






from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers.models.layoutlm.modeling_tf_layoutlm import (
		    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFLayoutLMForMaskedLM,
		    TFLayoutLMForQuestionAnswering,
		    TFLayoutLMForSequenceClassification,
		    TFLayoutLMForTokenClassification,
		    TFLayoutLMModel,
		)





class 	snake_case__ :
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=13      ,       lowerCAmelCase__=7      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=99      ,       lowerCAmelCase__=32      ,       lowerCAmelCase__=2      ,       lowerCAmelCase__=4      ,       lowerCAmelCase__=37      ,       lowerCAmelCase__="gelu"      ,       lowerCAmelCase__=0.1      ,       lowerCAmelCase__=0.1      ,       lowerCAmelCase__=5_12      ,       lowerCAmelCase__=16      ,       lowerCAmelCase__=2      ,       lowerCAmelCase__=0.0_2      ,       lowerCAmelCase__=3      ,       lowerCAmelCase__=4      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=10_00      ,       )							->				Any:
							__magic_name__      :       Optional[Any]       						= parent
							__magic_name__      :       Optional[Any]       						= batch_size
							__magic_name__      :       Optional[Any]       						= seq_length
							__magic_name__      :       Any       						= is_training
							__magic_name__      :       int       						= use_input_mask
							__magic_name__      :       str       						= use_token_type_ids
							__magic_name__      :       Optional[Any]       						= use_labels
							__magic_name__      :       Dict       						= vocab_size
							__magic_name__      :       int       						= hidden_size
							__magic_name__      :       Optional[int]       						= num_hidden_layers
							__magic_name__      :       List[Any]       						= num_attention_heads
							__magic_name__      :       int       						= intermediate_size
							__magic_name__      :       List[str]       						= hidden_act
							__magic_name__      :       Union[str, Any]       						= hidden_dropout_prob
							__magic_name__      :       int       						= attention_probs_dropout_prob
							__magic_name__      :       Optional[int]       						= max_position_embeddings
							__magic_name__      :       Dict       						= type_vocab_size
							__magic_name__      :       Dict       						= type_sequence_label_size
							__magic_name__      :       Union[str, Any]       						= initializer_range
							__magic_name__      :       List[str]       						= num_labels
							__magic_name__      :       Any       						= num_choices
							__magic_name__      :       Any       						= scope
							__magic_name__      :       Union[str, Any]       						= range_bbox
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Any       						= ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size			)

							# convert bbox to numpy since TF does not support item assignment
							__magic_name__      :       int       						= ids_tensor([self.batch_size, self.seq_length, 4]      ,       self.range_bbox			).numpy()
							# 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]:
																									__magic_name__      :       str       						= bbox[i, j, 3]
																									__magic_name__      :       List[Any]       						= bbox[i, j, 1]
																									__magic_name__      :       Dict       						= t
																			if bbox[i, j, 2] < bbox[i, j, 0]:
																									__magic_name__      :       Optional[Any]       						= bbox[i, j, 2]
																									__magic_name__      :       Union[str, Any]       						= bbox[i, j, 0]
																									__magic_name__      :       List[Any]       						= t
							__magic_name__      :       Optional[Any]       						= tf.convert_to_tensor(lowerCAmelCase__			)

							__magic_name__      :       Optional[Any]       						= None
							if self.use_input_mask:
													__magic_name__      :       Tuple       						= random_attention_mask([self.batch_size, self.seq_length]			)

							__magic_name__      :       List[str]       						= None
							if self.use_token_type_ids:
													__magic_name__      :       Optional[int]       						= ids_tensor([self.batch_size, self.seq_length]      ,       self.type_vocab_size			)

							__magic_name__      :       Union[str, Any]       						= None
							__magic_name__      :       Any       						= None
							__magic_name__      :       Dict       						= None
							if self.use_labels:
													__magic_name__      :       Dict       						= ids_tensor([self.batch_size]      ,       self.type_sequence_label_size			)
													__magic_name__      :       List[Any]       						= ids_tensor([self.batch_size, self.seq_length]      ,       self.num_labels			)
													__magic_name__      :       str       						= ids_tensor([self.batch_size]      ,       self.num_choices			)

							__magic_name__      :       Union[str, Any]       						= 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      ,       )

							return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				str:
							__magic_name__      :       Union[str, Any]       						= TFLayoutLMModel(config=lowerCAmelCase__			)

