


"""simple docstring"""






import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
       MODEL_MAPPING,
       EfficientFormerForImageClassification,
       EfficientFormerForImageClassificationWithTeacher,
       EfficientFormerModel,
   )
   from transformers.models.efficientformer.modeling_efficientformer import (
       EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
   )


if is_vision_available():
   from PIL import Image

   from transformers import EfficientFormerImageProcessor


class      __lowerCamelCase	:

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = 13     ,					__UpperCAmelCase = 64     ,					__UpperCAmelCase = 2     ,					__UpperCAmelCase = 3     ,					__UpperCAmelCase = 3     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase = 128     ,					__UpperCAmelCase=[16, 32, 64, 128]     ,					__UpperCAmelCase = 7     ,					__UpperCAmelCase = 4     ,					__UpperCAmelCase = 37     ,					__UpperCAmelCase = "gelu"     ,					__UpperCAmelCase = 0.1     ,					__UpperCAmelCase = 0.1     ,					__UpperCAmelCase = 10     ,					__UpperCAmelCase = 0.02     ,					__UpperCAmelCase = 2     ,					__UpperCAmelCase = 1     ,					__UpperCAmelCase = 128     ,					__UpperCAmelCase = [2, 2, 2, 2]     ,					__UpperCAmelCase = 2     ,					__UpperCAmelCase = 2     ,					)							->				List[str]:
          _a     						=					parent
          _a     						=					batch_size
          _a     						=					image_size
          _a     						=					patch_size
          _a     						=					num_channels
          _a     						=					is_training
          _a     						=					use_labels
          _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     						=					type_sequence_label_size
          _a     						=					initializer_range
          _a     						=					encoder_stride
          _a     						=					num_attention_outputs
          _a     						=					embed_dim
          _a     						=					embed_dim + 1
          _a     						=					resolution
          _a     						=					depths
          _a     						=					hidden_sizes
          _a     						=					dim
          _a     						=					mlp_expansion_ratio


     def 				_UpperCAmelCase   (   self				)							->				str:
          _a     						=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

          _a     						=					self.get_config()
          return config, pixel_values, labels


     def 				_UpperCAmelCase   (   self				)							->				int:
          return EfficientFormerConfig(
              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=__UpperCAmelCase     ,					initializer_range=self.initializer_range     ,					encoder_stride=self.encoder_stride     ,					resolution=self.resolution     ,					depths=self.depths     ,					hidden_sizes=self.hidden_sizes     ,					dim=self.dim     ,					mlp_expansion_ratio=self.mlp_expansion_ratio     ,					)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Union[str, Any]:
          _a     						=					EfficientFormerModel(config=__UpperCAmelCase				)
          model.to(__UpperCAmelCase				)
          model.eval()
          _a     						=					model(__UpperCAmelCase				)
          self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, self.seq_length, self.hidden_size)				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Optional[int]:
          _a     						=					self.type_sequence_label_size
          _a     						=					EfficientFormerForImageClassification(__UpperCAmelCase				)
          model.to(__UpperCAmelCase				)
          model.eval()
          _a     						=					model(__UpperCAmelCase     ,					labels=__UpperCAmelCase				)
          self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)				)

          # test greyscale images
          _a     						=					1
          _a     						=					EfficientFormerForImageClassification(__UpperCAmelCase				)
          model.to(__UpperCAmelCase				)
          model.eval()

          _a     						=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          _a     						=					model(__UpperCAmelCase				)
          self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)				)





     def 				_UpperCAmelCase   (   self				)							->				Dict:
          _a     						=					self.prepare_config_and_inputs()
          (
              (
              _a
          )						,   (
              _a
          )						,   (
              _a
          )						,   
          )     						=					config_and_inputs
          _a     						=					{'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_torch
class      __lowerCamelCase	(       a__	,       a__	,       unittest.TestCase					):

