

'''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 					A__      (      unittest.TestCase			):



    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      {
               """task_specific_params""": {
                   """summarization""": {"""length_penalty""": 1.0, """max_length""": 128, """min_length""": 12, """num_beams""": 4},
                   """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 142, """min_length""": 56, """num_beams""": 4},
                   """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
               }
           }
           A_						=      {
               """task_specific_params.summarization.length_penalty""": 1.0,
               """task_specific_params.summarization.max_length""": 128,
               """task_specific_params.summarization.min_length""": 12,
               """task_specific_params.summarization.num_beams""": 4,
               """task_specific_params.summarization_cnn.length_penalty""": 2.0,
               """task_specific_params.summarization_cnn.max_length""": 142,
               """task_specific_params.summarization_cnn.min_length""": 56,
               """task_specific_params.summarization_cnn.num_beams""": 4,
               """task_specific_params.summarization_xsum.length_penalty""": 1.0,
               """task_specific_params.summarization_xsum.max_length""": 62,
               """task_specific_params.summarization_xsum.min_length""": 11,
               """task_specific_params.summarization_xsum.num_beams""": 6,
           }

           self.assertEqual(flatten_dict(UpperCamelCase__       )		,				UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      List[str]:





           '''simple docstring'''
           A_						=      np.random.randn(3		,				4       )
           self.assertTrue(np.allclose(transpose(UpperCamelCase__       )		,				x.transpose()       )       )

           A_						=      np.random.randn(3		,				4		,				5       )
           self.assertTrue(np.allclose(transpose(UpperCamelCase__		,				axes=(1, 2, 0)       )		,				x.transpose((1, 2, 0)       )       )       )



    @require_torch
    def 					snake_case_			(					self       )						->      List[str]:





           '''simple docstring'''
           A_						=      np.random.randn(3		,				4       )
           A_						=      torch.tensor(UpperCamelCase__       )
           self.assertTrue(np.allclose(transpose(UpperCamelCase__       )		,				transpose(UpperCamelCase__       ).numpy()       )       )

           A_						=      np.random.randn(3		,				4		,				5       )
           A_						=      torch.tensor(UpperCamelCase__       )
           self.assertTrue(np.allclose(transpose(UpperCamelCase__		,				axes=(1, 2, 0)       )		,				transpose(UpperCamelCase__		,				axes=(1, 2, 0)       ).numpy()       )       )



    @require_tf
    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      np.random.randn(3		,				4       )
           A_						=      tf.constant(UpperCamelCase__       )
           self.assertTrue(np.allclose(transpose(UpperCamelCase__       )		,				transpose(UpperCamelCase__       ).numpy()       )       )

           A_						=      np.random.randn(3		,				4		,				5       )
           A_						=      tf.constant(UpperCamelCase__       )
           self.assertTrue(np.allclose(transpose(UpperCamelCase__		,				axes=(1, 2, 0)       )		,				transpose(UpperCamelCase__		,				axes=(1, 2, 0)       ).numpy()       )       )



    @require_flax
    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      np.random.randn(3		,				4       )
           A_						=      jnp.array(UpperCamelCase__       )
           self.assertTrue(np.allclose(transpose(UpperCamelCase__       )		,				np.asarray(transpose(UpperCamelCase__       )       )       )       )

           A_						=      np.random.randn(3		,				4		,				5       )
           A_						=      jnp.array(UpperCamelCase__       )
           self.assertTrue(np.allclose(transpose(UpperCamelCase__		,				axes=(1, 2, 0)       )		,				np.asarray(transpose(UpperCamelCase__		,				axes=(1, 2, 0)       )       )       )       )



    def 					snake_case_			(					self       )						->      List[str]:





           '''simple docstring'''
           A_						=      np.random.randn(3		,				4       )
           self.assertTrue(np.allclose(reshape(UpperCamelCase__		,				(4, 3)       )		,				np.reshape(UpperCamelCase__		,				(4, 3)       )       )       )

