


"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
 import jax.numpy as jnp

if is_tf_available():
 import tensorflow as tf

if is_torch_available():
 import torch



class 						snake_case_(				unittest.TestCase	):






     def    lowerCamelCase__   (		self     :    List[Any] ):
           lowerCAmelCase    :		Optional[int]				=      {
               '''task_specific_params''': {
                   '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 1_2_8, '''min_length''': 1_2, '''num_beams''': 4},
                   '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 1_4_2, '''min_length''': 5_6, '''num_beams''': 4},
                   '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 6_2, '''min_length''': 1_1, '''num_beams''': 6},
               }
           }
           lowerCAmelCase    :		int				=      {
               '''task_specific_params.summarization.length_penalty''': 1.0,
               '''task_specific_params.summarization.max_length''': 1_2_8,
               '''task_specific_params.summarization.min_length''': 1_2,
               '''task_specific_params.summarization.num_beams''': 4,
               '''task_specific_params.summarization_cnn.length_penalty''': 2.0,
               '''task_specific_params.summarization_cnn.max_length''': 1_4_2,
               '''task_specific_params.summarization_cnn.min_length''': 5_6,
               '''task_specific_params.summarization_cnn.num_beams''': 4,
               '''task_specific_params.summarization_xsum.length_penalty''': 1.0,
               '''task_specific_params.summarization_xsum.max_length''': 6_2,
               '''task_specific_params.summarization_xsum.min_length''': 1_1,
               '''task_specific_params.summarization_xsum.num_beams''': 6,
           }

           self.assertEqual(flatten_dict(UpperCamelCase_ )    , UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Dict ):
           lowerCAmelCase    :		Dict				=      np.random.randn(3    , 4 )
           self.assertTrue(np.allclose(transpose(UpperCamelCase_ )    , x.transpose() ) )

           lowerCAmelCase    :		List[Any]				=      np.random.randn(3    , 4    , 5 )
           self.assertTrue(np.allclose(transpose(UpperCamelCase_    , axes=(1, 2, 0) )    , x.transpose((1, 2, 0) ) ) )






     @require_torch
     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		Optional[int]				=      np.random.randn(3    , 4 )
           lowerCAmelCase    :		Any				=      torch.tensor(UpperCamelCase_ )
           self.assertTrue(np.allclose(transpose(UpperCamelCase_ )    , transpose(UpperCamelCase_ ).numpy() ) )

           lowerCAmelCase    :		Union[str, Any]				=      np.random.randn(3    , 4    , 5 )
           lowerCAmelCase    :		Tuple				=      torch.tensor(UpperCamelCase_ )
           self.assertTrue(np.allclose(transpose(UpperCamelCase_    , axes=(1, 2, 0) )    , transpose(UpperCamelCase_    , axes=(1, 2, 0) ).numpy() ) )






     @require_tf
     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		Any				=      np.random.randn(3    , 4 )
           lowerCAmelCase    :		str				=      tf.constant(UpperCamelCase_ )
           self.assertTrue(np.allclose(transpose(UpperCamelCase_ )    , transpose(UpperCamelCase_ ).numpy() ) )

           lowerCAmelCase    :		Optional[Any]				=      np.random.randn(3    , 4    , 5 )
           lowerCAmelCase    :		List[Any]				=      tf.constant(UpperCamelCase_ )
           self.assertTrue(np.allclose(transpose(UpperCamelCase_    , axes=(1, 2, 0) )    , transpose(UpperCamelCase_    , axes=(1, 2, 0) ).numpy() ) )






