






"""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 					_UpperCAmelCase       :




					def __init__(				self     :						Any    ,						A     :						Optional[int]    ,						A     :						Dict=13    ,						A     :						List[Any]=7    ,						A     :						List[str]=True    ,						A     :						Any=True    ,						A     :						List[Any]=True    ,						A     :						List[Any]=True    ,						A     :						Optional[int]=99    ,						A     :						Tuple=32    ,						A     :						Union[str, Any]=2    ,						A     :						Dict=4    ,						A     :						Any=37    ,						A     :						List[str]="gelu"    ,						A     :						Optional[int]=0.1    ,						A     :						Union[str, Any]=0.1    ,						A     :						str=5_12    ,						A     :						str=16    ,						A     :						Optional[int]=2    ,						A     :						Optional[Any]=0.02    ,						A     :						Dict=3    ,						A     :						Union[str, Any]=4    ,						A     :						Dict=None    ,						A     :						List[Any]=10_00    ,						)  ->  List[str]:
							lowercase_ : str							       =   parent
							lowercase_ : Optional[int]							       =   batch_size
							lowercase_ : str							       =   seq_length
							lowercase_ : int							       =   is_training
							lowercase_ : Union[str, Any]							       =   use_input_mask
							lowercase_ : int							       =   use_token_type_ids
							lowercase_ : List[str]							       =   use_labels
							lowercase_ : str							       =   vocab_size
							lowercase_ : Union[str, Any]							       =   hidden_size
							lowercase_ : str							       =   num_hidden_layers
							lowercase_ : Tuple							       =   num_attention_heads
							lowercase_ : List[str]							       =   intermediate_size
							lowercase_ : Dict							       =   hidden_act
							lowercase_ : Tuple							       =   hidden_dropout_prob
							lowercase_ : Any							       =   attention_probs_dropout_prob
							lowercase_ : Union[str, Any]							       =   max_position_embeddings
							lowercase_ : Tuple							       =   type_vocab_size
							lowercase_ : Tuple							       =   type_sequence_label_size
							lowercase_ : Dict							       =   initializer_range
							lowercase_ : List[Any]							       =   num_labels
							lowercase_ : Dict							       =   num_choices
							lowercase_ : int							       =   scope
							lowercase_ : str							       =   range_bbox




					def   A  (				self     :						str   )  ->  Tuple:
							lowercase_ : Any							       =   ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size   )

							# convert bbox to numpy since TF does not support item assignment
							lowercase_ : Any							       =   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]:
													lowercase_ : Any							       =   bbox[i, j, 3]
													lowercase_ : str							       =   bbox[i, j, 1]
													lowercase_ : List[str]							       =   t
											if bbox[i, j, 2] < bbox[i, j, 0]:
													lowercase_ : str							       =   bbox[i, j, 2]
													lowercase_ : Tuple							       =   bbox[i, j, 0]
													lowercase_ : Optional[int]							       =   t
							lowercase_ : Optional[int]							       =   tf.convert_to_tensor(A   )

							lowercase_ : Optional[int]							       =   None
							if self.use_input_mask:
									lowercase_ : List[str]							       =   random_attention_mask([self.batch_size, self.seq_length]   )

							lowercase_ : List[Any]							       =   None
							if self.use_token_type_ids:
									lowercase_ : Optional[Any]							       =   ids_tensor([self.batch_size, self.seq_length]    ,						self.type_vocab_size   )

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

							lowercase_ : Tuple							       =   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     :						Tuple    ,						A     :						Dict    ,						A     :						List[str]    ,						A     :						str    ,						A     :						Optional[Any]    ,						A     :						List[str]    ,						A     :						Tuple    ,						A     :						Tuple    ,						A     :						Union[str, Any]   )  ->  Any:
							lowercase_ : Optional[Any]							       =   TFLayoutLMModel(config=A   )

							lowercase_ : Any							       =   model(A    ,						A    ,						attention_mask=A    ,						token_type_ids=A   )
							lowercase_ : Optional[Any]							       =   model(A    ,						A    ,						token_type_ids=A   )
							lowercase_ : str							       =   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     :						str    ,						A     :						Optional[int]    ,						A     :						int    ,						A     :						Any    ,						A     :						Optional[int]    ,						A     :						int    ,						A     :						Any    ,						A     :						int    ,						A     :						Dict   )  ->  List[str]:
							lowercase_ : Optional[int]							       =   TFLayoutLMForMaskedLM(config=A   )

