


'''simple docstring'''







import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
           MODEL_FOR_QUESTION_ANSWERING_MAPPING,
           MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
           MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
           LayoutLMvaConfig,
           LayoutLMvaForQuestionAnswering,
           LayoutLMvaForSequenceClassification,
           LayoutLMvaForTokenClassification,
           LayoutLMvaModel,
       )
       from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
       from PIL import Image

       from transformers import LayoutLMvaImageProcessor
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__   ,						snake_case__=2   ,						snake_case__=3   ,						snake_case__=4   ,						snake_case__=2   ,						snake_case__=7   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=99   ,						snake_case__=36   ,						snake_case__=3   ,						snake_case__=4   ,						snake_case__=37   ,						snake_case__="gelu"   ,						snake_case__=0.1   ,						snake_case__=0.1   ,						snake_case__=512   ,						snake_case__=16   ,						snake_case__=2   ,						snake_case__=0.02   ,						snake_case__=6   ,						snake_case__=6   ,						snake_case__=3   ,						snake_case__=4   ,						snake_case__=None   ,						snake_case__=1000   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							parent
            _lowerCAmelCase     :				Optional[int]       =							batch_size
            _lowerCAmelCase     :				Optional[int]       =							num_channels
            _lowerCAmelCase     :				Dict       =							image_size
            _lowerCAmelCase     :				str       =							patch_size
            _lowerCAmelCase     :				List[Any]       =							text_seq_length
            _lowerCAmelCase     :				str       =							is_training
            _lowerCAmelCase     :				Optional[int]       =							use_input_mask
            _lowerCAmelCase     :				Union[str, Any]       =							use_token_type_ids
            _lowerCAmelCase     :				str       =							use_labels
            _lowerCAmelCase     :				Tuple       =							vocab_size
            _lowerCAmelCase     :				int       =							hidden_size
            _lowerCAmelCase     :				Optional[int]       =							num_hidden_layers
            _lowerCAmelCase     :				Optional[Any]       =							num_attention_heads
            _lowerCAmelCase     :				Any       =							intermediate_size
            _lowerCAmelCase     :				Optional[int]       =							hidden_act
            _lowerCAmelCase     :				List[Any]       =							hidden_dropout_prob
            _lowerCAmelCase     :				Tuple       =							attention_probs_dropout_prob
            _lowerCAmelCase     :				int       =							max_position_embeddings
            _lowerCAmelCase     :				List[Any]       =							type_vocab_size
            _lowerCAmelCase     :				int       =							type_sequence_label_size
            _lowerCAmelCase     :				Union[str, Any]       =							initializer_range
            _lowerCAmelCase     :				List[str]       =							coordinate_size
            _lowerCAmelCase     :				int       =							shape_size
            _lowerCAmelCase     :				Union[str, Any]       =							num_labels
            _lowerCAmelCase     :				str       =							num_choices
            _lowerCAmelCase     :				str       =							scope
            _lowerCAmelCase     :				Optional[int]       =							range_bbox

            # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
            _lowerCAmelCase     :				Optional[int]       =							text_seq_length
            _lowerCAmelCase     :				List[str]       =							(image_size // patch_size) ** 2 + 1
            _lowerCAmelCase     :				Optional[int]       =							self.text_seq_length + self.image_seq_length







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							ids_tensor([self.batch_size, self.text_seq_length]   ,						self.vocab_size )

            _lowerCAmelCase     :				Tuple       =							ids_tensor([self.batch_size, self.text_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]:
                                 _lowerCAmelCase     :				Optional[Any]       =							bbox[i, j, 3]
                                 _lowerCAmelCase     :				Tuple       =							bbox[i, j, 1]
                                 _lowerCAmelCase     :				List[str]       =							t
                          if bbox[i, j, 2] < bbox[i, j, 0]:
                                 _lowerCAmelCase     :				Union[str, Any]       =							bbox[i, j, 2]
                                 _lowerCAmelCase     :				int       =							bbox[i, j, 0]
                                 _lowerCAmelCase     :				Any       =							t

            _lowerCAmelCase     :				Union[str, Any]       =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

            _lowerCAmelCase     :				Optional[Any]       =							None
            if self.use_input_mask:
                   _lowerCAmelCase     :				List[str]       =							random_attention_mask([self.batch_size, self.text_seq_length] )

