


'''simple docstring'''






from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor




class  a     :
       def __init__( self      ,				__magic_name__      ,				__magic_name__=3      ,				__magic_name__=32      ,				__magic_name__=3      ,				__magic_name__=10      ,				__magic_name__=[10, 20, 30, 40]      ,				__magic_name__=[1, 1, 2, 1]      ,				__magic_name__=True      ,				__magic_name__=True      ,				__magic_name__="relu"      ,				__magic_name__=3      ,				__magic_name__=None      ,				)					->							int:
            _a			      =							parent
            _a			      =							batch_size
            _a			      =							image_size
            _a			      =							num_channels
            _a			      =							embeddings_size
            _a			      =							hidden_sizes
            _a			      =							depths
            _a			      =							is_training
            _a			      =							use_labels
            _a			      =							hidden_act
            _a			      =							num_labels
            _a			      =							scope
            _a			      =							len(__magic_name__						)
       def 							__UpperCAmelCase		( self						)					->							Dict:
            _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.num_labels						)

            _a			      =							self.get_config()
            return config, pixel_values, labels
       def 							__UpperCAmelCase		( self						)					->							List[Any]:
            return RegNetConfig(
                num_channels=self.num_channels      ,				embeddings_size=self.embeddings_size      ,				hidden_sizes=self.hidden_sizes      ,				depths=self.depths      ,				hidden_act=self.hidden_act      ,				num_labels=self.num_labels      ,				)
       def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Union[str, Any]:
            _a			      =							TFRegNetModel(config=__magic_name__						)
            _a			      =							model(__magic_name__      ,				training=__magic_name__						)
            # expected last hidden states: B, C, H // 32, W // 32
            self.parent.assertEqual(
                result.last_hidden_state.shape      ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)      ,				)
       def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Optional[int]:
            _a			      =							self.num_labels
            _a			      =							TFRegNetForImageClassification(__magic_name__						)
            _a			      =							model(__magic_name__      ,				labels=__magic_name__      ,				training=__magic_name__						)
            self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels)						)




       def 							__UpperCAmelCase		( self						)					->							Optional[int]:
            _a			      =							self.prepare_config_and_inputs()
            _a      ,   _a      ,   _a			      =							config_and_inputs
            _a			      =							{'pixel_values': pixel_values}
            return config, inputs_dict





@require_tf
class  a     (     _SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				unittest.TestCase       ):
       _lowerCAmelCase 					=	(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
       _lowerCAmelCase 					=	(
           {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
           if is_tf_available()
           else {}
       )

       _lowerCAmelCase 					=	False
       _lowerCAmelCase 					=	False
       _lowerCAmelCase 					=	False
       _lowerCAmelCase 					=	False
       _lowerCAmelCase 					=	False
       def 							__UpperCAmelCase		( self						)					->							Tuple:
            _a			      =							TFRegNetModelTester(self						)
            _a			      =							ConfigTester(self      ,				config_class=__magic_name__      ,				has_text_modality=__magic_name__						)
       def 							__UpperCAmelCase		( self						)					->							Tuple:
            return
       @unittest.skip(reason='RegNet does not use inputs_embeds'						)
       def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
            pass
       @unittest.skipIf(
           not is_tf_available() or len(tf.config.list_physical_devices('GPU'						)						) == 0      ,				reason='TF does not support backprop for grouped convolutions on CPU.'      ,				)
       @slow
       def 							__UpperCAmelCase		( self						)					->							int:
            super().test_keras_fit()
       @unittest.skip(reason='RegNet does not support input and output embeddings'						)
       def 							__UpperCAmelCase		( self						)					->							Optional[int]:
            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(__magic_name__						)
                 _a			      =							inspect.signature(model.call						)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 _a			      =							[*signature.parameters.keys()]

                 _a			      =							['pixel_values']
                 self.assertListEqual(arg_names[:1]      ,				__magic_name__						)
       def 							__UpperCAmelCase		( self						)					->							List[Any]:
            _a			      =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__magic_name__						)
       def 							__UpperCAmelCase		( self						)					->							Dict:
            def check_hidden_states_output(__magic_name__      ,				__magic_name__      ,				__magic_name__						):
                 _a			      =							model_class(__magic_name__						)
                 _a			      =							model(**self._prepare_for_class(__magic_name__      ,				__magic_name__						)      ,				training=__magic_name__						)

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

                 _a			      =							self.model_tester.num_stages
                 self.assertEqual(len(__magic_name__						)      ,				expected_num_stages + 1						)

                 # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-2:]						)      ,				[self.model_tester.image_size // 2, self.model_tester.image_size // 2]      ,				)

            _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()
            _a			      =							['basic', 'bottleneck']
            for model_class in self.all_model_classes:
                 for layer_type in layers_type:
                      _a			      =							layer_type
                      _a			      =							True
                      check_hidden_states_output(__magic_name__      ,				__magic_name__      ,				__magic_name__						)