							__magic_name__      :       Union[str, Any]       						= model(lowerCAmelCase__      ,       lowerCAmelCase__      ,       attention_mask=lowerCAmelCase__      ,       token_type_ids=lowerCAmelCase__			)
							__magic_name__      :       Any       						= model(lowerCAmelCase__      ,       lowerCAmelCase__      ,       token_type_ids=lowerCAmelCase__			)
							__magic_name__      :       Optional[Any]       						= model(lowerCAmelCase__      ,       lowerCAmelCase__			)
							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       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Tuple:
							__magic_name__      :       Optional[Any]       						= TFLayoutLMForMaskedLM(config=lowerCAmelCase__			)

							__magic_name__      :       List[str]       						= model(lowerCAmelCase__      ,       lowerCAmelCase__      ,       attention_mask=lowerCAmelCase__      ,       token_type_ids=lowerCAmelCase__      ,       labels=lowerCAmelCase__			)
							self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.vocab_size)			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				List[Any]:
							__magic_name__      :       Optional[Any]       						= self.num_labels
							__magic_name__      :       Dict       						= TFLayoutLMForSequenceClassification(config=lowerCAmelCase__			)

							__magic_name__      :       Dict       						= model(lowerCAmelCase__      ,       lowerCAmelCase__      ,       attention_mask=lowerCAmelCase__      ,       token_type_ids=lowerCAmelCase__			)
							self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				str:
							__magic_name__      :       Optional[Any]       						= self.num_labels
							__magic_name__      :       List[str]       						= TFLayoutLMForTokenClassification(config=lowerCAmelCase__			)

							__magic_name__      :       Dict       						= model(lowerCAmelCase__      ,       lowerCAmelCase__      ,       attention_mask=lowerCAmelCase__      ,       token_type_ids=lowerCAmelCase__      ,       labels=lowerCAmelCase__			)
							self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.num_labels)			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				str:
							__magic_name__      :       Optional[Any]       						= TFLayoutLMForQuestionAnswering(config=lowerCAmelCase__			)

							__magic_name__      :       List[Any]       						= model(lowerCAmelCase__      ,       lowerCAmelCase__      ,       attention_mask=lowerCAmelCase__      ,       token_type_ids=lowerCAmelCase__			)
							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			)							->				str:
							__magic_name__      :       Optional[int]       						= self.prepare_config_and_inputs()
							(
							    (
							    __magic_name__
							)						,(
							    __magic_name__
							)						,(
							    __magic_name__
							)						,(
							    __magic_name__
							)						,(
							    __magic_name__
							)						,(
							    __magic_name__
							)						,(
							    __magic_name__
							)						,(
							    __magic_name__
							)						,
							)      :       int       						= config_and_inputs
							__magic_name__      :       int       						= {
							    """input_ids""": input_ids,
							    """bbox""": bbox,
							    """token_type_ids""": token_type_ids,
							    """attention_mask""": input_mask,
							}
							return config, inputs_dict





@require_tf
class 	snake_case__ (							_lowerCAmelCase						,  _lowerCAmelCase						,  unittest.TestCase				):
	lowercase__						:					Optional[Any]											=   (
	    (
	        TFLayoutLMModel,
	        TFLayoutLMForMaskedLM,
	        TFLayoutLMForTokenClassification,
	        TFLayoutLMForSequenceClassification,
	        TFLayoutLMForQuestionAnswering,
	    )
	    if is_tf_available()
	    else ()
	)
	lowercase__						:					Dict											=   (
	    {
	        '''feature-extraction''': TFLayoutLMModel,
	        '''fill-mask''': TFLayoutLMForMaskedLM,
	        '''text-classification''': TFLayoutLMForSequenceClassification,
	        '''token-classification''': TFLayoutLMForTokenClassification,
	        '''zero-shot''': TFLayoutLMForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	lowercase__						:					List[Any]											=   False
	lowercase__						:					List[Any]											=   True
	lowercase__						:					Tuple											=   10
	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       int       						= TFLayoutLMModelTester(self			)
							__magic_name__      :       List[Any]       						= ConfigTester(self      ,       config_class=lowerCAmelCase__      ,       hidden_size=37			)
	def       __magic_name__    (    self			)							->				Optional[int]:
							self.config_tester.run_common_tests()
	def       __magic_name__    (    self			)							->				List[str]:
							__magic_name__      :       int       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Optional[int]       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				List[str]:
							__magic_name__      :       Any       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       int       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Any       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__			)
	@slow
	def       __magic_name__    (    self			)							->				int:
							for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__magic_name__      :       Union[str, Any]       						= TFLayoutLMModel.from_pretrained(lowerCAmelCase__			)
													self.assertIsNotNone(lowerCAmelCase__			)