            _lowerCAmelCase     :				Tuple       =							None
            if self.use_token_type_ids:
                   _lowerCAmelCase     :				Dict       =							ids_tensor([self.batch_size, self.text_seq_length]   ,						self.type_vocab_size )

            _lowerCAmelCase     :				List[str]       =							None
            _lowerCAmelCase     :				Any       =							None
            if self.use_labels:
                   _lowerCAmelCase     :				List[str]       =							ids_tensor([self.batch_size]   ,						self.type_sequence_label_size )
                   _lowerCAmelCase     :				Tuple       =							ids_tensor([self.batch_size, self.text_seq_length]   ,						self.num_labels )

            _lowerCAmelCase     :				List[str]       =							LayoutLMvaConfig(
                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   ,						coordinate_size=self.coordinate_size   ,						shape_size=self.shape_size   ,						input_size=self.image_size   ,						patch_size=self.patch_size   ,						)

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







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							LayoutLMvaModel(config=snake_case__ )
            model.to(snake_case__ )
            model.eval()

            # text + image
            _lowerCAmelCase     :				List[Any]       =							model(snake_case__   ,						pixel_values=snake_case__ )
            _lowerCAmelCase     :				str       =							model(
                snake_case__   ,						bbox=snake_case__   ,						pixel_values=snake_case__   ,						attention_mask=snake_case__   ,						token_type_ids=snake_case__ )
            _lowerCAmelCase     :				str       =							model(snake_case__   ,						bbox=snake_case__   ,						pixel_values=snake_case__   ,						token_type_ids=snake_case__ )
            _lowerCAmelCase     :				Dict       =							model(snake_case__   ,						bbox=snake_case__   ,						pixel_values=snake_case__ )

            self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size) )

            # text only
            _lowerCAmelCase     :				Union[str, Any]       =							model(snake_case__ )
            self.parent.assertEqual(
                result.last_hidden_state.shape   ,						(self.batch_size, self.text_seq_length, self.hidden_size) )

            # image only
            _lowerCAmelCase     :				Union[str, Any]       =							model(pixel_values=snake_case__ )
            self.parent.assertEqual(
                result.last_hidden_state.shape   ,						(self.batch_size, self.image_seq_length, self.hidden_size) )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							self.num_labels
            _lowerCAmelCase     :				Optional[int]       =							LayoutLMvaForSequenceClassification(snake_case__ )
            model.to(snake_case__ )
            model.eval()
            _lowerCAmelCase     :				Optional[Any]       =							model(
                snake_case__   ,						bbox=snake_case__   ,						pixel_values=snake_case__   ,						attention_mask=snake_case__   ,						token_type_ids=snake_case__   ,						labels=snake_case__   ,						)
            self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels) )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							self.num_labels
            _lowerCAmelCase     :				Optional[Any]       =							LayoutLMvaForTokenClassification(config=snake_case__ )
            model.to(snake_case__ )
            model.eval()
            _lowerCAmelCase     :				List[str]       =							model(
                snake_case__   ,						bbox=snake_case__   ,						pixel_values=snake_case__   ,						attention_mask=snake_case__   ,						token_type_ids=snake_case__   ,						labels=snake_case__   ,						)
            self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.text_seq_length, self.num_labels) )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							LayoutLMvaForQuestionAnswering(config=snake_case__ )
            model.to(snake_case__ )
            model.eval()
            _lowerCAmelCase     :				Any       =							model(
                snake_case__   ,						bbox=snake_case__   ,						pixel_values=snake_case__   ,						attention_mask=snake_case__   ,						token_type_ids=snake_case__   ,						start_positions=snake_case__   ,						end_positions=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 a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							self.prepare_config_and_inputs()
            (
                (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  
            )     :				Tuple       =							config_and_inputs
            _lowerCAmelCase     :				List[str]       =							{
                'input_ids': input_ids,
                'bbox': bbox,
                'pixel_values': pixel_values,
                'token_type_ids': token_type_ids,
                'attention_mask': input_mask,
            }
            return config, inputs_dict
@require_torch
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     False
     __magic_name__           =     False
     __magic_name__           =     False