     '''simple docstring'''
     A_							:						Union[str, Any]     =		(
         (
             EfficientFormerModel,
             EfficientFormerForImageClassificationWithTeacher,
             EfficientFormerForImageClassification,
         )
         if is_torch_available()
         else ()
     )
     A_							:						List[Any]     =		(
         {
             'feature-extraction': EfficientFormerModel,
             'image-classification': (
                 EfficientFormerForImageClassification,
                 EfficientFormerForImageClassificationWithTeacher,
             ),
         }
         if is_torch_available()
         else {}
     )
     A_							:						Any     =		False

     A_							:						List[Any]     =		False
     A_							:						Optional[int]     =		False
     A_							:						List[str]     =		False


     def 				_UpperCAmelCase   (   self				)							->				Any:
          _a     						=					EfficientFormerModelTester(self				)
          _a     						=					ConfigTester(
              self     ,					config_class=__UpperCAmelCase     ,					has_text_modality=__UpperCAmelCase     ,					hidden_size=37				)


     def 				_UpperCAmelCase   (   self				)							->				Optional[int]:
          self.config_tester.run_common_tests()


     @unittest.skip(reason='''EfficientFormer does not use inputs_embeds'''				)
     def 				_UpperCAmelCase   (   self				)							->				Optional[int]:
          pass


     @unittest.skip(reason='''EfficientFormer does not support input and output embeddings'''				)
     def 				_UpperCAmelCase   (   self				)							->				List[Any]:
          pass


     def 				_UpperCAmelCase   (   self				)							->				Any:
          _a						,   _a     						=					self.model_tester.prepare_config_and_inputs_for_common()

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

               _a     						=					['''pixel_values''']
               self.assertListEqual(arg_names[:1]     ,					__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Optional[int]:
          def check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):
               _a     						=					model_class(__UpperCAmelCase				)
               model.to(__UpperCAmelCase				)
               model.eval()

               with torch.no_grad():
                    _a     						=					model(**self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase				)				)

               _a     						=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               _a     						=					getattr(
                   self.model_tester     ,					'''expected_num_hidden_layers'''     ,					self.model_tester.num_hidden_layers + 1				)
               self.assertEqual(len(__UpperCAmelCase				)     ,					__UpperCAmelCase				)

               if hasattr(self.model_tester     ,					'''encoder_seq_length'''				):
                    _a     						=					self.model_tester.encoder_seq_length
                    if hasattr(self.model_tester     ,					'''chunk_length'''				) and self.model_tester.chunk_length > 1:
                         _a     						=					seq_length * self.model_tester.chunk_length
               else:
                    _a     						=					self.model_tester.seq_length

               self.assertListEqual(
                   list(hidden_states[-1].shape[-2:]				)     ,					[seq_length, self.model_tester.hidden_size]     ,					)

               if config.is_encoder_decoder:
                    _a     						=					outputs.decoder_hidden_states

                    self.assertIsInstance(__UpperCAmelCase     ,					(list, tuple)				)
                    self.assertEqual(len(__UpperCAmelCase				)     ,					__UpperCAmelCase				)
                    _a     						=					getattr(self.model_tester     ,					'''seq_length'''     ,					__UpperCAmelCase				)
                    _a     						=					getattr(self.model_tester     ,					'''decoder_seq_length'''     ,					__UpperCAmelCase				)

                    self.assertListEqual(
                        list(hidden_states[-1].shape[-2:]				)     ,					[decoder_seq_length, self.model_tester.hidden_size]     ,					)

          _a						,   _a     						=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               _a     						=					True
               check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               _a     						=					True

               check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=False				)							->				int:
          _a     						=					super()._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase     ,					return_labels=__UpperCAmelCase				)

          if return_labels:
               if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
                    del inputs_dict["labels"]

          return inputs_dict


     def 				_UpperCAmelCase   (   self				)							->				str:
          _a     						=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__UpperCAmelCase				)


     @unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet'''				)
     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          _a     						=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Dict:
          _a     						=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Dict:
          if not self.model_tester.is_training:
               return

