





"""simple docstring"""






import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
    from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image



class 	lowerCAmelCase					:






       def __init__(   self    ,  a__    ,  a__=13    ,  a__=64    ,  a__=2    ,  a__=3    ,  a__=True    ,  a__=True    ,  a__=32    ,  a__=5    ,  a__=4    ,  a__=37    ,  a__="gelu"    ,  a__=0.1    ,  a__=0.1    ,  a__=10    ,  a__=0.02    ,  a__=[1, 16, 4, 4]    ,  a__=None    ,  ):
              _UpperCAmelCase     =     parent
              _UpperCAmelCase     =     batch_size
              _UpperCAmelCase     =     image_size
              _UpperCAmelCase     =     patch_size
              _UpperCAmelCase     =     num_channels
              _UpperCAmelCase     =     is_training
              _UpperCAmelCase     =     use_labels
              _UpperCAmelCase     =     hidden_size
              _UpperCAmelCase     =     num_hidden_layers
              _UpperCAmelCase     =     num_attention_heads
              _UpperCAmelCase     =     intermediate_size
              _UpperCAmelCase     =     hidden_act
              _UpperCAmelCase     =     hidden_dropout_prob
              _UpperCAmelCase     =     attention_probs_dropout_prob
              _UpperCAmelCase     =     type_sequence_label_size
              _UpperCAmelCase     =     initializer_range
              _UpperCAmelCase     =     scope
              _UpperCAmelCase     =     backbone_featmap_shape

              # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
              # the number of patches is based on the feature map of the backbone, which by default uses an output stride
              # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
              _UpperCAmelCase     =     (self.image_size // 32) ** 2
              _UpperCAmelCase     =     num_patches + 1






       def 							__A (   self     ):
              _UpperCAmelCase     =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

              _UpperCAmelCase     =     None
              if self.use_labels:
                     _UpperCAmelCase     =     ids_tensor([self.batch_size]    ,  self.type_sequence_label_size     )

              _UpperCAmelCase     =     self.get_config()

              return config, pixel_values, labels






       def 							__A (   self     ):
              _UpperCAmelCase     =     {
                  'global_padding': 'same',
                  'layer_type': 'bottleneck',
                  'depths': [3, 4, 9],
                  'out_features': ['stage1', 'stage2', 'stage3'],
                  'embedding_dynamic_padding': True,
                  'hidden_sizes': [4, 8, 16, 32],
                  'num_groups': 2,
              }

              return ViTHybridConfig(
                  image_size=self.image_size    ,  patch_size=self.patch_size    ,  num_channels=self.num_channels    ,  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    ,  is_decoder=a__    ,  initializer_range=self.initializer_range    ,  backbone_featmap_shape=self.backbone_featmap_shape    ,  backbone_config=a__    ,  )






       def 							__A (   self    ,  a__    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     ViTHybridModel(config=a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__     )
              self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length, self.hidden_size)     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     self.type_sequence_label_size
              _UpperCAmelCase     =     ViTHybridForImageClassification(a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__    ,  labels=a__     )
              self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.type_sequence_label_size)     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.prepare_config_and_inputs()
              _UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase     =     config_and_inputs
              _UpperCAmelCase     =     {'pixel_values': pixel_values}
              return config, inputs_dict



@require_torch
class 	lowerCAmelCase					(      snake_case			,	snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
       lowerCAmelCase__						 =  (
           {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
           if is_torch_available()
           else {}
       )
       lowerCAmelCase__						 =  False
       lowerCAmelCase__						 =  False
       lowerCAmelCase__						 =  False






       def 							__A (   self     ):
              _UpperCAmelCase     =     ViTHybridModelTester(self     )
              _UpperCAmelCase     =     ConfigTester(self    ,  config_class=a__    ,  has_text_modality=a__    ,  hidden_size=37     )






       def 							__A (   self     ):
              self.config_tester.run_common_tests()






       @unittest.skip(reason='ViT does not use inputs_embeds'     )
       def 							__A (   self     ):
              pass






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     _UpperCAmelCase     =     model_class(a__     )
                     self.assertIsInstance(model.get_input_embeddings()    ,  (nn.Module)     )
                     _UpperCAmelCase     =     model.get_output_embeddings()
                     self.assertTrue(x is None or isinstance(a__    ,  nn.Linear     )     )