     __magic_name__           =     (
         (
             LayoutLMvaModel,
             LayoutLMvaForSequenceClassification,
             LayoutLMvaForTokenClassification,
             LayoutLMvaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     __magic_name__           =     (
         {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
         if is_torch_available()
         else {}
     )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            return True







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							LayoutLMvaModelTester(self )
            _lowerCAmelCase     :				Dict       =							ConfigTester(self   ,						config_class=snake_case__   ,						hidden_size=37 )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__=False ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							copy.deepcopy(snake_case__ )
            if model_class in get_values(snake_case__ ):
                   _lowerCAmelCase     :				Union[str, Any]       =							{
                       k: v.unsqueeze(1 ).expand(-1   ,						self.model_tester.num_choices   ,						-1 ).contiguous()
                       if isinstance(snake_case__   ,						torch.Tensor ) and v.ndim > 1
                       else v
                       for k, v in inputs_dict.items()
                   }
            if return_labels:
                   if model_class in get_values(snake_case__ ):
                          _lowerCAmelCase     :				Optional[int]       =							torch.ones(self.model_tester.batch_size   ,						dtype=torch.long   ,						device=snake_case__ )
                   elif model_class in get_values(snake_case__ ):
                          _lowerCAmelCase     :				Any       =							torch.zeros(
                              self.model_tester.batch_size   ,						dtype=torch.long   ,						device=snake_case__ )
                          _lowerCAmelCase     :				Union[str, Any]       =							torch.zeros(
                              self.model_tester.batch_size   ,						dtype=torch.long   ,						device=snake_case__ )
                   elif model_class in [
                       *get_values(snake_case__ ),
                   ]:
                          _lowerCAmelCase     :				int       =							torch.zeros(
                              self.model_tester.batch_size   ,						dtype=torch.long   ,						device=snake_case__ )
                   elif model_class in [
                       *get_values(snake_case__ ),
                   ]:
                          _lowerCAmelCase     :				Optional[int]       =							torch.zeros(
                              (self.model_tester.batch_size, self.model_tester.text_seq_length)   ,						dtype=torch.long   ,						device=snake_case__   ,						)

            return inputs_dict







     def a						(			self ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                   _lowerCAmelCase     :				str       =							type
                   self.model_tester.create_and_check_model(*snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*snake_case__ )







     @slow
     def a						(			self ):

            '''simple docstring'''

            for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _lowerCAmelCase     :				List[str]       =							LayoutLMvaModel.from_pretrained(snake_case__ )
                   self.assertIsNotNone(snake_case__ )






def       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				Any       =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
       return image
@require_torch
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     @cached_property
     def a						(			self ):

            '''simple docstring'''

            return LayoutLMvaImageProcessor(apply_ocr=snake_case__ ) if is_vision_available() else None







     @slow
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base' ).to(snake_case__ )

            _lowerCAmelCase     :				Optional[int]       =							self.default_image_processor
            _lowerCAmelCase     :				Dict       =							prepare_img()
            _lowerCAmelCase     :				List[str]       =							image_processor(images=snake_case__   ,						return_tensors='pt' ).pixel_values.to(snake_case__ )

            _lowerCAmelCase     :				List[str]       =							torch.tensor([[1, 2]] )
            _lowerCAmelCase     :				Union[str, Any]       =							torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )

            # forward pass
            _lowerCAmelCase     :				Dict       =							model(
                input_ids=input_ids.to(snake_case__ )   ,						bbox=bbox.to(snake_case__ )   ,						pixel_values=pixel_values.to(snake_case__ )   ,						)

            # verify the logits
            _lowerCAmelCase     :				Any       =							torch.Size((1, 199, 768) )
            self.assertEqual(outputs.last_hidden_state.shape   ,						snake_case__ )

            _lowerCAmelCase     :				Tuple       =							torch.tensor(
                [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] ).to(snake_case__ )

            self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]   ,						snake_case__   ,						atol=1E-4 ) )





'''simple docstring'''







def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Union[str, Any]       =							len(_A       )
       for i in range(length - 1       ):
              _lowerCAmelCase     :				List[Any]       =							i
              for k in range(i + 1 ,    _A       ):
                     if collection[k] < collection[least]:
                            _lowerCAmelCase     :				List[str]       =							k
              if least != i:
                     _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							(collection[i], collection[least])
       return collection


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

