


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 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase=1_3			,						__lowerCamelCase=7			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase=9_9			,						__lowerCamelCase=3_2			,						__lowerCamelCase=5			,						__lowerCamelCase=4			,						__lowerCamelCase=3_7			,						__lowerCamelCase="gelu"			,						__lowerCamelCase=0.1			,						__lowerCamelCase=0.1			,						__lowerCamelCase=5_1_2			,						__lowerCamelCase=1_6			,						__lowerCamelCase=2			,						__lowerCamelCase=0.0_2			,						__lowerCamelCase=3			,						__lowerCamelCase=4			,						__lowerCamelCase=None			,						__lowerCamelCase=1_0_0_0			,						)      ->    List[Any]:
            _A				:	Any       =			parent
            _A				:	Any       =			batch_size
            _A				:	Any       =			seq_length
            _A				:	Optional[Any]       =			is_training
            _A				:	Union[str, Any]       =			use_input_mask
            _A				:	Tuple       =			use_token_type_ids
            _A				:	Tuple       =			use_labels
            _A				:	int       =			vocab_size
            _A				:	Tuple       =			hidden_size
            _A				:	Union[str, Any]       =			num_hidden_layers
            _A				:	Tuple       =			num_attention_heads
            _A				:	str       =			intermediate_size
            _A				:	Optional[int]       =			hidden_act
            _A				:	List[Any]       =			hidden_dropout_prob
            _A				:	Union[str, Any]       =			attention_probs_dropout_prob
            _A				:	List[Any]       =			max_position_embeddings
            _A				:	Optional[int]       =			type_vocab_size
            _A				:	int       =			type_sequence_label_size
            _A				:	Union[str, Any]       =			initializer_range
            _A				:	Dict       =			num_labels
            _A				:	List[str]       =			num_choices
            _A				:	Dict       =			scope
            _A				:	Optional[int]       =			range_bbox


     def 			_lowerCamelCase			(						self)      ->    str:
            _A				:	Any       =			ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size)

            _A				:	str       =			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]:
                                 _A				:	List[Any]       =			bbox[i, j, 3]
                                 _A				:	Tuple       =			bbox[i, j, 1]
                                 _A				:	Union[str, Any]       =			t
                          if bbox[i, j, 2] < bbox[i, j, 0]:
                                 _A				:	List[Any]       =			bbox[i, j, 2]
                                 _A				:	List[Any]       =			bbox[i, j, 0]
                                 _A				:	Optional[Any]       =			t

            _A				:	int       =			None
            if self.use_input_mask:
                   _A				:	Any       =			ids_tensor([self.batch_size, self.seq_length]			,						vocab_size=2)

            _A				:	Tuple       =			None
            if self.use_token_type_ids:
                   _A				:	Union[str, Any]       =			ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size)

            _A				:	Any       =			None
            _A				:	List[Any]       =			None
            _A				:	int       =			None
            if self.use_labels:
                   _A				:	Any       =			ids_tensor([self.batch_size]			,						self.type_sequence_label_size)
                   _A				:	Any       =			ids_tensor([self.batch_size, self.seq_length]			,						self.num_labels)
                   _A				:	int       =			ids_tensor([self.batch_size]			,						self.num_choices)

            _A				:	str       =			self.get_config()

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


     def 			_lowerCamelCase			(						self)      ->    int:
            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 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Any:
            _A				:	int       =			LayoutLMModel(config=__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            _A				:	Optional[Any]       =			model(__lowerCamelCase			,						__lowerCamelCase			,						attention_mask=__lowerCamelCase			,						token_type_ids=__lowerCamelCase)
            _A				:	Tuple       =			model(__lowerCamelCase			,						__lowerCamelCase			,						token_type_ids=__lowerCamelCase)
            _A				:	int       =			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 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    str:
            _A				:	Dict       =			LayoutLMForMaskedLM(config=__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            _A				:	List[Any]       =			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 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    int:
            _A				:	Dict       =			self.num_labels
            _A				:	Optional[int]       =			LayoutLMForSequenceClassification(__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            _A				:	Tuple       =			model(
                __lowerCamelCase			,						__lowerCamelCase			,						attention_mask=__lowerCamelCase			,						token_type_ids=__lowerCamelCase			,						labels=__lowerCamelCase)
            self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels))