     @require_flax
     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		Optional[Any]				=      np.random.randn(3    , 4 )
           lowerCAmelCase    :		Union[str, Any]				=      jnp.array(UpperCamelCase_ )
           self.assertTrue(np.allclose(transpose(UpperCamelCase_ )    , np.asarray(transpose(UpperCamelCase_ ) ) ) )

           lowerCAmelCase    :		Tuple				=      np.random.randn(3    , 4    , 5 )
           lowerCAmelCase    :		Dict				=      jnp.array(UpperCamelCase_ )
           self.assertTrue(np.allclose(transpose(UpperCamelCase_    , axes=(1, 2, 0) )    , np.asarray(transpose(UpperCamelCase_    , axes=(1, 2, 0) ) ) ) )






     def    lowerCamelCase__   (		self     :    str ):
           lowerCAmelCase    :		Union[str, Any]				=      np.random.randn(3    , 4 )
           self.assertTrue(np.allclose(reshape(UpperCamelCase_    , (4, 3) )    , np.reshape(UpperCamelCase_    , (4, 3) ) ) )

           lowerCAmelCase    :		Union[str, Any]				=      np.random.randn(3    , 4    , 5 )
           self.assertTrue(np.allclose(reshape(UpperCamelCase_    , (1_2, 5) )    , np.reshape(UpperCamelCase_    , (1_2, 5) ) ) )






     @require_torch
     def    lowerCamelCase__   (		self     :    Any ):
           lowerCAmelCase    :		Optional[Any]				=      np.random.randn(3    , 4 )
           lowerCAmelCase    :		Optional[Any]				=      torch.tensor(UpperCamelCase_ )
           self.assertTrue(np.allclose(reshape(UpperCamelCase_    , (4, 3) )    , reshape(UpperCamelCase_    , (4, 3) ).numpy() ) )

           lowerCAmelCase    :		List[Any]				=      np.random.randn(3    , 4    , 5 )
           lowerCAmelCase    :		Union[str, Any]				=      torch.tensor(UpperCamelCase_ )
           self.assertTrue(np.allclose(reshape(UpperCamelCase_    , (1_2, 5) )    , reshape(UpperCamelCase_    , (1_2, 5) ).numpy() ) )






     @require_tf
     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		List[Any]				=      np.random.randn(3    , 4 )
           lowerCAmelCase    :		str				=      tf.constant(UpperCamelCase_ )
           self.assertTrue(np.allclose(reshape(UpperCamelCase_    , (4, 3) )    , reshape(UpperCamelCase_    , (4, 3) ).numpy() ) )

           lowerCAmelCase    :		int				=      np.random.randn(3    , 4    , 5 )
           lowerCAmelCase    :		Tuple				=      tf.constant(UpperCamelCase_ )
           self.assertTrue(np.allclose(reshape(UpperCamelCase_    , (1_2, 5) )    , reshape(UpperCamelCase_    , (1_2, 5) ).numpy() ) )






     @require_flax
     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase    :		Dict				=      np.random.randn(3    , 4 )
           lowerCAmelCase    :		Union[str, Any]				=      jnp.array(UpperCamelCase_ )
           self.assertTrue(np.allclose(reshape(UpperCamelCase_    , (4, 3) )    , np.asarray(reshape(UpperCamelCase_    , (4, 3) ) ) ) )

           lowerCAmelCase    :		List[str]				=      np.random.randn(3    , 4    , 5 )
           lowerCAmelCase    :		int				=      jnp.array(UpperCamelCase_ )
           self.assertTrue(np.allclose(reshape(UpperCamelCase_    , (1_2, 5) )    , np.asarray(reshape(UpperCamelCase_    , (1_2, 5) ) ) ) )






     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		List[Any]				=      np.random.randn(1    , 3    , 4 )
           self.assertTrue(np.allclose(squeeze(UpperCamelCase_ )    , np.squeeze(UpperCamelCase_ ) ) )

           lowerCAmelCase    :		Any				=      np.random.randn(1    , 4    , 1    , 5 )
           self.assertTrue(np.allclose(squeeze(UpperCamelCase_    , axis=2 )    , np.squeeze(UpperCamelCase_    , axis=2 ) ) )






     @require_torch
     def    lowerCamelCase__   (		self     :    int ):
           lowerCAmelCase    :		Any				=      np.random.randn(1    , 3    , 4 )
           lowerCAmelCase    :		Union[str, Any]				=      torch.tensor(UpperCamelCase_ )
           self.assertTrue(np.allclose(squeeze(UpperCamelCase_ )    , squeeze(UpperCamelCase_ ).numpy() ) )

           lowerCAmelCase    :		Dict				=      np.random.randn(1    , 4    , 1    , 5 )
           lowerCAmelCase    :		Tuple				=      torch.tensor(UpperCamelCase_ )
           self.assertTrue(np.allclose(squeeze(UpperCamelCase_    , axis=2 )    , squeeze(UpperCamelCase_    , axis=2 ).numpy() ) )