          _a						,   _a     						=					self.model_tester.prepare_config_and_inputs_for_common()
          _a     						=					True

          for model_class in self.all_model_classes:
               # EfficientFormerForImageClassificationWithTeacher supports inference-only
               if (
                   model_class in get_values(__UpperCAmelCase				)
                   or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
               ):
                    continue
               _a     						=					model_class(__UpperCAmelCase				)
               model.to(__UpperCAmelCase				)
               model.train()
               _a     						=					self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase     ,					return_labels=__UpperCAmelCase				)
               _a     						=					model(**__UpperCAmelCase				).loss
               loss.backward()


     def 				_UpperCAmelCase   (   self				)							->				str:
          _a						,   _a     						=					self.model_tester.prepare_config_and_inputs_for_common()

          _a     						=					[
              {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
              {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
              {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
          ]

          for model_class in self.all_model_classes:
               if (
                   model_class
                   not in [
                       *get_values(__UpperCAmelCase				),
                   ]
                   or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
               ):
                    continue

               for problem_type in problem_types:
                    with self.subTest(msg=F'Testing {model_class} with {problem_type["title"]}'				):
                         _a     						=					problem_type['''title''']
                         _a     						=					problem_type['''num_labels''']

                         _a     						=					model_class(__UpperCAmelCase				)
                         model.to(__UpperCAmelCase				)
                         model.train()

                         _a     						=					self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase     ,					return_labels=__UpperCAmelCase				)

                         if problem_type["num_labels"] > 1:
                              _a     						=					inputs['''labels'''].unsqueeze(1				).repeat(1     ,					problem_type['''num_labels''']				)

                         _a     						=					inputs['''labels'''].to(problem_type['''dtype''']				)

                         # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                         # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                         # they have the same size." which is a symptom something in wrong for the regression problem.
                         # See https://github.com/huggingface/transformers/issues/11780
                         with warnings.catch_warnings(record=__UpperCAmelCase				) as warning_list:
                              _a     						=					model(**__UpperCAmelCase				).loss
                         for w in warning_list:
                              if "Using a target size that is different to the input size" in str(w.message				):
                                   raise ValueError(
                                       F'Something is going wrong in the regression problem: intercepted {w.message}'				)

                         loss.backward()


     @slow
     def 				_UpperCAmelCase   (   self				)							->				Optional[Any]:
          for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _a     						=					EfficientFormerModel.from_pretrained(__UpperCAmelCase				)
               self.assertIsNotNone(__UpperCAmelCase				)





     def 				_UpperCAmelCase   (   self				)							->				Optional[int]:
          _a						,   _a     						=					self.model_tester.prepare_config_and_inputs_for_common()
          _a     						=					True

          _a     						=					getattr(self.model_tester     ,					'''seq_length'''     ,					__UpperCAmelCase				)
          _a     						=					getattr(self.model_tester     ,					'''encoder_seq_length'''     ,					__UpperCAmelCase				)
          _a     						=					getattr(self.model_tester     ,					'''key_length'''     ,					__UpperCAmelCase				)
          _a     						=					getattr(self.model_tester     ,					'''chunk_length'''     ,					__UpperCAmelCase				)

          if chunk_length is not None and hasattr(self.model_tester     ,					'''num_hashes'''				):
               _a     						=					encoder_seq_length * self.model_tester.num_hashes

          for model_class in self.all_model_classes:
               _a     						=					True
               _a     						=					False
               _a     						=					True
               _a     						=					model_class(__UpperCAmelCase				)
               model.to(__UpperCAmelCase				)
               model.eval()
               with torch.no_grad():
                    _a     						=					model(**self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase				)				)
               _a     						=					outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
               self.assertEqual(len(__UpperCAmelCase				)     ,					self.model_tester.num_attention_outputs				)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               _a     						=					True
               _a     						=					model_class(__UpperCAmelCase				)
               model.to(__UpperCAmelCase				)
               model.eval()
               with torch.no_grad():
                    _a     						=					model(**self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase				)				)
               _a     						=					outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
               self.assertEqual(len(__UpperCAmelCase				)     ,					self.model_tester.num_attention_outputs				)

               if chunk_length is not None:
                    self.assertListEqual(
                        list(attentions[0].shape[-4:]				)     ,					[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]     ,					)
               else:
                    self.assertListEqual(
                        list(attentions[0].shape[-3:]				)     ,					[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]     ,					)







def    A_ ( ):