           A_						=      np.random.randn(3		,				4		,				5       )
           self.assertTrue(np.allclose(reshape(UpperCamelCase__		,				(12, 5)       )		,				np.reshape(UpperCamelCase__		,				(12, 5)       )       )       )



    @require_torch
    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           A_						=      np.random.randn(3		,				4       )
           A_						=      torch.tensor(UpperCamelCase__       )
           self.assertTrue(np.allclose(reshape(UpperCamelCase__		,				(4, 3)       )		,				reshape(UpperCamelCase__		,				(4, 3)       ).numpy()       )       )

           A_						=      np.random.randn(3		,				4		,				5       )
           A_						=      torch.tensor(UpperCamelCase__       )
           self.assertTrue(np.allclose(reshape(UpperCamelCase__		,				(12, 5)       )		,				reshape(UpperCamelCase__		,				(12, 5)       ).numpy()       )       )



    @require_tf
    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      np.random.randn(3		,				4       )
           A_						=      tf.constant(UpperCamelCase__       )
           self.assertTrue(np.allclose(reshape(UpperCamelCase__		,				(4, 3)       )		,				reshape(UpperCamelCase__		,				(4, 3)       ).numpy()       )       )

           A_						=      np.random.randn(3		,				4		,				5       )
           A_						=      tf.constant(UpperCamelCase__       )
           self.assertTrue(np.allclose(reshape(UpperCamelCase__		,				(12, 5)       )		,				reshape(UpperCamelCase__		,				(12, 5)       ).numpy()       )       )



    @require_flax
    def 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           A_						=      np.random.randn(3		,				4       )
           A_						=      jnp.array(UpperCamelCase__       )
           self.assertTrue(np.allclose(reshape(UpperCamelCase__		,				(4, 3)       )		,				np.asarray(reshape(UpperCamelCase__		,				(4, 3)       )       )       )       )

           A_						=      np.random.randn(3		,				4		,				5       )
           A_						=      jnp.array(UpperCamelCase__       )
           self.assertTrue(np.allclose(reshape(UpperCamelCase__		,				(12, 5)       )		,				np.asarray(reshape(UpperCamelCase__		,				(12, 5)       )       )       )       )



    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           A_						=      np.random.randn(1		,				3		,				4       )
           self.assertTrue(np.allclose(squeeze(UpperCamelCase__       )		,				np.squeeze(UpperCamelCase__       )       )       )

           A_						=      np.random.randn(1		,				4		,				1		,				5       )
           self.assertTrue(np.allclose(squeeze(UpperCamelCase__		,				axis=2       )		,				np.squeeze(UpperCamelCase__		,				axis=2       )       )       )



    @require_torch
    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      np.random.randn(1		,				3		,				4       )
           A_						=      torch.tensor(UpperCamelCase__       )
           self.assertTrue(np.allclose(squeeze(UpperCamelCase__       )		,				squeeze(UpperCamelCase__       ).numpy()       )       )

           A_						=      np.random.randn(1		,				4		,				1		,				5       )
           A_						=      torch.tensor(UpperCamelCase__       )
           self.assertTrue(np.allclose(squeeze(UpperCamelCase__		,				axis=2       )		,				squeeze(UpperCamelCase__		,				axis=2       ).numpy()       )       )



    @require_tf
    def 					snake_case_			(					self       )						->      Optional[int]:





           '''simple docstring'''
           A_						=      np.random.randn(1		,				3		,				4       )
           A_						=      tf.constant(UpperCamelCase__       )
           self.assertTrue(np.allclose(squeeze(UpperCamelCase__       )		,				squeeze(UpperCamelCase__       ).numpy()       )       )

           A_						=      np.random.randn(1		,				4		,				1		,				5       )
           A_						=      tf.constant(UpperCamelCase__       )
           self.assertTrue(np.allclose(squeeze(UpperCamelCase__		,				axis=2       )		,				squeeze(UpperCamelCase__		,				axis=2       ).numpy()       )       )