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Optional[Any]:
            _A				:	List[str]       =			self.num_labels
            _A				:	List[Any]       =			LayoutLMForTokenClassification(config=__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            _A				:	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 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    List[str]:
            _A				:	int       =			LayoutLMForQuestionAnswering(config=__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            _A				:	List[str]       =			model(
                __lowerCamelCase			,						bbox=__lowerCamelCase			,						attention_mask=__lowerCamelCase			,						token_type_ids=__lowerCamelCase			,						start_positions=__lowerCamelCase			,						end_positions=__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 			_lowerCamelCase			(						self)      ->    Tuple:
            _A				:	int       =			self.prepare_config_and_inputs()
            (
                (
                _A
            )   ,      (
                _A
            )   ,      (
                _A
            )   ,      (
                _A
            )   ,      (
                _A
            )   ,      (
                _A
            )   ,      (
                _A
            )   ,      (
                _A
            )   ,      
            )				:	Dict       =			config_and_inputs
            _A				:	int       =			{
                "input_ids": input_ids,
                "bbox": bbox,
                "token_type_ids": token_type_ids,
                "attention_mask": input_mask,
            }
            return config, inputs_dict







@require_torch
class 					lowerCAmelCase__	(    a					,   a					,   unittest.TestCase):




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


     def 			_lowerCamelCase			(						self)      ->    Any:
            _A				:	Optional[Any]       =			LayoutLMModelTester(self)
            _A				:	Union[str, Any]       =			ConfigTester(self			,						config_class=__lowerCamelCase			,						hidden_size=3_7)


     def 			_lowerCamelCase			(						self)      ->    Tuple:
            self.config_tester.run_common_tests()


     def 			_lowerCamelCase			(						self)      ->    List[Any]:
            _A				:	Dict       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            _A				:	int       =			self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                   _A				:	Tuple       =			type
                   self.model_tester.create_and_check_model(*__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Tuple:
            _A				:	List[str]       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    List[Any]:
            _A				:	Tuple       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    int:
            _A				:	Tuple       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Any:
            _A				:	Union[str, Any]       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase)


     @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
     def 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            pass




def 							_UpperCAmelCase    ():
       # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
       # fmt: off
       _A				:	Union[str, Any]       =			torch.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]]    ,							device=UpperCamelCase__						)  # noqa: E231
       _A				:	Dict       =			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=UpperCamelCase__						)  # noqa: E231
       _A				:	Union[str, Any]       =			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=UpperCamelCase__						)  # noqa: E231
       _A				:	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=UpperCamelCase__						)  # noqa: E231
       # these are sequence labels (i.e. at the token level)
       _A				:	int       =			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=UpperCamelCase__						)  # noqa: E231
       # fmt: on

       return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class 					lowerCAmelCase__	(    unittest.TestCase):




     '''simple docstring'''


     @slow
     def 			_lowerCamelCase			(						self)      ->    List[str]:
            _A				:	Union[str, Any]       =			LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased").to(__lowerCamelCase)

            _A   ,      _A   ,      _A   ,      _A   ,      _A				:	Tuple       =			prepare_layoutlm_batch_inputs()

            # forward pass
            _A				:	Union[str, Any]       =			model(input_ids=__lowerCamelCase			,						bbox=__lowerCamelCase			,						attention_mask=__lowerCamelCase			,						token_type_ids=__lowerCamelCase)

            # test the sequence output on [0, :3, :3]
            _A				:	Dict       =			torch.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]]			,						device=__lowerCamelCase			,						)

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

            # test the pooled output on [1, :3]
            _A				:	List[str]       =			torch.tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]			,						device=__lowerCamelCase)

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


     @slow
     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            # initialize model with randomly initialized sequence classification head
            _A				:	Dict       =			LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"			,						num_labels=2).to(
                __lowerCamelCase)