     """simple docstring"""

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


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

     '''simple docstring'''


     @cached_property
     def 				_UpperCAmelCase   (   self				)							->				Any:
          return (
              EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300'''				)
              if is_vision_available()
              else None
          )


     @slow
     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          _a     						=					EfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300'''				).to(
              __UpperCAmelCase				)

          _a     						=					self.default_image_processor
          _a     						=					prepare_img()
          _a     						=					image_processor(images=__UpperCAmelCase     ,					return_tensors='''pt'''				).to(__UpperCAmelCase				)

          # forward pass
          with torch.no_grad():
               _a     						=					model(**__UpperCAmelCase				)

          # verify the logits
          _a     						=					(1, 1000)
          self.assertEqual(outputs.logits.shape     ,					__UpperCAmelCase				)

          _a     						=					torch.tensor([-0.0555, 0.4825, -0.0852]				).to(__UpperCAmelCase				)
          self.assertTrue(torch.allclose(outputs.logits[0][:3]     ,					__UpperCAmelCase     ,					atol=1e-4				)				)





     @slow
     def 				_UpperCAmelCase   (   self				)							->				Optional[int]:
          _a     						=					EfficientFormerForImageClassificationWithTeacher.from_pretrained(
              '''snap-research/efficientformer-l1-300'''				).to(__UpperCAmelCase				)

          _a     						=					self.default_image_processor
          _a     						=					prepare_img()
          _a     						=					image_processor(images=__UpperCAmelCase     ,					return_tensors='''pt'''				).to(__UpperCAmelCase				)

          # forward pass
          with torch.no_grad():
               _a     						=					model(**__UpperCAmelCase				)

          # verify the logits
          _a     						=					(1, 1000)
          self.assertEqual(outputs.logits.shape     ,					__UpperCAmelCase				)

          _a     						=					torch.tensor([-0.1312, 0.4353, -1.0499]				).to(__UpperCAmelCase				)
          self.assertTrue(torch.allclose(outputs.logits[0][:3]     ,					__UpperCAmelCase     ,					atol=1e-4				)				)



"""simple docstring"""






def    A_ ( _lowerCAmelCase  :       str,  _lowerCAmelCase  :       str							):






     """simple docstring"""

     if len(_lowerCAmelCase							) < len(_lowerCAmelCase							):
          return levenshtein_distance(_lowerCAmelCase,  _lowerCAmelCase							)

     if len(_lowerCAmelCase							) == 0:
          return len(_lowerCAmelCase							)

     _a     						=					list(range(len(_lowerCAmelCase							) + 1							)							)

     for i, ca in enumerate(_lowerCAmelCase							):
          _a     						=					[i + 1]

          for j, ca in enumerate(_lowerCAmelCase							):
               # Calculate insertions, deletions and substitutions
               _a     						=					previous_row[j + 1] + 1
               _a     						=					current_row[j] + 1
               _a     						=					previous_row[j] + (ca != ca)

               # Get the minimum to append to the current row
               current_row.append(min(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)							)

          # Store the previous row
          _a     						=					current_row

     # Returns the last element (distance)
     return previous_row[-1]


if __name__ == "__main__":
   __snake_case												=							input('''Enter the first word:\n''').strip()
   __snake_case												=							input('''Enter the second word:\n''').strip()

   __snake_case												=							levenshtein_distance(first_word, second_word)
   print(f'Levenshtein distance between {first_word} and {second_word} is {result}')