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

                      check_hidden_states_output(__magic_name__      ,				__magic_name__      ,				__magic_name__						)
       def 							__UpperCAmelCase		( self						)					->							Optional[int]:
            _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()

            def check_equivalence(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__={}						):
                 _a			      =							model(__magic_name__      ,				return_dict=__magic_name__      ,				**__magic_name__						)
                 _a			      =							model(__magic_name__      ,				return_dict=__magic_name__      ,				**__magic_name__						).to_tuple()

                 def recursive_check(__magic_name__      ,				__magic_name__						):
                      if isinstance(__magic_name__      ,				(List, Tuple)						):
                           for tuple_iterable_value, dict_iterable_value in zip(__magic_name__      ,				__magic_name__						):
                                recursive_check(__magic_name__      ,				__magic_name__						)
                      elif tuple_object is None:
                           return
                      else:
                           self.assertTrue(
                               all(tf.equal(__magic_name__      ,				__magic_name__						)						)      ,				msg=(
                                   'Tuple and dict output are not equal. Difference:'
                                   f' {tf.math.reduce_max(tf.abs(tuple_object - dict_object						)						)}'
                               )      ,				)

                      recursive_check(__magic_name__      ,				__magic_name__						)

            for model_class in self.all_model_classes:
                 _a			      =							model_class(__magic_name__						)

                 _a			      =							self._prepare_for_class(__magic_name__      ,				__magic_name__						)
                 _a			      =							self._prepare_for_class(__magic_name__      ,				__magic_name__						)
                 check_equivalence(__magic_name__      ,				__magic_name__      ,				__magic_name__						)

                 _a			      =							self._prepare_for_class(__magic_name__      ,				__magic_name__      ,				return_labels=__magic_name__						)
                 _a			      =							self._prepare_for_class(__magic_name__      ,				__magic_name__      ,				return_labels=__magic_name__						)
                 check_equivalence(__magic_name__      ,				__magic_name__      ,				__magic_name__						)

                 _a			      =							self._prepare_for_class(__magic_name__      ,				__magic_name__						)
                 _a			      =							self._prepare_for_class(__magic_name__      ,				__magic_name__						)
                 check_equivalence(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				{'output_hidden_states': True}						)

                 _a			      =							self._prepare_for_class(__magic_name__      ,				__magic_name__      ,				return_labels=__magic_name__						)
                 _a			      =							self._prepare_for_class(__magic_name__      ,				__magic_name__      ,				return_labels=__magic_name__						)
                 check_equivalence(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				{'output_hidden_states': True}						)
       def 							__UpperCAmelCase		( self						)					->							str:
            _a			      =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__magic_name__						)




       @slow
       def 							__UpperCAmelCase		( self						)					->							Any:
            for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _a			      =							TFRegNetModel.from_pretrained(__magic_name__						)
                 self.assertIsNotNone(__magic_name__						)






def 	_A				()   ->  Optional[Any]:




     '''simple docstring'''



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






@require_tf
@require_vision
class  a     (     unittest.TestCase       ):
       @cached_property
       def 							__UpperCAmelCase		( self						)					->							List[str]:
            return (
                AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
                if is_vision_available()
                else None
            )




       @slow
       def 							__UpperCAmelCase		( self						)					->							Any:
            _a			      =							TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

            _a			      =							self.default_image_processor
            _a			      =							prepare_img()
            _a			      =							image_processor(images=__magic_name__      ,				return_tensors='tf'						)

            # forward pass
            _a			      =							model(**__magic_name__      ,				training=__magic_name__						)

            # verify the logits
            _a			      =							tf.TensorShape((1, 10_00)						)
            self.assertEqual(outputs.logits.shape      ,				__magic_name__						)

            _a			      =							tf.constant([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]						)

            tf.debugging.assert_near(outputs.logits[0, :3]      ,				__magic_name__      ,				atol=1e-4						)





'''simple docstring'''






class  a     :
 def __init__( self						)					->							List[Any]:
      _a			      =							''
      _a			      =							''
      _a			      =							[]
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      if m == -1:
           return n + 1
      elif n == -1:
           return m + 1
      elif self.dp[m][n] > -1:
           return self.dp[m][n]
      else:
           if self.worda[m] == self.worda[n]:
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				n - 1						)
           else:
                _a			      =							self.__min_dist_top_down_dp(__magic_name__      ,				n - 1						)
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				__magic_name__						)
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				n - 1						)
                _a			      =							1 + min(__magic_name__      ,				__magic_name__      ,				__magic_name__						)

           return self.dp[m][n]
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      _a			      =							worda
      _a			      =							worda
      _a			      =							[[-1 for _ in range(len(__magic_name__						)						)] for _ in range(len(__magic_name__						)						)]

      return self.__min_dist_top_down_dp(len(__magic_name__						) - 1      ,				len(__magic_name__						) - 1						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      _a			      =							worda
      _a			      =							worda
      _a			      =							len(__magic_name__						)
      _a			      =							len(__magic_name__						)
      _a			      =							[[0 for _ in range(n + 1						)] for _ in range(m + 1						)]

      for i in range(m + 1						):
           for j in range(n + 1						):
                if i == 0:  # first string is empty
                     _a			      =							j
                elif j == 0:  # second string is empty
                     _a			      =							i
                elif worda[i - 1] == worda[j - 1]:  # last characters are equal
                     _a			      =							self.dp[i - 1][j - 1]
                else:
                     _a			      =							self.dp[i][j - 1]
                     _a			      =							self.dp[i - 1][j]
                     _a			      =							self.dp[i - 1][j - 1]
                     _a			      =							1 + min(__magic_name__      ,				__magic_name__      ,				__magic_name__						)
      return self.dp[m][n]


if __name__ == "__main__":
 a_					:    Dict          =    EditDistance()

 print("****************** Testing Edit Distance DP Algorithm ******************")
 print()

 a_					:    Tuple          =    input("Enter the first string: ").strip()
 a_					:    Dict          =    input("Enter the second string: ").strip()

 print()
 print(f'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
 print(f'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
 print()
 print("*************** End of Testing Edit Distance DP Algorithm ***************")

