
"""simple docstring"""





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 __lowerCAmelCase					:




			"""simple docstring"""







			def __init__(					self    :       Any							, _snake_case    :       Dict							, _snake_case    :       Union[str, Any]=13							, _snake_case    :       Any=7							, _snake_case    :       Tuple=True							, _snake_case    :       Union[str, Any]=True							, _snake_case    :       List[str]=True							, _snake_case    :       Optional[int]=True							, _snake_case    :       Dict=99							, _snake_case    :       Tuple=32							, _snake_case    :       Optional[Any]=2							, _snake_case    :       Dict=4							, _snake_case    :       Union[str, Any]=37							, _snake_case    :       Any="gelu"							, _snake_case    :       Optional[int]=0.1							, _snake_case    :       str=0.1							, _snake_case    :       str=512							, _snake_case    :       Optional[Any]=16							, _snake_case    :       Union[str, Any]=2							, _snake_case    :       Dict=0.0_2							, _snake_case    :       Dict=3							, _snake_case    :       Tuple=4							, _snake_case    :       Tuple=None							, _snake_case    :       List[Any]=1_000							, )   ->			Any:







								"""simple docstring"""
								A_								=    parent
								A_								=    batch_size
								A_								=    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_								=    num_labels
								A_								=    num_choices
								A_								=    scope
								A_								=    range_bbox




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			int:







								"""simple docstring"""
								A_								=    ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size						)

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

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

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

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

								A_								=    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   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       Dict							, _snake_case    :       Union[str, Any]							, _snake_case    :       int							, _snake_case    :       Optional[Any]							, _snake_case    :       Union[str, Any]							, _snake_case    :       Tuple							, _snake_case    :       int							, _snake_case    :       Optional[int]						)   ->			int:







								"""simple docstring"""
								A_								=    TFLayoutLMModel(config=_snake_case						)

								A_								=    model(_snake_case							, _snake_case							, attention_mask=_snake_case							, token_type_ids=_snake_case						)
								A_								=    model(_snake_case							, _snake_case							, token_type_ids=_snake_case						)
								A_								=    model(_snake_case							, _snake_case						)
								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   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       List[Any]							, _snake_case    :       Tuple							, _snake_case    :       int							, _snake_case    :       Tuple							, _snake_case    :       str							, _snake_case    :       Tuple							, _snake_case    :       List[str]							, _snake_case    :       Optional[Any]						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    TFLayoutLMForMaskedLM(config=_snake_case						)

								A_								=    model(_snake_case							, _snake_case							, attention_mask=_snake_case							, token_type_ids=_snake_case							, labels=_snake_case						)
								self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)						)




			def   lowerCamelCase__  (					self    :       Any							, _snake_case    :       Union[str, Any]							, _snake_case    :       int							, _snake_case    :       Union[str, Any]							, _snake_case    :       List[str]							, _snake_case    :       int							, _snake_case    :       Union[str, Any]							, _snake_case    :       List[str]							, _snake_case    :       List[Any]						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    self.num_labels
								A_								=    TFLayoutLMForSequenceClassification(config=_snake_case						)

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




			def   lowerCamelCase__  (					self    :       str							, _snake_case    :       Optional[int]							, _snake_case    :       str							, _snake_case    :       List[Any]							, _snake_case    :       Union[str, Any]							, _snake_case    :       Tuple							, _snake_case    :       Optional[Any]							, _snake_case    :       Union[str, Any]							, _snake_case    :       int						)   ->			int:







								"""simple docstring"""
								A_								=    self.num_labels
								A_								=    TFLayoutLMForTokenClassification(config=_snake_case						)

								A_								=    model(_snake_case							, _snake_case							, attention_mask=_snake_case							, token_type_ids=_snake_case							, labels=_snake_case						)
								self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.num_labels)						)




			def   lowerCamelCase__  (					self    :       List[str]							, _snake_case    :       Optional[Any]							, _snake_case    :       Union[str, Any]							, _snake_case    :       List[str]							, _snake_case    :       Union[str, Any]							, _snake_case    :       int							, _snake_case    :       int							, _snake_case    :       List[str]							, _snake_case    :       Union[str, Any]						)   ->			Dict:







								"""simple docstring"""
								A_								=    TFLayoutLMForQuestionAnswering(config=_snake_case						)

								A_								=    model(_snake_case							, _snake_case							, attention_mask=_snake_case							, token_type_ids=_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   lowerCamelCase__  (					self    :       Tuple						)   ->			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,
								    "token_type_ids": token_type_ids,
								    "attention_mask": input_mask,
								}
								return config, inputs_dict





