


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     __lowercase    :



		"""simple docstring"""

		def __init__(      self						,	A						,	A=13						,	A=7						,	A=True						,	A=True						,	A=True						,	A=True						,	A=99						,	A=32						,	A=2						,	A=4						,	A=37						,	A="gelu"						,	A=0.1						,	A=0.1						,	A=5_12						,	A=16						,	A=2						,	A=0.02						,	A=3						,	A=4						,	A=None						,	A=10_00						,	)   ->      Any:







					'''simple docstring'''







					lowerCamelCase  						=   parent
					lowerCamelCase  						=   batch_size
					lowerCamelCase  						=   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  						=   num_labels
					lowerCamelCase  						=   num_choices
					lowerCamelCase  						=   scope
					lowerCamelCase  						=   range_bbox







		def  __A					(      self     )   ->      Optional[int]:







					'''simple docstring'''







					lowerCamelCase  						=   ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size     )

					# convert bbox to numpy since TF does not support item assignment
					lowerCamelCase  						=   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]:
														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  						=   tf.convert_to_tensor(_UpperCamelCase     )

					lowerCamelCase  						=   None
					if self.use_input_mask:
								lowerCamelCase  						=   random_attention_mask([self.batch_size, self.seq_length]     )

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

					lowerCamelCase  						=   None
					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.seq_length]						,	self.num_labels     )
								lowerCamelCase  						=   ids_tensor([self.batch_size]						,	self.num_choices     )

					lowerCamelCase  						=   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  __A					(      self						,	A						,	A						,	A						,	A						,	A						,	A						,	A						,	A     )   ->      Optional[int]:







					'''simple docstring'''







					lowerCamelCase  						=   TFLayoutLMModel(config=_UpperCamelCase     )

					lowerCamelCase  						=   model(_UpperCamelCase						,	_UpperCamelCase						,	attention_mask=_UpperCamelCase						,	token_type_ids=_UpperCamelCase     )
					lowerCamelCase  						=   model(_UpperCamelCase						,	_UpperCamelCase						,	token_type_ids=_UpperCamelCase     )
					lowerCamelCase  						=   model(_UpperCamelCase						,	_UpperCamelCase     )
					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     )   ->      Optional[Any]:







					'''simple docstring'''







					lowerCamelCase  						=   TFLayoutLMForMaskedLM(config=_UpperCamelCase     )

					lowerCamelCase  						=   model(_UpperCamelCase						,	_UpperCamelCase						,	attention_mask=_UpperCamelCase						,	token_type_ids=_UpperCamelCase						,	labels=_UpperCamelCase     )
					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     )   ->      int:







					'''simple docstring'''







					lowerCamelCase  						=   self.num_labels
					lowerCamelCase  						=   TFLayoutLMForSequenceClassification(config=_UpperCamelCase     )

					lowerCamelCase  						=   model(_UpperCamelCase						,	_UpperCamelCase						,	attention_mask=_UpperCamelCase						,	token_type_ids=_UpperCamelCase     )
					self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)     )







		def  __A					(      self						,	A						,	A						,	A						,	A						,	A						,	A						,	A						,	A     )   ->      Optional[int]:







					'''simple docstring'''







					lowerCamelCase  						=   self.num_labels
					lowerCamelCase  						=   TFLayoutLMForTokenClassification(config=_UpperCamelCase     )

					lowerCamelCase  						=   model(_UpperCamelCase						,	_UpperCamelCase						,	attention_mask=_UpperCamelCase						,	token_type_ids=_UpperCamelCase						,	labels=_UpperCamelCase     )
					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     )   ->      Any:







					'''simple docstring'''







					lowerCamelCase  						=   TFLayoutLMForQuestionAnswering(config=_UpperCamelCase     )

					lowerCamelCase  						=   model(_UpperCamelCase						,	_UpperCamelCase						,	attention_mask=_UpperCamelCase						,	token_type_ids=_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  __A					(      self     )   ->      List[Any]:







					'''simple docstring'''







					lowerCamelCase  						=   self.prepare_config_and_inputs()
					(
					    lowerCamelCase  
					)  						=   config_and_inputs
					lowerCamelCase  						=   {
					    'input_ids': input_ids,
					    'bbox': bbox,
					    'token_type_ids': token_type_ids,
					    'attention_mask': input_mask,
					}
					return config, inputs_dict







@require_tf
class     __lowercase    (				_snake_case       ,				_snake_case       ,				unittest.TestCase   ):



		"""simple docstring"""

		UpperCamelCase			: int           =						(
		    (
		        TFLayoutLMModel,
		        TFLayoutLMForMaskedLM,
		        TFLayoutLMForTokenClassification,
		        TFLayoutLMForSequenceClassification,
		        TFLayoutLMForQuestionAnswering,
		    )
		    if is_tf_available()
		    else ()
		)
		UpperCamelCase			: List[Any]           =						(
		    {
		        '''feature-extraction''': TFLayoutLMModel,
		        '''fill-mask''': TFLayoutLMForMaskedLM,
		        '''text-classification''': TFLayoutLMForSequenceClassification,
		        '''token-classification''': TFLayoutLMForTokenClassification,
		        '''zero-shot''': TFLayoutLMForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		UpperCamelCase			: Optional[int]           =						False
		UpperCamelCase			: Dict           =						True
		UpperCamelCase			: Dict           =						1_0







		def  __A					(      self     )   ->      List[str]:







					'''simple docstring'''







					lowerCamelCase  						=   TFLayoutLMModelTester(self     )
					lowerCamelCase  						=   ConfigTester(self						,	config_class=_UpperCamelCase						,	hidden_size=37     )







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







					'''simple docstring'''







					self.config_tester.run_common_tests()







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







					'''simple docstring'''







					lowerCamelCase  						=   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_UpperCamelCase     )







		def  __A					(      self     )   ->      str:







					'''simple docstring'''







					lowerCamelCase  						=   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase     )







		def  __A					(      self     )   ->      Optional[int]:







					'''simple docstring'''







					lowerCamelCase  						=   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase     )







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







					'''simple docstring'''







					lowerCamelCase  						=   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase     )







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







					'''simple docstring'''







					lowerCamelCase  						=   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase     )







		@slow
		def  __A					(      self     )   ->      List[str]:







					'''simple docstring'''







					for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								lowerCamelCase  						=   TFLayoutLMModel.from_pretrained(_UpperCamelCase     )
								self.assertIsNotNone(_UpperCamelCase     )







		@unittest.skip("""Onnx compliancy broke with TF 2.10"""     )
		def  __A					(      self     )   ->      Tuple:







					'''simple docstring'''







					pass






def      __lowerCamelCase		(				):





			'''simple docstring'''




			lowerCamelCase  						=   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
			lowerCamelCase  						=   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
			lowerCamelCase  						=   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
			lowerCamelCase  						=   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)
			lowerCamelCase  						=   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     __lowercase    (				unittest.TestCase   ):



		"""simple docstring"""

		@slow
		def  __A					(      self     )   ->      Tuple:







					'''simple docstring'''







					lowerCamelCase  						=   TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""     )

					lowerCamelCase  						=   prepare_layoutlm_batch_inputs()

					# forward pass
					lowerCamelCase  						=   model(input_ids=_UpperCamelCase						,	bbox=_UpperCamelCase						,	attention_mask=_UpperCamelCase						,	token_type_ids=_UpperCamelCase     )

					# test the sequence output on [0, :3, :3]
					lowerCamelCase  						=   tf.convert_to_tensor(
					    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]						,	)

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

					# test the pooled output on [1, :3]
					lowerCamelCase  						=   tf.convert_to_tensor([-0.6580, -0.0214, 0.8552]     )

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







		@slow
		def  __A					(      self     )   ->      str:







					'''simple docstring'''







					lowerCamelCase  						=   TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""						,	num_labels=2     )

					lowerCamelCase  						=   prepare_layoutlm_batch_inputs()

					# forward pass
					lowerCamelCase  						=   model(
					    input_ids=_UpperCamelCase						,	bbox=_UpperCamelCase						,	attention_mask=_UpperCamelCase						,	token_type_ids=_UpperCamelCase						,	labels=tf.convert_to_tensor([1, 1]     )						,	)

					# test whether we get a loss as a scalar
					lowerCamelCase  						=   outputs.loss
					lowerCamelCase  						=   (2,)
					self.assertEqual(loss.shape						,	_UpperCamelCase     )

					# test the shape of the logits
					lowerCamelCase  						=   outputs.logits
					lowerCamelCase  						=   (2, 2)
					self.assertEqual(logits.shape						,	_UpperCamelCase     )







		@slow
		def  __A					(      self     )   ->      List[Any]:







					'''simple docstring'''







					lowerCamelCase  						=   TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""						,	num_labels=13     )

					lowerCamelCase  						=   prepare_layoutlm_batch_inputs()

					# forward pass
					lowerCamelCase  						=   model(
					    input_ids=_UpperCamelCase						,	bbox=_UpperCamelCase						,	attention_mask=_UpperCamelCase						,	token_type_ids=_UpperCamelCase						,	labels=_UpperCamelCase     )

					# test the shape of the logits
					lowerCamelCase  						=   outputs.logits
					lowerCamelCase  						=   tf.convert_to_tensor((2, 25, 13)     )
					self.assertEqual(logits.shape						,	_UpperCamelCase     )







		@slow
		def  __A					(      self     )   ->      List[str]:







					'''simple docstring'''







					lowerCamelCase  						=   TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""     )

					lowerCamelCase  						=   prepare_layoutlm_batch_inputs()

					# forward pass
					lowerCamelCase  						=   model(input_ids=_UpperCamelCase						,	bbox=_UpperCamelCase						,	attention_mask=_UpperCamelCase						,	token_type_ids=_UpperCamelCase     )

					# test the shape of the logits
					lowerCamelCase  						=   tf.convert_to_tensor((2, 25)     )
					self.assertEqual(outputs.start_logits.shape						,	_UpperCamelCase     )
					self.assertEqual(outputs.end_logits.shape						,	_UpperCamelCase     )









def      lowercase__      (     __snake_case    :							int     ,       __snake_case    :							int  ):


					'''simple docstring'''


					if a < 0 or b < 0:
										raise ValueError('the value of both inputs must be positive'  )

					UpperCAmelCase_    :      Tuple            =						str(bin(__snake_case  )  )[2:]  # remove the leading "0b"
					UpperCAmelCase_    :      Union[str, Any]            =						str(bin(__snake_case  )  )[2:]  # remove the leading "0b"

					UpperCAmelCase_    :      List[Any]            =						max(len(__snake_case  )     ,       len(__snake_case  )  )

					return "0b" + "".join(
					    str(int(char_a == '1' and char_b == '1'  )  )
					    for char_a, char_b in zip(a_binary.zfill(__snake_case  )     ,       b_binary.zfill(__snake_case  )  )  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