            _A   ,      _A   ,      _A   ,      _A   ,      _A				:	List[str]       =			prepare_layoutlm_batch_inputs()

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

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

            # test the shape of the logits
            _A				:	Union[str, Any]       =			outputs.logits
            _A				:	List[str]       =			torch.Size((2, 2))
            self.assertEqual(logits.shape			,						__lowerCamelCase)


     @slow
     def 			_lowerCamelCase			(						self)      ->    Dict:
            # initialize model with randomly initialized token classification head
            _A				:	int       =			LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"			,						num_labels=1_3).to(
                __lowerCamelCase)

            _A   ,      _A   ,      _A   ,      _A   ,      _A				:	List[str]       =			prepare_layoutlm_batch_inputs()

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

            # 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
            _A				:	Tuple       =			outputs.logits
            _A				:	int       =			torch.Size((2, 2_5, 1_3))
            self.assertEqual(logits.shape			,						__lowerCamelCase)


     @slow
     def 			_lowerCamelCase			(						self)      ->    List[Any]:
            # initialize model with randomly initialized token classification head
            _A				:	Tuple       =			LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased").to(__lowerCamelCase)

            _A   ,      _A   ,      _A   ,      _A   ,      _A				:	int       =			prepare_layoutlm_batch_inputs()

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

            # test the shape of the logits
            _A				:	int       =			torch.Size((2, 2_5))
            self.assertEqual(outputs.start_logits.shape			,						__lowerCamelCase)
            self.assertEqual(outputs.end_logits.shape			,						__lowerCamelCase)




def 							_UpperCAmelCase    (UpperCamelCase__					:  list    ,							UpperCamelCase__					:  int    ,							UpperCamelCase__					:  int						):
       if right <= left:
              return
       _A				:	str       =			left
       _A				:	List[str]       =			right
       _A				:	Union[str, Any]       =			sorting[left]
       while i <= b:
              if sorting[i] < pivot:
                     _A   ,      _A				:	Union[str, Any]       =			sorting[i], sorting[a]
                     a += 1
                     i += 1
              elif sorting[i] > pivot:
                     _A   ,      _A				:	str       =			sorting[i], sorting[b]
                     b -= 1
              else:
                     i += 1
       quick_sort_apartition(UpperCamelCase__    ,							UpperCamelCase__    ,							a - 1						)
       quick_sort_apartition(UpperCamelCase__    ,							b + 1    ,							UpperCamelCase__						)
def 							_UpperCAmelCase    (UpperCamelCase__					:  list    ,							UpperCamelCase__					:  int    ,							UpperCamelCase__					:  int						):
       if left < right:
              _A				:	Tuple       =			lomuto_partition(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						)
              quick_sort_lomuto_partition(UpperCamelCase__    ,							UpperCamelCase__    ,							pivot_index - 1						)
              quick_sort_lomuto_partition(UpperCamelCase__    ,							pivot_index + 1    ,							UpperCamelCase__						)
def 							_UpperCAmelCase    (UpperCamelCase__					:  list    ,							UpperCamelCase__					:  int    ,							UpperCamelCase__					:  int						):
       _A				:	Tuple       =			sorting[right]
       _A				:	Tuple       =			left
       for i in range(UpperCamelCase__    ,							UpperCamelCase__						):
              if sorting[i] < pivot:
                     _A   ,      _A				:	int       =			sorting[i], sorting[store_index]
                     store_index += 1
       _A   ,      _A				:	Dict       =			sorting[store_index], sorting[right]
       return store_index
def 							_UpperCAmelCase    (UpperCamelCase__					:  list						):
       if len(UpperCamelCase__						) <= 1:
              return sorting
       return (
           three_way_radix_quicksort([i for i in sorting if i < sorting[0]]						)
           + [i for i in sorting if i == sorting[0]]
           + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]						)
       )


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)

    lowerCAmelCase__						=			input('Enter numbers separated by a comma:\n').strip()
    lowerCAmelCase__						=			[int(item) for item in user_input.split(',')]
    quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
    print(unsorted)