							lowercase_ : Tuple							       =   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     :						Tuple    ,						A     :						List[str]    ,						A     :						Any    ,						A     :						List[str]    ,						A     :						Union[str, Any]    ,						A     :						List[str]    ,						A     :						Dict    ,						A     :						Optional[Any]    ,						A     :						List[Any]   )  ->  Union[str, Any]:
							lowercase_ : List[Any]							       =   self.num_labels
							lowercase_ : Optional[Any]							       =   TFLayoutLMForSequenceClassification(config=A   )

							lowercase_ : Tuple							       =   model(A    ,						A    ,						attention_mask=A    ,						token_type_ids=A   )
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)   )




					def   A  (				self     :						List[Any]    ,						A     :						List[str]    ,						A     :						Optional[int]    ,						A     :						Union[str, Any]    ,						A     :						Union[str, Any]    ,						A     :						int    ,						A     :						str    ,						A     :						Tuple    ,						A     :						Union[str, Any]   )  ->  Dict:
							lowercase_ : Tuple							       =   self.num_labels
							lowercase_ : List[Any]							       =   TFLayoutLMForTokenClassification(config=A   )

							lowercase_ : List[Any]							       =   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     :						str    ,						A     :						Tuple    ,						A     :						Any    ,						A     :						Dict    ,						A     :						Tuple    ,						A     :						Optional[int]    ,						A     :						Optional[int]    ,						A     :						List[str]    ,						A     :						Optional[int]   )  ->  int:
							lowercase_ : Any							       =   TFLayoutLMForQuestionAnswering(config=A   )

							lowercase_ : Any							       =   model(A    ,						A    ,						attention_mask=A    ,						token_type_ids=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     :						List[Any]   )  ->  Dict:
							lowercase_ : Any							       =   self.prepare_config_and_inputs()
							(
							    (
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					
							) : Union[str, Any]							       =   config_and_inputs
							lowercase_ : List[Any]							       =   {
							    '''input_ids''': input_ids,
							    '''bbox''': bbox,
							    '''token_type_ids''': token_type_ids,
							    '''attention_mask''': input_mask,
							}
							return config, inputs_dict





@require_tf
class 					_UpperCAmelCase       ( _A   ,						_A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Dict							    =   (
					    (
					        TFLayoutLMModel,
					        TFLayoutLMForMaskedLM,
					        TFLayoutLMForTokenClassification,
					        TFLayoutLMForSequenceClassification,
					        TFLayoutLMForQuestionAnswering,
					    )
					    if is_tf_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   (
					    {
					        "feature-extraction": TFLayoutLMModel,
					        "fill-mask": TFLayoutLMForMaskedLM,
					        "text-classification": TFLayoutLMForSequenceClassification,
					        "token-classification": TFLayoutLMForTokenClassification,
					        "zero-shot": TFLayoutLMForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					SCREAMING_SNAKE_CASE_		:				int							    =   False
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   True
					SCREAMING_SNAKE_CASE_		:				Any							    =   10




					def   A  (				self     :						List[Any]   )  ->  Union[str, Any]:
							lowercase_ : Tuple							       =   TFLayoutLMModelTester(self   )
							lowercase_ : Optional[int]							       =   ConfigTester(self    ,						config_class=A    ,						hidden_size=37   )




					def   A  (				self     :						Dict   )  ->  Tuple:
							self.config_tester.run_common_tests()




					def   A  (				self     :						List[Any]   )  ->  Union[str, Any]:
							lowercase_ : List[str]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A   )




					def   A  (				self     :						int   )  ->  List[str]:
							lowercase_ : Union[str, Any]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*A   )




					def   A  (				self     :						Dict   )  ->  str:
							lowercase_ : List[str]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*A   )




					def   A  (				self     :						int   )  ->  Optional[int]:
							lowercase_ : Dict							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*A   )




					def   A  (				self     :						Union[str, Any]   )  ->  List[Any]:
							lowercase_ : Dict							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*A   )




					@slow
					def   A  (				self     :						Any   )  ->  List[Any]:
							for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_ : Optional[int]							       =   TFLayoutLMModel.from_pretrained(A   )
									self.assertIsNotNone(A   )