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     _UpperCAmelCase     =     model_class(a__     )
                     _UpperCAmelCase     =     inspect.signature(model.forward     )
                     # signature.parameters is an OrderedDict => so arg_names order is deterministic
                     _UpperCAmelCase     =     [*signature.parameters.keys()]

                     _UpperCAmelCase     =     ['pixel_values']
                     self.assertListEqual(arg_names[:1]    ,  a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*a__     )






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()

              _UpperCAmelCase     =     _config_zero_init(a__     )
              for model_class in self.all_model_classes:
                     _UpperCAmelCase     =     model_class(config=a__     )
                     # Skip the check for the backbone
                     for name, module in model.named_modules():
                            if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                                   _UpperCAmelCase     =     [f"""{name}.{key}""" for key in module.state_dict().keys()]
                                   break

                     for name, param in model.named_parameters():
                            if param.requires_grad:
                                   if name in backbone_params:
                                          continue
                                   self.assertIn(
                                       ((param.data.mean() * 1E9).round() / 1E9).item()    ,  [0.0, 1.0]    ,  msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""    ,  )






       @slow
       def 							__A (   self     ):
              for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     _UpperCAmelCase     =     ViTHybridModel.from_pretrained(a__     )
                     self.assertIsNotNone(a__     )


def       __lowerCamelCase    (       )		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
       return image



@require_torch
@require_vision
class 	lowerCAmelCase					(      unittest.TestCase							):






       @cached_property
       def 							__A (   self     ):
              return (
                  ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
                  if is_vision_available()
                  else None
              )






       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(
                  a__     )

              _UpperCAmelCase     =     self.default_image_processor
              _UpperCAmelCase     =     prepare_img()
              _UpperCAmelCase     =     image_processor(images=a__    ,  return_tensors='pt'     ).to(a__     )

              # forward pass
              with torch.no_grad():
                     _UpperCAmelCase     =     model(**a__     )

              # verify the logits
              _UpperCAmelCase     =     torch.Size((1, 10_00)     )
              self.assertEqual(outputs.logits.shape    ,  a__     )

              _UpperCAmelCase     =     torch.tensor([-1.9_090, -0.4_993, -0.2_389]     ).to(a__     )

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






       @slow
       @require_accelerate
       def 							__A (   self     ):
              _UpperCAmelCase     =     ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'     )
              _UpperCAmelCase     =     ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'    ,  device_map='auto'     )

              _UpperCAmelCase     =     prepare_img()

              _UpperCAmelCase     =     image_processor(images=a__    ,  return_tensors='pt'     )
              _UpperCAmelCase     =     model(**a__     )
              _UpperCAmelCase     =     outputs.logits
              # model predicts one of the 1000 ImageNet classes
              _UpperCAmelCase     =     logits.argmax(-1     ).item()

              self.assertTrue(model.config.idalabel[predicted_class_idx]    ,  'tabby, tabby cat'     )













"""simple docstring"""






def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       list:






       """simple docstring"""




       _UpperCAmelCase     =     False
       while is_sorted is False:  # Until all the indices are traversed keep looping
              _UpperCAmelCase     =     True
              for i in range(0,len(SCREAMING_SNAKE_CASE							) - 1,2							):  # iterating over all even indices
                     if input_list[i] > input_list[i + 1]:
                            _UpperCAmelCase    ,							_UpperCAmelCase     =     input_list[i + 1], input_list[i]
                            # swapping if elements not in order
                            _UpperCAmelCase     =     False

              for i in range(1,len(SCREAMING_SNAKE_CASE							) - 1,2							):  # iterating over all odd indices
                     if input_list[i] > input_list[i + 1]:
                            _UpperCAmelCase    ,							_UpperCAmelCase     =     input_list[i + 1], input_list[i]
                            # swapping if elements not in order
                            _UpperCAmelCase     =     False
       return input_list


if __name__ == "__main__":
    print('''Enter list to be sorted''')
    lowerCAmelCase_			=							[int(x) for x in input().split()]
    # inputing elements of the list in one line
    lowerCAmelCase_			=							odd_even_sort(input_list)
    print('''The sorted list is''')
    print(sorted_list)