     @require_tf
     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase    :		str				=      np.random.randn(1    , 3    , 4 )
           lowerCAmelCase    :		List[str]				=      tf.constant(UpperCamelCase_ )
           self.assertTrue(np.allclose(squeeze(UpperCamelCase_ )    , squeeze(UpperCamelCase_ ).numpy() ) )

           lowerCAmelCase    :		Optional[Any]				=      np.random.randn(1    , 4    , 1    , 5 )
           lowerCAmelCase    :		Optional[int]				=      tf.constant(UpperCamelCase_ )
           self.assertTrue(np.allclose(squeeze(UpperCamelCase_    , axis=2 )    , squeeze(UpperCamelCase_    , axis=2 ).numpy() ) )






     @require_flax
     def    lowerCamelCase__   (		self     :    Any ):
           lowerCAmelCase    :		int				=      np.random.randn(1    , 3    , 4 )
           lowerCAmelCase    :		str				=      jnp.array(UpperCamelCase_ )
           self.assertTrue(np.allclose(squeeze(UpperCamelCase_ )    , np.asarray(squeeze(UpperCamelCase_ ) ) ) )

           lowerCAmelCase    :		List[str]				=      np.random.randn(1    , 4    , 1    , 5 )
           lowerCAmelCase    :		str				=      jnp.array(UpperCamelCase_ )
           self.assertTrue(np.allclose(squeeze(UpperCamelCase_    , axis=2 )    , np.asarray(squeeze(UpperCamelCase_    , axis=2 ) ) ) )






     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		List[Any]				=      np.random.randn(3    , 4 )
           self.assertTrue(np.allclose(expand_dims(UpperCamelCase_    , axis=1 )    , np.expand_dims(UpperCamelCase_    , axis=1 ) ) )






     @require_torch
     def    lowerCamelCase__   (		self     :    Dict ):
           lowerCAmelCase    :		Any				=      np.random.randn(3    , 4 )
           lowerCAmelCase    :		Any				=      torch.tensor(UpperCamelCase_ )
           self.assertTrue(np.allclose(expand_dims(UpperCamelCase_    , axis=1 )    , expand_dims(UpperCamelCase_    , axis=1 ).numpy() ) )






     @require_tf
     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           lowerCAmelCase    :		Tuple				=      np.random.randn(3    , 4 )
           lowerCAmelCase    :		Optional[int]				=      tf.constant(UpperCamelCase_ )
           self.assertTrue(np.allclose(expand_dims(UpperCamelCase_    , axis=1 )    , expand_dims(UpperCamelCase_    , axis=1 ).numpy() ) )






     @require_flax
     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		Dict				=      np.random.randn(3    , 4 )
           lowerCAmelCase    :		Tuple				=      jnp.array(UpperCamelCase_ )
           self.assertTrue(np.allclose(expand_dims(UpperCamelCase_    , axis=1 )    , np.asarray(expand_dims(UpperCamelCase_    , axis=1 ) ) ) )









'''simple docstring'''

import functools





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					str       ):
    A__				=			len(_lowerCamelCase       )
    A__				=			len(_lowerCamelCase       )

    @functools.cache
    def min_distance(_lowerCamelCase					:					int						,       _lowerCamelCase					:					int       ) -> int:
        # if first word index is overflow - delete all from the second word
        if indexa >= len_worda:
            return len_worda - indexa
        # if second word index is overflow - delete all from the first word
        if indexa >= len_worda:
            return len_worda - indexa
        A__				=			int(worda[indexa] != worda[indexa]       )  # current letters not identical
        return min(
            1 + min_distance(indexa + 1						,       _lowerCamelCase       )						,       1 + min_distance(_lowerCamelCase						,       indexa + 1       )						,       diff + min_distance(indexa + 1						,       indexa + 1       )						,       )

    return min_distance(0						,       0       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()