					@unittest.skip('''Onnx compliancy broke with TF 2.10'''   )
					def   A  (				self     :						int   )  ->  Tuple:
							pass



def 						lowercase    (				):
		# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
		# fmt: off
		lowercase_ : Union[str, Any]							       =   tf.convert_to_tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]	)  # noqa: E231
		lowercase_ : Any							       =   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
		lowercase_ : Tuple							       =   tf.convert_to_tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]	)  # noqa: E231
		lowercase_ : List[Any]							       =   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)
		lowercase_ : str							       =   tf.convert_to_tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]	)  # noqa: E231
		# fmt: on

		return input_ids, attention_mask, bbox, token_type_ids, labels






@require_tf
class 					_UpperCAmelCase       ( unittest.TestCase							):




					@slow
					def   A  (				self     :						List[str]   )  ->  Optional[Any]:
							lowercase_ : str							       =   TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''   )

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

							# forward pass
							lowercase_ : Tuple							       =   model(input_ids=A    ,						bbox=A    ,						attention_mask=A    ,						token_type_ids=A   )

							# test the sequence output on [0, :3, :3]
							lowercase_ : Dict							       =   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]    ,						A    ,						atol=1e-3   )   )

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

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




					@slow
					def   A  (				self     :						List[str]   )  ->  List[Any]:
							# initialize model with randomly initialized sequence classification head
							lowercase_ : Optional[int]							       =   TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''    ,						num_labels=2   )

							lowercase_       ,					lowercase_       ,					lowercase_       ,					lowercase_       ,					lowercase_ : Union[str, Any]							       =   prepare_layoutlm_batch_inputs()

							# forward pass
							lowercase_ : Tuple							       =   model(
							    input_ids=A    ,						bbox=A    ,						attention_mask=A    ,						token_type_ids=A    ,						labels=tf.convert_to_tensor([1, 1]   )    ,						)

							# test whether we get a loss as a scalar
							lowercase_ : Dict							       =   outputs.loss
							lowercase_ : List[str]							       =   (2,)
							self.assertEqual(loss.shape    ,						A   )

							# test the shape of the logits
							lowercase_ : Optional[Any]							       =   outputs.logits
							lowercase_ : Union[str, Any]							       =   (2, 2)
							self.assertEqual(logits.shape    ,						A   )




					@slow
					def   A  (				self     :						List[Any]   )  ->  List[str]:
							# initialize model with randomly initialized token classification head
							lowercase_ : str							       =   TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''    ,						num_labels=13   )

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

							# forward pass
							lowercase_ : Union[str, Any]							       =   model(
							    input_ids=A    ,						bbox=A    ,						attention_mask=A    ,						token_type_ids=A    ,						labels=A   )

							# test the shape of the logits
							lowercase_ : List[str]							       =   outputs.logits
							lowercase_ : Dict							       =   tf.convert_to_tensor((2, 25, 13)   )
							self.assertEqual(logits.shape    ,						A   )






					@slow
					def   A  (				self     :						int   )  ->  Any:
							# initialize model with randomly initialized token classification head
							lowercase_ : Tuple							       =   TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''   )

							lowercase_       ,					lowercase_       ,					lowercase_       ,					lowercase_       ,					lowercase_ : str							       =   prepare_layoutlm_batch_inputs()

							# forward pass
							lowercase_ : List[Any]							       =   model(input_ids=A    ,						bbox=A    ,						attention_mask=A    ,						token_type_ids=A   )

							# test the shape of the logits
							lowercase_ : Optional[Any]							       =   tf.convert_to_tensor((2, 25)   )
							self.assertEqual(outputs.start_logits.shape    ,						A   )
							self.assertEqual(outputs.end_logits.shape    ,						A   )











"""simple docstring"""



def 						lowercase    (				__snake_case				: list[int]	):
		lowercase_ : List[Any]							       =   len(__snake_case	)
		for i in range(__snake_case	):
				for j in range(i + 1		,					__snake_case	):
						if numbers[j] < numbers[i]:
								lowercase_       ,					lowercase_ : Optional[int]							       =   numbers[j], numbers[i]
		return numbers


if __name__ == "__main__":
						__A		:       int  				=		input('''Enter numbers separated by a comma:\n''').strip()
						__A		:       Any  				=		[int(item) for item in user_input.split(''',''')]
						print(exchange_sort(unsorted))



