							:		Optional[int]							      = num_hash
									UpperCamelCase							:		List[Any]							      = conv_window
									UpperCamelCase							:		Tuple							      = use_fast_hash
									UpperCamelCase							:		Dict							      = lsh_backward