    @require_flax
    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           A_						=      np.random.randn(1		,				3		,				4       )
           A_						=      jnp.array(UpperCamelCase__       )
           self.assertTrue(np.allclose(squeeze(UpperCamelCase__       )		,				np.asarray(squeeze(UpperCamelCase__       )       )       )       )

           A_						=      np.random.randn(1		,				4		,				1		,				5       )
           A_						=      jnp.array(UpperCamelCase__       )
           self.assertTrue(np.allclose(squeeze(UpperCamelCase__		,				axis=2       )		,				np.asarray(squeeze(UpperCamelCase__		,				axis=2       )       )       )       )



    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      np.random.randn(3		,				4       )
           self.assertTrue(np.allclose(expand_dims(UpperCamelCase__		,				axis=1       )		,				np.expand_dims(UpperCamelCase__		,				axis=1       )       )       )



    @require_torch
    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      np.random.randn(3		,				4       )
           A_						=      torch.tensor(UpperCamelCase__       )
           self.assertTrue(np.allclose(expand_dims(UpperCamelCase__		,				axis=1       )		,				expand_dims(UpperCamelCase__		,				axis=1       ).numpy()       )       )



    @require_tf
    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           A_						=      np.random.randn(3		,				4       )
           A_						=      tf.constant(UpperCamelCase__       )
           self.assertTrue(np.allclose(expand_dims(UpperCamelCase__		,				axis=1       )		,				expand_dims(UpperCamelCase__		,				axis=1       ).numpy()       )       )







    @require_flax
    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           A_						=      np.random.randn(3		,				4       )
           A_						=      jnp.array(UpperCamelCase__       )
           self.assertTrue(np.allclose(expand_dims(UpperCamelCase__		,				axis=1       )		,				np.asarray(expand_dims(UpperCamelCase__		,				axis=1       )       )       )       )



'''simple docstring'''







import os

__lowerCamelCase		       =    {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      0

       A_						=      0
       while index < len(UpperCAmelCase__				) - 1:
              A_						=      SYMBOLS[numerals[index]]
              A_						=      SYMBOLS[numerals[index + 1]]
              if current_value < next_value:
                     total_value -= current_value
              else:
                     total_value += current_value
              index += 1
       total_value += SYMBOLS[numerals[index]]

       return total_value
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			str:
       A_						=      """"""

       A_						=      num // 10_00
       numerals += m_count * "M"
       num %= 10_00

       A_						=      num // 1_00
       if c_count == 9:
              numerals += "CM"
              c_count -= 9
       elif c_count == 4:
              numerals += "CD"
              c_count -= 4
       if c_count >= 5:
              numerals += "D"
              c_count -= 5
       numerals += c_count * "C"
       num %= 1_00

       A_						=      num // 10
       if x_count == 9:
              numerals += "XC"
              x_count -= 9
       elif x_count == 4:
              numerals += "XL"
              x_count -= 4
       if x_count >= 5:
              numerals += "L"
              x_count -= 5
       numerals += x_count * "X"
       num %= 10

       if num == 9:
              numerals += "IX"
              num -= 9
       elif num == 4:
              numerals += "IV"
              num -= 4
       if num >= 5:
              numerals += "V"
              num -= 5
       numerals += num * "I"

       return numerals
def       UpperCAmelCase__    (      UpperCAmelCase__ = "/p089_roman.txt"				)			->			int:
       A_						=      0

       with open(os.path.dirname(UpperCAmelCase__				) + roman_numerals_filename				) as filea:
              A_						=      filea.readlines()

       for line in lines:
              A_						=      line.strip()
              A_						=      parse_roman_numerals(UpperCAmelCase__				)
              A_						=      generate_roman_numerals(UpperCAmelCase__				)
              savings += len(UpperCAmelCase__				) - len(UpperCAmelCase__				)

       return savings


if __name__ == "__main__":
      print(f"""{solution() = }""")