	@unittest.skip("""Onnx compliancy broke with TF 2.10"""			)
	def       __magic_name__    (    self			)							->				Optional[Any]:
							pass



def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       str       						= tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]			)  # noqa: E231
						__magic_name__      :       str       						= tf.convert_to_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],]			)  # noqa: E231
						__magic_name__      :       int       						= tf.convert_to_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]]]			)  # noqa: E231
						__magic_name__      :       Optional[int]       						= tf.convert_to_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]]			)  # noqa: E231
						# these are sequence labels (i.e. at the token level)
						__magic_name__      :       Union[str, Any]       						= tf.convert_to_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]]			)  # noqa: E231
						# fmt: on

						return input_ids, attention_mask, bbox, token_type_ids, labels





@require_tf
class 	snake_case__ (							unittest.TestCase				):
	@slow
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       Union[str, Any]       						= TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""			)

							__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__      :       Tuple       						= prepare_layoutlm_batch_inputs()

							# forward pass
							__magic_name__      :       Tuple       						= model(input_ids=lowerCAmelCase__      ,       bbox=lowerCAmelCase__      ,       attention_mask=lowerCAmelCase__      ,       token_type_ids=lowerCAmelCase__			)

							# test the sequence output on [0, :3, :3]
							__magic_name__      :       Optional[Any]       						= tf.convert_to_tensor(
							    [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]      ,       )

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

							# test the pooled output on [1, :3]
							__magic_name__      :       Union[str, Any]       						= tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]			)

							self.assertTrue(np.allclose(outputs.pooler_output[1, :3]      ,       lowerCAmelCase__      ,       atol=1e-3			)			)
	@slow
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							# initialize model with randomly initialized sequence classification head
							__magic_name__      :       List[Any]       						= TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""      ,       num_labels=2			)

							__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__      :       Optional[int]       						= prepare_layoutlm_batch_inputs()

							# forward pass
							__magic_name__      :       int       						= model(
							    input_ids=lowerCAmelCase__      ,       bbox=lowerCAmelCase__      ,       attention_mask=lowerCAmelCase__      ,       token_type_ids=lowerCAmelCase__      ,       labels=tf.convert_to_tensor([1, 1]			)      ,       )

							# test whether we get a loss as a scalar
							__magic_name__      :       Any       						= outputs.loss
							__magic_name__      :       Optional[Any]       						= (2,)
							self.assertEqual(loss.shape      ,       lowerCAmelCase__			)

							# test the shape of the logits
							__magic_name__      :       Tuple       						= outputs.logits
							__magic_name__      :       Any       						= (2, 2)
							self.assertEqual(logits.shape      ,       lowerCAmelCase__			)
	@slow
	def       __magic_name__    (    self			)							->				Any:
							# initialize model with randomly initialized token classification head
							__magic_name__      :       Tuple       						= TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""      ,       num_labels=13			)

							__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__      :       Optional[Any]       						= prepare_layoutlm_batch_inputs()

							# forward pass
							__magic_name__      :       Optional[int]       						= model(
							    input_ids=lowerCAmelCase__      ,       bbox=lowerCAmelCase__      ,       attention_mask=lowerCAmelCase__      ,       token_type_ids=lowerCAmelCase__      ,       labels=lowerCAmelCase__			)

							# test the shape of the logits
							__magic_name__      :       Optional[Any]       						= outputs.logits
							__magic_name__      :       List[Any]       						= tf.convert_to_tensor((2, 25, 13)			)
							self.assertEqual(logits.shape      ,       lowerCAmelCase__			)







	@slow
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							# initialize model with randomly initialized token classification head
							__magic_name__      :       List[str]       						= TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""			)

							__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__						,__magic_name__      :       Any       						= prepare_layoutlm_batch_inputs()

							# forward pass
							__magic_name__      :       Optional[int]       						= model(input_ids=lowerCAmelCase__      ,       bbox=lowerCAmelCase__      ,       attention_mask=lowerCAmelCase__      ,       token_type_ids=lowerCAmelCase__			)

							# test the shape of the logits
							__magic_name__      :       str       						= tf.convert_to_tensor((2, 25)			)
							self.assertEqual(outputs.start_logits.shape      ,       lowerCAmelCase__			)
							self.assertEqual(outputs.end_logits.shape      ,       lowerCAmelCase__			)








__magic_name__:					List[Any]							=     "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

__magic_name__:					Dict							=     [{"type": "code", "content": INSTALL_CONTENT}]
__magic_name__:					Optional[Any]							=     {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}