@require_tf
class __lowerCAmelCase					(       _lowercase  ,					_lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







			snake_case						   =					(
			    (
			        TFLayoutLMModel,
			        TFLayoutLMForMaskedLM,
			        TFLayoutLMForTokenClassification,
			        TFLayoutLMForSequenceClassification,
			        TFLayoutLMForQuestionAnswering,
			    )
			    if is_tf_available()
			    else ()
			)
			snake_case						   =					(
			    {
			        "feature-extraction": TFLayoutLMModel,
			        "fill-mask": TFLayoutLMForMaskedLM,
			        "text-classification": TFLayoutLMForSequenceClassification,
			        "token-classification": TFLayoutLMForTokenClassification,
			        "zero-shot": TFLayoutLMForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			snake_case						   =					False
			snake_case						   =					True
			snake_case						   =					10




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







								"""simple docstring"""
								A_								=    TFLayoutLMModelTester(self						)
								A_								=    ConfigTester(self							, config_class=_snake_case							, hidden_size=37						)




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







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




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







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case						)




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







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*_snake_case						)




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			Any:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*_snake_case						)




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			Any:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*_snake_case						)




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







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*_snake_case						)




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







								"""simple docstring"""
								for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A_								=    TFLayoutLMModel.from_pretrained(_snake_case						)
													self.assertIsNotNone(_snake_case						)






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







								"""simple docstring"""
								pass





def 				A_  ():

					'''simple docstring'''





					A_								=    tf.convert_to_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]]					)  # noqa: E231
					A_								=    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
					A_								=    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
					A_								=    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)
					A_								=    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 __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			@slow
			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Union[str, Any]:







								"""simple docstring"""
								A_								=    TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"						)

								A_			,     A_			,     A_			,     A_			,     A_								=    prepare_layoutlm_batch_inputs()

								# forward pass
								A_								=    model(input_ids=_snake_case							, bbox=_snake_case							, attention_mask=_snake_case							, token_type_ids=_snake_case						)

								# test the sequence output on [0, :3, :3]
								A_								=    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]							, _snake_case							, atol=1e-3						)						)

								# test the pooled output on [1, :3]
								A_								=    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]							, _snake_case							, atol=1e-3						)						)




			@slow
			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			Optional[int]:







								"""simple docstring"""
								# initialize model with randomly initialized sequence classification head
								A_								=    TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"							, num_labels=2						)

								A_			,     A_			,     A_			,     A_			,     A_								=    prepare_layoutlm_batch_inputs()

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

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

								# test the shape of the logits
								A_								=    outputs.logits
								A_								=    (2, 2)
								self.assertEqual(logits.shape							, _snake_case						)




			@slow
			def   lowerCamelCase__  (					self    :       Tuple						)   ->			Tuple:







								"""simple docstring"""
								# initialize model with randomly initialized token classification head
								A_								=    TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"							, num_labels=13						)

								A_			,     A_			,     A_			,     A_			,     A_								=    prepare_layoutlm_batch_inputs()

								# forward pass
								A_								=    model(
								    input_ids=_snake_case							, bbox=_snake_case							, attention_mask=_snake_case							, token_type_ids=_snake_case							, labels=_snake_case						)

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






			@slow
			def   lowerCamelCase__  (					self    :       Tuple						)   ->			str:







								"""simple docstring"""
								# initialize model with randomly initialized token classification head
								A_								=    TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"						)

								A_			,     A_			,     A_			,     A_			,     A_								=    prepare_layoutlm_batch_inputs()

								# forward pass
								A_								=    model(input_ids=_snake_case							, bbox=_snake_case							, attention_mask=_snake_case							, token_type_ids=_snake_case						)

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






"""simple docstring"""





import operator
def 				A_  (__a					, __a = False					, __a = None					):

					'''simple docstring'''





					A_								=    operator.lt if reverse else operator.gt
					A_								=    solution or []

					if not arr:
										return solution

					A_								=    [arr.pop(0					)]
					for i, item in enumerate(__a					):
										if _operator(__a					, sublist[-1]					):
															sublist.append(__a					)
															arr.pop(__a					)

    #  merging sublist into solution list
					if not solution:
										solution.extend(__a					)
					else:
										while sublist:
															A_								=    sublist.pop(0					)
															for i, xx in enumerate(__a					):
																				if not _operator(__a					, __a					):
																									solution.insert(__a					, __a					)
																									break
															else:
																				solution.append(__a					)

					strand_sort(__a					, __a					, __a					)
					return solution


if __name__ == "__main__":
		assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
		assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]




